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Tag #learning

5675 papers:

PADLPADL-2020-NguyenZJXD #artificial reality #named #programming #set
VRASP: A Virtual Reality Environment for Learning Answer Set Programming (VTN, YZ0, KJ, WX, TD), pp. 82–91.
ASPLOSASPLOS-2020-AngstadtJW #automaton #bound #kernel #legacy #string
Accelerating Legacy String Kernels via Bounded Automata Learning (KA, JBJ, WW), pp. 235–249.
ASPLOSASPLOS-2020-HuangJ0 #gpu #memory management #named
SwapAdvisor: Pushing Deep Learning Beyond the GPU Memory Limit via Smart Swapping (CCH, GJ, JL0), pp. 1341–1355.
ASPLOSASPLOS-2020-HuLL0Z0XDLSX #architecture #framework #named
DeepSniffer: A DNN Model Extraction Framework Based on Learning Architectural Hints (XH, LL, SL, LD0, PZ, YJ0, XX, YD, CL, TS, YX), pp. 385–399.
ASPLOSASPLOS-2020-MireshghallahTR #named #privacy
Shredder: Learning Noise Distributions to Protect Inference Privacy (FM, MT, PR, AJ, DMT, HE), pp. 3–18.
ASPLOSASPLOS-2020-PengSD0MXYQ #gpu #memory management #named
Capuchin: Tensor-based GPU Memory Management for Deep Learning (XP, XS, HD, HJ0, WM, QX, FY, XQ), pp. 891–905.
CCCC-2020-BrauckmannGEC #graph #modelling
Compiler-based graph representations for deep learning models of code (AB, AG, SE, JC), pp. 201–211.
CGOCGO-2020-Haj-AliAWSAS #named
NeuroVectorizer: end-to-end vectorization with deep reinforcement learning (AHA, NKA, TLW, YSS, KA, IS), pp. 242–255.
EDMEDM-2019-AiCGZWFW #concept #online #recommendation
Concept-Aware Deep Knowledge Tracing and Exercise Recommendation in an Online Learning System (FA, YC, YG, YZ, ZW, GF, GW).
EDMEDM-2019-AusinABC #induction #policy
Leveraging Deep Reinforcement Learning for Pedagogical Policy Induction in an Intelligent Tutoring System (MSA, HA, TB, MC).
EDMEDM-2019-BroisinH #automation #design #evaluation #programming #semantics
Design and evaluation of a semantic indicator for automatically supporting programming learning (JB, CH).
EDMEDM-2019-CaoPB #analysis #performance
Incorporating Prior Practice Difficulty into Performance Factor Analysis to Model Mandarin Tone Learning (MC, PIPJ, GMB).
EDMEDM-2019-ChoffinPBV #distributed #modelling #named #scheduling #student
DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills (BC, FP, YB, JJV).
EDMEDM-2019-ChopraKMG #difference #gender
Gender Differences in Work-Integrated Learning Assessments (SC, AK, MM, LG).
EDMEDM-2019-DavisRF #difference #student
Individual Differences in Student Learning Aid Usage (AKD, YJR, DF).
EDMEDM-2019-EmondV #3d #performance #predict #visualisation
Visualizing Learning Performance Data and Model Predictions as Objects in a 3D Space (BE, JJV).
EDMEDM-2019-Furr #clustering #interactive #online #visualisation
Visualization and clustering of learner pathways in an interactive online learning environment (DF).
EDMEDM-2019-GagnonLBD
Filtering non-relevant short answers in peer learning applications (VG, AL, SB, MCD).
EDMEDM-2019-GuthrieC #behaviour #online #quality #student
Adding duration-based quality labels to learning events for improved description of students' online learning behavior (MWG, ZC).
EDMEDM-2019-HarmonW #education #online
Measuring Item Teaching Value in an Online Learning Environment (JH, RW).
EDMEDM-2019-HarrakBLB #automation #identification #self
Automatic identification of questions in MOOC forums and association with self-regulated learning (FH, FB, VL, RB).
EDMEDM-2019-Ikeda #analysis #education #quality #using
Learning Feature Analysis for Quality Improvement of Web-Based Teaching Materials Using Mouse Cursor Tracking (MI).
EDMEDM-2019-JiangIDLW #student
Measuring students' thermal comfort and its impact on learning (HJ, MI, SVD, SL, JW).
EDMEDM-2019-JiangP
Binary Q-matrix Learning with dAFM (NJ, ZAP).
EDMEDM-2019-JoYKL #analysis #comparative #education #effectiveness #online #word
A Comparative Analysis of Emotional Words for Learning Effectiveness in Online Education (JJ, YY, GK, HL).
EDMEDM-2019-JuZABC #identification
Identifying Critical Pedagogical Decisions through Adversarial Deep Reinforcement Learning (SJ, GZ, HA, TB, MC).
EDMEDM-2019-KraussMA #modelling #recommendation
Smart Learning Object Recommendations based on Time-Dependent Learning Need Models (CK, AM, SA).
EDMEDM-2019-LiangYZPG #case study #concept #partial order #strict
Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations (CL0, JY, HZ0, BP, CLG).
EDMEDM-2019-MussackFSC #behaviour #problem #similarity #towards
Towards discovering problem similarity through deep learning: combining problem features and user behavior (DM, RF, PS, PCL).
EDMEDM-2019-NazaretskyHA #clustering #education
Kappa Learning: A New Item-Similarity Method for Clustering Educational Items from Response Data (TN, SH, GA).
EDMEDM-2019-NguyenWSM #component #comprehension #game studies #modelling #using
Using Knowledge Component Modeling to Increase Domain Understanding in a Digital Learning Game (HN, YW, JCS, BMM).
EDMEDM-2019-RamirezYCRS #student #towards #using
Toward Instrumenting Makerspaces: Using Motion Sensors to Capture Students' Affective States in Open-Ended Learning Environments (LR, WY, EC, IR, BS).
EDMEDM-2019-Reddick #algorithm #using
Using a Glicko-based Algorithm to Measure In-Course Learning (RR).
EDMEDM-2019-ReillyD #assessment
Exploring Stealth Assessment via Deep Learning in an Open-Ended Virtual Environment (JMR, CD).
EDMEDM-2019-Sher #mobile #using
Anatomy of mobile learners: Using learning analytics to unveil learning in presence of mobile devices (VS).
EDMEDM-2019-SherHG #mobile #power of #predict #student
Investigating effects of considering mobile and desktop learning data on predictive power of learning management system (LMS) features on student success (VS, MH, DG).
EDMEDM-2019-ShimadaMTOTK #optimisation #process #student
Optimizing Assignment of Students to Courses based on Learning Activity Analytics (AS, KM, YT, HO, RiT, SK).
EDMEDM-2019-WeitekampHMRK #predict #student #towards #using
Toward Near Zero-Parameter Prediction Using a Computational Model of Student Learning (DWI, EH, CJM, NR, KRK).
EDMEDM-2019-WhitehillAH #crowdsourcing #predict #what
Do Learners Know What's Good for Them? Crowdsourcing Subjective Ratings of OERs to Predict Learning Gains (JW, CA, BH).
EDMEDM-2019-YangBSHL #detection #student
Active Learning for Student Affect Detection (TYY, RSB, CS, NTH, ASL).
EDMEDM-2019-Yeung #named #using
Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory (CKY).
EDMEDM-2019-ZaidiCDMBR #modelling #student #using
Accurate Modelling of Language Learning Tasks and Students Using Representations of Grammatical Proficiency (AHZ, AC, CD, RM, PB, AR).
EDMEDM-2019-ZhangDYS #student
Student Knowledge Diagnosis on Response Data via the Model of Sparse Factor Learning (YZ, HD, YY, XS).
ICPCICPC-2019-SchnappingerOPF #classification #maintenance #predict #static analysis #tool support
Learning a classifier for prediction of maintainability based on static analysis tools (MS, MHO, AP, AF), pp. 243–248.
ICPCICPC-2019-XieQMZ #named #programming #visual notation
DeepVisual: a visual programming tool for deep learning systems (CX, HQ, LM0, JZ), pp. 130–134.
ICSMEICSME-2019-BarbezKG #anti #metric
Deep Learning Anti-Patterns from Code Metrics History (AB, FK, YGG), pp. 114–124.
ICSMEICSME-2019-Ha0 #configuration management #fourier
Performance-Influence Model for Highly Configurable Software with Fourier Learning and Lasso Regression (HH, HZ0), pp. 470–480.
ICSMEICSME-2019-MillsEBKCH #classification #traceability
Tracing with Less Data: Active Learning for Classification-Based Traceability Link Recovery (CM, JEA, AB, GK, SC, SH), pp. 103–113.
ICSMEICSME-2019-OumazizF0BK #product line
Handling Duplicates in Dockerfiles Families: Learning from Experts (MAO, JRF, XB0, TFB, JK), pp. 524–535.
ICSMEICSME-2019-PalacioMMBPS #identification #network #using
Learning to Identify Security-Related Issues Using Convolutional Neural Networks (DNP, DM, KM, CBC, DP, CS), pp. 140–144.
ICSMEICSME-2019-TufanoWBPWP #how #source code
Learning How to Mutate Source Code from Bug-Fixes (MT, CW, GB, MDP, MW, DP), pp. 301–312.
MSRMSR-2019-HoangDK0U #fault #framework #named #predict
DeepJIT: an end-to-end deep learning framework for just-in-time defect prediction (TH, HKD, YK, DL0, NU), pp. 34–45.
MSRMSR-2019-PerezC #abstract syntax tree #clone detection #detection #syntax
Cross-language clone detection by learning over abstract syntax trees (DP, SC), pp. 518–528.
MSRMSR-2019-TheetenVC #library
Import2vec learning embeddings for software libraries (BT, FV, TVC), pp. 18–28.
SANERSANER-2019-MaJXLLLZ #combinator #named #testing
DeepCT: Tomographic Combinatorial Testing for Deep Learning Systems (LM0, FJX, MX, BL0, LL0, YL0, JZ), pp. 614–618.
SANERSANER-2019-WhiteTMMP #program repair #sorting
Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities (MW, MT, MM, MM, DP), pp. 479–490.
SANERSANER-2019-XieCYLHDZ #approach #graph #named
DeepLink: A Code Knowledge Graph Based Deep Learning Approach for Issue-Commit Link Recovery (RX, LC, WY0, ZL, TH, DD, SZ), pp. 434–444.
SANERSANER-2019-YangASLHCS #industrial #model inference
Improving Model Inference in Industry by Combining Active and Passive Learning (NY, KA, RRHS, LL, DH, LC, AS), pp. 253–263.
SANERSANER-2019-YuBLKYX #empirical #fault #predict #rank
An Empirical Study of Learning to Rank Techniques for Effort-Aware Defect Prediction (XY, KEB, JL0, JWK, XY, ZX), pp. 298–309.
FMFM-2019-Sheinvald #automaton #infinity
Learning Deterministic Variable Automata over Infinite Alphabets (SS), pp. 633–650.
FMFM-2019-TapplerA0EL #markov #process
L*-Based Learning of Markov Decision Processes (MT, BKA, GB0, ME, KGL), pp. 651–669.
IFM-2019-DamascenoMS #adaptation #evolution #reuse
Learning to Reuse: Adaptive Model Learning for Evolving Systems (CDND, MRM, AdSS), pp. 138–156.
SEFMSEFM-2019-AvellanedaP #approach #automaton #satisfiability
Learning Minimal DFA: Taking Inspiration from RPNI to Improve SAT Approach (FA, AP), pp. 243–256.
AIIDEAIIDE-2019-BontragerKASST #game studies #network
“Superstition” in the Network: Deep Reinforcement Learning Plays Deceptive Games (PB, AK, DA, MS, CS, JT), pp. 10–16.
AIIDEAIIDE-2019-FrazierR
Improving Deep Reinforcement Learning in Minecraft with Action Advice (SF, MR), pp. 146–152.
AIIDEAIIDE-2019-GaoKHT #case study #on the
On Hard Exploration for Reinforcement Learning: A Case Study in Pommerman (CG, BK, PHL, MET), pp. 24–30.
AIIDEAIIDE-2019-Hernandez-LealK #modelling
Agent Modeling as Auxiliary Task for Deep Reinforcement Learning (PHL, BK, MET), pp. 31–37.
AIIDEAIIDE-2019-KartalHT #predict
Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning (BK, PHL, MET), pp. 38–44.
AIIDEAIIDE-2019-KartalHT19a
Action Guidance with MCTS for Deep Reinforcement Learning (BK, PHL, MET), pp. 153–159.
AIIDEAIIDE-2019-LinXR #named #semantics
GenerationMania: Learning to Semantically Choreograph (ZL, KX, MR), pp. 52–58.
AIIDEAIIDE-2019-Marino #game studies #programming #realtime #search-based
Learning Strategies for Real-Time Strategy Games with Genetic Programming (JRHM), pp. 219–220.
AIIDEAIIDE-2019-WangSZ #behaviour #modelling
Beyond Winning and Losing: Modeling Human Motivations and Behaviors with Vector-Valued Inverse Reinforcement Learning (BW, TS, XSZ), pp. 195–201.
AIIDEAIIDE-2019-XuKZHLS #metaprogramming
Macro Action Selection with Deep Reinforcement Learning in StarCraft (SX, HK, ZZ, RH, YL, HS), pp. 94–99.
CoGCoG-2019-AshleyCKB #evolution
Learning to Select Mates in Evolving Non-playable Characters (DRA, VC, BK, VB), pp. 1–8.
CoGCoG-2019-ChenL #game studies #metaprogramming
Macro and Micro Reinforcement Learning for Playing Nine-ball Pool (YC, YL), pp. 1–4.
CoGCoG-2019-ChenYL #abstraction #game studies #object-oriented #video
Object-Oriented State Abstraction in Reinforcement Learning for Video Games (YC, HY, YL), pp. 1–4.
CoGCoG-2019-DockhornLVBGL #game studies #modelling
Learning Local Forward Models on Unforgiving Games (AD, SML, VV, IB, RDG, DPL), pp. 1–4.
CoGCoG-2019-FendtA #education #game studies #student #using
Using Learning Games to Teach Texas Civil War History to Public Middle School Students (MWF, EA), pp. 1–4.
CoGCoG-2019-GainaS #game studies #video
“Did You Hear That?” Learning to Play Video Games from Audio Cues (RDG, MS), pp. 1–4.
CoGCoG-2019-GeorgiadisLBW #assessment
Learning Analytics Should Analyse the Learning: Proposing a Generic Stealth Assessment Tool (KG, GvL, KB, WW), pp. 1–8.
CoGCoG-2019-HarriesLRHD #3d #benchmark #metric #named
MazeExplorer: A Customisable 3D Benchmark for Assessing Generalisation in Reinforcement Learning (LH, SL, JR, KH, SD), pp. 1–4.
CoGCoG-2019-IlhanGP #education #multi
Teaching on a Budget in Multi-Agent Deep Reinforcement Learning (EI, JG, DPL), pp. 1–8.
CoGCoG-2019-JooK #game studies #how #question #using #visualisation
Visualization of Deep Reinforcement Learning using Grad-CAM: How AI Plays Atari Games? (HTJ, KJK), pp. 1–2.
CoGCoG-2019-KamaldinovM #game studies
Deep Reinforcement Learning in Match-3 Game (IK, IM), pp. 1–4.
CoGCoG-2019-KanagawaK #challenge #named
Rogue-Gym: A New Challenge for Generalization in Reinforcement Learning (YK, TK), pp. 1–8.
CoGCoG-2019-KanervistoH #named
ToriLLE: Learning Environment for Hand-to-Hand Combat (AK, VH), pp. 1–8.
CoGCoG-2019-KatonaSHDBDW #predict #using
Time to Die: Death Prediction in Dota 2 using Deep Learning (AK, RJS, VJH, SD, FB, AD, JAW), pp. 1–8.
CoGCoG-2019-KeehlS
Monster Carlo 2: Integrating Learning and Tree Search for Machine Playtesting (OK, AMS), pp. 1–8.
CoGCoG-2019-KhaustovBM #game studies
Pass in Human Style: Learning Soccer Game Patterns from Spatiotemporal Data (VK, GMB, MM), pp. 1–2.
CoGCoG-2019-Konen #education #game studies #research
General Board Game Playing for Education and Research in Generic AI Game Learning (WK), pp. 1–8.
CoGCoG-2019-LiapisKMSY #multimodal
Fusing Level and Ruleset Features for Multimodal Learning of Gameplay Outcomes (AL, DK, KM, KS, GNY), pp. 1–8.
CoGCoG-2019-LucasDVBGBPMK #approach #game studies
A Local Approach to Forward Model Learning: Results on the Game of Life Game (SML, AD, VV, CB, RDG, IB, DPL, SM, RK), pp. 1–8.
CoGCoG-2019-NaderiBRH #approach
A Reinforcement Learning Approach To Synthesizing Climbing Movements (KN, AB, SR, PH), pp. 1–7.
CoGCoG-2019-NaikJ #agile #development #game studies #gamification
Relax, It's a Game: Utilising Gamification in Learning Agile Scrum Software Development (NN, PJ), pp. 1–4.
CoGCoG-2019-NamI #game studies #generative #using
Generation of Diverse Stages in Turn-Based Role-Playing Game using Reinforcement Learning (SN, KI), pp. 1–8.
CoGCoG-2019-PiergigliRMG #multi
Deep Reinforcement Learning to train agents in a multiplayer First Person Shooter: some preliminary results (DP, LAR, DM, DG), pp. 1–8.
CoGCoG-2019-PleinesZB
Action Spaces in Deep Reinforcement Learning to Mimic Human Input Devices (MP, FZ, VPB), pp. 1–8.
CoGCoG-2019-RebstockSB #policy
Learning Policies from Human Data for Skat (DR, CS, MB), pp. 1–8.
CoGCoG-2019-SoemersPSB #policy #self
Learning Policies from Self-Play with Policy Gradients and MCTS Value Estimates (DJNJS, ÉP, MS, CB), pp. 1–8.
CoGCoG-2019-SpickCW #generative #using
Procedural Generation using Spatial GANs for Region-Specific Learning of Elevation Data (RJS, PC, JAW), pp. 1–8.
CoGCoG-2019-ZhangPFAJ #game studies #lr
1GBDT, LR & Deep Learning for Turn-based Strategy Game AI (LZ, HP, QF, CA, YJ), pp. 1–8.
CoGCoG-2019-ZuinV #game studies
Learning a Resource Scale for Collectible Card Games (GLZ, AV), pp. 1–8.
DiGRADiGRA-2019-KultimaL #game studies
Sami Game Jam - Learning, Exploring, Reflecting and Sharing Indigenous Culture through Game Jamming (AK, OL).
FDGFDG-2019-GuitartTRCP #game studies #predict #video
From non-paying to premium: predicting user conversion in video games with ensemble learning (AG, SHT, AFdR, PPC, ÁP), p. 9.
FDGFDG-2019-JemmaliKBARE #concept #design #game studies #programming #using
Using game design mechanics as metaphors to enhance learning of introductory programming concepts (CJ, EK, SB, MVA, ER, MSEN), p. 5.
FDGFDG-2019-KarthS
Addressing the fundamental tension of PCGML with discriminative learning (IK, AMS), p. 9.
FDGFDG-2019-Ruch #development #education #game studies
Trans-pacific project-based learning: game production curriculum development (AWR), p. 9.
FDGFDG-2019-WangCYPTA #game studies #synthesis
Goal-based progression synthesis in a korean learning game (SW, BC, SY, JYP, NT, EA), p. 9.
CoGVS-Games-2019-Hohl #architecture #game studies #interactive #visualisation
Game-Based Learning - Developing a Business Game for Interactive Architectural Visualization (WH), pp. 1–4.
CoGVS-Games-2019-ZhangBJ #artificial reality #interactive
Exploring Effects of Interactivity on Learning with Interactive Storytelling in Immersive Virtual Reality (LZ, DAB, CNJ), pp. 1–8.
CIKMCIKM-2019-00090S #representation
Hyper-Path-Based Representation Learning for Hyper-Networks (JH0, XL0, YS), pp. 449–458.
CIKMCIKM-2019-BhutaniZJ #composition #knowledge base #query
Learning to Answer Complex Questions over Knowledge Bases with Query Composition (NB, XZ, HVJ), pp. 739–748.
CIKMCIKM-2019-BoiarovT #metric #recognition #scalability
Large Scale Landmark Recognition via Deep Metric Learning (AB, ET), pp. 169–178.
CIKMCIKM-2019-BozarthDHJMPPQS #deployment #ubiquitous
Model Asset eXchange: Path to Ubiquitous Deep Learning Deployment (AB, BD, FH, DJ, KM, NP, SP, GdQ, SS, PT, XW, HX0, FRR, VB), pp. 2953–2956.
CIKMCIKM-2019-ChengLCHHCMH #named
DIRT: Deep Learning Enhanced Item Response Theory for Cognitive Diagnosis (SC, QL0, EC, ZH, ZH, YC, HM, GH), pp. 2397–2400.
CIKMCIKM-2019-ChenJZPNYWLXG #e-commerce
Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds (DC, JJ, WZ0, FP, LN, CY, JW0, HL, JX, KG), pp. 2527–2535.
CIKMCIKM-2019-ChenTL #query #social
Query Embedding Learning for Context-based Social Search (YCC, YCT, CTL), pp. 2441–2444.
CIKMCIKM-2019-DuanZYZLWWZS0 #mining #multi #summary
Legal Summarization for Multi-role Debate Dialogue via Controversy Focus Mining and Multi-task Learning (XD, YZ, LY, XZ, XL, TW, RW, QZ, CS, FW0), pp. 1361–1370.
CIKMCIKM-2019-EladGNKR #personalisation
Learning to Generate Personalized Product Descriptions (GE, IG, SN, BK, KR), pp. 389–398.
CIKMCIKM-2019-ElMS #data analysis #named
ATENA: An Autonomous System for Data Exploration Based on Deep Reinforcement Learning (OBE, TM, AS), pp. 2873–2876.
CIKMCIKM-2019-FanHZLLW #named #scalability
MONOPOLY: Learning to Price Public Facilities for Revaluing Private Properties with Large-Scale Urban Data (MF, JH, AZ, YL, PL0, HW), pp. 2655–2663.
CIKMCIKM-2019-FanZDCSL #approach #graph #identification #network #novel
Learning to Identify High Betweenness Centrality Nodes from Scratch: A Novel Graph Neural Network Approach (CF, LZ, YD, MC, YS, ZL), pp. 559–568.
CIKMCIKM-2019-GongZ00XWH #community #detection #developer #online #using
Detecting Malicious Accounts in Online Developer Communities Using Deep Learning (QG, JZ, YC0, QL0, YX, XW, PH), pp. 1251–1260.
CIKMCIKM-2019-GuHDM #analysis #named
LinkRadar: Assisting the Analysis of Inter-app Page Links via Transfer Learning (DG, ZH, SD, YM0), pp. 2077–2080.
CIKMCIKM-2019-HanMNKURNS #detection #image #using
Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images (CH, KM, TN, YK, FU, LR, HN, SS), pp. 119–127.
CIKMCIKM-2019-HosseiniH #feature model #kernel #multi #prototype #representation
Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection (BH, BH), pp. 1863–1872.
CIKMCIKM-2019-HuangSZWC #network #self
Similarity-Aware Network Embedding with Self-Paced Learning (CH0, BS, XZ, XW, NVC), pp. 2113–2116.
CIKMCIKM-2019-HuangYX #detection #graph
System Deterioration Detection and Root Cause Learning on Time Series Graphs (HH, SY, YX), pp. 2537–2545.
CIKMCIKM-2019-JenkinsFWL #multimodal #representation
Unsupervised Representation Learning of Spatial Data via Multimodal Embedding (PJ, AF, SW, ZL), pp. 1993–2002.
CIKMCIKM-2019-JiangCBWYN #predict #smarttech
Learning to Predict Human Stress Level with Incomplete Sensor Data from Wearable Devices (JYJ, ZC, ALB, WW0, SDY, DN), pp. 2773–2781.
CIKMCIKM-2019-JiangWZSLL #detection #graph #representation
Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation Learning (ZJ, JW, LZ, CS, YL, XL), pp. 289–298.
CIKMCIKM-2019-JinOLLLC #graph #semantics #similarity
Learning Region Similarity over Spatial Knowledge Graphs with Hierarchical Types and Semantic Relations (XJ, BO, SL, DL, KHL, LC), pp. 669–678.
CIKMCIKM-2019-KangHLY #recommendation
Semi-Supervised Learning for Cross-Domain Recommendation to Cold-Start Users (SK, JH, DL, HY), pp. 1563–1572.
CIKMCIKM-2019-KimSRLW #predict
Deep Learning for Blast Furnaces: Skip-Dense Layers Deep Learning Model to Predict the Remaining Time to Close Tap-holes for Blast Furnaces (KK, BS, SHR, SL, SSW), pp. 2733–2741.
CIKMCIKM-2019-KuziLSJZ #adaptation #analysis #information retrieval #rank
Analysis of Adaptive Training for Learning to Rank in Information Retrieval (SK, SL, SKKS, PPJ, CZ), pp. 2325–2328.
CIKMCIKM-2019-LiuWSL
Loopless Semi-Stochastic Gradient Descent with Less Hard Thresholding for Sparse Learning (XL, BW, FS, HL), pp. 881–890.
CIKMCIKM-2019-LiuWYZSMZGZYQ #graph #mobile #optimisation #representation
Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing (ZL, DW, QY, ZZ, YS, JM, WZ, JG, JZ, SY, YQ), pp. 2577–2584.
CIKMCIKM-2019-LiuZYCY #generative #refinement
Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System (YL, CZ, XY, YC, PSY), pp. 1643–1652.
CIKMCIKM-2019-LiWWLYLW #multi #platform
Partially Shared Adversarial Learning For Semi-supervised Multi-platform User Identity Linkage (CL, SW, HW, YL, PSY, ZL, WW), pp. 249–258.
CIKMCIKM-2019-LuoSAZ0 #multi #retrieval
Cross-modal Image-Text Retrieval with Multitask Learning (JL, YS, XA, ZZ, MY0), pp. 2309–2312.
CIKMCIKM-2019-LuoZWZ #framework #named #representation
ResumeGAN: An Optimized Deep Representation Learning Framework for Talent-Job Fit via Adversarial Learning (YL, HZ, YW, XZ), pp. 1101–1110.
CIKMCIKM-2019-LuYGWLC #clustering #realtime
Reinforcement Learning with Sequential Information Clustering in Real-Time Bidding (JL, CY, XG, LW, CL, GC), pp. 1633–1641.
CIKMCIKM-2019-MaAWSCTY #data analysis #graph #similarity
Deep Graph Similarity Learning for Brain Data Analysis (GM, NKA, TLW, DS, MWC, NBTB, PSY), pp. 2743–2751.
CIKMCIKM-2019-MaoSSSS #process
Investigating the Learning Process in Job Search: A Longitudinal Study (JM, DS, SS, FS, MS), pp. 2461–2464.
CIKMCIKM-2019-NeutatzMA #detection #fault #named
ED2: A Case for Active Learning in Error Detection (FN, MM, ZA), pp. 2249–2252.
CIKMCIKM-2019-RaoSPJCTGK #evolution #recommendation
Learning to be Relevant: Evolution of a Course Recommendation System (SR, KS, GP, MJ, SC, VT, JG, DK), pp. 2625–2633.
CIKMCIKM-2019-RizosHS #classification
Augment to Prevent: Short-Text Data Augmentation in Deep Learning for Hate-Speech Classification (GR, KH, BWS), pp. 991–1000.
CIKMCIKM-2019-ShenTB #graph #representation
GRLA 2019: The first International Workshop on Graph Representation Learning and its Applications (HS, JT, PB), pp. 2997–2998.
CIKMCIKM-2019-ShresthaMAV #behaviour #graph #interactive #social
Learning from Dynamic User Interaction Graphs to Forecast Diverse Social Behavior (PS, SM, DA, SV), pp. 2033–2042.
CIKMCIKM-2019-SongS0DX0T #automation #feature model #interactive #named #network #self
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks (WS, CS, ZX0, ZD, YX, MZ0, JT), pp. 1161–1170.
CIKMCIKM-2019-TanYHD #multi #segmentation #semantics
Batch Mode Active Learning for Semantic Segmentation Based on Multi-Clue Sample Selection (YT, LY, QH, ZD), pp. 831–840.
CIKMCIKM-2019-TaoGFCYZ #game studies #multi #named #online #predict
GMTL: A GART Based Multi-task Learning Model for Multi-Social-Temporal Prediction in Online Games (JT, LG, CF, LC, DY, SZ), pp. 2841–2849.
CIKMCIKM-2019-TrittenbachB #detection #multi
One-Class Active Learning for Outlier Detection with Multiple Subspaces (HT, KB), pp. 811–820.
CIKMCIKM-2019-Wang0C #graph #reasoning #recommendation
Learning and Reasoning on Graph for Recommendation (XW, XH0, TSC), pp. 2971–2972.
CIKMCIKM-2019-WangJH0YZWHWLXG #adaptation #realtime
Learning Adaptive Display Exposure for Real-Time Advertising (WW, JJ, JH, CC0, CY, WZ0, JW0, XH, YW, HL, JX, KG), pp. 2595–2603.
CIKMCIKM-2019-WangL #behaviour #network
Spotting Terrorists by Learning Behavior-aware Heterogeneous Network Embedding (PCW, CTL), pp. 2097–2100.
CIKMCIKM-2019-WangRCR0R #graph #predict
Order-free Medicine Combination Prediction with Graph Convolutional Reinforcement Learning (SW, PR, ZC, ZR, JM0, MdR), pp. 1623–1632.
CIKMCIKM-2019-WeiXZZZC0ZXL #named
CoLight: Learning Network-level Cooperation for Traffic Signal Control (HW, NX, HZ, GZ, XZ, CC, WZ0, YZ, KX, ZL), pp. 1913–1922.
CIKMCIKM-2019-WuLZQ #recommendation
Long- and Short-term Preference Learning for Next POI Recommendation (YW, KL, GZ, XQ), pp. 2301–2304.
CIKMCIKM-2019-WuPDTZD #distance #graph #network
Long-short Distance Aggregation Networks for Positive Unlabeled Graph Learning (MW, SP, LD, IWT, XZ, BD), pp. 2157–2160.
CIKMCIKM-2019-WuWZJ #effectiveness #performance #recommendation
Learning to Effectively Estimate the Travel Time for Fastest Route Recommendation (NW, JW, WXZ, YJ), pp. 1923–1932.
CIKMCIKM-2019-XiaoLM #collaboration
Dynamic Collaborative Recurrent Learning (TX, SL, ZM), pp. 1151–1160.
CIKMCIKM-2019-XiaoRMSL #metric #personalisation
Dynamic Bayesian Metric Learning for Personalized Product Search (TX, JR, ZM, HS, SL), pp. 1693–1702.
CIKMCIKM-2019-XiaWY #comprehension #multi
Incorporating Relation Knowledge into Commonsense Reading Comprehension with Multi-task Learning (JX, CW, MY), pp. 2393–2396.
CIKMCIKM-2019-XiongZXL
Learning Traffic Signal Control from Demonstrations (YX, GZ, KX, ZL), pp. 2289–2292.
CIKMCIKM-2019-XuHY #graph #network #scalability
Scalable Causal Graph Learning through a Deep Neural Network (CX, HH, SY), pp. 1853–1862.
CIKMCIKM-2019-YangDTTZQD #composition #predict #relational #visual notation
Learning Compositional, Visual and Relational Representations for CTR Prediction in Sponsored Search (XY, TD, WT, XT, JZ, SQ, ZD), pp. 2851–2859.
CIKMCIKM-2019-ZhangLZLWWX #benchmark #metric #multi #named #representation
Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning (DZ, JL, HZ, YL, LW, PW, HX), pp. 2763–2771.
CIKMCIKM-2019-ZhangLZZLWCZ #word
Learning Chinese Word Embeddings from Stroke, Structure and Pinyin of Characters (YZ, YL, JZ, ZZ, XL, WW, ZC, SZ), pp. 1011–1020.
CIKMCIKM-2019-ZhangMLZ0MXT #ranking
Context-Aware Ranking by Constructing a Virtual Environment for Reinforcement Learning (JZ, JM, YL, RZ, MZ0, SM, JX0, QT), pp. 1603–1612.
CIKMCIKM-2019-ZhangYWH #automation #e-commerce #named #ranking #realtime
Autor3: Automated Real-time Ranking with Reinforcement Learning in E-commerce Sponsored Search Advertising (YZ, ZY, LW, LH), pp. 2499–2507.
CIKMCIKM-2019-ZhaoCY #comprehension #e-commerce #query
A Dynamic Product-aware Learning Model for E-commerce Query Intent Understanding (JZ, HC, DY), pp. 1843–1852.
CIKMCIKM-2019-ZhaoSSW #graph #named #precise #retrieval
GRAPHENE: A Precise Biomedical Literature Retrieval Engine with Graph Augmented Deep Learning and External Knowledge Empowerment (SZ, CS, AS, FW), pp. 149–158.
CIKMCIKM-2019-ZhengXZFWZLXL #contest
Learning Phase Competition for Traffic Signal Control (GZ, YX, XZ, JF, HW, HZ, YL0, KX, ZL), pp. 1963–1972.
CIKMCIKM-2019-ZhouJZQJWWYY #multi
Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching (MZ, JJ, WZ0, ZQ, YJ, CW, GW, YY0, JY), pp. 2645–2653.
CIKMCIKM-2019-ZouK
Learning to Ask: Question-based Sequential Bayesian Product Search (JZ, EK), pp. 369–378.
CIKMCIKM-2019-ZouLAWZ #multi #named #rank
MarlRank: Multi-agent Reinforced Learning to Rank (SZ, ZL, MA, JW0, PZ), pp. 2073–2076.
ECIRECIR-p1-2019-BalikasDMAA #semantics #using
Learning Lexical-Semantic Relations Using Intuitive Cognitive Links (GB, GD, RM, HA, MRA), pp. 3–18.
ECIRECIR-p1-2019-FlorescuJ #graph #representation
A Supervised Keyphrase Extraction System Based on Graph Representation Learning (CF, WJ), pp. 197–212.
ECIRECIR-p2-2019-Landin #recommendation
Learning User and Item Representations for Recommender Systems (AL), pp. 337–342.
ECIRECIR-p2-2019-SyedIGSV #detection #induction #natural language #query
Inductive Transfer Learning for Detection of Well-Formed Natural Language Search Queries (BS, VI, MG0, MS0, VV), pp. 45–52.
ICMLICML-2019-0002CZG #adaptation #invariant #on the
On Learning Invariant Representations for Domain Adaptation (HZ0, RTdC, KZ0, GJG), pp. 7523–7532.
ICMLICML-2019-0002H
Target-Based Temporal-Difference Learning (DL0, NH), pp. 3713–3722.
ICMLICML-2019-0002VBB #performance
Provably Efficient Imitation Learning from Observation Alone (WS0, AV, BB, DB), pp. 6036–6045.
ICMLICML-2019-0002VY #constraints #policy
Batch Policy Learning under Constraints (HML0, CV, YY), pp. 3703–3712.
ICMLICML-2019-AbelsRLNS #multi
Dynamic Weights in Multi-Objective Deep Reinforcement Learning (AA, DMR, TL, AN, DS), pp. 11–20.
ICMLICML-2019-AcharyaSFS #communication #distributed #sublinear
Distributed Learning with Sublinear Communication (JA, CDS, DJF, KS), pp. 40–50.
ICMLICML-2019-AdamsJWS #fault #metric #modelling
Learning Models from Data with Measurement Error: Tackling Underreporting (RA, YJ, XW, SS), pp. 61–70.
ICMLICML-2019-AdelW #approach #named #visual notation
TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning (TA, AW), pp. 71–81.
ICMLICML-2019-AgarwalLS0
Learning to Generalize from Sparse and Underspecified Rewards (RA, CL, DS, MN0), pp. 130–140.
ICMLICML-2019-Allen-ZhuLS #convergence
A Convergence Theory for Deep Learning via Over-Parameterization (ZAZ, YL, ZS), pp. 242–252.
ICMLICML-2019-AllenSST #infinity #prototype
Infinite Mixture Prototypes for Few-shot Learning (KRA, ES, HS, JBT), pp. 232–241.
ICMLICML-2019-AssranLBR #distributed #probability
Stochastic Gradient Push for Distributed Deep Learning (MA, NL, NB, MR), pp. 344–353.
ICMLICML-2019-BalduzziGB0PJG #game studies #symmetry
Open-ended learning in symmetric zero-sum games (DB, MG, YB, WC0, JP, MJ, TG), pp. 434–443.
ICMLICML-2019-BaranchukPSB #graph #similarity
Learning to Route in Similarity Graphs (DB, DP, AS, AB), pp. 475–484.
ICMLICML-2019-BehpourLZ #predict #probability
Active Learning for Probabilistic Structured Prediction of Cuts and Matchings (SB, AL, BDZ), pp. 563–572.
ICMLICML-2019-BelilovskyEO
Greedy Layerwise Learning Can Scale To ImageNet (EB, ME, EO), pp. 583–593.
ICMLICML-2019-BenzingGMMS #approximate #realtime
Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning (FB, MMG, AM, AM, AS), pp. 604–613.
ICMLICML-2019-BhagojiCMC #lens
Analyzing Federated Learning through an Adversarial Lens (ANB, SC, PM, SBC), pp. 634–643.
ICMLICML-2019-BibautMVL #evaluation #performance
More Efficient Off-Policy Evaluation through Regularized Targeted Learning (AB, IM, NV, MJvdL), pp. 654–663.
ICMLICML-2019-BrownGNN
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations (DSB, WG, PN, SN), pp. 783–792.
ICMLICML-2019-BunneA0J #generative #modelling
Learning Generative Models across Incomparable Spaces (CB, DAM, AK0, SJ), pp. 851–861.
ICMLICML-2019-ByrdL #question #what
What is the Effect of Importance Weighting in Deep Learning? (JB, ZCL), pp. 872–881.
ICMLICML-2019-CaoS #multi #problem
Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit Problem (JC, WS), pp. 912–920.
ICMLICML-2019-ChandakTKJT
Learning Action Representations for Reinforcement Learning (YC, GT, JK, SMJ, PST), pp. 941–950.
ICMLICML-2019-CharoenphakdeeL #on the #symmetry
On Symmetric Losses for Learning from Corrupted Labels (NC, JL, MS), pp. 961–970.
ICMLICML-2019-ChatterjiPB #kernel #online
Online learning with kernel losses (NSC, AP, PLB), pp. 971–980.
ICMLICML-2019-Chen0LJQS #generative #recommendation
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System (XC, SL0, HL, SJ, YQ, LS), pp. 1052–1061.
ICMLICML-2019-ChengVOCYB
Control Regularization for Reduced Variance Reinforcement Learning (RC, AV, GO, SC, YY, JB), pp. 1141–1150.
ICMLICML-2019-ChenJ
Information-Theoretic Considerations in Batch Reinforcement Learning (JC, NJ), pp. 1042–1051.
ICMLICML-2019-ChuBG #functional #probability
Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning (CC, JHB, PWG), pp. 1213–1222.
ICMLICML-2019-CobbeKHKS
Quantifying Generalization in Reinforcement Learning (KC, OK, CH, TK, JS), pp. 1282–1289.
ICMLICML-2019-CohenKM
Learning Linear-Quadratic Regulators Efficiently with only √T Regret (AC, TK, YM), pp. 1300–1309.
ICMLICML-2019-ColasOSFC #composition #motivation #multi #named
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning (CC, PYO, OS, PF, MC), pp. 1331–1340.
ICMLICML-2019-CortesDGMY #feedback #graph #online
Online Learning with Sleeping Experts and Feedback Graphs (CC, GD, CG, MM, SY), pp. 1370–1378.
ICMLICML-2019-CortesDMZG #graph
Active Learning with Disagreement Graphs (CC, GD, MM, NZ, CG), pp. 1379–1387.
ICMLICML-2019-CreagerMJWSPZ #representation
Flexibly Fair Representation Learning by Disentanglement (EC, DM, JHJ, MAW, KS, TP, RSZ), pp. 1436–1445.
ICMLICML-2019-CutkoskyS #online
Matrix-Free Preconditioning in Online Learning (AC, TS), pp. 1455–1464.
ICMLICML-2019-CvitkovicK #statistics
Minimal Achievable Sufficient Statistic Learning (MC, GK), pp. 1465–1474.
ICMLICML-2019-CvitkovicSA #source code
Open Vocabulary Learning on Source Code with a Graph-Structured Cache (MC, BS, AA), pp. 1475–1485.
ICMLICML-2019-DadashiBTRS
The Value Function Polytope in Reinforcement Learning (RD, MGB, AAT, NLR, DS), pp. 1486–1495.
ICMLICML-2019-Dann0WB #policy #towards
Policy Certificates: Towards Accountable Reinforcement Learning (CD, LL0, WW, EB), pp. 1507–1516.
ICMLICML-2019-DaoGERR #algorithm #linear #performance #using
Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations (TD, AG, ME, AR, CR), pp. 1517–1527.
ICMLICML-2019-DereliOG #algorithm #analysis #biology #kernel #multi
A Multitask Multiple Kernel Learning Algorithm for Survival Analysis with Application to Cancer Biology (OD, CO, MG), pp. 1576–1585.
ICMLICML-2019-DiaconuW #approach
Learning to Convolve: A Generalized Weight-Tying Approach (ND, DEW), pp. 1586–1595.
ICMLICML-2019-DoanMR #analysis #approximate #distributed #finite #linear #multi
Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation on Multi-Agent Reinforcement Learning (TTD, STM, JR), pp. 1626–1635.
ICMLICML-2019-DoerrVTTD
Trajectory-Based Off-Policy Deep Reinforcement Learning (AD, MV, MT, ST, CD), pp. 1636–1645.
ICMLICML-2019-Duetting0NPR
Optimal Auctions through Deep Learning (PD, ZF0, HN, DCP, SSR), pp. 1706–1715.
ICMLICML-2019-DuklerLLM #generative #modelling
Wasserstein of Wasserstein Loss for Learning Generative Models (YD, WL, ATL, GM), pp. 1716–1725.
ICMLICML-2019-DuN
Task-Agnostic Dynamics Priors for Deep Reinforcement Learning (YD, KN), pp. 1696–1705.
ICMLICML-2019-DunckerBBS #modelling #probability
Learning interpretable continuous-time models of latent stochastic dynamical systems (LD, GB, JB, MS), pp. 1726–1734.
ICMLICML-2019-ElfekiCRE #named #process #using
GDPP: Learning Diverse Generations using Determinantal Point Processes (ME, CC, MR, ME), pp. 1774–1783.
ICMLICML-2019-FatemiSSK
Dead-ends and Secure Exploration in Reinforcement Learning (MF, SS, HvS, SEK), pp. 1873–1881.
ICMLICML-2019-Feige #invariant #multi #representation
Invariant-Equivariant Representation Learning for Multi-Class Data (IF), pp. 1882–1891.
ICMLICML-2019-FoersterSHBDWBB #multi
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning (JNF, HFS, EH, NB, ID, SW, MB, MB), pp. 1942–1951.
ICMLICML-2019-FranceschiNPH #graph #network
Learning Discrete Structures for Graph Neural Networks (LF, MN, MP, XH), pp. 1972–1982.
ICMLICML-2019-FrancP #nondeterminism #on the #predict
On discriminative learning of prediction uncertainty (VF, DP), pp. 1963–1971.
ICMLICML-2019-FujimotoMP
Off-Policy Deep Reinforcement Learning without Exploration (SF, DM, DP), pp. 2052–2062.
ICMLICML-2019-GamrianG
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation (SG, YG), pp. 2063–2072.
ICMLICML-2019-GaoJWWYZ #generative
Deep Generative Learning via Variational Gradient Flow (YG, YJ, YW, YW0, CY, SZ), pp. 2093–2101.
ICMLICML-2019-GeladaKBNB #modelling #named #representation
DeepMDP: Learning Continuous Latent Space Models for Representation Learning (CG, SK, JB, ON, MGB), pp. 2170–2179.
ICMLICML-2019-GhadikolaeiGFS #big data #dataset
Learning and Data Selection in Big Datasets (HSG, HGG, CF, MS), pp. 2191–2200.
ICMLICML-2019-GhaziPW #composition #recursion #sketching
Recursive Sketches for Modular Deep Learning (BG, RP, JRW), pp. 2211–2220.
ICMLICML-2019-GilboaB0 #performance #taxonomy
Efficient Dictionary Learning with Gradient Descent (DG, SB, JW0), pp. 2252–2259.
ICMLICML-2019-GillickREEB #sequence
Learning to Groove with Inverse Sequence Transformations (JG, AR, JHE, DE, DB), pp. 2269–2279.
ICMLICML-2019-GolovnevPS
The information-theoretic value of unlabeled data in semi-supervised learning (AG, DP, BS), pp. 2328–2336.
ICMLICML-2019-GreenfeldGBYK #multi
Learning to Optimize Multigrid PDE Solvers (DG, MG, RB, IY, RK), pp. 2415–2423.
ICMLICML-2019-GreffKKWBZMBL #multi #representation
Multi-Object Representation Learning with Iterative Variational Inference (KG, RLK, RK, NW, CB, DZ, LM, MB, AL), pp. 2424–2433.
ICMLICML-2019-GuoSH #dependence #graph #relational
Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs (LG, ZS, WH0), pp. 2505–2514.
ICMLICML-2019-HacohenW #education #network #on the #power of
On The Power of Curriculum Learning in Training Deep Networks (GH, DW), pp. 2535–2544.
ICMLICML-2019-HafnerLFVHLD
Learning Latent Dynamics for Planning from Pixels (DH, TPL, IF, RV, DH, HL, JD), pp. 2555–2565.
ICMLICML-2019-HanS
Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning (SH, YS), pp. 2586–2595.
ICMLICML-2019-HanSDXWSLZ #game studies #multi #video
Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI (LH, PS, YD, JX, QW, XS, HL, TZ), pp. 2576–2585.
ICMLICML-2019-HeidariNG #algorithm #on the #policy #social
On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning (HH, VN, KPG), pp. 2692–2701.
ICMLICML-2019-HendrickxOS #graph
Graph Resistance and Learning from Pairwise Comparisons (JMH, AO, VS), pp. 2702–2711.
ICMLICML-2019-HoferKND #persistent #representation
Connectivity-Optimized Representation Learning via Persistent Homology (CDH, RK, MN, MD), pp. 2751–2760.
ICMLICML-2019-HoLCSA #performance #policy
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules (DH, EL, XC0, IS, PA), pp. 2731–2741.
ICMLICML-2019-HoulsbyGJMLGAG
Parameter-Efficient Transfer Learning for NLP (NH, AG, SJ, BM, QdL, AG, MA, SG), pp. 2790–2799.
ICMLICML-2019-HuangDGZ
Unsupervised Deep Learning by Neighbourhood Discovery (JH, QD0, SG, XZ), pp. 2849–2858.
ICMLICML-2019-InnesL #problem
Learning Structured Decision Problems with Unawareness (CI, AL), pp. 2941–2950.
ICMLICML-2019-IqbalS #multi
Actor-Attention-Critic for Multi-Agent Reinforcement Learning (SI, FS), pp. 2961–2970.
ICMLICML-2019-IshidaNMS #modelling
Complementary-Label Learning for Arbitrary Losses and Models (TI, GN, AKM, MS), pp. 2971–2980.
ICMLICML-2019-JacqGPP
Learning from a Learner (AJ, MG, AP, OP), pp. 2990–2999.
ICMLICML-2019-JagielskiKMORSU
Differentially Private Fair Learning (MJ, MJK, JM, AO, AR0, SSM, JU), pp. 3000–3008.
ICMLICML-2019-JangLHS #what
Learning What and Where to Transfer (YJ, HL, SJH, JS), pp. 3030–3039.
ICMLICML-2019-JaquesLHGOSLF #motivation #multi #social
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning (NJ, AL, EH, ÇG, PAO, DS, JZL, NdF), pp. 3040–3049.
ICMLICML-2019-JayRGST #internet
A Deep Reinforcement Learning Perspective on Internet Congestion Control (NJ, NHR, BG, MS, AT), pp. 3050–3059.
ICMLICML-2019-JeongS19a
Learning Discrete and Continuous Factors of Data via Alternating Disentanglement (YJ, HOS), pp. 3091–3099.
ICMLICML-2019-JiangL #logic
Neural Logic Reinforcement Learning (ZJ, SL), pp. 3110–3119.
ICMLICML-2019-KaplanisSC #policy
Policy Consolidation for Continual Reinforcement Learning (CK, MS, CC), pp. 3242–3251.
ICMLICML-2019-KaplanMMS #concept #geometry
Differentially Private Learning of Geometric Concepts (HK, YM, YM, US), pp. 3233–3241.
ICMLICML-2019-KempkaKW #adaptation #algorithm #invariant #linear #modelling #online
Adaptive Scale-Invariant Online Algorithms for Learning Linear Models (MK, WK, MKW), pp. 3321–3330.
ICMLICML-2019-KhadkaMNDTMLT #collaboration
Collaborative Evolutionary Reinforcement Learning (SK, SM, TN, ZD, ET, SM, YL, KT), pp. 3341–3350.
ICMLICML-2019-KipfLDZSGKB #composition #execution #named
CompILE: Compositional Imitation Learning and Execution (TK, YL, HD, VFZ, ASG, EG, PK, PWB), pp. 3418–3428.
ICMLICML-2019-KonstantinovL #robust
Robust Learning from Untrusted Sources (NK, CL), pp. 3488–3498.
ICMLICML-2019-LawLSZ #distance
Lorentzian Distance Learning for Hyperbolic Representations (MTL, RL, JS, RSZ), pp. 3672–3681.
ICMLICML-2019-LawrenceEC #dependence #multi #named #parametricity
DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures (ARL, CHE, NDFC), pp. 3682–3691.
ICMLICML-2019-LiDMMHH #named #network
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning (HYL, WD, XM, CM, FH, BGH), pp. 3825–3834.
ICMLICML-2019-LiGDVK #graph #network #similarity
Graph Matching Networks for Learning the Similarity of Graph Structured Objects (YL, CG, TD, OV, PK), pp. 3835–3845.
ICMLICML-2019-LiLS #online #rank
Online Learning to Rank with Features (SL, TL, CS), pp. 3856–3865.
ICMLICML-2019-LiLWZG #black box #named #network
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks (YL, LL, LW, TZ, BG), pp. 3866–3876.
ICMLICML-2019-LimA #kernel #markov #process #robust
Kernel-Based Reinforcement Learning in Robust Markov Decision Processes (SHL, AA), pp. 3973–3981.
ICMLICML-2019-LiSK #physics
Adversarial camera stickers: A physical camera-based attack on deep learning systems (JL0, FRS, JZK), pp. 3896–3904.
ICMLICML-2019-LiSSG #exponential #kernel #product line
Learning deep kernels for exponential family densities (WL, DJS, HS, AG), pp. 6737–6746.
ICMLICML-2019-LiuS #multi
Sparse Extreme Multi-label Learning with Oracle Property (WL, XS0), pp. 4032–4041.
ICMLICML-2019-LiuSH
The Implicit Fairness Criterion of Unconstrained Learning (LTL, MS, MH), pp. 4051–4060.
ICMLICML-2019-LiuSX #performance
Taming MAML: Efficient unbiased meta-reinforcement learning (HL, RS, CX), pp. 4061–4071.
ICMLICML-2019-LiZWSX #framework
Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting (XL, YZ, TW, RS, CX), pp. 3925–3934.
ICMLICML-2019-LocatelloBLRGSB
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations (FL, SB, ML, GR, SG, BS, OB), pp. 4114–4124.
ICMLICML-2019-MahloujifarMM #multi
Data Poisoning Attacks in Multi-Party Learning (SM, MM, AM), pp. 4274–4283.
ICMLICML-2019-MalikKSNSE #modelling
Calibrated Model-Based Deep Reinforcement Learning (AM, VK, JS, DN, HS, SE), pp. 4314–4323.
ICMLICML-2019-MannGGHJLS #recommendation
Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems (TAM, SG, AG, HH, RJ, BL, PS), pp. 4324–4332.
ICMLICML-2019-MaryCK
Fairness-Aware Learning for Continuous Attributes and Treatments (JM, CC, NEK), pp. 4382–4391.
ICMLICML-2019-MavrinYKWY #performance
Distributional Reinforcement Learning for Efficient Exploration (BM, HY, LK, KW, YY), pp. 4424–4434.
ICMLICML-2019-MenschBP #geometry
Geometric Losses for Distributional Learning (AM, MB, GP), pp. 4516–4525.
ICMLICML-2019-MetelliGR #configuration management
Reinforcement Learning in Configurable Continuous Environments (AMM, EG, MR), pp. 4546–4555.
ICMLICML-2019-MishneCC
Co-manifold learning with missing data (GM, ECC, RRC), pp. 4605–4614.
ICMLICML-2019-MohriSS
Agnostic Federated Learning (MM, GS, ATS), pp. 4615–4625.
ICMLICML-2019-NabiMS #policy
Learning Optimal Fair Policies (RN, DM, IS), pp. 4674–4682.
ICMLICML-2019-NaganoY0K
A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning (YN, SY, YF0, MK), pp. 4693–4702.
ICMLICML-2019-NamKMPSF #classification #multi #permutation
Learning Context-dependent Label Permutations for Multi-label Classification (JN, YBK, ELM, SP, RS, JF), pp. 4733–4742.
ICMLICML-2019-NedelecKP
Learning to bid in revenue-maximizing auctions (TN, NEK, VP), pp. 4781–4789.
ICMLICML-2019-Nguyen #on the #set
On Connected Sublevel Sets in Deep Learning (QN), pp. 4790–4799.
ICMLICML-2019-NiekerkJER
Composing Value Functions in Reinforcement Learning (BvN, SJ, ACE, BR), pp. 6401–6409.
ICMLICML-2019-NyeHTS #sketching
Learning to Infer Program Sketches (MIN, LBH, JBT, ASL), pp. 4861–4870.
ICMLICML-2019-OglicG #kernel #scalability
Scalable Learning in Reproducing Kernel Krein Spaces (DO, TG0), pp. 4912–4921.
ICMLICML-2019-OymakS #question
Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path? (SO, MS), pp. 4951–4960.
ICMLICML-2019-PaulOW #optimisation #policy #robust
Fingerprint Policy Optimisation for Robust Reinforcement Learning (SP, MAO, SW), pp. 5082–5091.
ICMLICML-2019-PengHSS
Domain Agnostic Learning with Disentangled Representations (XP, ZH, XS, KS), pp. 5102–5112.
ICMLICML-2019-PingPSZRW #normalisation #representation
Differentiable Dynamic Normalization for Learning Deep Representation (LP, ZP, WS, RZ, JR, LW), pp. 4203–4211.
ICMLICML-2019-QuMX
Nonlinear Distributional Gradient Temporal-Difference Learning (CQ, SM, HX), pp. 5251–5260.
ICMLICML-2019-RadanovicDPS #markov #process
Learning to Collaborate in Markov Decision Processes (GR, RD, DCP, AS), pp. 5261–5270.
ICMLICML-2019-RakellyZFLQ #performance #probability
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables (KR, AZ, CF, SL, DQ), pp. 5331–5340.
ICMLICML-2019-ReslerM #online
Adversarial Online Learning with noise (AR, YM), pp. 5429–5437.
ICMLICML-2019-RollandKISC #performance #probability #testing
Efficient learning of smooth probability functions from Bernoulli tests with guarantees (PR, AK, AI, AS, VC), pp. 5459–5467.
ICMLICML-2019-RowlandDKMBD #statistics
Statistics and Samples in Distributional Reinforcement Learning (MR, RD, SK, RM, MGB, WD), pp. 5528–5536.
ICMLICML-2019-SaunshiPAKK #analysis #representation
A Theoretical Analysis of Contrastive Unsupervised Representation Learning (NS, OP, SA, MK, HK), pp. 5628–5637.
ICMLICML-2019-SchroeterSM #locality
Weakly-Supervised Temporal Localization via Occurrence Count Learning (JS, KAS, ADM), pp. 5649–5659.
ICMLICML-2019-ShahGAD #bias #on the
On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference (RS, NG, PA, ADD), pp. 5670–5679.
ICMLICML-2019-ShaniEM #revisited
Exploration Conscious Reinforcement Learning Revisited (LS, YE, SM), pp. 5680–5689.
ICMLICML-2019-ShenLL
Learning to Clear the Market (WS, SL, RPL), pp. 5710–5718.
ICMLICML-2019-ShenS
Learning with Bad Training Data via Iterative Trimmed Loss Minimization (YS, SS), pp. 5739–5748.
ICMLICML-2019-Shi0 #multi #performance
Fast Direct Search in an Optimally Compressed Continuous Target Space for Efficient Multi-Label Active Learning (WS, QY0), pp. 5769–5778.
ICMLICML-2019-SinglaWFF #approximate #comprehension #higher-order
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation (SS0, EW, SF, SF), pp. 5848–5856.
ICMLICML-2019-SongK0 #named #robust
SELFIE: Refurbishing Unclean Samples for Robust Deep Learning (HS, MK, JGL0), pp. 5907–5915.
ICMLICML-2019-SonKKHY #multi #named
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning (KS, DK, WJK, DH, YY), pp. 5887–5896.
ICMLICML-2019-Stickland0 #adaptation #multi #performance
BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning (ACS, IM0), pp. 5986–5995.
ICMLICML-2019-Streeter #linear
Learning Optimal Linear Regularizers (MS), pp. 5996–6004.
ICMLICML-2019-SundinSSVSK
Active Learning for Decision-Making from Imbalanced Observational Data (IS, PS, ES, AV, SS, SK), pp. 6046–6055.
ICMLICML-2019-SuW #distance #metric #sequence
Learning Distance for Sequences by Learning a Ground Metric (BS, YW), pp. 6015–6025.
ICMLICML-2019-SuWSJ #adaptation #evaluation #named #policy
CAB: Continuous Adaptive Blending for Policy Evaluation and Learning (YS, LW, MS, TJ), pp. 6005–6014.
ICMLICML-2019-TesslerEM #robust
Action Robust Reinforcement Learning and Applications in Continuous Control (CT, YE, SM), pp. 6215–6224.
ICMLICML-2019-ThulasidasanBBC #using
Combating Label Noise in Deep Learning using Abstention (ST, TB, JAB, GC, JMY), pp. 6234–6243.
ICMLICML-2019-TranDRC #generative
Bayesian Generative Active Deep Learning (TT, TTD, IDR0, GC), pp. 6295–6304.
ICMLICML-2019-TrouleauEGKT #process
Learning Hawkes Processes Under Synchronization Noise (WT, JE, MG, NK, PT), pp. 6325–6334.
ICMLICML-2019-VarmaSHRR #dependence #modelling
Learning Dependency Structures for Weak Supervision Models (PV, FS, AH, AR, CR), pp. 6418–6427.
ICMLICML-2019-VinayakKVK #estimation #parametricity
Maximum Likelihood Estimation for Learning Populations of Parameters (RKV, WK, GV, SMK), pp. 6448–6457.
ICMLICML-2019-VorobevUGS #ranking
Learning to select for a predefined ranking (AV, AU, GG, PS), pp. 6477–6486.
ICMLICML-2019-Wang0 #modelling
Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models (DW, QL0), pp. 6576–6585.
ICMLICML-2019-WangCAD #estimation #policy #random
Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation (RW, CC, PVA, YD), pp. 6536–6544.
ICMLICML-2019-WangDWK #logic #named #reasoning #satisfiability #using
SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver (PWW, PLD, BW, JZK), pp. 6545–6554.
ICMLICML-2019-WangZ0Q #random #recommendation #robust
Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random (XW, RZ0, YS0, JQ0), pp. 6638–6647.
ICMLICML-2019-WangZXS #on the
On the Generalization Gap in Reparameterizable Reinforcement Learning (HW, SZ, CX, RS), pp. 6648–6658.
ICMLICML-2019-WiqvistMPF #approximate #architecture #network #statistics #summary
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation (SW, PAM, UP, JF), pp. 6798–6807.
ICMLICML-2019-WonXL
Projection onto Minkowski Sums with Application to Constrained Learning (JHW, JX, KL), pp. 3642–3651.
ICMLICML-2019-WuCBTS
Imitation Learning from Imperfect Demonstration (YHW, NC, HB, VT, MS), pp. 6818–6827.
ICMLICML-2019-WuDSYHSRK #matrix #metric
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling (SW, AD, SS, FXY, DNHR, DS, AR, SK), pp. 6828–6839.
ICMLICML-2019-XuLZC #graph
Gromov-Wasserstein Learning for Graph Matching and Node Embedding (HX, DL, HZ, LC), pp. 6932–6941.
ICMLICML-2019-XuRDLF
Learning a Prior over Intent via Meta-Inverse Reinforcement Learning (KX, ER, ADD, SL, CF), pp. 6952–6962.
ICMLICML-2019-YangD #proving #theorem
Learning to Prove Theorems via Interacting with Proof Assistants (KY, JD), pp. 6984–6994.
ICMLICML-2019-YinCRB #distributed
Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning (DY, YC0, KR, PLB), pp. 7074–7084.
ICMLICML-2019-YoonSM #adaptation #named #network
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning (SWY, JS, JM), pp. 7115–7123.
ICMLICML-2019-YoungBN #generative #modelling #synthesis
Learning Neurosymbolic Generative Models via Program Synthesis (HY, OB, MN), pp. 7144–7153.
ICMLICML-2019-YuCGY #graph #named #network
DAG-GNN: DAG Structure Learning with Graph Neural Networks (YY, JC, TG, MY), pp. 7154–7163.
ICMLICML-2019-YunZYLA #analysis #optimisation #statistics
Trimming the l₁ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning (JY, PZ, EY, ACL, AYA), pp. 7242–7251.
ICMLICML-2019-YurochkinAGGHK #network #parametricity
Bayesian Nonparametric Federated Learning of Neural Networks (MY, MA, SG, KHG, TNH, YK), pp. 7252–7261.
ICMLICML-2019-YuSE #multi
Multi-Agent Adversarial Inverse Reinforcement Learning (LY, JS, SE), pp. 7194–7201.
ICMLICML-2019-YuTRKSAZL #distributed #network
Distributed Learning over Unreliable Networks (CY, HT, CR, SK, AS, DA, CZ, JL0), pp. 7202–7212.
ICMLICML-2019-ZablockiBSPG #recognition
Context-Aware Zero-Shot Learning for Object Recognition (EZ, PB, LS, BP, PG), pp. 7292–7303.
ICMLICML-2019-ZanetteB #bound #problem #using
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds (AZ, EB), pp. 7304–7312.
ICMLICML-2019-ZengLLY #convergence #coordination
Global Convergence of Block Coordinate Descent in Deep Learning (JZ, TTKL, SL, YY0), pp. 7313–7323.
ICMLICML-2019-ZhangHY #named #performance #recognition #visual notation
LatentGNN: Learning Efficient Non-local Relations for Visual Recognition (SZ, XH, SY), pp. 7374–7383.
ICMLICML-2019-ZhangL #incremental #kernel #online #random #sketching
Incremental Randomized Sketching for Online Kernel Learning (XZ, SL), pp. 7394–7403.
ICMLICML-2019-ZhangS #network
Co-Representation Network for Generalized Zero-Shot Learning (FZ, GS), pp. 7434–7443.
ICMLICML-2019-ZhangVSA0L #modelling #named
SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning (MZ, SV, LS, PA, MJJ0, SL), pp. 7444–7453.
ICMLICML-2019-ZhangYT #novel #policy
Learning Novel Policies For Tasks (YZ, WY, GT), pp. 7483–7492.
ICMLICML-2019-ZhaoST #multi
Maximum Entropy-Regularized Multi-Goal Reinforcement Learning (RZ, XS0, VT), pp. 7553–7562.
ICMLICML-2019-ZhuangCO #online #optimisation #probability
Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization (ZZ, AC, FO), pp. 7664–7672.
ICMLICML-2019-ZhuSLHB #fault tolerance #graph
Improved Dynamic Graph Learning through Fault-Tolerant Sparsification (CJZ, SS, KyL, SH, JB), pp. 7624–7633.
ICMLICML-2019-ZhuWS #classification
Learning Classifiers for Target Domain with Limited or No Labels (PZ, HW, VS), pp. 7643–7653.
KDDKDD-2019-BabaevSTU
E.T.-RNN: Applying Deep Learning to Credit Loan Applications (DB, MS, AT, DU), pp. 2183–2190.
KDDKDD-2019-CenZZYZ0 #multi #network #representation
Representation Learning for Attributed Multiplex Heterogeneous Network (YC, XZ, JZ, HY, JZ, JT0), pp. 1358–1368.
KDDKDD-2019-ChenreddyPNCA #named #optimisation
SMOILE: A Shopper Marketing Optimization and Inverse Learning Engine (ARC, PP, SN, RC, RA), pp. 2934–2942.
KDDKDD-2019-Chien #comprehension #mining
Deep Bayesian Mining, Learning and Understanding (JTC), pp. 3197–3198.
KDDKDD-2019-DengRN #graph #predict #social
Learning Dynamic Context Graphs for Predicting Social Events (SD, HR, YN), pp. 1007–1016.
KDDKDD-2019-deWetO
Finding Users Who Act Alike: Transfer Learning for Expanding Advertiser Audiences (Sd, JO), pp. 2251–2259.
KDDKDD-2019-DiSC
Relation Extraction via Domain-aware Transfer Learning (SD, YS, LC), pp. 1348–1357.
KDDKDD-2019-EsfandiariWAR #online #optimisation
Optimizing Peer Learning in Online Groups with Affinities (ME, DW, SAY, SBR), pp. 1216–1226.
KDDKDD-2019-FanZPLZYWWPH #multi
Multi-Horizon Time Series Forecasting with Temporal Attention Learning (CF, YZ, YP, XL, CZ0, RY, DW, WW, JP, HH), pp. 2527–2535.
KDDKDD-2019-FeiTL #multi #predict #word
Hierarchical Multi-Task Word Embedding Learning for Synonym Prediction (HF, ST, PL0), pp. 834–842.
KDDKDD-2019-GaoJ #graph #network #representation
Graph Representation Learning via Hard and Channel-Wise Attention Networks (HG, SJ), pp. 741–749.
KDDKDD-2019-HaldarARXYDZBTC
Applying Deep Learning to Airbnb Search (MH, MA, PR, TX, SY, HD, QZ, NBW, BCT, BMC, TL), pp. 1927–1935.
KDDKDD-2019-HaoCYSW #concept #knowledge base #ontology #representation
Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts (JH, MC, WY, YS, WW), pp. 1709–1719.
KDDKDD-2019-HeLLH #network
Learning Network-to-Network Model for Content-rich Network Embedding (ZH, JL0, NL, YH), pp. 1037–1045.
KDDKDD-2019-HeXZMZY #multi
Off-policy Learning for Multiple Loggers (LH, LX, WZ, ZMM, YZ, DY), pp. 1184–1193.
KDDKDD-2019-HossainR #process #recognition
Active Deep Learning for Activity Recognition with Context Aware Annotator Selection (HMSH, NR), pp. 1862–1870.
KDDKDD-2019-HouCLCY #framework #graph #representation
A Representation Learning Framework for Property Graphs (YH, HC, CL, JC, MCY), pp. 65–73.
KDDKDD-2019-Huang0DLLPST0Y0 #algorithm #network #theory and practice
Learning From Networks: Algorithms, Theory, and Applications (XH, PC0, YD, JL, HL0, JP, LS, JT0, FW0, HY, WZ0), pp. 3221–3222.
KDDKDD-2019-HuFS #network
Adversarial Learning on Heterogeneous Information Networks (BH, YF0, CS), pp. 120–129.
KDDKDD-2019-HughesCZ #generative
Generating Better Search Engine Text Advertisements with Deep Reinforcement Learning (JWH, KhC, RZ), pp. 2269–2277.
KDDKDD-2019-HuH #named #network #set
Sets2Sets: Learning from Sequential Sets with Neural Networks (HH, XH0), pp. 1491–1499.
KDDKDD-2019-HulsebosHBZSKDH #approach #data type #detection #named #semantics
Sherlock: A Deep Learning Approach to Semantic Data Type Detection (MH, KZH, MAB, EZ, AS, TK, ÇD, CAH), pp. 1500–1508.
KDDKDD-2019-InabaFKZ #approach #distance #energy #metric
A Free Energy Based Approach for Distance Metric Learning (SI, CTF, RVK, KZ), pp. 5–13.
KDDKDD-2019-JiaLZKK #how #question #robust #towards
Towards Robust and Discriminative Sequential Data Learning: When and How to Perform Adversarial Training? (XJ, SL0, HZ, SK, VK), pp. 1665–1673.
KDDKDD-2019-JiaSSB #graph
Graph-based Semi-Supervised & Active Learning for Edge Flows (JJ, MTS, SS, ARB), pp. 761–771.
KDDKDD-2019-KeXZBL #framework #named #online #predict
DeepGBM: A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks (GK, ZX, JZ, JB0, TYL), pp. 384–394.
KDDKDD-2019-KillianWSCDT #using
Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data (JAK, BW, AS, VC, BD, MT), pp. 2430–2438.
KDDKDD-2019-Li0WGYK #adaptation #kernel #multi #predict
Sample Adaptive Multiple Kernel Learning for Failure Prediction of Railway Points (ZL, JZ0, QW0, YG, JY, CK), pp. 2848–2856.
KDDKDD-2019-LiuFWWBL #automation #multi
Automating Feature Subspace Exploration via Multi-Agent Reinforcement Learning (KL, YF, PW, LW, RB, XL), pp. 207–215.
KDDKDD-2019-LiuLDCG #named #recommendation
DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation (DL, JL0, BD, JC, RG), pp. 344–352.
KDDKDD-2019-LiuTLZCMW #adaptation
Exploiting Cognitive Structure for Adaptive Learning (QL0, ST, CL, HZ, EC, HM, SW), pp. 627–635.
KDDKDD-2019-LiZY #effectiveness #performance
Efficient and Effective Express via Contextual Cooperative Reinforcement Learning (YL, YZ, QY), pp. 510–519.
KDDKDD-2019-MingXQR #prototype #sequence
Interpretable and Steerable Sequence Learning via Prototypes (YM, PX, HQ, LR), pp. 903–913.
KDDKDD-2019-OhI #detection #using
Sequential Anomaly Detection using Inverse Reinforcement Learning (MhO, GI), pp. 1480–1490.
KDDKDD-2019-PanLW00Z #predict #using
Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning (ZP, YL, WW, YY0, YZ0, JZ), pp. 1720–1730.
KDDKDD-2019-PanMRSF #multi #online #predict
Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising (JP, YM, ALR, YS, AF), pp. 2689–2697.
KDDKDD-2019-ParkLHHLC #quality
Learning Sleep Quality from Daily Logs (SP, CTL, SH, CH, SWL, MC), pp. 2421–2429.
KDDKDD-2019-Qin0Y
Deep Reinforcement Learning with Applications in Transportation (Z(Q, JT0, JY), pp. 3201–3202.
KDDKDD-2019-RawatLY #multi #using
Naranjo Question Answering using End-to-End Multi-task Learning Model (BPSR, FL, HY0), pp. 2547–2555.
KDDKDD-2019-SahooHKWLALH #image #named #recognition
FoodAI: Food Image Recognition via Deep Learning for Smart Food Logging (DS, WH, SK, XW, HL, PA, EPL, SCHH), pp. 2260–2268.
KDDKDD-2019-Salakhutdinov
Integrating Domain-Knowledge into Deep Learning (RS), p. 3176.
KDDKDD-2019-ShangYLQMY #re-engineering #recommendation
Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation (WS, YY, QL, ZQ, YM, JY), pp. 566–576.
KDDKDD-2019-ShenVAAHN #monitoring #smarttech #using
Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning (YS, MV, AA, AA, AYH, AYN), pp. 1909–1916.
KDDKDD-2019-SpathisRFMR #multi #self #sequence
Sequence Multi-task Learning to Forecast Mental Wellbeing from Sparse Self-reported Data (DS, SSR, KF, CM, JR), pp. 2886–2894.
KDDKDD-2019-SuzukiWN #scheduling
TV Advertisement Scheduling by Learning Expert Intentions (YS, WMW, IN), pp. 3071–3081.
KDDKDD-2019-TangXWZL #multi
Retaining Privileged Information for Multi-Task Learning (FT, CX, FW, JZ, LWHL), pp. 1369–1377.
KDDKDD-2019-TokuiOANOSSUVV #framework #named #research
Chainer: A Deep Learning Framework for Accelerating the Research Cycle (ST, RO, TA, YN, TO, SS, SS, KU, BV, HYV), pp. 2002–2011.
KDDKDD-2019-WangFXL #mobile #profiling #representation
Adversarial Substructured Representation Learning for Mobile User Profiling (PW, YF, HX, XL), pp. 130–138.
KDDKDD-2019-WangLCJ #effectiveness #game studies #performance #representation #retrieval
Effective and Efficient Sports Play Retrieval with Deep Representation Learning (ZW, CL, GC, CJ), pp. 499–509.
KDDKDD-2019-WangLZ #adaptation #ambiguity #graph
Adaptive Graph Guided Disambiguation for Partial Label Learning (DBW, LL0, MLZ), pp. 83–91.
KDDKDD-2019-WangQWLGZHZCZ #game studies
A Minimax Game for Instance based Selective Transfer Learning (BW, MQ, XW, YL, YG, XZ, JH0, BZ, DC, JZ), pp. 34–43.
KDDKDD-2019-WangXLLCDWS #framework #multi #named #network #social
MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network (HW, TX, QL0, DL, EC, DD, HW, WS), pp. 1064–1072.
KDDKDD-2019-WangYCZ #convergence #performance
ADMM for Efficient Deep Learning with Global Convergence (JW, FY, XC0, LZ0), pp. 111–119.
KDDKDD-2019-WeiCZWGXL #coordination #named #network
PressLight: Learning Max Pressure Control to Coordinate Traffic Signals in Arterial Network (HW, CC, GZ, KW, VVG, KX, ZL), pp. 1290–1298.
KDDKDD-2019-XieH
Learning Class-Conditional GANs with Active Sampling (MKX, SJH), pp. 998–1006.
KDDKDD-2019-XuTZ #kernel #multi
Isolation Set-Kernel and Its Application to Multi-Instance Learning (BCX, KMT, ZHZ), pp. 941–949.
KDDKDD-2019-YangZZX0 #adaptation #capacity #incremental #modelling #scalability
Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and Sustainability (YY, DWZ, DCZ, HX, YJ0), pp. 74–82.
KDDKDD-2019-YaoCC #clustering #multi #robust
Robust Task Grouping with Representative Tasks for Clustered Multi-Task Learning (YY, JC0, HC), pp. 1408–1417.
KDDKDD-2019-YoshidaTK #graph #metric #mining
Learning Interpretable Metric between Graphs: Convex Formulation and Computation with Graph Mining (TY, IT, MK), pp. 1026–1036.
KDDKDD-2019-YuGNCPH #constraints #incremental
Tackle Balancing Constraint for Incremental Semi-Supervised Support Vector Learning (SY, BG, KN, HC, JP, HH), pp. 1587–1595.
KDDKDD-2019-ZhaiWTPR #visual notation
Learning a Unified Embedding for Visual Search at Pinterest (AZ, HYW, ET, DHP, CR), pp. 2412–2420.
KDDKDD-2019-ZhangFWL0
Unifying Inter-region Autocorrelation and Intra-region Structures for Spatial Embedding via Collective Adversarial Learning (YZ, YF, PW, XL, YZ0), pp. 1700–1708.
KDDKDD-2019-ZhangYY #robust
Adversarial Variational Embedding for Robust Semi-supervised Learning (XZ0, LY, FY), pp. 139–147.
KDDKDD-2019-ZhangZJZ
Learning from Incomplete and Inaccurate Supervision (ZyZ, PZ, YJ0, ZHZ), pp. 1017–1025.
KDDKDD-2019-ZhaoDSZLX #multi #network #relational
Multiple Relational Attention Network for Multi-task Learning (JZ, BD, LS, FZ, WL, HX), pp. 1123–1131.
KDDKDD-2019-ZhaoZWGGQNCL #multi #personalisation
Personalized Attraction Enhanced Sponsored Search with Multi-task Learning (WZ0, BZ, BW, ZG, WG, GQ, WN, JC, HL), pp. 2632–2642.
KDDKDD-2019-ZhouGHZXJLX #collaboration #framework #refinement
A Collaborative Learning Framework to Tag Refinement for Points of Interest (JZ, SG, RH, DZ, JX, AJ, YL, HX), pp. 1752–1761.
KDDKDD-2019-ZhouM0H #education #optimisation
Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching (YZ, FM, JG0, JH), pp. 3231–3232.
KDDKDD-2019-ZouXDS0Y #recommendation
Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems (LZ, LX, ZD, JS, WL0, DY), pp. 2810–2818.
MoDELSMoDELS-2019-BencomoP #modelling #named #ram #runtime #using
RaM: Causally-Connected and Requirements-Aware Runtime Models using Bayesian Learning (NB, LHGP), pp. 216–226.
OnwardOnward-2019-BaniassadBHKA #design
Learning to listen for design (ELAB, IB, RH, GK, MA), pp. 179–186.
OnwardOnward-2019-CambroneroDV0WR #re-engineering
Active learning for software engineering (JPC, THYD, NV, JS0, JW, MCR), pp. 62–78.
OOPSLAOOPSLA-2019-BaderSP0 #automation #debugging #named
Getafix: learning to fix bugs automatically (JB, AS, MP, SC0), p. 27.
OOPSLAOOPSLA-2019-CambroneroR #named #source code
AL: autogenerating supervised learning programs (JPC, MCR), p. 28.
OOPSLAOOPSLA-2019-ChenWFBD #relational #using #verification
Relational verification using reinforcement learning (JC, JW, YF, OB, ID), p. 30.
OOPSLAOOPSLA-2019-LiWNN #debugging #detection #network #representation
Improving bug detection via context-based code representation learning and attention-based neural networks (YL, SW0, TNN, SVN), p. 30.
OOPSLAOOPSLA-2019-WuCHS0 #approach #fault #generative #precise #specification
Generating precise error specifications for C: a zero shot learning approach (BW, JPCI, YH, AS, SC0), p. 30.
PLATEAUPLATEAU-2019-ZhaoF0I #live programming #network #programming #visualisation
Live Programming Environment for Deep Learning with Instant and Editable Neural Network Visualization (CZ, TF, JK0, TI), p. 5.
PLDIPLDI-2019-0001R #database #modelling #using
Using active learning to synthesize models of applications that access databases (JS0, MCR), pp. 269–285.
PLDIPLDI-2019-AstorgaMSWX #generative
Learning stateful preconditions modulo a test generator (AA, PM, SS, SW, TX0), pp. 775–787.
PLDIPLDI-2019-EberhardtSRV #alias #api #specification
Unsupervised learning of API aliasing specifications (JE, SS, VR, MTV), pp. 745–759.
PLDIPLDI-2019-ZhuXMJ #framework #induction #synthesis
An inductive synthesis framework for verifiable reinforcement learning (HZ0, ZX, SM, SJ), pp. 686–701.
POPLPOPL-2019-AlonZLY #distributed #named
code2vec: learning distributed representations of code (UA0, MZ, OL, EY), p. 29.
SASSAS-2019-NeiderS0M #algorithm #invariant #named
Sorcar: Property-Driven Algorithms for Learning Conjunctive Invariants (DN, SS, PG0, PM), pp. 323–346.
ASEASE-2019-GuoCXMHLLZL #deployment #development #empirical #framework #platform #towards
An Empirical Study Towards Characterizing Deep Learning Development and Deployment Across Different Frameworks and Platforms (QG, SC, XX, LM0, QH, HL, YL0, JZ, XL), pp. 810–822.
ASEASE-2019-Hu0XY0Z #framework #mutation testing #testing
DeepMutation++: A Mutation Testing Framework for Deep Learning Systems (QH, LM0, XX, BY, YL0, JZ), pp. 1158–1161.
ASEASE-2019-NejadgholiY #approximate #case study #library #testing
A Study of Oracle Approximations in Testing Deep Learning Libraries (MN, JY0), pp. 785–796.
ASEASE-2019-SaifullahAR #api
Learning from Examples to Find Fully Qualified Names of API Elements in Code Snippets (CMKS, MA, CKR), pp. 243–254.
ASEASE-2019-WanSSXZ0Y #multi #network #retrieval #semantics #source code
Multi-modal Attention Network Learning for Semantic Source Code Retrieval (YW, JS, YS, GX, ZZ, JW0, PSY), pp. 13–25.
ASEASE-2019-ZhangC #adaptation #approach #modelling #named
Apricot: A Weight-Adaptation Approach to Fixing Deep Learning Models (HZ, WKC), pp. 376–387.
ASEASE-2019-ZhangYFSL0 #modelling #named #visualisation
NeuralVis: Visualizing and Interpreting Deep Learning Models (XZ, ZY, YF0, QS, JL, ZC0), pp. 1106–1109.
ASEASE-2019-ZhengFXS0HMLSC #automation #game studies #named #online #testing #using
Wuji: Automatic Online Combat Game Testing Using Evolutionary Deep Reinforcement Learning (YZ, CF, XX, TS, LM0, JH, ZM, YL0, RS, YC), pp. 772–784.
ESEC-FSEESEC-FSE-2019-BuiYJ #api #named
SAR: learning cross-language API mappings with little knowledge (NDQB, YY, LJ), pp. 796–806.
ESEC-FSEESEC-FSE-2019-CambroneroLKS0 #code search
When deep learning met code search (JC, HL, SK, KS, SC0), pp. 964–974.
ESEC-FSEESEC-FSE-2019-ChenCLML #analysis #approach #named #re-engineering #sentiment
SEntiMoji: an emoji-powered learning approach for sentiment analysis in software engineering (ZC, YC, XL, QM, XL), pp. 841–852.
ESEC-FSEESEC-FSE-2019-DuXLM0Z #analysis #modelling #named
DeepStellar: model-based quantitative analysis of stateful deep learning systems (XD, XX, YL0, LM0, YL0, JZ), pp. 477–487.
ESEC-FSEESEC-FSE-2019-IslamNPR #debugging
A comprehensive study on deep learning bug characteristics (MJI, GN, RP, HR), pp. 510–520.
ESEC-FSEESEC-FSE-2019-Kwiatkowska #robust #safety
Safety and robustness for deep learning with provable guarantees (keynote) (MK), p. 2.
ESEC-FSEESEC-FSE-2019-MesbahRJGA #compilation #fault #named
DeepDelta: learning to repair compilation errors (AM, AR, EJ, NG, EA), pp. 925–936.
ESEC-FSEESEC-FSE-2019-WuJYBSPX #grammar inference #named
REINAM: reinforcement learning for input-grammar inference (ZW, EJ, WY0, OB, DS, JP, TX0), pp. 488–498.
ESEC-FSEESEC-FSE-2019-Zhou0X0JLXH #fault #locality #predict
Latent error prediction and fault localization for microservice applications by learning from system trace logs (XZ, XP0, TX, JS0, CJ, DL, QX, CH), pp. 683–694.
ICSE-2019-FanLLWNZL #analysis #android #graph #using
Graph embedding based familial analysis of Android malware using unsupervised learning (MF, XL, JL0, MW, CN, QZ, TL0), pp. 771–782.
ICSE-2019-KimFY #testing #using
Guiding deep learning system testing using surprise adequacy (JK, RF, SY), pp. 1039–1049.
ICSE-2019-Liu0BKKKKT #consistency
Learning to spot and refactor inconsistent method names (KL0, DK0, TFB, TyK, KK, AK, SK, YLT), pp. 1–12.
ICSE-2019-PhamLQT #debugging #detection #library #locality #named #validation
CRADLE: cross-backend validation to detect and localize bugs in deep learning libraries (HVP, TL, WQ, LT0), pp. 1027–1038.
ICSE-2019-TufanoPWBP #on the
On learning meaningful code changes via neural machine translation (MT, JP, CW, GB, DP), pp. 25–36.
ICSE-2019-WeiLC #android #correlation #detection #named
Pivot: learning API-device correlations to facilitate Android compatibility issue detection (LW, YL, SCC), pp. 878–888.
ASPLOSASPLOS-2019-ChoOPJL #named
FA3C: FPGA-Accelerated Deep Reinforcement Learning (HC, PO, JP, WJ, JL), pp. 499–513.
ASPLOSASPLOS-2019-SivathanuCSZ #named #predict
Astra: Exploiting Predictability to Optimize Deep Learning (MS, TC, SSS, LZ), pp. 909–923.
CASECASE-2019-AyoobiCVV #using
Handling Unforeseen Failures Using Argumentation-Based Learning (HA, MC0, RV, BV), pp. 1699–1704.
CASECASE-2019-CronrathAL
Enhancing Digital Twins through Reinforcement Learning (CC, ARA, BL), pp. 293–298.
CASECASE-2019-FarooquiF #modelling #synthesis #using
Synthesis of Supervisors for Unknown Plant Models Using Active Learning (AF, MF), pp. 502–508.
CASECASE-2019-FoxBSG #automation #multi
Multi-Task Hierarchical Imitation Learning for Home Automation (RF, RB, IS, KG), pp. 1–8.
CASECASE-2019-GamsRNSKSU #quality #visual notation
Robotic Learning for Increased Productivity: Autonomously Improving Speed of Robotic Visual Quality Inspection (AG, SR, BN, JS, RK, JS, AU), pp. 1275–1281.
CASECASE-2019-GaoZ0 #behaviour #modelling #navigation
Modeling Socially Normative Navigation Behaviors from Demonstrations with Inverse Reinforcement Learning (XG, XZ, MT0), pp. 1333–1340.
CASECASE-2019-HongCL #locality #mobile #realtime #using
Real-time Visual-Based Localization for Mobile Robot Using Structured-View Deep Learning (YFH, YMC, CHGL), pp. 1353–1358.
CASECASE-2019-Huang0C #policy
Machine Preventive Replacement Policy for Serial Production Lines Based on Reinforcement Learning (JH0, QC0, NC), pp. 523–528.
CASECASE-2019-KanekolTPODKM #image #modelling #process
Learning Deep Dynamical Models of a Waste Incineration Plant from In-furnace Images and Process Data (TK, YT, JP, YO, YD, KK, TM), pp. 873–878.
CASECASE-2019-KazmiNVRC #detection #recognition #using
Vehicle tire (tyre) detection and text recognition using deep learning (WK, IN, GV, PR, AC), pp. 1074–1079.
CASECASE-2019-LaiL #detection #using
Video-Based Windshield Rain Detection and Wiper Control Using Holistic-View Deep Learning (CCL, CHGL), pp. 1060–1065.
CASECASE-2019-LiuHS #approach #exponential #scheduling
A new solution approach for flow shop scheduling with an exponential time-dependent learning effect (LL, HH, LS), pp. 468–473.
CASECASE-2019-LiuZHZWW #estimation #network #using
sEMG-Based Continuous Estimation of Knee Joint Angle Using Deep Learning with Convolutional Neural Network (GL, LZ, BH, TZ, ZW, PW), pp. 140–145.
CASECASE-2019-PotluriD #detection #injection #performance #process
Deep Learning based Efficient Anomaly Detection for Securing Process Control Systems against Injection Attacks (SP, CD), pp. 854–860.
CASECASE-2019-QianAX0 #performance
Improved Production Performance Through Manufacturing System Learning (YQ, JA, GX, QC0), pp. 517–522.
CASECASE-2019-RazaL #approach #multi #policy
Constructive Policy: Reinforcement Learning Approach for Connected Multi-Agent Systems (SJAR, ML), pp. 257–262.
CASECASE-2019-ShkorutaCMR
Iterative learning control for power profile shaping in selective laser melting (AS, WC, SM, SR), pp. 655–660.
CASECASE-2019-SoniGAS #hybrid #named
HMC: A Hybrid Reinforcement Learning Based Model Compression for Healthcare Applications (RS, JG, GA, VRS), pp. 146–151.
CASECASE-2019-WangY0 #approach #monitoring
A Deep Learning Approach for Heating and Cooling Equipment Monitoring (YW, CY, WS0), pp. 228–234.
CASECASE-2019-WuZQX #precise
Deep Reinforcement Learning of Robotic Precision Insertion Skill Accelerated by Demonstrations (XW, DZ, FQ, DX), pp. 1651–1656.
CASECASE-2019-XuLWZCQ #approach #performance
An Improved GA-KRR Nested Learning Approach for Refrigeration Compressor Performance Forecasting* (CX, XL, JW, JZ, JC, WQ), pp. 622–627.
CASECASE-2019-XuMZLKZ #adaptation #word
An Adaptive Wordpiece Language Model for Learning Chinese Word Embeddings (BX, LM, LZ, HL, QK, MZ), pp. 812–817.
CASECASE-2019-YangLYK #classification #realtime
Investigation of Deep Learning for Real-Time Melt Pool Classification in Additive Manufacturing (ZY, YL, HY, SK), pp. 640–647.
CASECASE-2019-ZhangCZXL #detection #fault
Weld Defect Detection Based on Deep Learning Method (HZ, ZC, CZ, JX, XL), pp. 1574–1579.
CASECASE-2019-ZhangLGWL #algorithm #classification #taxonomy
A Shapelet Dictionary Learning Algorithm for Time Series Classification (JZ, XL, LG0, LW, GL), pp. 299–304.
CASECASE-2019-ZhangLWGG #fault #network #using
Fault Diagnosis Using Unsupervised Transfer Learning Based on Adversarial Network (ZZ, XL, LW, LG0, YG), pp. 305–310.
CASECASE-2019-ZhouWXS00G #approach #modelling #personalisation #predict
A Model-Driven Learning Approach for Predicting the Personalized Dynamic Thermal Comfort in Ordinary Office Environment (YZ, XW, ZX, YS, TL0, CS0, XG), pp. 739–744.
CADECADE-2019-ChenWAZKZ #named
NIL: Learning Nonlinear Interpolants (MC, JW0, JA, BZ, DK, NZ), pp. 178–196.
CADECADE-2019-FioriW #modelling
SCL Clause Learning from Simple Models (AF, CW), pp. 233–249.
ICSTICST-2019-KooS0B #automation #generative #named #testing #worst-case
PySE: Automatic Worst-Case Test Generation by Reinforcement Learning (JK, CS, MK0, SB), pp. 136–147.
ICSTICST-2019-WangWZK #alloy
Learning to Optimize the Alloy Analyzer (WW, KW, MZ, SK), pp. 228–239.
ICSTICST-2019-ZhaoLWSH #framework #fuzzing #industrial #named #perspective #protocol
SeqFuzzer: An Industrial Protocol Fuzzing Framework from a Deep Learning Perspective (HZ, ZL, HW, JS, YH), pp. 59–67.
ICTSSICTSS-2019-ArcainiGR #regular expression #testing
Regular Expression Learning with Evolutionary Testing and Repair (PA, AG, ER), pp. 22–40.
TAPTAP-2019-AichernigPSW #case study #predict #testing
Predicting and Testing Latencies with Deep Learning: An IoT Case Study (BKA, FP, RS, AW), pp. 93–111.
TAPTAP-2019-PetrenkoA #communication #state machine
Learning Communicating State Machines (AP, FA), pp. 112–128.
JCDLJCDL-2018-ColeASSCJ #design #framework #platform #research
Designing a Research Platform for Engaged Learning (NC, AAR, RS, CES, SC, RJ), pp. 315–316.
JCDLJCDL-2018-MaiGS #performance #semantics #using
Using Deep Learning for Title-Based Semantic Subject Indexing to Reach Competitive Performance to Full-Text (FM, LG, AS), pp. 169–178.
EDMEDM-2018-AguerrebereCW #deployment #online #process #student
Estimating the Treatment Effect of New Device Deployment on Uruguayan Students' Online Learning Activity (CA, CC, JW).
EDMEDM-2018-AkramMWMBL #assessment #game studies
Improving Stealth Assessment in Game-based Learning with LSTM-based Analytics (BA, WM, ENW, BWM, KB, JCL).
EDMEDM-2018-CarvalhoGMK #online #process
Analyzing the relative learning benefits of completing required activities and optional readings in online courses (PFC, MG, BM, KK).
EDMEDM-2018-ChenLCBC #analysis #behaviour #scalability
Behavioral Analysis at Scale: Learning Course Prerequisite Structures from Learner Clickstreams (WC, ASL, DC, CGB, MC).
EDMEDM-2018-ChopraG #mining
Job Description Mining to Understand Work-Integrated Learning (SC, LG).
EDMEDM-2018-DuDP #analysis #behaviour #named
ELBA: Exceptional Learning Behavior Analysis (XD, WD, MP).
EDMEDM-2018-FangSLCSFGCCPFG #clustering
Clustering the Learning Patterns of Adults with Low Literacy Skills Interacting with an Intelligent Tutoring System (YF, KTS, AL, QC, GS, SF, JG, SC, ZC, PIP, JF, DG, ACG).
EDMEDM-2018-KarumbaiahBS #game studies #predict #student
Predicting Quitting in Students Playing a Learning Game (SK, RSB, VJS).
EDMEDM-2018-KimVG #named #performance #predict #student
GritNet: Student Performance Prediction with Deep Learning (BHK, EV, VG).
EDMEDM-2018-MatayoshiGDUC #adaptation #assessment #testing
Forgetting curves and testing effect in an adaptive learning and assessment system (JM, UG, CD, HU, EC).
EDMEDM-2018-RajendranKCLB #behaviour #predict
Predicting Learning by Analyzing Eye-Gaze Data of Reading Behavior (RR, AK, KEC, DTL, GB).
EDMEDM-2018-ReillyRS #collaboration #multi #using
Exploring Collaboration Using Motion Sensors and Multi-Modal Learning Analytics (JMR, MR, BS).
EDMEDM-2018-SawyerRAL #analysis #behaviour #game studies #problem #student
Filtered Time Series Analyses of Student Problem-Solving Behaviors in Game-based Learning (RS, JPR, RA, JCL).
EDMEDM-2018-SinghSCD #behaviour #modelling #multi #student
Modeling Hint-Taking Behavior and Knowledge State of Students with Multi-Task Learning (HS, SKS, RC, PD).
EDMEDM-2018-TranLCGSBM #design #documentation #generative
Document Chunking and Learning Objective Generation for Instruction Design (KNT, JHL, DC, UG, BS, CJB, MKM).
EDMEDM-2018-WinchellMLGP #predict #student
Textbook annotations as an early predictor of student learning (AW, MM, ASL, PG, HP).
ICPCICPC-2018-LiNJWHW #behaviour #evolution #named
Logtracker: learning log revision behaviors proactively from software evolution history (SL, XN, ZJ, JW, HH, TW0), pp. 178–188.
ICPCICPC-2018-OttAHBAFL #network #programming language #using
Learning lexical features of programming languages from imagery using convolutional neural networks (JO, AA, PH, NB, HA, CF, EL), pp. 336–339.
MSRMSR-2018-MajumderBBFM08 #case study #mining #performance #stack overflow
500+ times faster than deep learning: a case study exploring faster methods for text mining stackoverflow (SM, NB, KB, WF0, TM), pp. 554–563.
MSRMSR-2018-OttAHBL08 #approach #identification #image #source code #video
A deep learning approach to identifying source code in images and video (JO, AA, PH, AB, EL), pp. 376–386.
MSRMSR-2018-TufanoWBPWP08 #source code
Deep learning similarities from different representations of source code (MT, CW, GB, MDP, MW, DP), pp. 542–553.
MSRMSR-2018-YinDCVN08 #natural language #stack overflow
Learning to mine aligned code and natural language pairs from stack overflow (PY, BD, EC, BV, GN), pp. 476–486.
SANERSANER-2018-FakhouryANKA #detection #question #smell
Keep it simple: Is deep learning good for linguistic smell detection? (SF, VA, CN, FK, GA), pp. 602–611.
SANERSANER-2018-XuLLZ #analysis #component #fault #hybrid #kernel #predict
Cross-version defect prediction via hybrid active learning with kernel principal component analysis (ZX, JL0, XL, TZ0), pp. 209–220.
FMFM-2018-AkazakiLYDH #cyber-physical #using
Falsification of Cyber-Physical Systems Using Deep Reinforcement Learning (TA, SL, YY, YD, JH), pp. 456–465.
SEFMSEFM-2018-BabaeeGF #framework #predict #runtime #statistics #using #verification
Prevent : A Predictive Run-Time Verification Framework Using Statistical Learning (RB, AG, SF), pp. 205–220.
ICFP-2018-StampoulisC #functional #higher-order #logic programming #prolog #prototype #using
Prototyping a functional language using higher-order logic programming: a functional pearl on learning the ways of λProlog/Makam (AS, AC), p. 30.
AIIDEAIIDE-2018-LeeTZXDA #architecture #composition
Modular Architecture for StarCraft II with Deep Reinforcement Learning (DL, HT, JOZ, HX, TD, PA), pp. 187–193.
AIIDEAIIDE-2018-PackardO #case study #user study
A User Study on Learning from Human Demonstration (BP, SO), pp. 208–214.
CoGCIG-2018-AndersenGG #game studies #realtime
Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games (PAA, MG, OCG), pp. 1–8.
CoGCIG-2018-AungBDCKYW #dataset #predict #scalability
Predicting Skill Learning in a Large, Longitudinal MOBA Dataset (MA, VB, AD, PIC, AVK, CY, ARW), pp. 1–7.
CoGCIG-2018-BulitkoD #heuristic #realtime
Anxious Learning in Real-Time Heuristic Search (VB, KD), pp. 1–4.
CoGCIG-2018-DockhornA #approximate #game studies #video
Forward Model Approximation for General Video Game Learning (AD, DA), pp. 1–8.
CoGCIG-2018-GlavinM #experience #using
Skilled Experience Catalogue: A Skill-Balancing Mechanism for Non-Player Characters using Reinforcement Learning (FGG, MGM), pp. 1–8.
CoGCIG-2018-GudmundssonEPNP
Human-Like Playtesting with Deep Learning (SFG, PE, EP, AN, SP, BK, RM, LC), pp. 1–8.
CoGCIG-2018-HarmerGVHBOSN #3d #concurrent #game studies
Imitation Learning with Concurrent Actions in 3D Games (JH, LG, JdV, HH, JB, TO, KS, MN), pp. 1–8.
CoGCIG-2018-JustesenR #automation #education
Automated Curriculum Learning by Rewarding Temporally Rare Events (NJ, SR), pp. 1–8.
CoGCIG-2018-KaczmarekP #interactive #motivation
Promotion of Learning Motivation through Individualization of Learner-Game Interaction (SK, SP), pp. 1–8.
CoGCIG-2018-KowalskiK #regular expression
Regular Language Inference for Learning Rules of Simplified Boardgames (JK, AK), pp. 1–8.
CoGCIG-2018-ShaoZLZ
Learning Battles in ViZDoom via Deep Reinforcement Learning (KS, DZ, NL, YZ), pp. 1–4.
CoGCIG-2018-SpyrouVPAL #personalisation
Exploiting IoT Technologies for Personalized Learning (ES, NV, AP, SA, HCL), pp. 1–8.
CoGCIG-2018-SwiechowskiTJ #algorithm
Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms (MS, TT, AJ), pp. 1–8.
CoGCIG-2018-TavaresC #game studies #realtime
Tabular Reinforcement Learning in Real-Time Strategy Games via Options (ART, LC), pp. 1–8.
CoGCIG-2018-TorradoBT0P #game studies #video
Deep Reinforcement Learning for General Video Game AI (RRT, PB, JT, JL0, DPL), pp. 1–8.
CoGCIG-2018-WoofC #game studies #network
Learning to Play General Video-Games via an Object Embedding Network (WW, KC), pp. 1–8.
CoGCIG-2018-YangO #evaluation #game studies #independence #realtime
Learning Map-Independent Evaluation Functions for Real-Time Strategy Games (ZY, SO), pp. 1–7.
DiGRADiGRA-2018-RichardMA #collaboration #contest
Collegiate eSports as Learning Ecologies: Investigating Collaborative Learning and Cognition During Competitions (GTR, ZAM, RWA).
DiGRADiGRA-2018-Wu #education #game studies #video
Video Games, Learning, and the Shifting Educational Landscape (HAW).
FDGFDG-2018-Maureira #game studies #named #tool support
CURIO: a game-based learning toolkit for fostering curiosity (MAGM), p. 6.
CoGVS-Games-2018-KutunS #game studies
Rallye Game: Learning by Playing with Racing Cars (BK, WS), pp. 1–2.
CoGVS-Games-2018-Perez-ColadoRFM #game studies #multi
Multi-Level Game Learning Analytics for Serious Games (IJPC, DCR, MFM, IMO, BFM), pp. 1–4.
CoGVS-Games-2018-RallisLGVDD #analysis #artificial reality #game studies #using #visualisation
An Embodied Learning Game Using Kinect and Labanotation for Analysis and Visualization of Dance Kinesiology (IR, AL, IG, AV, ND, AD), pp. 1–8.
CIKMCIKM-2018-0013H #consistency #interactive #modelling #multi
Interactions Modeling in Multi-Task Multi-View Learning with Consistent Task Diversity (XL0, JH), pp. 853–861.
CIKMCIKM-2018-AiMLC #rank #theory and practice
Unbiased Learning to Rank: Theory and Practice (QA, JM, YL, WBC), pp. 2305–2306.
CIKMCIKM-2018-BiessmannSSSL
“Deep” Learning for Missing Value Imputationin Tables with Non-Numerical Data (FB, DS, SS, PS, DL), pp. 2017–2025.
CIKMCIKM-2018-DaveZHAK #approach #recommendation #representation
A Combined Representation Learning Approach for Better Job and Skill Recommendation (VSD, BZ, MAH, KA, MK), pp. 1997–2005.
CIKMCIKM-2018-DingTZ #generative #graph
Semi-supervised Learning on Graphs with Generative Adversarial Nets (MD, JT, JZ), pp. 913–922.
CIKMCIKM-2018-FerroLM0 #continuation #education #rank
Continuation Methods and Curriculum Learning for Learning to Rank (NF0, CL, MM, RP0), pp. 1523–1526.
CIKMCIKM-2018-HashemiWKZC18a #identification
Impact of Domain and User's Learning Phase on Task and Session Identification in Smart Speaker Intelligent Assistants (SHH, KW, AEK, IZ, PAC), pp. 1193–1202.
CIKMCIKM-2018-JinSLGWZ #multi #realtime
Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising (JJ, CS, HL, KG, JW0, WZ0), pp. 2193–2201.
CIKMCIKM-2018-KimLCCK #comprehension #scheduling
Learning User Preferences and Understanding Calendar Contexts for Event Scheduling (DK, JL, DC, JC, JK), pp. 337–346.
CIKMCIKM-2018-KrishnanSS #behaviour #online
Insights from the Long-Tail: Learning Latent Representations of Online User Behavior in the Presence of Skew and Sparsity (AK, AS, HS), pp. 297–306.
CIKMCIKM-2018-LiuZHL #representation #visual notation
Adversarial Learning of Answer-Related Representation for Visual Question Answering (YL, XZ0, FH, ZL), pp. 1013–1022.
CIKMCIKM-2018-LoyolaGS #debugging #locality #rank
Bug Localization by Learning to Rank and Represent Bug Inducing Changes (PL, KG, FS), pp. 657–665.
CIKMCIKM-2018-LuoWHYZ #segmentation #semantics
Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning (YL, ZW, ZH, YY0, CZ), pp. 237–246.
CIKMCIKM-2018-MedinaVY #online #testing
Online Learning for Non-Stationary A/B Tests (AMM, SV, DY), pp. 317–326.
CIKMCIKM-2018-MelidisSN
Learning under Feature Drifts in Textual Streams (DPM, MS, EN), pp. 527–536.
CIKMCIKM-2018-MoraesPH #process
Contrasting Search as a Learning Activity with Instructor-designed Learning (FM, SRP, CH), pp. 167–176.
CIKMCIKM-2018-NishidaSOAT #comprehension #information retrieval #multi #named
Retrieve-and-Read: Multi-task Learning of Information Retrieval and Reading Comprehension (KN, IS, AO, HA, JT), pp. 647–656.
CIKMCIKM-2018-OhSL #graph #multi
Knowledge Graph Completion by Context-Aware Convolutional Learning with Multi-Hop Neighborhoods (BO, SS, KHL), pp. 257–266.
CIKMCIKM-2018-OosterhuisR #online #rank
Differentiable Unbiased Online Learning to Rank (HO, MdR), pp. 1293–1302.
CIKMCIKM-2018-PandeyKS #recommendation #using
Recommending Serendipitous Items using Transfer Learning (GP0, DK, AS), pp. 1771–1774.
CIKMCIKM-2018-PauleMMO #fine-grained #twitter
Learning to Geolocalise Tweets at a Fine-Grained Level (JDGP, YM, CM, IO), pp. 1675–1678.
CIKMCIKM-2018-RamanathIPHGOWK #representation #towards
Towards Deep and Representation Learning for Talent Search at LinkedIn (RR, HI, GP, BH, QG, CO, XW, KK, SCG), pp. 2253–2261.
CIKMCIKM-2018-RenFZLLZYW #multi #online
Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising (KR, YF, WZ0, SL, JL, YZ, YY0, JW0), pp. 1433–1442.
CIKMCIKM-2018-ShenKBQM #clustering #email #multi #query #ranking
Multi-Task Learning for Email Search Ranking with Auxiliary Query Clustering (JS, MK, MB, ZQ, DM), pp. 2127–2135.
CIKMCIKM-2018-SongZWTZJC #graph #named #rank
TGNet: Learning to Rank Nodes in Temporal Graphs (QS, BZ, YW, LAT, HZ, GJ, HC), pp. 97–106.
CIKMCIKM-2018-SuLK #distributed #hybrid #metric
Communication-Efficient Distributed Deep Metric Learning with Hybrid Synchronization (YS, MRL, IK), pp. 1463–1472.
CIKMCIKM-2018-WuCYWTZXG
Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising (DW, XC, XY, HW, QT, XZ, JX, KG), pp. 1443–1451.
CIKMCIKM-2018-WuLZ #retrieval #semantics #taxonomy
Joint Dictionary Learning and Semantic Constrained Latent Subspace Projection for Cross-Modal Retrieval (JW, ZL, HZ), pp. 1663–1666.
CIKMCIKM-2018-WuWL #classification #multi #sentiment
Imbalanced Sentiment Classification with Multi-Task Learning (FW, CW, JL), pp. 1631–1634.
CIKMCIKM-2018-WuWL18a #collaboration #detection #microblog #social
Semi-Supervised Collaborative Learning for Social Spammer and Spam Message Detection in Microblogging (FW, CW, JL), pp. 1791–1794.
CIKMCIKM-2018-WuZA #classification #graph
A Quest for Structure: Jointly Learning the Graph Structure and Semi-Supervised Classification (XW, LZ, LA), pp. 87–96.
CIKMCIKM-2018-XiaJSZWS #modelling #multi #recommendation
Modeling Consumer Buying Decision for Recommendation Based on Multi-Task Deep Learning (QX, PJ, FS, YZ, XW, ZS), pp. 1703–1706.
CIKMCIKM-2018-YangS #multi #named #performance
FALCON: A Fast Drop-In Replacement of Citation KNN for Multiple Instance Learning (SY, XS), pp. 67–76.
CIKMCIKM-2018-ZamaniDCLK #ranking #representation
From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing (HZ, MD0, WBC, EGLM, JK), pp. 497–506.
CIKMCIKM-2018-ZhaoX0ZLZ #approach #comprehension #on the #predict
On Prediction of User Destination by Sub-Trajectory Understanding: A Deep Learning based Approach (JZ, JX, RZ0, PZ, CL, FZ), pp. 1413–1422.
CIKMCIKM-2018-ZhuLYZ0W #framework #predict
A Supervised Learning Framework for Prediction of Incompatible Herb Pair in Traditional Chinese Medicine (JZ, YL, SY, SZ, ZY0, CW), pp. 1799–1802.
ECIRECIR-2018-00010VPRP #multi #twitter
Multi-task Learning for Extraction of Adverse Drug Reaction Mentions from Tweets (SG0, MG0, VV, SP, NR, GKP), pp. 59–71.
ECIRECIR-2018-AgrawalA #detection #multi #platform #social #social media
Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms (SA, AA), pp. 141–153.
ECIRECIR-2018-HerreraPP #microblog #retrieval
Learning to Leverage Microblog Information for QA Retrieval (JMH, BP, DP), pp. 507–520.
ECIRECIR-2018-Jalan0V #classification #using
Medical Forum Question Classification Using Deep Learning (RSJ, MG0, VV), pp. 45–58.
ECIRECIR-2018-McDonaldMO #overview #perspective
Active Learning Strategies for Technology Assisted Sensitivity Review (GM, CM, IO), pp. 439–453.
ECIRECIR-2018-NiculaRR #multi
Improving Deep Learning for Multiple Choice Question Answering with Candidate Contexts (BN, SR, TR), pp. 678–683.
ECIRECIR-2018-TianLWWQLLS #semantics #similarity
An Adversarial Joint Learning Model for Low-Resource Language Semantic Textual Similarity (JT, ML, YW, JW, LQ, SL0, JL, LS), pp. 89–101.
ECIRECIR-2018-WilkensZF #documentation #ranking
Document Ranking Applied to Second Language Learning (RW, LZ, CF), pp. 618–624.
ICMLICML-2018-0001JADYD
Hierarchical Imitation and Reinforcement Learning (HML0, NJ, AA, MD, YY, HDI), pp. 2923–2932.
ICMLICML-2018-AbelALL #abstraction
State Abstractions for Lifelong Reinforcement Learning (DA, DA, LL, MLL), pp. 10–19.
ICMLICML-2018-AbelJGKL #policy
Policy and Value Transfer in Lifelong Reinforcement Learning (DA, YJ, SYG, GDK, MLL), pp. 20–29.
ICMLICML-2018-AchlioptasDMG #3d #generative #modelling
Learning Representations and Generative Models for 3D Point Clouds (PA, OD, IM, LJG), pp. 40–49.
ICMLICML-2018-AlaaS18a #automation #kernel #modelling #named #optimisation
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning (AMA, MvdS), pp. 139–148.
ICMLICML-2018-AlmahairiRSBC
Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data (AA, SR, AS, PB, ACC), pp. 195–204.
ICMLICML-2018-AsadiML #modelling
Lipschitz Continuity in Model-based Reinforcement Learning (KA, DM, MLL), pp. 264–273.
ICMLICML-2018-BalcanDSV #branch
Learning to Branch (MFB, TD, TS, EV), pp. 353–362.
ICMLICML-2018-BalestrieroCGB
Spline Filters For End-to-End Deep Learning (RB, RC, HG, RGB), pp. 373–382.
ICMLICML-2018-BargiacchiVRNH #coordination #graph #multi #problem
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems (EB, TV, DMR, AN, HvH), pp. 491–499.
ICMLICML-2018-BarretoBQSSHMZM #policy #using
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement (AB, DB, JQ, TS, DS, MH, DJM, AZ, RM), pp. 510–519.
ICMLICML-2018-BelkinMM #kernel
To Understand Deep Learning We Need to Understand Kernel Learning (MB, SM, SM), pp. 540–548.
ICMLICML-2018-CalandrielloKLV #graph #scalability
Improved Large-Scale Graph Learning through Ridge Spectral Sparsification (DC, IK, AL, MV), pp. 687–696.
ICMLICML-2018-CaoGWSHT #coordination
Adversarial Learning with Local Coordinate Coding (JC, YG, QW, CS, JH, MT), pp. 706–714.
ICMLICML-2018-CharlesP #algorithm
Stability and Generalization of Learning Algorithms that Converge to Global Optima (ZBC, DSP), pp. 744–753.
ICMLICML-2018-Chatterjee
Learning and Memorization (SC), pp. 754–762.
ICMLICML-2018-ChengDH #rank
Extreme Learning to Rank via Low Rank Assumption (MC, ID, CJH), pp. 950–959.
ICMLICML-2018-ChenLW #scalability #using
Scalable Bilinear Learning Using State and Action Features (YC, LL0, MW), pp. 833–842.
ICMLICML-2018-ChenMS
Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations (TC0, MRM, YS), pp. 853–862.
ICMLICML-2018-ChenSWJ
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation (JC, LS, MJW, MIJ), pp. 882–891.
ICMLICML-2018-ChenXG #multi
End-to-End Learning for the Deep Multivariate Probit Model (DC, YX, CPG), pp. 931–940.
ICMLICML-2018-Chierichetti0T #multi
Learning a Mixture of Two Multinomial Logits (FC, RK0, AT), pp. 960–968.
ICMLICML-2018-ChowNG #consistency
Path Consistency Learning in Tsallis Entropy Regularized MDPs (YC, ON, MG), pp. 978–987.
ICMLICML-2018-Co-ReyesLGEAL #self
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings (JDCR, YL, AG0, BE, PA, SL), pp. 1008–1017.
ICMLICML-2018-ColasSO #algorithm #named
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms (CC, OS, PYO), pp. 1038–1047.
ICMLICML-2018-CorneilGB #performance
Efficient ModelBased Deep Reinforcement Learning with Variational State Tabulation (DSC, WG, JB), pp. 1057–1066.
ICMLICML-2018-CortesDGMY #online
Online Learning with Abstention (CC, GD, CG, MM, SY), pp. 1067–1075.
ICMLICML-2018-CzarneckiJJHTHO #education
Mix & Match Agent Curricula for Reinforcement Learning (WMC, SMJ, MJ, LH, YWT, NH, SO, RP), pp. 1095–1103.
ICMLICML-2018-DabneyOSM #network
Implicit Quantile Networks for Distributional Reinforcement Learning (WD, GO, DS, RM), pp. 1104–1113.
ICMLICML-2018-DaiKDSS #algorithm #graph
Learning Steady-States of Iterative Algorithms over Graphs (HD, ZK, BD, AJS, LS), pp. 1114–1122.
ICMLICML-2018-DaiS0XHLCS #approximate #convergence #named
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation (BD, AS, LL0, LX, NH, ZL0, JC, LS), pp. 1133–1142.
ICMLICML-2018-DepewegHDU #composition #nondeterminism #performance
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning (SD, JMHL, FDV, SU), pp. 1192–1201.
ICMLICML-2018-DibangoyeB #distributed
Learning to Act in Decentralized Partially Observable MDPs (JSD, OB), pp. 1241–1250.
ICMLICML-2018-DietterichTC
Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning (TGD, GT, ZC), pp. 1261–1269.
ICMLICML-2018-DimakopoulouR #concurrent #coordination
Coordinated Exploration in Concurrent Reinforcement Learning (MD, BVR), pp. 1270–1278.
ICMLICML-2018-EfroniDSM #approach
Beyond the One-Step Greedy Approach in Reinforcement Learning (YE, GD, BS, SM), pp. 1386–1395.
ICMLICML-2018-FalahatgarJOPR #ranking
The Limits of Maxing, Ranking, and Preference Learning (MF, AJ, AO, VP, VR), pp. 1426–1435.
ICMLICML-2018-FengWCS #multi #network #parametricity #using
Nonparametric variable importance using an augmented neural network with multi-task learning (JF, BDW, MC, NS), pp. 1495–1504.
ICMLICML-2018-FlorensaHGA #automation #generative
Automatic Goal Generation for Reinforcement Learning Agents (CF, DH, XG, PA), pp. 1514–1523.
ICMLICML-2018-FruitPLO #performance
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning (RF, MP, AL, RO), pp. 1573–1581.
ICMLICML-2018-GaneaBH
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings (OEG, GB, TH), pp. 1632–1641.
ICMLICML-2018-GaninKBEV #image #source code #using
Synthesizing Programs for Images using Reinforced Adversarial Learning (YG, TK, IB, SMAE, OV), pp. 1652–1661.
ICMLICML-2018-GaoW #network #parallel
Parallel Bayesian Network Structure Learning (TG, DW), pp. 1671–1680.
ICMLICML-2018-GarciaCEd #predict
Structured Output Learning with Abstention: Application to Accurate Opinion Prediction (AG0, CC, SE, FdB), pp. 1681–1689.
ICMLICML-2018-Georgogiannis #fault #taxonomy
The Generalization Error of Dictionary Learning with Moreau Envelopes (AG), pp. 1710–1718.
ICMLICML-2018-GhassamiSKB #design #empirical
Budgeted Experiment Design for Causal Structure Learning (AG, SS, NK, EB), pp. 1719–1728.
ICMLICML-2018-GhoshalH #modelling #polynomial #predict
Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time (AG, JH), pp. 1749–1757.
ICMLICML-2018-GhoshYD #network
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors (SG, JY, FDV), pp. 1739–1748.
ICMLICML-2018-GilraG #network
Non-Linear Motor Control by Local Learning in Spiking Neural Networks (AG, WG), pp. 1768–1777.
ICMLICML-2018-GoelKM
Learning One Convolutional Layer with Overlapping Patches (SG, ARK, RM), pp. 1778–1786.
ICMLICML-2018-GroverAGBE #multi #policy
Learning Policy Representations in Multiagent Systems (AG, MAS, JKG, YB, HE), pp. 1797–1806.
ICMLICML-2018-GuezWASVWMS
Learning to Search with MCTSnets (AG, TW, IA, KS, OV, DW, RM, DS), pp. 1817–1826.
ICMLICML-2018-HaarnojaHAL #policy
Latent Space Policies for Hierarchical Reinforcement Learning (TH, KH, PA, SL), pp. 1846–1855.
ICMLICML-2018-HaarnojaZAL #probability
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (TH, AZ, PA, SL), pp. 1856–1865.
ICMLICML-2018-HammN #optimisation #performance
K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning (JH, YKN), pp. 1876–1884.
ICMLICML-2018-HashemiSSALCKR #data access #memory management
Learning Memory Access Patterns (MH, KS, JAS, GA, HL, JC, CK, PR), pp. 1924–1933.
ICMLICML-2018-HeinonenYMIL #modelling #process
Learning unknown ODE models with Gaussian processes (MH, CY, HM, JI, HL), pp. 1964–1973.
ICMLICML-2018-Huang0S #markov #modelling #topic
Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling (KH, XF0, NDS), pp. 2073–2082.
ICMLICML-2018-HuangA0S #using
Learning Deep ResNet Blocks Sequentially using Boosting Theory (FH, JTA, JL0, RES), pp. 2063–2072.
ICMLICML-2018-HuNSS #classification #question #robust
Does Distributionally Robust Supervised Learning Give Robust Classifiers? (WH, GN, IS, MS), pp. 2034–2042.
ICMLICML-2018-IcarteKVM #composition #specification #using
Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning (RTI, TQK, RAV, SAM), pp. 2112–2121.
ICMLICML-2018-IglZLWW
Deep Variational Reinforcement Learning for POMDPs (MI, LMZ, TAL, FW, SW), pp. 2122–2131.
ICMLICML-2018-IlseTW #multi
Attention-based Deep Multiple Instance Learning (MI, JMT, MW), pp. 2132–2141.
ICMLICML-2018-JaffeWCKN #approach #modelling
Learning Binary Latent Variable Models: A Tensor Eigenpair Approach (AJ, RW, SC, YK, BN), pp. 2201–2210.
ICMLICML-2018-JawanpuriaM #framework #matrix #rank
A Unified Framework for Structured Low-rank Matrix Learning (PJ, BM), pp. 2259–2268.
ICMLICML-2018-JeongS #performance
Efficient end-to-end learning for quantizable representations (YJ, HOS), pp. 2269–2278.
ICMLICML-2018-JiangEL
Feedback-Based Tree Search for Reinforcement Learning (DRJ, EE, HL), pp. 2289–2298.
ICMLICML-2018-JiangZLLF #data-driven #education #named #network
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels (LJ0, ZZ, TL, LJL, LFF0), pp. 2309–2318.
ICMLICML-2018-JinKL18a
Regret Minimization for Partially Observable Deep Reinforcement Learning (PHJ, KK, SL), pp. 2347–2356.
ICMLICML-2018-Johnson0 #functional #generative #modelling
Composite Functional Gradient Learning of Generative Adversarial Models (RJ, TZ0), pp. 2376–2384.
ICMLICML-2018-KalimerisSSW #using
Learning Diffusion using Hyperparameters (DK, YS, KS, UW), pp. 2425–2433.
ICMLICML-2018-KalyanLKB #multi
Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations (AK, SL, AK, DB), pp. 2454–2463.
ICMLICML-2018-KamnitsasCFWTRG #clustering
Semi-Supervised Learning via Compact Latent Space Clustering (KK, DCC, LLF, IW, RT, DR, BG, AC, AVN), pp. 2464–2473.
ICMLICML-2018-KaplanisSC
Continual Reinforcement Learning with Complex Synapses (CK, MS, CC), pp. 2502–2511.
ICMLICML-2018-KatharopoulosF
Not All Samples Are Created Equal: Deep Learning with Importance Sampling (AK, FF), pp. 2530–2539.
ICMLICML-2018-KearnsNRW
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness (MJK, SN, AR0, ZSW), pp. 2569–2577.
ICMLICML-2018-KennamerKIS #classification #named
ContextNet: Deep learning for Star Galaxy Classification (NK, DK, ATI, FJSL), pp. 2587–2595.
ICMLICML-2018-KhanNTLGS #performance #scalability
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam (MEK, DN, VT, WL, YG, AS), pp. 2616–2625.
ICMLICML-2018-KuleshovFE #nondeterminism #using
Accurate Uncertainties for Deep Learning Using Calibrated Regression (VK, NF, SE), pp. 2801–2809.
ICMLICML-2018-LeeKCL #case study #game studies
Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling (KL, SAK, JC, SWL), pp. 2943–2952.
ICMLICML-2018-LeeYH #multi #symmetry
Deep Asymmetric Multi-task Feature Learning (HL, EY, SJH), pp. 2962–2970.
ICMLICML-2018-LehtinenMHLKAA #image #named
Noise2Noise: Learning Image Restoration without Clean Data (JL, JM, JH, SL, TK, MA, TA), pp. 2971–2980.
ICMLICML-2018-LiangLNMFGGJS #abstraction #distributed #named
RLlib: Abstractions for Distributed Reinforcement Learning (EL, RL, RN, PM, RF, KG, JG, MIJ, IS), pp. 3059–3068.
ICMLICML-2018-LiaoC18a #approach #matrix #random
The Dynamics of Learning: A Random Matrix Approach (ZL, RC), pp. 3078–3087.
ICMLICML-2018-LiGD #bias #induction #network
Explicit Inductive Bias for Transfer Learning with Convolutional Networks (XL0, YG, FD), pp. 2830–2839.
ICMLICML-2018-LiH #approach #network
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks (QL, SH), pp. 2991–3000.
ICMLICML-2018-LinC #distributed #multi #probability
Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods (JL, VC), pp. 3098–3107.
ICMLICML-2018-LongLMD #named
PDE-Net: Learning PDEs from Data (ZL, YL, XM, BD0), pp. 3214–3222.
ICMLICML-2018-LuoSZLZW
End-to-end Active Object Tracking via Reinforcement Learning (WL, PS, FZ, WL0, TZ0, YW), pp. 3292–3301.
ICMLICML-2018-MaBB #comprehension #effectiveness #power of
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning (SM, RB, MB), pp. 3331–3340.
ICMLICML-2018-MadrasCPZ
Learning Adversarially Fair and Transferable Representations (DM, EC, TP, RSZ), pp. 3381–3390.
ICMLICML-2018-MalikPFHRD #performance
An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning (DM, MP, JFF, DHM, SJR, ADD), pp. 3391–3399.
ICMLICML-2018-MaWHZEXWB
Dimensionality-Driven Learning with Noisy Labels (XM, YW0, MEH, SZ0, SME, STX, SNRW, JB0), pp. 3361–3370.
ICMLICML-2018-MeyersonM #multi #pseudo
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing-and Back (EM, RM), pp. 3508–3517.
ICMLICML-2018-MhamdiGR #distributed
The Hidden Vulnerability of Distributed Learning in Byzantium (EMEM, RG, SR), pp. 3518–3527.
ICMLICML-2018-MishchenkoIMA #algorithm #distributed
A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning (KM, FI, JM, MRA), pp. 3584–3592.
ICMLICML-2018-Nachum0TS #policy
Smoothed Action Value Functions for Learning Gaussian Policies (ON, MN0, GT, DS), pp. 3689–3697.
ICMLICML-2018-NguyenSH #on the
On Learning Sparsely Used Dictionaries from Incomplete Samples (TVN, AS, CH), pp. 3766–3775.
ICMLICML-2018-NickelK #geometry
Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry (MN, DK), pp. 3776–3785.
ICMLICML-2018-OglicG #kernel
Learning in Reproducing Kernel Krein Spaces (DO, TG0), pp. 3856–3864.
ICMLICML-2018-OhGSL #self
Self-Imitation Learning (JO, YG, SS, HL), pp. 3875–3884.
ICMLICML-2018-OkunoHS #framework #multi #network #probability
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks (AO, TH, HS), pp. 3885–3894.
ICMLICML-2018-OsamaZS #locality #modelling #streaming
Learning Localized Spatio-Temporal Models From Streaming Data (MO, DZ, TBS), pp. 3924–3932.
ICMLICML-2018-Oymak #network
Learning Compact Neural Networks with Regularization (SO), pp. 3963–3972.
ICMLICML-2018-PaassenGMH #adaptation #distance #edit distance
Tree Edit Distance Learning via Adaptive Symbol Embeddings (BP, CG, AM, BH), pp. 3973–3982.
ICMLICML-2018-PanFWNGN #difference #equation
Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control (YP, AmF, MW, SN, PG, DN), pp. 3983–3992.
ICMLICML-2018-PanS #predict
Learning to Speed Up Structured Output Prediction (XP, VS), pp. 3993–4002.
ICMLICML-2018-PanZD #analysis
Theoretical Analysis of Image-to-Image Translation with Adversarial Learning (XP, MZ, DD), pp. 4003–4012.
ICMLICML-2018-ParascandoloKRS #independence
Learning Independent Causal Mechanisms (GP, NK, MRC, BS), pp. 4033–4041.
ICMLICML-2018-PardoTLK
Time Limits in Reinforcement Learning (FP, AT, VL, PK), pp. 4042–4051.
ICMLICML-2018-PearceBZN #approach #predict
High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach (TP, AB, MZ, AN), pp. 4072–4081.
ICMLICML-2018-PretoriusKK #linear
Learning Dynamics of Linear Denoising Autoencoders (AP, SK, HK), pp. 4138–4147.
ICMLICML-2018-PuDGWWZHC #generative #multi #named
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets (YP, SD, ZG, WW, GW0, YZ, RH, LC), pp. 4148–4157.
ICMLICML-2018-RaeDDL #parametricity #performance
Fast Parametric Learning with Activation Memorization (JWR, CD, PD, TPL), pp. 4225–4234.
ICMLICML-2018-RaghuIAKLK #game studies #question
Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games? (MR, AI, JA, RK, QVL, JMK), pp. 4235–4243.
ICMLICML-2018-RaileanuDSF #modelling #multi #using
Modeling Others using Oneself in Multi-Agent Reinforcement Learning (RR, ED, AS, RF), pp. 4254–4263.
ICMLICML-2018-RashidSWFFW #multi #named
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning (TR, MS, CSdW, GF, JNF, SW), pp. 4292–4301.
ICMLICML-2018-RavuriMRV #generative #modelling
Learning Implicit Generative Models with the Method of Learned Moments (SVR, SM, MR, OV), pp. 4311–4320.
ICMLICML-2018-RenZYU #robust
Learning to Reweight Examples for Robust Deep Learning (MR, WZ, BY, RU), pp. 4331–4340.
ICMLICML-2018-RiedmillerHLNDW #game studies
Learning by Playing Solving Sparse Reward Tasks from Scratch (MAR, RH, TL, MN, JD, TVdW, VM, NH, JTS), pp. 4341–4350.
ICMLICML-2018-RobertsERHE #music
A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music (AR, JHE, CR, CH, DE), pp. 4361–4370.
ICMLICML-2018-RosenfeldBGS #combinator
Learning to Optimize Combinatorial Functions (NR, EB, AG, YS), pp. 4371–4380.
ICMLICML-2018-SahooLM #equation
Learning Equations for Extrapolation and Control (SSS, CHL, GM), pp. 4439–4447.
ICMLICML-2018-SchmitJ
Learning with Abandonment (SS, RJ), pp. 4516–4524.
ICMLICML-2018-SchwabKMMSK #multi
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care (PS, EK, CM, DJM, CS, WK), pp. 4525–4534.
ICMLICML-2018-Schwarz0LGTPH #framework #scalability
Progress & Compress: A scalable framework for continual learning (JS, WC0, JL, AGB, YWT, RP, RH), pp. 4535–4544.
ICMLICML-2018-ShazeerS #adaptation #memory management #named #sublinear
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost (NS, MS), pp. 4603–4611.
ICMLICML-2018-SheldonWS #automation #difference #integer #modelling
Learning in Integer Latent Variable Models with Nested Automatic Differentiation (DS, KW, DS), pp. 4622–4630.
ICMLICML-2018-ShenMZZQ #communication #convergence #distributed #performance #probability #towards
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication (ZS, AM, TZ, PZ, HQ), pp. 4631–4640.
ICMLICML-2018-ShiarlisWSWP #composition #named
TACO: Learning Task Decomposition via Temporal Alignment for Control (KS, MW, SS, SW, IP), pp. 4661–4670.
ICMLICML-2018-SibliniMK #clustering #multi #performance #random
CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning (WS, FM, PK), pp. 4671–4680.
ICMLICML-2018-SmithHP #policy
An Inference-Based Policy Gradient Method for Learning Options (MS, HvH, JP), pp. 4710–4719.
ICMLICML-2018-SrinivasJALF #network
Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control (AS, AJ, PA, SL, CF), pp. 4739–4748.
ICMLICML-2018-SroujiZS
Structured Control Nets for Deep Reinforcement Learning (MS, JZ, RS), pp. 4749–4758.
ICMLICML-2018-SunZWZLG #composition #kernel #process
Differentiable Compositional Kernel Learning for Gaussian Processes (SS, GZ, CW, WZ, JL, RBG), pp. 4835–4844.
ICMLICML-2018-SuW
Learning Low-Dimensional Temporal Representations (BS, YW), pp. 4768–4777.
ICMLICML-2018-Talvitie
Learning the Reward Function for a Misspecified Model (ET), pp. 4845–4854.
ICMLICML-2018-ThomasDB
Decoupling Gradient-Like Learning Rules from Representations (PST, CD, EB), pp. 4924–4932.
ICMLICML-2018-TianZZ #named
CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions (KT, TZ, JZ), pp. 4933–4942.
ICMLICML-2018-TirinzoniSPR
Importance Weighted Transfer of Samples in Reinforcement Learning (AT, AS, MP, MR), pp. 4943–4952.
ICMLICML-2018-TrinhDLL #dependence
Learning Longer-term Dependencies in RNNs with Auxiliary Losses (THT, AMD, TL, QVL), pp. 4972–4981.
ICMLICML-2018-TschannenKA #multi #named
StrassenNets: Deep Learning with a Multiplication Budget (MT, AK, AA), pp. 4992–5001.
ICMLICML-2018-TuckerBGTGL
The Mirage of Action-Dependent Baselines in Reinforcement Learning (GT, SB, SG, RET, ZG, SL), pp. 5022–5031.
ICMLICML-2018-TuR #difference #linear #polynomial
Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator (ST, BR), pp. 5012–5021.
ICMLICML-2018-VermaMSKC
Programmatically Interpretable Reinforcement Learning (AV, VM, RS, PK, SC), pp. 5052–5061.
ICMLICML-2018-VogelBC #optimisation #probability #similarity
A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization (RV, AB, SC), pp. 5062–5071.
ICMLICML-2018-WagnerGKM #data type
Semi-Supervised Learning on Data Streams via Temporal Label Propagation (TW, SG, SPK, NM), pp. 5082–5091.
ICMLICML-2018-WangGLWY #predict #towards
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning (YW, ZG, ML, JW0, PSY), pp. 5110–5119.
ICMLICML-2018-WangK #multi
Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations (XW, DK), pp. 5130–5138.
ICMLICML-2018-WangSQ #modelling #multi #performance #scalability #visual notation
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models (BW, AS, YQ), pp. 5148–5157.
ICMLICML-2018-WeinshallCA #education #network
Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks (DW, GC, DA), pp. 5235–5243.
ICMLICML-2018-WeiZHY
Transfer Learning via Learning to Transfer (YW, YZ, JH, QY), pp. 5072–5081.
ICMLICML-2018-XiaTTQYL
Model-Level Dual Learning (YX, XT, FT, TQ, NY, TYL), pp. 5379–5388.
ICMLICML-2018-XieWZX #analysis #distance #metric
Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis (PX, WW, YZ, EPX), pp. 5399–5408.
ICMLICML-2018-XieZCC #adaptation #semantics
Learning Semantic Representations for Unsupervised Domain Adaptation (SX, ZZ, LC0, CC), pp. 5419–5428.
ICMLICML-2018-XuCZ #process
Learning Registered Point Processes from Idiosyncratic Observations (HX, LC, HZ), pp. 5439–5448.
ICMLICML-2018-XuLTSKJ #graph #network #representation
Representation Learning on Graphs with Jumping Knowledge Networks (KX, CL, YT, TS, KiK, SJ), pp. 5449–5458.
ICMLICML-2018-XuLZP
Learning to Explore via Meta-Policy Gradient (TX, QL0, LZ, JP0), pp. 5459–5468.
ICMLICML-2018-XuZFLB #semantics
A Semantic Loss Function for Deep Learning with Symbolic Knowledge (JX, ZZ, TF, YL, GVdB), pp. 5498–5507.
ICMLICML-2018-YanCJ
Active Learning with Logged Data (SY, KC, TJ), pp. 5517–5526.
ICMLICML-2018-YangKU #equivalence #graph
Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions (KDY, AK, CU), pp. 5537–5546.
ICMLICML-2018-YangLLZZW #multi
Mean Field Multi-Agent Reinforcement Learning (YY, RL, ML, MZ, WZ0, JW0), pp. 5567–5576.
ICMLICML-2018-YenKYHKR #composition #performance #scalability
Loss Decomposition for Fast Learning in Large Output Spaces (IEHY, SK, FXY, DNHR, SK, PR), pp. 5626–5635.
ICMLICML-2018-YinCRB #distributed #statistics #towards
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates (DY, YC0, KR, PLB), pp. 5636–5645.
ICMLICML-2018-YonaR #approximate
Probably Approximately Metric-Fair Learning (GY, GNR), pp. 5666–5674.
ICMLICML-2018-ZanetteB #bound #identification #problem
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs (AZ, EB), pp. 5732–5740.
ICMLICML-2018-ZhangLSD #dependence #fourier
Learning Long Term Dependencies via Fourier Recurrent Units (JZ, YL, ZS, ISD), pp. 5810–5818.
ICMLICML-2018-ZhangYL0B #distributed #multi
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents (KZ, ZY, HL0, TZ0, TB), pp. 5867–5876.
ICMLICML-2018-Zhao0FYW #estimation #feature model
MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning (BZ, XS0, YF, YY0, YW), pp. 5907–5916.
ICMLICML-2018-ZhaoDBZ #topic #word
Inter and Intra Topic Structure Learning with Word Embeddings (HZ, LD, WLB, MZ), pp. 5887–5896.
ICPRICPR-2018-AfonsoPSP #classification #using
Improving Optimum- Path Forest Classification Using Unsupervised Manifold Learning (LCSA, DCGP, ANdS, JPP), pp. 560–565.
ICPRICPR-2018-Aldana-LopezCZG #approach #network
Dynamic Learning Rate for Neural Networks: A Fixed-Time Stability Approach (RAL, LECM, JZ, DGG, AC), pp. 1378–1383.
ICPRICPR-2018-BiFW #constraints #metric
Cayley- Klein Metric Learning with Shrinkage-Expansion Constraints (YB, BF, FW), pp. 43–48.
ICPRICPR-2018-CaoCHP #identification #metric
Region-specific Metric Learning for Person Re-identification (MC, CC0, XH, SP), pp. 794–799.
ICPRICPR-2018-CaoGWXW #detection
Gaze-Aided Eye Detection via Appearance Learning (LC, CG, KW, GX, FYW0), pp. 1965–1970.
ICPRICPR-2018-CaoLL0JJC #detection #image
Deep Learning Based Bioresorbable Vascular Scaffolds Detection in IVOCT Images (YC, YL, JL, RZ0, QJ, JJ, YC), pp. 3778–3783.
ICPRICPR-2018-CuiB00JH #graph #hybrid #kernel #network
A Deep Hybrid Graph Kernel Through Deep Learning Networks (LC, LB0, LR0, YW0, YJ0, ERH), pp. 1030–1035.
ICPRICPR-2018-CuiZZH #multi #network #recognition #using
Multi-source Learning for Skeleton -based Action Recognition Using Deep LSTM Networks (RC, AZ, SZ, GH0), pp. 547–552.
ICPRICPR-2018-DasRBP #classification #documentation #image #network
Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks (AD, SR, UB, SKP), pp. 3180–3185.
ICPRICPR-2018-Dey0GVLP #image #multi #retrieval #sketching #using
Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch (SD, AD0, SKG, EV, JL0, UP0), pp. 916–921.
ICPRICPR-2018-DuCWP #distributed #named #representation
Zone2Vec: Distributed Representation Learning of Urban Zones (JD, YC, YW0, JP), pp. 880–885.
ICPRICPR-2018-EleziTVP #network
Transductive Label Augmentation for Improved Deep Network Learning (IE, AT, SV, MP), pp. 1432–1437.
ICPRICPR-2018-FuGA #detection #scalability
Simultaneous Context Feature Learning and Hashing for Large Scale Loop Closure Detection (ZF, YG, WA), pp. 1689–1694.
ICPRICPR-2018-GaoDS
Discernibility Matrix-Based Ensemble Learning (SG, JD, HS), pp. 952–957.
ICPRICPR-2018-GaolLH0W #automation #multi #predict
Automatic Facial Attractiveness Prediction by Deep Multi-Task Learning (LG, WL, ZH, DH0, YW), pp. 3592–3597.
ICPRICPR-2018-GrelssonF #exponential #linear #network
Improved Learning in Convolutional Neural Networks with Shifted Exponential Linear Units (ShELUs) (BG, MF), pp. 517–522.
ICPRICPR-2018-GuptaMSM #image #order #ranking #similarity
Learning an Order Preserving Image Similarity through Deep Ranking (NG, SM, SS, SM), pp. 1115–1120.
ICPRICPR-2018-HailatK0
Deep Semi-Supervised Learning (ZH, AK, XwC0), pp. 2154–2159.
ICPRICPR-2018-HanXW #generative #multi #network #representation
Learning Multi-view Generator Network for Shared Representation (TH0, XX, YNW), pp. 2062–2068.
ICPRICPR-2018-HanXZL #composition #image #network
Learning Intrinsic Image Decomposition by Deep Neural Network with Perceptual Loss (GH, XX, WSZ, JL), pp. 91–96.
ICPRICPR-2018-HaoDWT #fine-grained #named #representation #retrieval
DeepFirearm: Learning Discriminative Feature Representation for Fine-grained Firearm Retrieval (JH, JD0, WW0, TT), pp. 3335–3340.
ICPRICPR-2018-HeGG #network
Structure Learning of Bayesian Networks by Finding the Optimal Ordering (CCH, XGG, ZgG), pp. 177–182.
ICPRICPR-2018-HuangWDSL #image #lightweight
Lightweight Deep Residue Learning for Joint Color Image Demosaicking and Denoising (TH, FW, WD, GS, XL0), pp. 127–132.
ICPRICPR-2018-HuKLFLZD #named #representation
FV-Net: learning a finger-vein feature representation based on a CNN (HH, WK, YL, YF, HL, JZ, FD), pp. 3489–3494.
ICPRICPR-2018-JiangLSWZW #identification #similarity
Orientation-Guided Similarity Learning for Person Re-identification (NJ, JL, CS, YW, ZZ, WW), pp. 2056–2061.
ICPRICPR-2018-LeiZH0HL #classification #multi #rank
Multi-classification of Parkinson's Disease via Sparse Low-Rank Learning (HL, YZ, ZH, FZ0, LH, BL), pp. 3268–3272.
ICPRICPR-2018-LiCQWW #adaptation #network #semantics
Cross-domain Semantic Feature Learning via Adversarial Adaptation Networks (RL, WmC0, SQ, HSW, SW), pp. 37–42.
ICPRICPR-2018-LiL #metric
Riemannian Metric Learning based on Curvature Flow (YL, RL), pp. 806–811.
ICPRICPR-2018-LingLZG #classification #image #network
Semi-Supervised Learning via Convolutional Neural Network for Hyperspectral Image Classification (ZL, XL, WZ, SG), pp. 1–6.
ICPRICPR-2018-LiuDWZWZ #classification #education #image
Teaching Squeeze-and-Excitation PyramidNet for Imbalanced Image Classification with GAN-based Curriculum Learning (JL, AD, CW0, HZ, NW0, BZ), pp. 2444–2449.
ICPRICPR-2018-LiWK18a #framework #image #using
Infrared and Visible Image Fusion using a Deep Learning Framework (HL0, XJW, JK), pp. 2705–2710.
ICPRICPR-2018-LuoZLW #clustering #graph #image
Graph Embedding-Based Ensemble Learning for Image Clustering (XL, LZ0, FL, BW), pp. 213–218.
ICPRICPR-2018-LyuYCZZ #classification #detection
Learning Fixation Point Strategy for Object Detection and Classification (JL0, ZY, DC, YZ, HZ), pp. 2081–2086.
ICPRICPR-2018-MaBCX0 #collaboration #visual notation
Learning Collaborative Model for Visual Tracking (DM, WB, YC, YX, XW0), pp. 2582–2587.
ICPRICPR-2018-MadapanaW #gesture #recognition
Hard Zero Shot Learning for Gesture Recognition (NM, JPW), pp. 3574–3579.
ICPRICPR-2018-MaierSSWSCF #network #precise #towards #using
Precision Learning: Towards Use of Known Operators in Neural Networks (AKM, FS, CS, TW, SS, JHC, RF), pp. 183–188.
ICPRICPR-2018-ManessiR
Learning Combinations of Activation Functions (FM, AR), pp. 61–66.
ICPRICPR-2018-NguyenNSADF #recognition
Meta Transfer Learning for Facial Emotion Recognition (DNT, KN0, SS, IA, DD, CF), pp. 3543–3548.
ICPRICPR-2018-NguyenTL #data-driven #using
Are French Really That Different? Recognizing Europeans from Faces Using Data-Driven Learning (VDN, MT, JL), pp. 2729–2734.
ICPRICPR-2018-NieLQZJ #algorithm #classification #incremental #multi
An Incremental Multi-view Active Learning Algorithm for PolSAR Data Classification (XN, YL, HQ, BZ0, ZPJ), pp. 2251–2255.
ICPRICPR-2018-NiuHSC #named
SynRhythm: Learning a Deep Heart Rate Estimator from General to Specific (XN, HH, SS, XC), pp. 3580–3585.
ICPRICPR-2018-NiuS0 #graph
Enhancing Knowledge Graph Completion with Positive Unlabeled Learning (JN, ZS, WZ0), pp. 296–301.
ICPRICPR-2018-PangDWH
Dynamic Ensemble Active Learning: A Non-Stationary Bandit with Expert Advice (KP, MD, YW, TMH), pp. 2269–2276.
ICPRICPR-2018-PeiFR #multi
Learning with Latent Label Hierarchy from Incomplete Multi-Label Data (YP, XZF, RR), pp. 2075–2080.
ICPRICPR-2018-PengLMSL #detection #image #sequence #video
Driving Maneuver Detection via Sequence Learning from Vehicle Signals and Video Images (XP, RL, YLM, SS, YL), pp. 1265–1270.
ICPRICPR-2018-RenZLLWY #rank #representation #robust #taxonomy
Robust Projective Low-Rank and Sparse Representation by Robust Dictionary Learning (JR, ZZ0, SL0, GL, MW0, SY), pp. 1851–1856.
ICPRICPR-2018-RibaFLF #graph #message passing #network
Learning Graph Distances with Message Passing Neural Networks (PR, AF0, JL0, AF), pp. 2239–2244.
ICPRICPR-2018-RoyT #higher-order #using
Learning to Learn Second-Order Back-Propagation for CNNs Using LSTMs (AR, ST), pp. 97–102.
ICPRICPR-2018-RuedaF #process #recognition #representation
Learning Attribute Representation for Human Activity Recognition (FMR, GAF), pp. 523–528.
ICPRICPR-2018-SahaVJ #named
Class2Str: End to End Latent Hierarchy Learning (SS, GV, CVJ), pp. 1000–1005.
ICPRICPR-2018-SiddiquiV0 #approach #recognition
Face Recognition for Newborns, Toddlers, and Pre-School Children: A Deep Learning Approach (SS, MV, RS0), pp. 3156–3161.
ICPRICPR-2018-SuiZYC #detection #framework #novel #recognition
A Novel Integrated Framework for Learning both Text Detection and Recognition (WS, QZ, JY, WC), pp. 2233–2238.
ICPRICPR-2018-SunCWX #coordination #metric #online #parallel #rank
Online Low-Rank Metric Learning via Parallel Coordinate Descent Method (GS, YC, QW0, XX), pp. 207–212.
ICPRICPR-2018-SunZJLWY #adaptation #robust #taxonomy
Robust Discriminative Projective Dictionary Pair Learning by Adaptive Representations (YS, ZZ0, WJ, GL, MW0, SY), pp. 621–626.
ICPRICPR-2018-SunZWJ #behaviour #detection
Weak Supervised Learning Based Abnormal Behavior Detection (XS, SZ, SW, XYJ), pp. 1580–1585.
ICPRICPR-2018-TayanovKS #classification #predict #using
Prediction-based classification using learning on Riemannian manifolds (VT, AK, CYS), pp. 591–596.
ICPRICPR-2018-VinayavekhinCMA #comprehension #using #what
Focusing on What is Relevant: Time-Series Learning and Understanding using Attention (PV, SC, AM, DJA, GDM, DK, RT), pp. 2624–2629.
ICPRICPR-2018-WangHJ #using
Focus on Scene Text Using Deep Reinforcement Learning (HW, SH, LJ), pp. 3759–3765.
ICPRICPR-2018-WangSSL #metric
Nonlinear Metric Learning through Geodesic Interpolation within Lie Groups (ZW, BS, CDS, JL), pp. 898–903.
ICPRICPR-2018-WangWCK #classification #image #metric #multi #set
Multiple Manifolds Metric Learning with Application to Image Set Classification (RW, XJW, KXC, JK), pp. 627–632.
ICPRICPR-2018-WangWL #education #performance
Weakly- and Semi-supervised Faster R-CNN with Curriculum Learning (JW, XW, WL0), pp. 2416–2421.
ICPRICPR-2018-WenWSY #adaptation #recognition #representation
Adaptive convolution local and global learning for class-level joint representation of face recognition with single sample per person (WW, XW0, LS, MY0), pp. 3537–3542.
ICPRICPR-2018-WitmerB #classification #image #multi #using
Multi-label Classification of Stem Cell Microscopy Images Using Deep Learning (AW, BB), pp. 1408–1413.
ICPRICPR-2018-WuLCW #multi #semantics
Learning a Hierarchical Latent Semantic Model for Multimedia Data (SHW, YSL, SHC, JCW), pp. 2995–3000.
ICPRICPR-2018-WuYSZ #identification #ranking
Joint Semi-supervised Learning and Re-ranking for Vehicle Re-identification (FW, SY, JSS, BZ), pp. 278–283.
ICPRICPR-2018-XuCG #modelling #multi #random #using
Common Random Subgraph Modeling Using Multiple Instance Learning (TX, DKYC, IG), pp. 1205–1210.
ICPRICPR-2018-XuWK #correlation #representation
Non-negative Subspace Representation Learning Scheme for Correlation Filter Based Tracking (TX, XJW, JK), pp. 1888–1893.
ICPRICPR-2018-XuZL18a #incremental #kernel #linear #online
A Linear Incremental Nyström Method for Online Kernel Learning (SX, XZ, SL), pp. 2256–2261.
ICPRICPR-2018-YangDWL
Masked Label Learning for Optical Flow Regression (GY, ZD, SW, ZL), pp. 1139–1144.
ICPRICPR-2018-YanWSLZ #image #network #using
Image Captioning using Adversarial Networks and Reinforcement Learning (SY, FW, JSS, WL, BZ), pp. 248–253.
ICPRICPR-2018-Ye0 #classification #image #invariant
Rotational Invariant Discriminant Subspace Learning For Image Classification (QY, ZZ0), pp. 1217–1222.
ICPRICPR-2018-YuanTLDZ #automation #multi #segmentation #using
Fully Automatic Segmentation of the Left Ventricle Using Multi-Scale Fusion Learning (TY, QT, XL, XD, JZ), pp. 3838–3843.
ICPRICPR-2018-YuanWXZ #empirical #estimation #multi
Multiple- Instance Learning with Empirical Estimation Guided Instance Selection (LY, XW, HX, LZ), pp. 770–775.
ICPRICPR-2018-YuanZLQ0S #adaptation #canonical #correlation #parallel #recognition
Learning Parallel Canonical Correlations for Scale-Adaptive Low Resolution Face Recognition (YY, ZZ, YL0, JPQ, BL0, XBS), pp. 922–927.
ICPRICPR-2018-ZengZQQB0 #image
Single Image Super-Resolution With Learning Iteratively Non-Linear Mapping Between Low- and High-Resolution Sparse Representations (KZ, HZ, YQ, XQ, LB, ZC0), pp. 507–512.
ICPRICPR-2018-ZhangJCXP #approach #graph #kernel #network
Kernel-Weighted Graph Convolutional Network: A Deep Learning Approach for Traffic Forecasting (QZ, QJ, JC, SX, CP), pp. 1018–1023.
ICPRICPR-2018-ZhangWG #adaptation #multi #representation
Adaptive Latent Representation for Multi-view Subspace Learning (YZ, XW, XG), pp. 1229–1234.
ICPRICPR-2018-ZhangWGWXL #detection #effectiveness #network
An Effective Deep Learning Based Scheme for Network Intrusion Detection (HZ, CQW, SG, ZW, YX, YL), pp. 682–687.
ICPRICPR-2018-ZhaoPL0DWQ #locality #semantics #topic #using
Learning Topics Using Semantic Locality (ZZ, KP, SL, ZL0, CD, YW, QQ), pp. 3710–3715.
ICPRICPR-2018-ZhouL0LL #estimation
Scale and Orientation Aware EPI-Patch Learning for Light Field Depth Estimation (WZ, LL, HZ, AL, LL), pp. 2362–2367.
ICPRICPR-2018-ZhouMMB #detection
Learning Training Samples for Occlusion Edge Detection and Its Application in Depth Ordering Inference (YZ0, JM, AM, XB), pp. 541–546.
ICPRICPR-2018-ZhouWD #online #realtime #robust
Online Learning of Spatial-Temporal Convolution Response for Robust Real-Time Tracking (JZ, RW, JD), pp. 1821–1826.
ICPRICPR-2018-Zhuang0CW #classification #multi
Multi-task Learning of Cascaded CNN for Facial Attribute Classification (NZ, YY0, SC, HW), pp. 2069–2074.
ICPRICPR-2018-ZhuangTYMZJX #image #named #representation #segmentation #semantics
RelationNet: Learning Deep-Aligned Representation for Semantic Image Segmentation (YZ, LT, FY, CM, ZZ, HJ, XX), pp. 1506–1511.
ICPRICPR-2018-ZhuX #approximate #graph #scalability
Scalable Semi-Supervised Learning by Graph Construction with Approximate Anchors Embedding (HZ, MX), pp. 1331–1336.
ICPRICPR-2018-ZhuZZ
Learning Evasion Strategy in Pursuit-Evasion by Deep Q-network (JZ, WZ, ZZ), pp. 67–72.
ICPRICPR-2018-ZhuZZ18b #recognition #representation
End-to-end Video-level Representation Learning for Action Recognition (JZ, ZZ, WZ), pp. 645–650.
KDDKDD-2018-0009QG0H #realtime
Deep Reinforcement Learning for Sponsored Search Real-time Bidding (JZ0, GQ, ZG, WZ0, XH), pp. 1021–1030.
KDDKDD-2018-BaiZEV #representation
Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time (TB, SZ, BLE, SV), pp. 43–51.
KDDKDD-2018-CaiWGSJ #multi
Deep Adversarial Learning for Multi-Modality Missing Data Completion (LC, ZW, HG, DS, SJ), pp. 1158–1166.
KDDKDD-2018-CardosoDV #personalisation #recommendation #semistructured data #towards
Product Characterisation towards Personalisation: Learning Attributes from Unstructured Data to Recommend Fashion Products (ÂC, FD, SV), pp. 80–89.
KDDKDD-2018-Chen0DTHT #online #recommendation
Stabilizing Reinforcement Learning in Dynamic Environment with Application to Online Recommendation (SYC, YY0, QD, JT, HKH, HHT), pp. 1187–1196.
KDDKDD-2018-DasSCHLCKC #named #performance #using
SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression (ND, MS, STC, FH, SL, LC, MEK, DHC), pp. 196–204.
KDDKDD-2018-DiPSC #morphism
Transfer Learning via Feature Isomorphism Discovery (SD, JP, YS, LC), pp. 1301–1309.
KDDKDD-2018-DonnatZHL
Learning Structural Node Embeddings via Diffusion Wavelets (CD, MZ, DH, JL), pp. 1320–1329.
KDDKDD-2018-FoxAJPW #multi #predict
Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories (IF, LA, MJ, RPB, JW), pp. 1387–1395.
KDDKDD-2018-FuWHW #approximate #fault #reduction #scalability
Scalable Active Learning by Approximated Error Reduction (WF, MW, SH, XW0), pp. 1396–1405.
KDDKDD-2018-GohSVH #predict #rule-based #using
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction (GBG, CS, AV, NOH), pp. 302–310.
KDDKDD-2018-GorovitsGPB #community #named
LARC: Learning Activity-Regularized Overlapping Communities Across Time (AG, EG, EEP, PB), pp. 1465–1474.
KDDKDD-2018-GuYCH #algorithm #incremental
New Incremental Learning Algorithm for Semi-Supervised Support Vector Machine (BG, XTY, SC, HH), pp. 1475–1484.
KDDKDD-2018-HanSSZ #collaboration #multi #semistructured data
Multi-label Learning with Highly Incomplete Data via Collaborative Embedding (YH, GS, YS, XZ0), pp. 1494–1503.
KDDKDD-2018-HongCL #kernel
Disturbance Grassmann Kernels for Subspace-Based Learning (JH, HC, FL), pp. 1521–1530.
KDDKDD-2018-HuaiMLSSZ #metric #probability
Metric Learning from Probabilistic Labels (MH, CM, YL, QS, LS, AZ), pp. 1541–1550.
KDDKDD-2018-HuDZ0X #analysis #e-commerce #formal method #rank
Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application (YH, QD, AZ, YY0, YX), pp. 368–377.
KDDKDD-2018-Janakiraman #multi #safety #using
Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning (VMJ), pp. 406–415.
KDDKDD-2018-JeongJ #multi
Variable Selection and Task Grouping for Multi-Task Learning (JYJ, CHJ), pp. 1589–1598.
KDDKDD-2018-KumagaiI #bound
Learning Dynamics of Decision Boundaries without Additional Labeled Data (AK, TI), pp. 1627–1636.
KDDKDD-2018-Le0V #memory management
Dual Memory Neural Computer for Asynchronous Two-view Sequential Learning (HL, TT0, SV), pp. 1637–1645.
KDDKDD-2018-LeeAVN #collaboration #comprehension #metric #video
Collaborative Deep Metric Learning for Video Understanding (JL, SAEH, BV, AN), pp. 481–490.
KDDKDD-2018-LiaoZWMCYGW #predict #sequence
Deep Sequence Learning with Auxiliary Information for Traffic Prediction (BL, JZ, CW0, DM, TC, SY, YG, FW), pp. 537–546.
KDDKDD-2018-LiFWSYL #estimation #multi #representation
Multi-task Representation Learning for Travel Time Estimation (YL, KF, ZW, CS, JY, YL0), pp. 1695–1704.
KDDKDD-2018-LinZXZ #multi #performance #scalability
Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning (KL, RZ, ZX, JZ), pp. 1774–1783.
KDDKDD-2018-LiuZC #metric #performance
Efficient Similar Region Search with Deep Metric Learning (YL, KZ0, GC), pp. 1850–1859.
KDDKDD-2018-LiY #classification #network #policy
Learning Adversarial Networks for Semi-Supervised Text Classification via Policy Gradient (YL, JY), pp. 1715–1723.
KDDKDD-2018-LiZLHMC #behaviour #recommendation
Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors (ZL, HZ, QL0, ZH, TM, EC), pp. 1734–1743.
KDDKDD-2018-LiZY #approach
Dynamic Bike Reposition: A Spatio-Temporal Reinforcement Learning Approach (YL, YZ, QY), pp. 1724–1733.
KDDKDD-2018-LuJZDZW #named #semantics #visual notation
R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering (PL, LJ, WZ0, ND, MZ0, JW), pp. 1880–1889.
KDDKDD-2018-LuoCTSLCY #information management #invariant #named #network
TINET: Learning Invariant Networks via Knowledge Transfer (CL, ZC, LAT, AS, ZL, HC, JY), pp. 1890–1899.
KDDKDD-2018-MaZYCHC #modelling #multi
Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts (JM, ZZ, XY, JC, LH, EHC), pp. 1930–1939.
KDDKDD-2018-NieHL #multi
Calibrated Multi-Task Learning (FN, ZH, XL), pp. 2012–2021.
KDDKDD-2018-NiOLLOZS #e-commerce #multi
Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks (YN, DO, SL, XL, WO, AZ, LS), pp. 596–605.
KDDKDD-2018-OshriHACDWBLE #assessment #framework #quality #using
Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning (BO, AH, PA, XC, PD, JW, MB, DBL, SE), pp. 616–625.
KDDKDD-2018-PangCCL #detection #random
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection (GP, LC, LC, HL), pp. 2041–2050.
KDDKDD-2018-QiuTMDW0 #named #predict #social
DeepInf: Social Influence Prediction with Deep Learning (JQ, JT, HM, YD, KW, JT0), pp. 2110–2119.
KDDKDD-2018-RaoTL #comprehension #framework #multi #network #platform #query
Multi-Task Learning with Neural Networks for Voice Query Understanding on an Entertainment Platform (JR, FT, JL), pp. 636–645.
KDDKDD-2018-SamelM
Active Deep Learning to Tune Down the Noise in Labels (KS, XM), pp. 685–694.
KDDKDD-2018-SatoNHMHAM #detection
Managing Computer-Assisted Detection System Based on Transfer Learning with Negative Transfer Inhibition (IS, YN, SH, SM, NH, OA, YM), pp. 695–704.
KDDKDD-2018-ShiZGZ0 #network
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks (YS, QZ, FG, CZ0, JH0), pp. 2190–2199.
KDDKDD-2018-SureshGG #multi
Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU (HS, JJG, JVG), pp. 802–810.
KDDKDD-2018-TangW #modelling #performance #ranking #recommendation
Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System (JT, KW), pp. 2289–2298.
KDDKDD-2018-Teh #big data #on the #problem
On Big Data Learning for Small Data Problems (YWT), p. 3.
KDDKDD-2018-VandalKDGNG #nondeterminism
Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning (TV, EK, JGD, SG, RRN, ARG), pp. 2377–2386.
KDDKDD-2018-WangFY
Learning to Estimate the Travel Time (ZW, KF, JY), pp. 858–866.
KDDKDD-2018-WangFZWZA #analysis #behaviour #how #representation
You Are How You Drive: Peer and Temporal-Aware Representation Learning for Driving Behavior Analysis (PW, YF, JZ, PW, YZ, CCA), pp. 2457–2466.
KDDKDD-2018-WangJZEC #behaviour #multi
Multi-Type Itemset Embedding for Learning Behavior Success (DW, MJ0, QZ, ZE, NVC), pp. 2397–2406.
KDDKDD-2018-WangOWW #modelling
Learning Credible Models (JW, JO, HW, JW), pp. 2417–2426.
KDDKDD-2018-WangZ #problem #towards
Towards Mitigating the Class-Imbalance Problem for Partial Label Learning (JW, MLZ), pp. 2427–2436.
KDDKDD-2018-WangZBZCY #mobile #performance #privacy
Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud (JW0, JZ, WB, XZ, BC, PSY), pp. 2407–2416.
KDDKDD-2018-WangZHZ #network #recommendation
Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation (LW, WZ0, XH, HZ), pp. 2447–2456.
KDDKDD-2018-WeiZYL #approach #named
IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control (HW, GZ, HY, ZL), pp. 2496–2505.
KDDKDD-2018-WuYC #realtime
Deep Censored Learning of the Winning Price in the Real Time Bidding (WCHW, MYY, MSC), pp. 2526–2535.
KDDKDD-2018-WuYYZ #process
Decoupled Learning for Factorial Marked Temporal Point Processes (WW, JY, XY, HZ), pp. 2516–2525.
KDDKDD-2018-XuLDH #metric #robust #using
New Robust Metric Learning Model Using Maximum Correntropy Criterion (JX0, LL, CD, HH), pp. 2555–2564.
KDDKDD-2018-XuLGZLNLBY #approach #on-demand #order #platform #scalability
Large-Scale Order Dispatch in On-Demand Ride-Hailing Platforms: A Learning and Planning Approach (ZX, ZL, QG, DZ, QL, JN, CL, WB, JY), pp. 905–913.
KDDKDD-2018-YangZTWCH #case study #contest #image #recognition
Deep Learning for Practical Image Recognition: Case Study on Kaggle Competitions (XY, ZZ, SGT, LW, VC0, SCHH), pp. 923–931.
KDDKDD-2018-YoshidaTK #distance #metric
Safe Triplet Screening for Distance Metric Learning (TY, IT, MK), pp. 2653–2662.
KDDKDD-2018-YuanZY #approach #named #predict
Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Temporal Data (ZY, XZ, TY), pp. 984–992.
KDDKDD-2018-YuZCASZCW #network
Learning Deep Network Representations with Adversarially Regularized Autoencoders (WY, CZ, WC, CCA, DS, BZ, HC, WW0), pp. 2663–2671.
KDDKDD-2018-ZangC0 #empirical
Learning and Interpreting Complex Distributions in Empirical Data (CZ, PC0, WZ0), pp. 2682–2691.
KDDKDD-2018-ZhangWLTYY #matrix #self
Discrete Ranking-based Matrix Factorization with Self-Paced Learning (YZ0, HW, DL, IWT, HY, GY), pp. 2758–2767.
KDDKDD-2018-ZhangZCMHWT #adaptation #online #symmetry
Online Adaptive Asymmetric Active Learning for Budgeted Imbalanced Data (YZ0, PZ, JC, WM, JH, QW, MT), pp. 2768–2777.
KDDKDD-2018-ZhaoLSY #e-commerce #representation
Learning and Transferring IDs Representation in E-commerce (KZ, YL, ZS, CY), pp. 1031–1039.
KDDKDD-2018-ZhaoZDXTY #feedback #recommendation
Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning (XZ, LZ, ZD, LX, JT, DY), pp. 1040–1048.
KDDKDD-2018-ZhuLZLHLG #recommendation
Learning Tree-based Deep Model for Recommender Systems (HZ, XL, PZ, GL, JH, HL, KG), pp. 1079–1088.
ECOOPECOOP-2018-ChenHZHK0 #execution #program transformation #symbolic computation
Learning to Accelerate Symbolic Execution via Code Transformation (JC0, WH, LZ, DH, SK, LZ0), p. 27.
OnwardOnward-2018-RinardSM #source code
Active learning for inference and regeneration of computer programs that store and retrieve data (MCR, JS0, VM), pp. 12–28.
OOPSLAOOPSLA-2018-EzudheenND0M #contract #invariant
Horn-ICE learning for synthesizing invariants and contracts (PE, DN, DD, PG0, PM), p. 25.
OOPSLAOOPSLA-2018-PradelS #approach #debugging #detection #named
DeepBugs: a learning approach to name-based bug detection (MP, KS), p. 25.
PLDIPLDI-2018-Bastani0AL #points-to #specification
Active learning of points-to specifications (OB, RS0, AA, PL), pp. 678–692.
PLDIPLDI-2018-FengMBD #synthesis #using
Program synthesis using conflict-driven learning (YF, RM, OB, ID), pp. 420–435.
SASSAS-2018-PrabhuMV #behaviour #proving #safety
Efficiently Learning Safety Proofs from Appearance as well as Behaviours (SP, KM, RV), pp. 326–343.
ASEASE-2018-ChaLO #online #testing
Template-guided concolic testing via online learning (SC, SL, HO), pp. 408–418.
ASEASE-2018-GaoYFJS #named #platform #semantics
VulSeeker: a semantic learning based vulnerability seeker for cross-platform binary (JG, XY, YF, YJ0, JS), pp. 896–899.
ASEASE-2018-HabibP #documentation #graph #thread #using
Is this class thread-safe? inferring documentation using graph-based learning (AH, MP), pp. 41–52.
ASEASE-2018-HanYL #debugging #named #performance
PerfLearner: learning from bug reports to understand and generate performance test frames (XH, TY, DL0), pp. 17–28.
ASEASE-2018-LiuXZ #detection
Deep learning based feature envy detection (HL, ZX, YZ), pp. 385–396.
ASEASE-2018-MaJZSXLCSLLZW #multi #named #testing
DeepGauge: multi-granularity testing criteria for deep learning systems (LM0, FJX, FZ, JS, MX, BL0, CC, TS, LL0, YL0, JZ, YW), pp. 120–131.
ASEASE-2018-TufanoWBPWP #empirical
An empirical investigation into learning bug-fixing patches in the wild via neural machine translation (MT, CW, GB, MDP, MW, DP), pp. 832–837.
ASEASE-2018-WanZYXY0Y #automation #source code #summary
Improving automatic source code summarization via deep reinforcement learning (YW, ZZ, MY0, GX, HY, JW0, PSY), pp. 397–407.
ESEC-FSEESEC-FSE-2018-GaoYFJSS #named #semantics
VulSeeker-pro: enhanced semantic learning based binary vulnerability seeker with emulation (JG, XY, YF, YJ0, HS, JS), pp. 803–808.
ESEC-FSEESEC-FSE-2018-GuoJZCS #difference #fuzzing #named #testing
DLFuzz: differential fuzzing testing of deep learning systems (JG, YJ0, YZ, QC, JS), pp. 739–743.
ESEC-FSEESEC-FSE-2018-HellendoornBBA #type inference
Deep learning type inference (VJH, CB, ETB, MA), pp. 152–162.
ESEC-FSEESEC-FSE-2018-JamshidiVKS #configuration management #modelling #performance
Learning to sample: exploiting similarities across environments to learn performance models for configurable systems (PJ, MV, CK, NS), pp. 71–82.
ESEC-FSEESEC-FSE-2018-Meijer #concept #framework #programming language
Behind every great deep learning framework is an even greater programming languages concept (keynote) (EM0), p. 1.
ESEC-FSEESEC-FSE-2018-ZhaoH #functional #named #similarity
DeepSim: deep learning code functional similarity (GZ, JH0), pp. 141–151.
ICSE-2018-PhanNTTNN #api #online #statistics
Statistical learning of API fully qualified names in code snippets of online forums (HP, HAN, NMT, LHT, ATN0, TNN), pp. 632–642.
ASPLOSASPLOS-2018-MishraILH #energy #latency #named #predict
CALOREE: Learning Control for Predictable Latency and Low Energy (NM, CI, JDL, HH), pp. 184–198.
CASECASE-2018-BanerjeeRP #towards
A Step Toward Learning to Control Tens of Optically Actuated Microrobots in Three Dimensions (AGB, KR, BP), pp. 1460–1465.
CASECASE-2018-ChuBIICS #multi #online #power management #using
Plug-and-Play Power Management Control of All-Electric Vehicles Using Multi-Agent System and On-line Gaussian Learning (KCC, GB, MI, AI, CC, KS), pp. 1599–1604.
CASECASE-2018-FarooquiFF #automation #modelling #simulation #towards
Towards Automatic Learning of Discrete-Event Models from Simulations (AF, PF, MF), pp. 857–862.
CASECASE-2018-HuaH #concept #induction #logic programming #semantics
Concept Learning in AutomationML with Formal Semantics and Inductive Logic Programming (YH, BH), pp. 1542–1547.
CASECASE-2018-JiKPFG #2d
Learning 2D Surgical Camera Motion From Demonstrations (JJJ, SK, VP, DF, KG), pp. 35–42.
CASECASE-2018-LeeLFG #constraints #estimation
Constraint Estimation and Derivative-Free Recovery for Robot Learning from Demonstrations (JL, ML, RF, KG), pp. 270–277.
CASECASE-2018-NagahamaTYYYI
A Learning Method for a Daily Assistive Robot for Opening and Closing Doors Based on Simple Instructions (KN, KT, HY, KY, TY, MI), pp. 599–605.
CASECASE-2018-NeumannNKM #classification
Material Classification through Knocking and Grasping by Learning of Structure-Borne Sound under Changing Acoustic Conditions (MN, KN, IK, ZCM), pp. 1269–1275.
CASECASE-2018-ParkHGS #process
Robot Model Learning with Gaussian Process Mixture Model (SP, YH, CFG, KS), pp. 1263–1268.
CASECASE-2018-RenWLG #behaviour #online #video
Learning Traffic Behaviors by Extracting Vehicle Trajectories from Online Video Streams (XR, DW, ML, KG), pp. 1276–1283.
CASECASE-2018-SeichterESG #detection #how
How to Improve Deep Learning based Pavement Distress Detection while Minimizing Human Effort (DS, ME0, RS, HMG), pp. 63–70.
CASECASE-2018-TanCPP #analysis #automation #design #visual notation
Transfer Learning with PipNet: For Automated Visual Analysis of Piping Design (WCT, IMC, DP, SJP), pp. 1296–1301.
CASECASE-2018-TanGCC #analysis #automation
Learning with Corrosion Feature: For Automated Quantitative Risk Analysis of Corrosion Mechanism (WCT, PCG, KHC, IMC), pp. 1290–1295.
CASECASE-2018-TsengWCMSVVCOG #automation #image #precise #towards
Towards Automating Precision Irrigation: Deep Learning to Infer Local Soil Moisture Conditions from Synthetic Aerial Agricultural Images (DT, DWLW, CC, LM, WS, JV, SV, SC, JAO, KG), pp. 284–291.
CASECASE-2018-WangKZY #concept #data type #detection #multi
A Multiscale Concept Drift Detection Method for Learning from Data Streams (XW, QK, MZ, SY), pp. 786–790.
CASECASE-2018-YangZCTK
Intelligent Diagnosis of Forging Die based on Deep Learning (HCY, CHZ, YZC, CMT, YCK), pp. 199–204.
ESOPESOP-2018-MertenBS #algorithm #complexity #distributed #game studies
Verified Learning Without Regret - From Algorithmic Game Theory to Distributed Systems with Mechanized Complexity Guarantees (SM, AB, GS0), pp. 561–588.
CAVCAV-2018-DreossiJS #semantics
Semantic Adversarial Deep Learning (TD, SJ, SAS), pp. 3–26.
CAVCAV-2018-KelmendiKKW #algorithm #game studies #probability
Value Iteration for Simple Stochastic Games: Stopping Criterion and Learning Algorithm (EK, JK, JK, MW), pp. 623–642.
CAVCAV-2018-SinghPV #performance #program analysis
Fast Numerical Program Analysis with Reinforcement Learning (GS, MP, MTV), pp. 211–229.
CAVCAV-2018-WangADM #abstraction #synthesis
Learning Abstractions for Program Synthesis (XW0, GA, ID, KLM), pp. 407–426.
CAVCAV-2018-ZhouL
Safety-Aware Apprenticeship Learning (WZ, WL), pp. 662–680.
ICTSSICTSS-2018-SalvaBL #component #data analysis #modelling
Combining Model Learning and Data Analysis to Generate Models of Component-Based Systems (SS, EB, PL), pp. 142–148.
IJCARIJCAR-2018-PiotrowskiU #feedback #named
ATPboost: Learning Premise Selection in Binary Setting with ATP Feedback (BP, JU), pp. 566–574.
VMCAIVMCAI-2018-LiTZS #automaton
Learning to Complement Büchi Automata (YL0, AT, LZ0, SS), pp. 313–335.
JCDLJCDL-2017-WeihsE #metric #predict
Learning to Predict Citation-Based Impact Measures (LW, OE), pp. 49–58.
JCDLJCDL-2017-YangHHOZKG #identification #library #using
Smart Library: Identifying Books on Library Shelves Using Supervised Deep Learning for Scene Text Reading (XY, DH, WH, AO, ZZ, DK, CLG), pp. 245–248.
CSEETCSEET-2017-BinderNRM #challenge #development #education #mobile
Challenge Based Learning Applied to Mobile Software Development Teaching (FVB, MN, SSR, AM), pp. 57–64.
CSEETCSEET-2017-LeildeR #assessment #process
Does Process Assessment Drive Process Learning? The Case of a Bachelor Capstone Project (VL, VR), pp. 197–201.
EDMEDM-2017-AgrawalNM #student
Grouping Students for Maximizing Learning from Peers (RA, SN, NMM).
EDMEDM-2017-BaoCH #multi #on the #online
On the Prevalence of Multiple-Account Cheating in Massive Open Online Learning (YB, GC, CH).
EDMEDM-2017-BeckCB #data mining #education #mining
Workshop proposal: deep learning for educational data mining (JB, MC, RSB).
EDMEDM-2017-CaiEDPGS #analysis #chat #collaboration #modelling #network #topic
Epistemic Network Analysis and Topic Modeling for Chat Data from Collaborative Learning Environment (ZC, BRE, ND, JWP, ACG, DWS).
EDMEDM-2017-DongB #behaviour #modelling #student
An Extended Learner Modeling Method to Assess Students' Learning Behaviors (YD, GB).
EDMEDM-2017-EkambaramMDKSN #physics
Tell Me More: Digital Eyes to the Physical World for Early Childhood Learning (VE, RSM, PD, RK, AKS, SVN).
EDMEDM-2017-FangNPXGH #online #persistent
Online Learning Persistence and Academic Achievement (YF, BN, PIPJ, YX, ACG, XH).
EDMEDM-2017-GrawemeyerWSHMP #graph #modelling #student #using
Using Graph-based Modelling to explore changes in students' affective states during exploratory learning tasks (BG, AW, SGS, WH, MM, AP).
EDMEDM-2017-HongB #predict #using
A Prediction and Early Alert Model Using Learning Management System Data and Grounded in Learning Science Theory (WJH, MLB).
EDMEDM-2017-LalleCATM #on the #self #student
On the Influence on Learning of Student Compliance with Prompts Fostering Self-Regulated Learning (SL, CC, RA, MT, NM).
EDMEDM-2017-LiuK #automation #data-driven
Closing the loop: Automated data-driven cognitive model discoveries lead to improved instruction and learning (RL0, KRK).
EDMEDM-2017-MaM #composition
Intelligent Composition of Test Papers based on MOOC Learning Data (LM, YM).
EDMEDM-2017-NamFC #predict #semantics #word
Predicting Short- and Long-Term Vocabulary Learning via Semantic Features of Partial Word Knowledge (SN, GAF, KCT).
EDMEDM-2017-RomeroEGGM #automation #classification #towards
Towards Automatic Classification of Learning Objects: Reducing the Number of Used Features (CR, PGE, EG, AZG, VHM).
EDMEDM-2017-ShiPG #analysis #performance #using
Using an Additive Factor Model and Performance Factor Analysis to Assess Learning Gains in a Tutoring System to Help Adults with Reading Difficulties (GS, PIPJ, ACG).
EDMEDM-2017-SuprajaHTK #automation #towards
Toward the Automatic Labeling of Course Questions for Ensuring their Alignment with Learning Outcomes (SS, KH, ST, AWHK).
EDMEDM-2017-ThanasuanCW #mining #student
Emerging Patterns in Student's Learning Attributes through Text Mining (KT, WC, CW).
EDMEDM-2017-WangSLP #programming #student #using
Learning to Represent Student Knowledge on Programming Exercises Using Deep Learning (LW, AS, LL, CP).
EDMEDM-2017-WatersGLB
Short-Answer Responses to STEM Exercises: Measuring Response Validity and Its Impact on Learning (AEW, PG, ASL, RGB).
EDMEDM-2017-XieMSEBH #adaptation #online #predict #student
Student Learning Strategies to Predict Success in an Online Adaptive Mathematics Tutoring System (JX, SM, KTS, AE, RSB, XH).
EDMEDM-2017-ZhouWLC #policy #towards
Towards Closing the Loop: Bridging Machine-induced Pedagogical Policies to Learning Theories (GZ, JW, CL, MC).
ICPCICPC-2017-LamNNN #debugging #information retrieval #locality
Bug localization with combination of deep learning and information retrieval (ANL, ATN0, HAN, TNN), pp. 218–229.
ICSMEICSME-2017-DeshmukhMPSD #debugging #retrieval #towards #using
Towards Accurate Duplicate Bug Retrieval Using Deep Learning Techniques (JD, KMA, SP, SS, ND), pp. 115–124.
ICSMEICSME-2017-HanLXLF #predict #using
Learning to Predict Severity of Software Vulnerability Using Only Vulnerability Description (ZH, XL0, ZX, HL, ZF0), pp. 125–136.
ICSMEICSME-2017-LiJZZ #fault #kernel #multi #predict
Heterogeneous Defect Prediction Through Multiple Kernel Learning and Ensemble Learning (ZL0, XYJ, XZ, HZ0), pp. 91–102.
ICSMEICSME-2017-VerwerH #automaton #named
flexfringe: A Passive Automaton Learning Package (SV, CAH), pp. 638–642.
ICSMEICSME-2017-WiemanALVD #case study #experience #scalability
An Experience Report on Applying Passive Learning in a Large-Scale Payment Company (RW, MFA, WL, SV, AvD), pp. 564–573.
SANERSANER-2017-GoerFM #execution #named
scat: Learning from a single execution of a binary (FdG, CF, LM), pp. 492–496.
SANERSANER-2017-SharmaTSLY #developer #twitter
Harnessing Twitter to support serendipitous learning of developers (AS0, YT0, AS, DL0, AFY), pp. 387–391.
IFM-2017-SilvettiPB #approach #black box #cyber-physical
An Active Learning Approach to the Falsification of Black Box Cyber-Physical Systems (SS, AP, LB), pp. 3–17.
SEFMSEFM-2017-CabodiCPPV #bound #model checking
Interpolation-Based Learning as a Mean to Speed-Up Bounded Model Checking (Short Paper) (GC, PC, MP, PP, DV), pp. 382–387.
AIIDEAIIDE-2017-BarrigaSB #game studies #realtime
Combining Strategic Learning with Tactical Search in Real-Time Strategy Games (NAB, MS, MB), pp. 9–15.
AIIDEAIIDE-2017-CampbellV
Learning Combat in NetHack (JC, CV), pp. 16–22.
AIIDEAIIDE-2017-SigurdsonB #algorithm #heuristic #realtime
Deep Learning for Real-Time Heuristic Search Algorithm Selection (DS, VB), pp. 108–114.
CHI-PLAYCHI-PLAY-2017-ArroyoMCOHR #game studies #multi #smarttech
Wearable Learning: Multiplayer Embodied Games for Math (IA, MM, JC, EO, TH, MMTR), pp. 205–216.
CHI-PLAYCHI-PLAY-2017-JohansonGM #3d #game studies #navigation #performance
The Effects of Navigation Assistance on Spatial Learning and Performance in a 3D Game (CJ, CG, RLM), pp. 341–353.
CHI-PLAYCHI-PLAY-2017-ScozziIL #approach #design #game studies
A Mixed Method Approach for Evaluating and Improving the Design of Learning in Puzzle Games (MVS, II, CL), pp. 217–228.
CoGCIG-2017-IlhanE #game studies #monte carlo #video
Monte Carlo tree search with temporal-difference learning for general video game playing (EI, ASEU), pp. 317–324.
CoGCIG-2017-JustesenR #using
Learning macromanagement in starcraft from replays using deep learning (NJ, SR), pp. 162–169.
CoGCIG-2017-MinK #game studies #using #visual notation
Learning to play visual doom using model-free episodic control (BJM, KJK), pp. 223–225.
CoGCIG-2017-NguyenRGM #automation #network
Automated learning of hierarchical task networks for controlling minecraft agents (CN, NR, SG, HMA), pp. 226–231.
CoGCIG-2017-OonishiI #game studies #using
Improving generalization ability in a puzzle game using reinforcement learning (HO, HI), pp. 232–239.
CoGCIG-2017-OsbornSM #automation #design #game studies
Automated game design learning (JCO, AS, MM), pp. 240–247.
CoGCIG-2017-PhucNK #behaviour #statistics #using
Learning human-like behaviors using neuroevolution with statistical penalties (LHP, KN, KI), pp. 207–214.
CoGCIG-2017-PoulsenTFR #named #visual notation
DLNE: A hybridization of deep learning and neuroevolution for visual control (APP, MT, MHF, SR), pp. 256–263.
CoGCIG-2017-ZhangB #policy
Improving hearthstone AI by learning high-level rollout policies and bucketing chance node events (SZ, MB), pp. 309–316.
DiGRADiGRA-2017-Loban #game studies #video
Digitising Diplomacy: Grand Strategy Video Games as an Introductory Tool for Learning Diplomacy and International Relations (RL).
DiGRADiGRA-2017-TyackW #adaptation #design #game studies
Adapting Epic Theatre Principles for the Design of Games for Learning (AT, PW).
FDGFDG-2017-BauerBP #architecture #design #game studies #problem
Dragon architect: open design problems for guided learning in a creative computational thinking sandbox game (AB0, EB, ZP), p. 6.
FDGFDG-2017-KaravolosLY #game studies #multi
Learning the patterns of balance in a multi-player shooter game (DK, AL, GNY), p. 10.
FDGFDG-2017-LaffeyGSLSGKWW #game studies
Mission HydroSci: a progress report on a transformational role playing game for science learning (JML, JG, JS, SL, TDS, SPG, SMK, EW, AJW), p. 4.
FDGFDG-2017-Valls-VargasZO #game studies #generative #grammarware #graph grammar #parallel #programming
Graph grammar-based controllable generation of puzzles for a learning game about parallel programming (JVV, JZ, SO), p. 10.
ICGJICGJ-2017-PollockMY #development #game studies
Brain jam: STEAM learning through neuroscience-themed game development (IP, JM, BY), pp. 15–21.
CoGVS-Games-2017-BlomeDRBM #artificial reality
VReanimate - Non-verbal guidance and learning in virtual reality (TB, AD, SR, KB, SvM), pp. 23–30.
CoGVS-Games-2017-CobelloBMZ #aspect-oriented #community #education #experience #gamification #social
The value of establishing a community of teachers for the gamification of prosocial learning: Pegadogical, social and developmental aspects of a teachers' community space experience (SC, PPB, EM, NZ), pp. 189–192.
CoGVS-Games-2017-MullerPGLJ #case study
Learning mechanical engineering in a virtual workshop: A preliminary study on utilisability, utility and acceptability (NM, DP, MG, PL, JPJ), pp. 55–62.
CoGVS-Games-2017-PanzoliCPDOBLBG #biology #game studies
Learning the cell cycle with a game: Virtual experiments in cell biology (DP, SCB, JP, JD, MO, LB, VL, EB, FG, CPL, BD, YD), pp. 47–54.
CoGVS-Games-2017-TsatsouVD #adaptation #case study #experience #modelling #multi
Modelling learning experiences in adaptive multi-agent learning environments (DT, NV, PD), pp. 193–200.
CIKMCIKM-2017-0001KGR #constraints #named
TaCLe: Learning Constraints in Tabular Data (SP0, SK, TG, LDR), pp. 2511–2514.
CIKMCIKM-2017-0002L #representation
Region Representation Learning via Mobility Flow (HW0, ZL), pp. 237–246.
CIKMCIKM-2017-Abu-El-HaijaPA #rank #symmetry
Learning Edge Representations via Low-Rank Asymmetric Projections (SAEH, BP, RAR), pp. 1787–1796.
CIKMCIKM-2017-BiegaGFGW #community #online
Learning to Un-Rank: Quantifying Search Exposure for Users in Online Communities (AJB, AG, HF, KPG, GW), pp. 267–276.
CIKMCIKM-2017-BouadjenekVZ #biology #sequence #using
Learning Biological Sequence Types Using the Literature (MRB, KV, JZ), pp. 1991–1994.
CIKMCIKM-2017-CavallariZCCC #community #detection #graph
Learning Community Embedding with Community Detection and Node Embedding on Graphs (SC, VWZ, HC, KCCC, EC), pp. 377–386.
CIKMCIKM-2017-ChaiLTS #multi
Compact Multiple-Instance Learning (JC, WL0, IWT, XBS), pp. 2007–2010.
CIKMCIKM-2017-ChenDWXCCM #detection #spreadsheet
Spreadsheet Property Detection With Rule-assisted Active Learning (ZC, SD, RW, GX, DC, MJC, JDM), pp. 999–1008.
CIKMCIKM-2017-DangCWZC #classification #kernel
Unsupervised Matrix-valued Kernel Learning For One Class Classification (SD, XC, YW0, JZ, FC0), pp. 2031–2034.
CIKMCIKM-2017-DehghaniRAF #query
Learning to Attend, Copy, and Generate for Session-Based Query Suggestion (MD0, SR, EA, PF), pp. 1747–1756.
CIKMCIKM-2017-EnsanBZK #empirical #rank
An Empirical Study of Embedding Features in Learning to Rank (FE, EB, AZ, AK), pp. 2059–2062.
CIKMCIKM-2017-FanGLXPC #visual notation #web
Learning Visual Features from Snapshots for Web Search (YF, JG, YL, JX0, LP, XC), pp. 247–256.
CIKMCIKM-2017-FuLL #named #network #representation
HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning (TYF, WCL, ZL), pp. 1797–1806.
CIKMCIKM-2017-HuangPLLMC #predict
An Ad CTR Prediction Method Based on Feature Learning of Deep and Shallow Layers (ZH, ZP, QL0, BL, HM, EC), pp. 2119–2122.
CIKMCIKM-2017-LeiLLZ #personalisation #ranking
Alternating Pointwise-Pairwise Learning for Personalized Item Ranking (YL, WL0, ZL, MZ), pp. 2155–2158.
CIKMCIKM-2017-LiCY #graph #recommendation
Learning Graph-based Embedding For Time-Aware Product Recommendation (YL, WC, HY), pp. 2163–2166.
CIKMCIKM-2017-LiDHTCL #network
Attributed Network Embedding for Learning in a Dynamic Environment (JL, HD, XH, JT, YC, HL0), pp. 387–396.
CIKMCIKM-2017-LiTZYW #recommendation #representation
Content Recommendation by Noise Contrastive Transfer Learning of Feature Representation (YL, GT, WZ0, YY0, JW0), pp. 1657–1665.
CIKMCIKM-2017-Liu0MLLM
A Two-step Information Accumulation Strategy for Learning from Highly Imbalanced Data (BL, MZ0, WM, XL0, YL, SM), pp. 1289–1298.
CIKMCIKM-2017-LyuHLP #collaboration #privacy #process #recognition
Privacy-Preserving Collaborative Deep Learning with Application to Human Activity Recognition (LL, XH, YWL, MP), pp. 1219–1228.
CIKMCIKM-2017-MansouriZRO0 #ambiguity #query #web
Learning Temporal Ambiguity in Web Search Queries (BM, MSZ, MR, FO, RC0), pp. 2191–2194.
CIKMCIKM-2017-MehrotraY #query #using
Task Embeddings: Learning Query Embeddings using Task Context (RM, EY), pp. 2199–2202.
CIKMCIKM-2017-Moon0S #graph
Learning Entity Type Embeddings for Knowledge Graph Completion (CM, PJ0, NFS), pp. 2215–2218.
CIKMCIKM-2017-Ni0ZYM #fine-grained #metric #similarity #using
Fine-grained Patient Similarity Measuring using Deep Metric Learning (JN, JL0, CZ, DY, ZM), pp. 1189–1198.
CIKMCIKM-2017-OosterhuisR17a #information retrieval #online #quality #rank
Balancing Speed and Quality in Online Learning to Rank for Information Retrieval (HO, MdR), pp. 277–286.
CIKMCIKM-2017-PangXCZ #category theory #detection
Selective Value Coupling Learning for Detecting Outliers in High-Dimensional Categorical Data (GP, HX, LC, WZ), pp. 807–816.
CIKMCIKM-2017-QianPS #scalability
Active Learning for Large-Scale Entity Resolution (KQ0, LP0, PS), pp. 1379–1388.
CIKMCIKM-2017-QuTSR00 #collaboration #framework #multi #network #representation
An Attention-based Collaboration Framework for Multi-View Network Representation Learning (MQ, JT0, JS, XR, MZ0, JH0), pp. 1767–1776.
CIKMCIKM-2017-SahaJHH #modelling #representation
Regularized and Retrofitted models for Learning Sentence Representation with Context (TKS, SRJ, NH, MAH), pp. 547–556.
CIKMCIKM-2017-ShiPW #modelling #student
Modeling Student Learning Styles in MOOCs (YS, ZP, HW), pp. 979–988.
CIKMCIKM-2017-TanZW #graph #representation #scalability
Representation Learning of Large-Scale Knowledge Graphs via Entity Feature Combinations (ZT, XZ0, WW0), pp. 1777–1786.
CIKMCIKM-2017-TengLW #detection #multi #network #using
Anomaly Detection in Dynamic Networks using Multi-view Time-Series Hypersphere Learning (XT, YRL, XW), pp. 827–836.
CIKMCIKM-2017-XiangJ #multimodal #network
Common-Specific Multimodal Learning for Deep Belief Network (CX, XJ), pp. 2387–2390.
CIKMCIKM-2017-XiaoMZLM #personalisation #recommendation #social
Learning and Transferring Social and Item Visibilities for Personalized Recommendation (XL0, MZ0, YZ, YL, SM), pp. 337–346.
CIKMCIKM-2017-XuLLX #rank
Learning to Rank with Query-level Semi-supervised Autoencoders (BX0, HL, YL0, KX), pp. 2395–2398.
CIKMCIKM-2017-Yang
When Deep Learning Meets Transfer Learning (QY), p. 5.
CIKMCIKM-2017-ZhangACC #recommendation #representation
Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources (YZ, QA, XC, WBC), pp. 1449–1458.
CIKMCIKM-2017-ZhangCYL #community #detection #enterprise #named
BL-ECD: Broad Learning based Enterprise Community Detection via Hierarchical Structure Fusion (JZ, LC, PSY, YL), pp. 859–868.
CIKMCIKM-2017-ZhangXKZ #graph #interactive
Learning Node Embeddings in Interaction Graphs (YZ, YX, XK, YZ), pp. 397–406.
CIKMCIKM-2017-ZhaoWLL
Missing Value Learning (ZLZ, CDW, KYL, JHL), pp. 2427–2430.
CIKMCIKM-2017-ZhaoXYYZFQ #image
Dual Learning for Cross-domain Image Captioning (WZ, WX, MY0, JY, ZZ, YF, YQ), pp. 29–38.
CIKMCIKM-2017-ZhouZL0
Learning Knowledge Embeddings by Combining Limit-based Scoring Loss (XZ, QZ, PL, LG0), pp. 1009–1018.
CIKMCIKM-2017-ZhuZHWZZY #collaboration #multi #recommendation
Broad Learning based Multi-Source Collaborative Recommendation (JZ, JZ, LH0, QW, BZ0, CZ, PSY), pp. 1409–1418.
CIKMCIKM-2017-ZohrevandGTSSS #framework
Deep Learning Based Forecasting of Critical Infrastructure Data (ZZ, UG, MAT, HYS, MS, AYS), pp. 1129–1138.
ECIRECIR-2017-AlkhawaldehPJY #clustering #information retrieval #named #query
LTRo: Learning to Route Queries in Clustered P2P IR (RSA, DP0, JMJ, FY), pp. 513–519.
ECIRECIR-2017-AyadiKHDJ #image #using
Learning to Re-rank Medical Images Using a Bayesian Network-Based Thesaurus (HA, MTK, JXH, MD, MBJ), pp. 160–172.
ECIRECIR-2017-GuptaS
Learning to Classify Inappropriate Query-Completions (PG, JS), pp. 548–554.
ECIRECIR-2017-RomeoMBM #approach #multi #ranking
A Multiple-Instance Learning Approach to Sentence Selection for Question Ranking (SR, GDSM, ABC, AM), pp. 437–449.
ECIRECIR-2017-SoldainiG #approach #health #rank #semantics
Learning to Rank for Consumer Health Search: A Semantic Approach (LS, NG), pp. 640–646.
ICMLICML-2017-0001N #composition #modelling #scalability
Relative Fisher Information and Natural Gradient for Learning Large Modular Models (KS0, FN), pp. 3289–3298.
ICMLICML-2017-0004K
Follow the Moving Leader in Deep Learning (SZ0, JTK), pp. 4110–4119.
ICMLICML-2017-0007MW #effectiveness
Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible (KZ0, WM, LW0), pp. 4130–4139.
ICMLICML-2017-AgarwalS #difference #online #privacy
The Price of Differential Privacy for Online Learning (NA, KS), pp. 32–40.
ICMLICML-2017-AlaaHS #process
Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis (AMA, SH, MvdS), pp. 60–69.
ICMLICML-2017-AllamanisCKS #semantics
Learning Continuous Semantic Representations of Symbolic Expressions (MA, PC, PK, CAS), pp. 80–88.
ICMLICML-2017-Allen-ZhuL17b #online #performance
Follow the Compressed Leader: Faster Online Learning of Eigenvectors and Faster MMWU (ZAZ, YL), pp. 116–125.
ICMLICML-2017-AndreasKL #composition #multi #policy #sketching
Modular Multitask Reinforcement Learning with Policy Sketches (JA, DK, SL), pp. 166–175.
ICMLICML-2017-AnschelBS #named #reduction
Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning (OA, NB, NS), pp. 176–185.
ICMLICML-2017-AsadiL
An Alternative Softmax Operator for Reinforcement Learning (KA, MLL), pp. 243–252.
ICMLICML-2017-AzarOM #bound
Minimax Regret Bounds for Reinforcement Learning (MGA, IO, RM), pp. 263–272.
ICMLICML-2017-BachHRR #generative #modelling
Learning the Structure of Generative Models without Labeled Data (SHB, BDH, AR, CR), pp. 273–282.
ICMLICML-2017-BachmanST #algorithm
Learning Algorithms for Active Learning (PB, AS, AT), pp. 301–310.
ICMLICML-2017-BalleM #finite #policy
Spectral Learning from a Single Trajectory under Finite-State Policies (BB, OAM), pp. 361–370.
ICMLICML-2017-BaramACM
End-to-End Differentiable Adversarial Imitation Learning (NB, OA, IC, SM), pp. 390–399.
ICMLICML-2017-BarmannPS #online #optimisation
Emulating the Expert: Inverse Optimization through Online Learning (AB, SP, OS), pp. 400–410.
ICMLICML-2017-BelangerYM #energy #network #predict
End-to-End Learning for Structured Prediction Energy Networks (DB, BY, AM), pp. 429–439.
ICMLICML-2017-BelilovskyKVB #modelling #visual notation
Learning to Discover Sparse Graphical Models (EB, KK, GV, MBB), pp. 440–448.
ICMLICML-2017-BellemareDM
A Distributional Perspective on Reinforcement Learning (MGB, WD, RM), pp. 449–458.
ICMLICML-2017-BelloZVL
Neural Optimizer Search with Reinforcement Learning (IB, BZ, VV, QVL), pp. 459–468.
ICMLICML-2017-BergmannJV
Learning Texture Manifolds with the Periodic Spatial GAN (UB, NJ, RV), pp. 469–477.
ICMLICML-2017-BernsteinMSSHM #modelling #using #visual notation
Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models (GB, RM, TS, DS, MH, GM), pp. 478–487.
ICMLICML-2017-BeygelzimerOZ #multi #online #performance
Efficient Online Bandit Multiclass Learning with Õ(√T) Regret (AB, FO, CZ), pp. 488–497.
ICMLICML-2017-BojanowskiJ #predict
Unsupervised Learning by Predicting Noise (PB, AJ), pp. 517–526.
ICMLICML-2017-BotevRB #optimisation
Practical Gauss-Newton Optimisation for Deep Learning (AB, HR, DB), pp. 557–565.
ICMLICML-2017-ChebotarHZSSL #modelling
Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning (YC, KH, MZ, GSS, SS, SL), pp. 703–711.
ICMLICML-2017-ChenHCDLBF
Learning to Learn without Gradient Descent by Gradient Descent (YC, MWH, SGC, MD, TPL, MB, NdF), pp. 748–756.
ICMLICML-2017-ChenZLHH
Learning to Aggregate Ordinal Labels by Maximizing Separating Width (GC, SZ, DL, HH0, PAH), pp. 787–796.
ICMLICML-2017-ChouMS #policy #probability #using
Improving Stochastic Policy Gradients in Continuous Control with Deep Reinforcement Learning using the Beta Distribution (PWC, DM, SAS), pp. 834–843.
ICMLICML-2017-CortesGKMY #adaptation #named #network
AdaNet: Adaptive Structural Learning of Artificial Neural Networks (CC, XG, VK, MM, SY), pp. 874–883.
ICMLICML-2017-DevlinUBSMK #named
RobustFill: Neural Program Learning under Noisy I/O (JD, JU, SB, RS, ArM, PK), pp. 990–998.
ICMLICML-2017-FoersterNFATKW #experience #multi
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning (JNF, NN, GF, TA, PHST, PK, SW), pp. 1146–1155.
ICMLICML-2017-FutomaHH #classification #detection #multi #process
Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier (JF, SH, KAH), pp. 1174–1182.
ICMLICML-2017-GalIG #image
Deep Bayesian Active Learning with Image Data (YG, RI, ZG), pp. 1183–1192.
ICMLICML-2017-GaoFC #network
Local-to-Global Bayesian Network Structure Learning (TG, KPF, MC), pp. 1193–1202.
ICMLICML-2017-GehringAGYD #sequence
Convolutional Sequence to Sequence Learning (JG, MA, DG, DY, YND), pp. 1243–1252.
ICMLICML-2017-GravesBMMK #automation #education #network
Automated Curriculum Learning for Neural Networks (AG, MGB, JM, RM, KK), pp. 1311–1320.
ICMLICML-2017-HaarnojaTAL #energy #policy
Reinforcement Learning with Deep Energy-Based Policies (TH, HT, PA, SL), pp. 1352–1361.
ICMLICML-2017-HarandiSH #geometry #metric #reduction
Joint Dimensionality Reduction and Metric Learning: A Geometric Take (MTH, MS, RIH), pp. 1404–1413.
ICMLICML-2017-HigginsPRMBPBBL #named
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning (IH, AP, AAR, LM, CB, AP, MB, CB, AL), pp. 1480–1490.
ICMLICML-2017-Hoffman #markov #modelling #monte carlo
Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo (MDH), pp. 1510–1519.
ICMLICML-2017-HongHZ #algorithm #distributed #named #network #optimisation #performance
Prox-PDA: The Proximal Primal-Dual Algorithm for Fast Distributed Nonconvex Optimization and Learning Over Networks (MH, DH, MMZ), pp. 1529–1538.
ICMLICML-2017-HuMTMS #self
Learning Discrete Representations via Information Maximizing Self-Augmented Training (WH, TM, ST, EM, MS), pp. 1558–1567.
ICMLICML-2017-JabbariJKMR
Fairness in Reinforcement Learning (SJ, MJ, MJK, JM, AR0), pp. 1617–1626.
ICMLICML-2017-JainMR #generative #modelling #multi #scalability
Scalable Generative Models for Multi-label Learning with Missing Labels (VJ, NM, PR), pp. 1636–1644.
ICMLICML-2017-JerniteCS #classification #estimation
Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation (YJ, AC, DAS), pp. 1665–1674.
ICMLICML-2017-KattOA #monte carlo
Learning in POMDPs with Monte Carlo Tree Search (SK, FAO, CA), pp. 1819–1827.
ICMLICML-2017-KhasanovaF #graph #invariant #representation
Graph-based Isometry Invariant Representation Learning (RK, PF), pp. 1847–1856.
ICMLICML-2017-KimCKLK #generative #network
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks (TK, MC, HK, JKL, JK), pp. 1857–1865.
ICMLICML-2017-KimPKH #named #network #parallel #parametricity #reduction #semantics
SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization (JK, YP, GK, SJH), pp. 1866–1874.
ICMLICML-2017-KocaogluDV #graph
Cost-Optimal Learning of Causal Graphs (MK, AD, SV), pp. 1875–1884.
ICMLICML-2017-KrishnamurthyAH #classification
Active Learning for Cost-Sensitive Classification (AK, AA, TKH, HDI, JL0), pp. 1915–1924.
ICMLICML-2017-LawUZ #clustering
Deep Spectral Clustering Learning (MTL, RU, RSZ), pp. 1985–1994.
ICMLICML-2017-LeeHPS #multi
Confident Multiple Choice Learning (KL, CH, KP, JS), pp. 2014–2023.
ICMLICML-2017-LevyW #source code
Learning to Align the Source Code to the Compiled Object Code (DL, LW), pp. 2043–2051.
ICMLICML-2017-LeY0L #coordination #multi
Coordinated Multi-Agent Imitation Learning (HML0, YY, PC0, PL), pp. 1995–2003.
ICMLICML-2017-LivniCG #infinity #kernel #network
Learning Infinite Layer Networks Without the Kernel Trick (RL, DC, AG), pp. 2198–2207.
ICMLICML-2017-LongZ0J #adaptation #network
Deep Transfer Learning with Joint Adaptation Networks (ML, HZ, JW0, MIJ), pp. 2208–2217.
ICMLICML-2017-Luo #architecture #network
Learning Deep Architectures via Generalized Whitened Neural Networks (PL0), pp. 2238–2246.
ICMLICML-2017-LvJL
Learning Gradient Descent: Better Generalization and Longer Horizons (KL, SJ, JL), pp. 2247–2255.
ICMLICML-2017-MacGlashanHLPWR #feedback #interactive
Interactive Learning from Policy-Dependent Human Feedback (JM, MKH, RTL, BP, GW, DLR, MET, MLL), pp. 2285–2294.
ICMLICML-2017-MachadoBB #framework
A Laplacian Framework for Option Discovery in Reinforcement Learning (MCM, MGB, MHB), pp. 2295–2304.
ICMLICML-2017-MaystreG #approach #effectiveness #exclamation
Just Sort It! A Simple and Effective Approach to Active Preference Learning (LM, MG), pp. 2344–2353.
ICMLICML-2017-MirhoseiniPLSLZ #optimisation
Device Placement Optimization with Reinforcement Learning (AM, HP, QVL, BS, RL0, YZ, NK, MN0, SB, JD), pp. 2430–2439.
ICMLICML-2017-MohajerSE #rank
Active Learning for Top-K Rank Aggregation from Noisy Comparisons (SM, CS, AE), pp. 2488–2497.
ICMLICML-2017-OhSLK #multi
Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning (JO, SPS, HL, PK), pp. 2661–2670.
ICMLICML-2017-OmidshafieiPAHV #distributed #multi
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability (SO, JP, CA, JPH, JV), pp. 2681–2690.
ICMLICML-2017-OsbandR #question #why
Why is Posterior Sampling Better than Optimism for Reinforcement Learning? (IO, BVR), pp. 2701–2710.
ICMLICML-2017-OsogamiKS #bidirectional #modelling
Bidirectional Learning for Time-series Models with Hidden Units (TO, HK, TS), pp. 2711–2720.
ICMLICML-2017-PadSCTU #taxonomy
Dictionary Learning Based on Sparse Distribution Tomography (PP, FS, LEC, PT, MU), pp. 2731–2740.
ICMLICML-2017-PentinaL #multi
Multi-task Learning with Labeled and Unlabeled Tasks (AP, CHL), pp. 2807–2816.
ICMLICML-2017-PintoDSG #robust
Robust Adversarial Reinforcement Learning (LP, JD, RS, AG0), pp. 2817–2826.
ICMLICML-2017-RiquelmeGL #estimation #linear #modelling
Active Learning for Accurate Estimation of Linear Models (CR, MG, AL), pp. 2931–2939.
ICMLICML-2017-Shalev-ShwartzS
Failures of Gradient-Based Deep Learning (SSS, OS, SS), pp. 3067–3075.
ICMLICML-2017-ShamirS #feedback #online #permutation
Online Learning with Local Permutations and Delayed Feedback (OS, LS), pp. 3086–3094.
ICMLICML-2017-ShrikumarGK #difference
Learning Important Features Through Propagating Activation Differences (AS, PG, AK), pp. 3145–3153.
ICMLICML-2017-SilverHHSGHDRRB #predict
The Predictron: End-To-End Learning and Planning (DS, HvH, MH, TS, AG, TH, GDA, DPR, NCR, AB, TD), pp. 3191–3199.
ICMLICML-2017-SunRMW #named
meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting (XS0, XR, SM, HW), pp. 3299–3308.
ICMLICML-2017-SunVGBB #predict
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction (WS0, AV, GJG, BB, JAB), pp. 3309–3318.
ICMLICML-2017-TandonLDK #distributed
Gradient Coding: Avoiding Stragglers in Distributed Learning (RT, QL, AGD, NK), pp. 3368–3376.
ICMLICML-2017-TanM #modelling
Partitioned Tensor Factorizations for Learning Mixed Membership Models (ZT, SM0), pp. 3358–3367.
ICMLICML-2017-ToshD
Diameter-Based Active Learning (CT, SD), pp. 3444–3452.
ICMLICML-2017-UmlauftH #probability
Learning Stable Stochastic Nonlinear Dynamical Systems (JU, SH), pp. 3502–3510.
ICMLICML-2017-UrschelBMR #process
Learning Determinantal Point Processes with Moments and Cycles (JU, VEB, AM, PR), pp. 3511–3520.
ICMLICML-2017-VaswaniKWGLS #independence #online
Model-Independent Online Learning for Influence Maximization (SV, BK, ZW, MG, LVSL, MS), pp. 3530–3539.
ICMLICML-2017-VezhnevetsOSHJS #network
FeUdal Networks for Hierarchical Reinforcement Learning (ASV, SO, TS, NH, MJ, DS, KK), pp. 3540–3549.
ICMLICML-2017-VillegasYZSLL #predict
Learning to Generate Long-term Future via Hierarchical Prediction (RV, JY, YZ, SS, XL, HL), pp. 3560–3569.
ICMLICML-2017-VorontsovTKP #dependence #network #on the #orthogonal
On orthogonality and learning recurrent networks with long term dependencies (EV, CT, SK, CP), pp. 3570–3578.
ICMLICML-2017-WangKS0 #distributed #performance
Efficient Distributed Learning with Sparsity (JW, MK, NS, TZ0), pp. 3636–3645.
ICMLICML-2017-WangLJK #kernel #optimisation
Batched High-dimensional Bayesian Optimization via Structural Kernel Learning (ZW, CL, SJ, PK), pp. 3656–3664.
ICMLICML-2017-White #specification
Unifying Task Specification in Reinforcement Learning (MW), pp. 3742–3750.
ICMLICML-2017-XiaQCBYL
Dual Supervised Learning (YX, TQ, WC0, JB0, NY, TYL), pp. 3789–3798.
ICMLICML-2017-XieDZKYZX #constraints #modelling
Learning Latent Space Models with Angular Constraints (PX, YD, YZ, AK, YY, JZ, EPX), pp. 3799–3810.
ICMLICML-2017-XuLZ #process #sequence
Learning Hawkes Processes from Short Doubly-Censored Event Sequences (HX, DL, HZ), pp. 3831–3840.
ICMLICML-2017-YangFSH #clustering #towards
Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering (BY, XF0, NDS, MH), pp. 3861–3870.
ICMLICML-2017-ZenkePG
Continual Learning Through Synaptic Intelligence (FZ, BP, SG), pp. 3987–3995.
ICMLICML-2017-Zhang0KALZ #linear #modelling #named #precise
ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning (HZ, JL0, KK, DA, JL0, CZ), pp. 4035–4043.
ICMLICML-2017-ZhangHTC
Re-revisiting Learning on Hypergraphs: Confidence Interval and Subgradient Method (CZ, SH, ZGT, THHC), pp. 4026–4034.
ICMLICML-2017-ZhangZZHZ #distributed #network #online
Projection-free Distributed Online Learning in Networks (WZ0, PZ, WZ0, SCHH, TZ), pp. 4054–4062.
ICMLICML-2017-ZhaoSE #generative #modelling
Learning Hierarchical Features from Deep Generative Models (SZ, JS, SE), pp. 4091–4099.
ICMLICML-2017-ZhaoYKJB #architecture
Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture (MZ, SY, DK, TSJ, MTB), pp. 4100–4109.
ICMLICML-2017-ZoghiTGKSW #modelling #online #probability #rank
Online Learning to Rank in Stochastic Click Models (MZ, TT, MG, BK, CS, ZW), pp. 4199–4208.
KDDKDD-2017-0013H #paradigm #predict
Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics (XL0, JH), pp. 285–294.
KDDKDD-2017-AmandH #composition #metric
Sparse Compositional Local Metric Learning (JSA, JH), pp. 1097–1104.
KDDKDD-2017-AngelinoLASR
Learning Certifiably Optimal Rule Lists (EA, NLS, DA, MS, CR), pp. 35–44.
KDDKDD-2017-ChoiBSSS #graph #named #representation
GRAM: Graph-based Attention Model for Healthcare Representation Learning (EC, MTB, LS, WFS, JS), pp. 787–795.
KDDKDD-2017-DadkhahiM #detection #embedded #network
Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices (HD, BMM), pp. 1773–1781.
KDDKDD-2017-DebGIPVYY #automation #named #network #policy #predict
AESOP: Automatic Policy Learning for Predicting and Mitigating Network Service Impairments (SD, ZG, SI, SCP, SV, HY, JY), pp. 1783–1792.
KDDKDD-2017-DongCS #named #network #representation #scalability
metapath2vec: Scalable Representation Learning for Heterogeneous Networks (YD, NVC, AS), pp. 135–144.
KDDKDD-2017-EmraniMX #multi #using
Prognosis and Diagnosis of Parkinson's Disease Using Multi-Task Learning (SE, AM, WX), pp. 1457–1466.
KDDKDD-2017-How #nondeterminism #theory and practice
Planning and Learning under Uncertainty: Theory and Practice (JPH), p. 19.
KDDKDD-2017-IosifidisN #scalability #sentiment
Large Scale Sentiment Learning with Limited Labels (VI, EN), pp. 1823–1832.
KDDKDD-2017-LabutovHBH #mining
Semi-Supervised Techniques for Mining Learning Outcomes and Prerequisites (IL, YH0, PB, DH), pp. 907–915.
KDDKDD-2017-LiuPH #distributed #multi
Distributed Multi-Task Relationship Learning (SL, SJP, QH), pp. 937–946.
KDDKDD-2017-LuoZQYYWYW #functional #multi
Functional Annotation of Human Protein Coding Isoforms via Non-convex Multi-Instance Learning (TL, WZ, SQ, YY, DY, GW, JY, JW0), pp. 345–354.
KDDKDD-2017-OvadiaHKLNPZS
Learning to Count Mosquitoes for the Sterile Insect Technique (YO, YH, DK, JL, DN, RP, TZ, DS), pp. 1943–1949.
KDDKDD-2017-RibeiroSF #named
struc2vec: Learning Node Representations from Structural Identity (LFRR, PHPS, DRF), pp. 385–394.
KDDKDD-2017-ShenHGC #comprehension #named
ReasoNet: Learning to Stop Reading in Machine Comprehension (YS, PSH, JG, WC), pp. 1047–1055.
KDDKDD-2017-SpringS #random #scalability
Scalable and Sustainable Deep Learning via Randomized Hashing (RS, AS), pp. 445–454.
KDDKDD-2017-TangW0M
End-to-end Learning for Short Text Expansion (JT, YW, KZ0, QM), pp. 1105–1113.
KDDKDD-2017-TongKIYKSV #multi
Learning to Generate Rock Descriptions from Multivariate Well Logs with Hierarchical Attention (BT, MK, MI, TY, YK, AS, RV), pp. 2031–2040.
KDDKDD-2017-UesakaMSKMAY #multi #visual notation
Multi-view Learning over Retinal Thickness and Visual Sensitivity on Glaucomatous Eyes (TU, KM, HS, TK, HM, RA, KY), pp. 2041–2050.
KDDKDD-2017-WangYRTZYW #editing #recommendation
Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors' Demonstration (XW, LY, KR, GT, WZ0, YY0, JW0), pp. 2051–2059.
KDDKDD-2017-XiaoGVT #behaviour
Learning Temporal State of Diabetes Patients via Combining Behavioral and Demographic Data (HX, JG0, LHV, DST), pp. 2081–2089.
KDDKDD-2017-XieBLZ #distributed #multi #privacy
Privacy-Preserving Distributed Multi-Task Learning with Asynchronous Updates (LX, IMB, KL, JZ), pp. 1195–1204.
KDDKDD-2017-YangBZY0 #approach #collaboration #recommendation
Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation (CY, LB, CZ0, QY0, JH0), pp. 1245–1254.
KDDKDD-2017-YeZMPB #network
Learning from Labeled and Unlabeled Vertices in Networks (WY0, LZ, DM, CP, CB), pp. 1265–1274.
KDDKDD-2017-YouX0T #education #multi #network
Learning from Multiple Teacher Networks (SY, CX0, CX0, DT), pp. 1285–1294.
KDDKDD-2017-ZhangCTSS #effectiveness #multi #named
LEAP: Learning to Prescribe Effective and Safe Treatment Combinations for Multimorbidity (YZ, RC, JT0, WFS, JS), pp. 1315–1324.
KDDKDD-2017-ZhanZ #induction #multi
Inductive Semi-supervised Multi-Label Learning with Co-Training (WZ, MLZ), pp. 1305–1314.
KDDKDD-2017-ZhengBLL #detection #metric
Contextual Spatial Outlier Detection with Metric Learning (GZ, SLB, TL, ZL), pp. 2161–2170.
KDDKDD-2017-ZhouLZCLYCYCDQ #distributed #named #parametricity
KunPeng: Parameter Server based Distributed Learning Systems and Its Applications in Alibaba and Ant Financial (JZ, XL, PZ, CC, LL, XY, QC, JY, XC, YD, Y(Q), pp. 1693–1702.
OOPSLAOOPSLA-2017-ChaeOHY #automation #generative #heuristic #program analysis
Automatically generating features for learning program analysis heuristics for C-like languages (KC, HO, KH, HY), p. 25.
OOPSLAOOPSLA-2017-SantolucitoZDSP #specification
Synthesizing configuration file specifications with association rule learning (MS, EZ, RD, AS, RP), p. 20.
OOPSLAOOPSLA-2017-SeidelSCWJ #data-driven #fault
Learning to blame: localizing novice type errors with data-driven diagnosis (ELS, HS, KC, WW, RJ), p. 27.
OOPSLAOOPSLA-2017-WuCC #error message
Learning user friendly type-error messages (BW, JPCI, SC0), p. 29.
PADLPADL-2017-Vennekens #api #declarative #programming #python
Lowering the Learning Curve for Declarative Programming: A Python API for the IDP System (JV), pp. 86–102.
POPLPOPL-2017-MoermanS0KS #automaton
Learning nominal automata (JM, MS, AS0, BK, MS), pp. 613–625.
PPDPPPDP-2017-HoweRK #symmetry
Theory learning with symmetry breaking (JMH, ER, AK), pp. 85–96.
SASSAS-2017-BrockschmidtCKK #analysis
Learning Shape Analysis (MB, YC, PK, SK, DT), pp. 66–87.
ASEASE-2017-JamshidiSVKPA #analysis #configuration management #modelling #performance
Transfer learning for performance modeling of configurable systems: an exploratory analysis (PJ, NS, MV, CK, AP, YA), pp. 497–508.
ASEASE-2017-Krishna #effectiveness
Learning effective changes for software projects (RK), pp. 1002–1005.
ASEASE-2017-RafiqDRBYSLCPN #adaptation #network #online #re-engineering #social
Learning to share: engineering adaptive decision-support for online social networks (YR, LD, AR, AKB, MY, AS, ML, GC, BAP, BN), pp. 280–285.
ESEC-FSEESEC-FSE-2017-FuM #case study
Easy over hard: a case study on deep learning (WF0, TM), pp. 49–60.
ESEC-FSEESEC-FSE-2017-FuM17a #fault #predict
Revisiting unsupervised learning for defect prediction (WF0, TM), pp. 72–83.
ESEC-FSEESEC-FSE-2017-LeeHLKJ #automation #debugging #industrial
Applying deep learning based automatic bug triager to industrial projects (SRL, MJH, CGL, MK, GJ), pp. 926–931.
ESEC-FSEESEC-FSE-2017-MuraliCJ #api #fault #specification
Bayesian specification learning for finding API usage errors (VM, SC, CJ), pp. 151–162.
ICSE-2017-0004CC #semantics #traceability #using
Semantically enhanced software traceability using deep learning techniques (JG0, JC, JCH), pp. 3–14.
ICSE-2017-ChenBHXZX #compilation #source code #testing
Learning to prioritize test programs for compiler testing (JC0, YB, DH, YX, HZ0, BX), pp. 700–711.
ICSE-2017-RolimSDPGGSH #program transformation
Learning syntactic program transformations from examples (RR, GS, LD, OP, SG, RG, RS, BH), pp. 404–415.
GPCEGPCE-2017-MartiniH #automation #case study #experience #generative
Automatic generation of virtual learning spaces driven by CaVaDSL: an experience report (RGM, PRH), pp. 233–245.
ASPLOSASPLOS-2017-LiCCZ #modelling #named #topic
SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs (KL, JC0, WC, JZ0), pp. 497–509.
CASECASE-2017-ChuckLKJFG #automation #statistics
Statistical data cleaning for deep learning of automation tasks from demonstrations (CC, ML, SK, RJ, RF, KG), pp. 1142–1149.
CASECASE-2017-GohSS #modelling #predict
A model-based learning controller with predictor augmentation for non-stationary conditions and time delay in water shooting (CFG, GLGS, KS), pp. 1110–1117.
CASECASE-2017-HanPM #approach #linear #modelling
Model-based reinforcement learning approach for deformable linear object manipulation (HH, GP, TM), pp. 750–755.
CASECASE-2017-KapadiaSJG #named
EchoBot: Facilitating data collection for robot learning with the Amazon echo (RK, SS, LJ, KG), pp. 159–165.
CASECASE-2017-LaiJG #energy #parametricity #predict
An integrated physical-based and parameter learning method for ship energy prediction under varying operating conditions (XL, XJ, XG), pp. 1180–1185.
CASECASE-2017-LiangMLLG #automation #industrial #using
Using dVRK teleoperation to facilitate deep learning of automation tasks for an industrial robot (JL, JM, ML, PL, KG), pp. 1–8.
CASECASE-2017-LiXLK #approach #classification #physics
Improving colorectal polyp classification based on physical examination data - A ensemble learning approach (CL, XX, JL, NK), pp. 193–194.
CASECASE-2017-LiXZ #analysis #complexity
Complexity analysis of reinforcement learning and its application to robotics (BL, LX, QZ), pp. 1425–1426.
CASECASE-2017-LuRSW #detection #visual notation
Visual guided deep learning scheme for fall detection (NL, XR, JS, YW), pp. 801–806.
CASECASE-2017-PengZH #distributed #fault
Distributed fault diagnosis with shared-basis and B-splines-based matched learning (CP, YZ, QH), pp. 536–541.
CASECASE-2017-RenWJ #equivalence
Engineering effect equivalence enabled transfer learning (JR, HW, XJ), pp. 1174–1179.
CASECASE-2017-SunLZJ #framework #functional #using
Exploring functional variant using a deep learning framework (TS, ZL, XMZ, RJ), pp. 98–99.
CASECASE-2017-ZhaoCDW #fault #multi #taxonomy
TQWT-based multi-scale dictionary learning for rotating machinery fault diagnosis (ZZ, XC, BD, SW), pp. 554–559.
CASECASE-2017-ZhengLC #comparison #policy #realtime
Comparison study of two reinforcement learning based real-time control policies for two-machine-one-buffer production system (WZ, YL, QC), pp. 1163–1168.
CASECASE-2017-ZhouWY #approach
Dynamic dispatching for re-entrant production lines - A deep learning approach (FYZ, CHW, CJY), pp. 1026–1031.
CGOCGO-2017-OgilvieP0L #compilation #cost analysis
Minimizing the cost of iterative compilation with active learning (WFO, PP, ZW0, HL), pp. 245–256.
CAVCAV-2017-BielikRV
Learning a Static Analyzer from Data (PB, VR, MTV), pp. 233–253.
CAVCAV-2017-Vazquez-Chanlatte #clustering #logic
Logical Clustering and Learning for Time-Series Data (MVC, JVD, XJ, SAS), pp. 305–325.
CSLCSL-2017-AngluinAF #polynomial #query
Query Learning of Derived Omega-Tree Languages in Polynomial Time (DA, TA, DF), p. 21.
CSLCSL-2017-HeerdtS0 #automaton #category theory #framework #named
CALF: Categorical Automata Learning Framework (GvH, MS, AS0), p. 24.
ICSTICST-2017-TapplerAB #automaton #communication #modelling #testing
Model-Based Testing IoT Communication via Active Automata Learning (MT, BKA, RB), pp. 276–287.
ICTSSICTSS-2017-MaAYE #execution #testing
Fragility-Oriented Testing with Model Execution and Reinforcement Learning (TM, SA0, TY0, ME), pp. 3–20.
CSEETCSEET-2016-DaunSWPT #case study #experience #industrial #requirements
Project-Based Learning with Examples from Industry in University Courses: An Experience Report from an Undergraduate Requirements Engineering Course (MD, AS, TW, KP, BT), pp. 184–193.
CSEETCSEET-2016-FreitasSM #student #using
Using an Active Learning Environment to Increase Students' Engagement (SAAdF, WCMPS, GM), pp. 232–236.
CSEETCSEET-2016-GeorgasPM #architecture #runtime #using #visualisation
Supporting Software Architecture Learning Using Runtime Visualization (JCG, JDP, MJM), pp. 101–110.
CSEETCSEET-2016-LetouzeSS #case study #generative #web
Generating Software Engineers by Developing Web Systems: A Project-Based Learning Case Study (PL, JIMdS, VMDS), pp. 194–203.
CSEETCSEET-2016-ShutoWKFYO #education #effectiveness #re-engineering
Learning Effectiveness of Team Discussions in Various Software Engineering Education Courses (MS, HW, KK, YF, SY, MO), pp. 227–231.
CSEETCSEET-2016-SunagaSWKFYO #effectiveness #question
Which Combinations of Personal Characteristic Types are more Effective in Different Project-Based Learning Courses? (YS, MS, HW, KK, YF, SY, MO), pp. 137–141.
EDMEDM-2016-BhartiyaCBSM #documentation #segmentation
Document Segmentation for Labeling with Academic Learning Objectives (DB, DC, SB, BS, MKM), pp. 282–287.
EDMEDM-2016-BuffumFBWML #assessment #collaboration #embedded #mining #sequence
Mining Sequences of Gameplay for Embedded Assessment in Collaborative Learning (PSB, MF, KEB, ENW, BWM, JCL), pp. 575–576.
EDMEDM-2016-ChoiLHLRW #data-driven #interactive
Exploring Learning Management System Interaction Data: Combining Data-driven and Theory-driven Approaches (HC, JEL, WJH, KL, MR, AW), pp. 324–329.
EDMEDM-2016-CraigHXFH #behaviour #identification #persistent #predict
Identifying relevant user behavior and predicting learning and persistence in an ITS-based afterschool program (SDC, XH, JX, YF, XH), pp. 581–582.
EDMEDM-2016-CutumisuS #assessment #feedback #game studies
Choosing versus Receiving Feedback: The Impact of Feedback Valence on Learning in an Assessment Game (MC, DLS), pp. 341–346.
EDMEDM-2016-DaiAY #analysis #recommendation #towards
Course Content Analysis: An Initiative Step toward Learning Object Recommendation Systems for MOOC Learners (YD, YA, MY), pp. 347–352.
EDMEDM-2016-DavisCHH
Gauging MOOC Learners' Adherence to the Designed Learning Path (DD, GC, CH, GJH), pp. 54–61.
EDMEDM-2016-DianaESK #metric #self #student
Extracting Measures of Active Learning and Student Self-Regulated Learning Strategies from MOOC Data (ND, ME, JCS, KRK), pp. 583–584.
EDMEDM-2016-DibieSMQ #community #online #social
Exploring Social Influence on the Usage of Resources in an Online Learning Community (OD, TS, KEM, DQ), pp. 585–586.
EDMEDM-2016-DominguezBU #modelling #predict
Predicting STEM Achievement with Learning Management System Data: Prediction Modeling and a Test of an Early Warning System (MD, MLB, PMU), pp. 589–590.
EDMEDM-2016-DongKB #comparison #mining #multi #process
Comparison of Selection Criteria for Multi-Feature Hierarchical Activity Mining in Open Ended Learning Environments (YD, JSK, GB), pp. 591–592.
EDMEDM-2016-FeildLZRE #automation #feedback #framework #platform #scalability
A Scalable Learning Analytics Platform for Automated Writing Feedback (JLF, NL, NLZ, MR, AE), pp. 688–693.
EDMEDM-2016-HuangB #framework #modelling #student #towards
Towards Modeling Chunks in a Knowledge Tracing Framework for Students' Deep Learning (YH0, PB), pp. 666–668.
EDMEDM-2016-HuttMWDD #detection
The Eyes Have It: Gaze-based Detection of Mind Wandering during Learning with an Intelligent Tutoring System (SH, CM, SW, PJD, SKD), pp. 86–93.
EDMEDM-2016-JoL #how #online
How to Judge Learning on Online Learning: Minimum Learning Judgment System (JJ, HL), pp. 597–598.
EDMEDM-2016-JoTFRG #behaviour #modelling #social
Expediting Support for Social Learning with Behavior Modeling (YJ, GT, OF, CPR, DG), pp. 400–405.
EDMEDM-2016-Kay #people
Enabling people to harness and control EDM for lifelong, life-wide learning (JK), p. 4.
EDMEDM-2016-Kay16a #people
Enabling people to harness and control EDM for lifelong, life-wide learning (JK), pp. 10–20.
EDMEDM-2016-LabutovL #web
Web as a textbook: Curating Targeted Learning Paths through the Heterogeneous Learning Resources on the Web (IL, HL), pp. 110–118.
EDMEDM-2016-LanB #framework #personalisation
A Contextual Bandits Framework for Personalized Learning Action Selection (ASL, RGB), pp. 424–429.
EDMEDM-2016-LeeRBY #analysis #approach #clustering #heatmap #interactive #visualisation
Hierarchical Cluster Analysis Heatmaps and Pattern Analysis: An Approach for Visualizing Learning Management System Interaction Data (JEL, MR, AB, MY), pp. 603–604.
EDMEDM-2016-Linn
WISE Ways to Strengthen Inquiry Science Learning (MCL), p. 3.
EDMEDM-2016-MacLellanHPK #architecture #education
The Apprentice Learner architecture: Closing the loop between learning theory and educational data (CJM, EH, RP, KRK), pp. 151–158.
EDMEDM-2016-Nam #adaptation #behaviour #predict
Predicting Off-task Behaviors for Adaptive Vocabulary Learning System (SN), pp. 672–674.
EDMEDM-2016-NgHLK #modelling #sequence #using
Modelling the way: Using action sequence archetypes to differentiate learning pathways from learning outcomes (KHRN, KH, KL, AWHK), pp. 167–174.
EDMEDM-2016-NiuNZWKY #algorithm #clustering
A Coupled User Clustering Algorithm for Web-based Learning Systems (KN, ZN, XZ, CW, KK, MY), pp. 175–182.
EDMEDM-2016-QuigleyDSHPSAP
Equity of Learning Opportunities in the Chicago City of Learning Program (DQ, OD, MAS, KVH, WRP, TS, UA, NP), pp. 618–619.
EDMEDM-2016-Rau #concept #mining #physics #social
Pattern mining uncovers social prompts of conceptual learning with physical and virtual representations (MAR), pp. 478–483.
EDMEDM-2016-RauMN16a #how #similarity #visual notation
How to Model Implicit Knowledge? Similarity Learning Methods to Assess Perceptions of Visual Representations (MAR, BM, RDN), pp. 199–206.
EDMEDM-2016-RauMN16a_ #how #similarity #visual notation
How to Model Implicit Knowledge? Similarity Learning Methods to Assess Perceptions of Visual Representations (MAR, BM, RDN), pp. 199–206.
EDMEDM-2016-RoweAEHBBE #game studies #metric #validation
Validating Game-based Measures of Implicit Science Learning (ER, JAC, ME, AH, TB, RB, TE), pp. 490–495.
EDMEDM-2016-Sande #component #multi #problem
Learning Curves for Problems with Multiple Knowledge Components (BvdS), pp. 523–526.
EDMEDM-2016-Sande16a #analysis #component #problem
Learning curves versus problem difficulty: an analysis of the Knowledge Component picture for a given context (BvdS), pp. 646–647.
EDMEDM-2016-ShenC #feature model #modelling
Aim Low: Correlation-based Feature Selection for Model-based Reinforcement Learning (SS, MC), pp. 507–512.
EDMEDM-2016-SlaterOBSIH #problem #semantics #student
Semantic Features of Math Problems: Relationships to Student Learning and Engagement (SS, JO, RSB, PS, PSI, NTH), pp. 223–230.
EDMEDM-2016-SnowKPFB #how #online #performance #student
Quantifying How Students Use an Online Learning System: A Focus on Transitions and Performance (ELS, AEK, TEP, MF, AJB), pp. 640–641.
EDMEDM-2016-StapelZP #online #performance #predict #student
An Ensemble Method to Predict Student Performance in an Online Math Learning Environment (MS, ZZ, NP), pp. 231–238.
EDMEDM-2016-SunY #community #online #personalisation
Personalization of Learning Paths in Online Communities of Creators (MS, SY), pp. 513–516.
EDMEDM-2016-Wang #concept #design #interactive #personalisation
Designing Interactive and Personalized Concept Mapping Learning Environments (SW0), pp. 678–680.
EDMEDM-2016-WenMWDHR #collaboration #integration #online #predict
Transactivity as a Predictor of Future Collaborative Knowledge Integration in Team-Based Learning in Online Courses (MW, KM, XW0, SD, JDH, CPR), pp. 533–538.
EDMEDM-2016-YadavSKSD #framework #named #platform
TutorSpace: Content-centric Platform for Enabling Blended Learning in Developing Countries (KY, KS, RK, SS, OD), pp. 705–706.
EDMEDM-2016-Yee-Kingd #collaboration #markov #online #process #social
Stimulating collaborative activity in online social learning environments with Markov decision processes (MYK, Md), pp. 652–653.
EDMEDM-2016-Yee-KingGd #collaboration #metric #online #predict #social #student #using
Predicting student grades from online, collaborative social learning metrics using K-NN (MYK, AGR, Md), pp. 654–655.
EDMEDM-2016-ZhangSC #automation #clustering #effectiveness #modelling #student
Deep Learning + Student Modeling + Clustering: a Recipe for Effective Automatic Short Answer Grading (YZ, RS, MC), pp. 562–567.
EDMEDM-2016-ZhengSP #exclamation #student
Perfect Scores Indicate Good Students !? The Case of One Hundred Percenters in a Math Learning System (ZZ, MS, NP), pp. 660–661.
ICPCICPC-2016-TianWLG #debugging #rank #recommendation
Learning to rank for bug report assignee recommendation (YT0, DW, DL0, CLG), pp. 1–10.
ICSMEICSME-2016-YeXFLK #api #natural language
Learning to Extract API Mentions from Informal Natural Language Discussions (DY, ZX, CYF, JL0, NK), pp. 389–399.
FMFM-2016-ChenP0 #cyber-physical #invariant #towards #verification
Towards Learning and Verifying Invariants of Cyber-Physical Systems by Code Mutation (YC0, CMP, JS0), pp. 155–163.
FMFM-2016-GiantamidisT
Learning Moore Machines from Input-Output Traces (GG, ST), pp. 291–309.
IFM-2016-BosSV #automaton #metric
Enhancing Automata Learning by Log-Based Metrics (PvdB, RS, FWV), pp. 295–310.
IFM-2016-SchutsHV #case study #equivalence #experience #industrial #legacy #refactoring #using
Refactoring of Legacy Software Using Model Learning and Equivalence Checking: An Industrial Experience Report (MS, JH, FWV), pp. 311–325.
ICFP-2016-Abadi #named #scalability
TensorFlow: learning functions at scale (MA), p. 1.
AIIDEAIIDE-2016-HarrisonR #crowdsourcing #using
Learning From Stories: Using Crowdsourced Narratives to Train Virtual Agents (BH, MOR), pp. 183–189.
AIIDEAIIDE-2016-SummervilleM #design
Mystical Tutor: A Magic: The Gathering Design Assistant via Denoising Sequence-to-Sequence Learning (AJS, MM), pp. 86–92.
CHI-PLAYCHI-PLAY-2016-BonsignoreHKVF #game studies #people
Roles People Play: Key Roles Designed to Promote Participation and Learning in Alternate Reality Games (EB, DLH, KK, AV, AF), pp. 78–90.
CoGCIG-2016-Bursztein #statistics #using
I am a legend: Hacking hearthstone using statistical learning methods (EB), pp. 1–8.
CoGCIG-2016-CazenaveLTT #game studies #random #using
Learning opening books in partially observable games: Using random seeds in Phantom Go (TC, JL0, FT, OT), pp. 1–7.
CoGCIG-2016-ChuIHT #game studies #video
Position-based reinforcement learning biased MCTS for General Video Game Playing (CYC, SI, TH, RT), pp. 1–8.
CoGCIG-2016-KempkaWRTJ #framework #named #platform #research #visual notation
ViZDoom: A Doom-based AI research platform for visual reinforcement learning (MK, MW, GR, JT, WJ), pp. 1–8.
CoGCIG-2016-Shaker #framework #generative #motivation
Intrinsically motivated reinforcement learning: A promising framework for procedural content generation (NS), pp. 1–8.
CoGCIG-2016-ShakerA #experience #predict
Transfer learning for cross-game prediction of player experience (NS, MAZ), pp. 1–8.
CoGCIG-2016-ShiC #generative #online
Online level generation in Super Mario Bros via learning constructive primitives (PS, KC0), pp. 1–8.
CoGCIG-2016-SifaSDOB #game studies #predict #representation
Predicting Retention in Sandbox Games with Tensor Factorization-based Representation Learning (RS, SS, AD, CO, CB), pp. 1–8.
CoGCIG-2016-SungurS #algorithm #behaviour
Voluntary behavior on cortical learning algorithm based agents (AKS, ES), pp. 1–7.
DiGRADiGRA-FDG-2016-MelcerI #design #framework #game studies #physics #simulation
Bridging the Physical Learning Divides: A Design Framework for Embodied Learning Games and Simulations (EFM, KI).
CoGVS-Games-2016-FerreiraGH #case study #design #education #game studies
Game Based Learning: A Case Study on Designing an Educational Game for Children in Developing Countries (SMF, CGV, RH), pp. 1–8.
CoGVS-Games-2016-GauthierJ #game studies #problem #research
Woes of an RCT for Game-Based Learning Research - Past Problems and Potential Solutions (AG, JJ), pp. 1–2.
CoGVS-Games-2016-GomezC #design #development #game studies #prototype
Bridging Design Prototypes in the Development of Games for Formal Learning Environments (GG, DC), pp. 1–5.
CoGVS-Games-2016-PatinoRP #analysis #game studies
Analysis of Game and Learning Mechanics According to the Learning Theories (AP, MR, JNP), pp. 1–4.
CoGVS-Games-2016-RamosP #game studies
Program with Ixquic: Educative Games and Learning in Augmented and Virtual Environments (CR, TP), pp. 1–2.
CIKMCIKM-2016-0001H #adaptation #interactive #multi #named #using
aptMTVL: Nailing Interactions in Multi-Task Multi-View Multi-Label Learning using Adaptive-basis Multilinear Factor Analyzers (XL0, JH), pp. 1171–1180.
CIKMCIKM-2016-0064NRR #detection #framework #identification #multi
A Multiple Instance Learning Framework for Identifying Key Sentences and Detecting Events (WW0, YN, HR, NR), pp. 509–518.
CIKMCIKM-2016-AmandH
Discriminative View Learning for Single View Co-Training (JSA, JH), pp. 2221–2226.
CIKMCIKM-2016-BaruahZGLSV #optimisation
Optimizing Nugget Annotations with Active Learning (GB, HZ0, RG, JJL, MDS, OV), pp. 2359–2364.
CIKMCIKM-2016-ChenOX #recommendation
Learning Points and Routes to Recommend Trajectories (DC, CSO, LX), pp. 2227–2232.
CIKMCIKM-2016-CheungL #rank #robust #scalability
Scalable Spectral k-Support Norm Regularization for Robust Low Rank Subspace Learning (YmC, JL), pp. 1151–1160.
CIKMCIKM-2016-CormackG #classification #reliability #scalability
Scalability of Continuous Active Learning for Reliable High-Recall Text Classification (GVC, MRG), pp. 1039–1048.
CIKMCIKM-2016-DeveaudMN #rank
Learning to Rank System Configurations (RD, JM, JYN), pp. 2001–2004.
CIKMCIKM-2016-FengXZ #distributed
Distributed Deep Learning for Question Answering (MF, BX, BZ), pp. 2413–2416.
CIKMCIKM-2016-GyselRK
Learning Latent Vector Spaces for Product Search (CVG, MdR, EK), pp. 165–174.
CIKMCIKM-2016-HanSBW
Routing an Autonomous Taxi with Reinforcement Learning (MH, PS, SB, HW0), pp. 2421–2424.
CIKMCIKM-2016-HeTOKYC #query
Learning to Rewrite Queries (YH, JT, HO, CK, DY, YC), pp. 1443–1452.
CIKMCIKM-2016-KhabsaCAZAW #metric
Learning to Account for Good Abandonment in Search Success Metrics (MK, ACC, AHA, IZ, TA, KW), pp. 1893–1896.
CIKMCIKM-2016-LiSNLF #hashtag #rank #recommendation #topic #twitter
Hashtag Recommendation Based on Topic Enhanced Embedding, Tweet Entity Data and Learning to Rank (QL, SS, AN, XL, RF), pp. 2085–2088.
CIKMCIKM-2016-MartinoBR0M #community #using
Learning to Re-Rank Questions in Community Question Answering Using Advanced Features (GDSM, ABC, SR, AU0, AM), pp. 1997–2000.
CIKMCIKM-2016-RenZRZYW #optimisation #performance
User Response Learning for Directly Optimizing Campaign Performance in Display Advertising (KR, WZ0, YR, HZ, YY0, JW0), pp. 679–688.
CIKMCIKM-2016-SilvaGAG #rank
Compression-Based Selective Sampling for Learning to Rank (RMS, GdCMG, MSA, MAG), pp. 247–256.
CIKMCIKM-2016-SousaCRMG #feature model #rank
Incorporating Risk-Sensitiveness into Feature Selection for Learning to Rank (DXdS, SDC, TCR, WSM, MAG), pp. 257–266.
CIKMCIKM-2016-TymoshenkoBM #rank #web
Learning to Rank Non-Factoid Answers: Comment Selection in Web Forums (KT, DB, AM), pp. 2049–2052.
CIKMCIKM-2016-WangJYJM #using
Learning to Extract Conditional Knowledge for Question Answering using Dialogue (PW, LJ, JY0, LJ, WYM), pp. 277–286.
CIKMCIKM-2016-WangWW
Learning Hidden Features for Contextual Bandits (HW, QW, HW), pp. 1633–1642.
CIKMCIKM-2016-XieWY
Active Zero-Shot Learning (SX, SW, PSY), pp. 1889–1892.
CIKMCIKM-2016-XieYWXCW #graph #recommendation
Learning Graph-based POI Embedding for Location-based Recommendation (MX, HY, HW, FX, WC, SW), pp. 15–24.
CIKMCIKM-2016-YuanGJCYZ #named #ranking #using
LambdaFM: Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates (FY, GG, JMJ, LC0, HY, WZ0), pp. 227–236.
CIKMCIKM-2016-ZhaoK #online #rank #reliability
Constructing Reliable Gradient Exploration for Online Learning to Rank (TZ, IK), pp. 1643–1652.
CIKMCIKM-2016-ZhengC #classification #constraints #probability
Regularizing Structured Classifier with Conditional Probabilistic Constraints for Semi-supervised Learning (VWZ, KCCC), pp. 1029–1038.
CIKMCIKM-2016-ZhengW #graph #multi
Graph-Based Multi-Modality Learning for Clinical Decision Support (ZZ, XW0), pp. 1945–1948.
CIKMCIKM-2016-ZhuangLPXH #adaptation
Ensemble of Anchor Adapters for Transfer Learning (FZ, PL0, SJP, HX, QH), pp. 2335–2340.
ECIRECIR-2016-AlmasriBC #comparison #feedback #pseudo #query
A Comparison of Deep Learning Based Query Expansion with Pseudo-Relevance Feedback and Mutual Information (MA, CB, JPC), pp. 709–715.
ECIRECIR-2016-BotevaGSR #dataset #information retrieval #rank
A Full-Text Learning to Rank Dataset for Medical Information Retrieval (VB, DGG, AS, SR), pp. 716–722.
ECIRECIR-2016-CroceB #kernel #scalability
Large-Scale Kernel-Based Language Learning Through the Ensemble Nystr đdoto o ¨ m Methods (DC, RB0), pp. 100–112.
ECIRECIR-2016-IencoRRRT #mining #modelling #multi
MultiLingMine 2016: Modeling, Learning and Mining for Cross/Multilinguality (DI, MR, SR, PR, AT), pp. 869–873.
ECIRECIR-2016-LiWPA #analysis #empirical #sentiment
An Empirical Study of Skip-Gram Features and Regularization for Learning on Sentiment Analysis (CL, BW, VP, JAA), pp. 72–87.
ECIRECIR-2016-MiottoLD #health #predict
Deep Learning to Predict Patient Future Diseases from the Electronic Health Records (RM, LL0, JTD), pp. 768–774.
ECIRECIR-2016-MustoSGL #recommendation #wiki #word
Learning Word Embeddings from Wikipedia for Content-Based Recommender Systems (CM, GS, MdG, PL), pp. 729–734.
ECIRECIR-2016-NiuLC #approach #named #twitter
LExL: A Learning Approach for Local Expert Discovery on Twitter (WN, ZL, JC), pp. 803–809.
ECIRECIR-2016-WangGLXC #multi #predict #representation
Multi-task Representation Learning for Demographic Prediction (PW, JG, YL, JX0, XC), pp. 88–99.
ECIRECIR-2016-ZhangDW #case study #category theory #multi #predict
Deep Learning over Multi-field Categorical Data - - A Case Study on User Response Prediction (WZ0, TD, JW0), pp. 45–57.
ICMLICML-2016-AJFMS #cumulative #predict
Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control (PLA, CJ, MCF0, SIM, CS), pp. 1406–1415.
ICMLICML-2016-AkrourNAA #optimisation
Model-Free Trajectory Optimization for Reinforcement Learning (RA, GN, HA, AA), pp. 2961–2970.
ICMLICML-2016-AroraMM #multi #optimisation #probability #representation #using
Stochastic Optimization for Multiview Representation Learning using Partial Least Squares (RA, PM, TVM), pp. 1786–1794.
ICMLICML-2016-BaiRWS #classification #difference #geometry
Differential Geometric Regularization for Supervised Learning of Classifiers (QB, SR, ZW, SS), pp. 1879–1888.
ICMLICML-2016-BalkanskiMKS #combinator
Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization (EB, BM, AK0, YS), pp. 2207–2216.
ICMLICML-2016-CohenHK #feedback #graph #online
Online Learning with Feedback Graphs Without the Graphs (AC, TH, TK), pp. 811–819.
ICMLICML-2016-DaneshmandLH #adaptation
Starting Small - Learning with Adaptive Sample Sizes (HD, AL, TH), pp. 1463–1471.
ICMLICML-2016-DuanCHSA #benchmark #metric
Benchmarking Deep Reinforcement Learning for Continuous Control (YD, XC0, RH, JS, PA), pp. 1329–1338.
ICMLICML-2016-FernandoG #classification #video
Learning End-to-end Video Classification with Rank-Pooling (BF, SG), pp. 1187–1196.
ICMLICML-2016-FinnLA #optimisation #policy
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization (CF, SL, PA), pp. 49–58.
ICMLICML-2016-FriesenD #modelling #theorem
The Sum-Product Theorem: A Foundation for Learning Tractable Models (ALF, PMD), pp. 1909–1918.
ICMLICML-2016-GalG #approximate #nondeterminism #representation
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning (YG, ZG), pp. 1050–1059.
ICMLICML-2016-GlaudeP #automaton #probability
PAC learning of Probabilistic Automaton based on the Method of Moments (HG, OP), pp. 820–829.
ICMLICML-2016-GuanRW #markov #multi #performance #process #recognition #using
Efficient Multi-Instance Learning for Activity Recognition from Time Series Data Using an Auto-Regressive Hidden Markov Model (XG, RR, WKW), pp. 2330–2339.
ICMLICML-2016-HammCB #multi
Learning privately from multiparty data (JH, YC, MB), pp. 555–563.
ICMLICML-2016-HashimotoGJ #generative
Learning Population-Level Diffusions with Generative RNNs (TBH, DKG, TSJ), pp. 2417–2426.
ICMLICML-2016-HeB #modelling
Opponent Modeling in Deep Reinforcement Learning (HH0, JLBG), pp. 1804–1813.
ICMLICML-2016-HoGE #optimisation #policy
Model-Free Imitation Learning with Policy Optimization (JH, JKG, SE), pp. 2760–2769.
ICMLICML-2016-JiangL #evaluation #robust
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning (NJ, LL0), pp. 652–661.
ICMLICML-2016-JohanssonSS
Learning Representations for Counterfactual Inference (FDJ, US, DAS), pp. 3020–3029.
ICMLICML-2016-Kasiviswanathan #empirical #performance
Efficient Private Empirical Risk Minimization for High-dimensional Learning (SPK, HJ), pp. 488–497.
ICMLICML-2016-KatariyaKSW #multi #rank
DCM Bandits: Learning to Rank with Multiple Clicks (SK, BK, CS, ZW), pp. 1215–1224.
ICMLICML-2016-KawakitaT
Barron and Cover's Theory in Supervised Learning and its Application to Lasso (MK, JT), pp. 1958–1966.
ICMLICML-2016-LeeYH #multi #symmetry
Asymmetric Multi-task Learning based on Task Relatedness and Confidence (GL, EY, SJH), pp. 230–238.
ICMLICML-2016-LeKYC #online #predict #sequence
Smooth Imitation Learning for Online Sequence Prediction (HML0, AK, YY, PC0), pp. 680–688.
ICMLICML-2016-LererGF #physics
Learning Physical Intuition of Block Towers by Example (AL, SG, RF), pp. 430–438.
ICMLICML-2016-LiuSSF #markov #network
Structure Learning of Partitioned Markov Networks (SL0, TS, MS, KF), pp. 439–448.
ICMLICML-2016-LiuY #multi
Cross-Graph Learning of Multi-Relational Associations (HL, YY), pp. 2235–2243.
ICMLICML-2016-LiZALH #optimisation #probability
Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning (XL, TZ, RA, HL0, JDH), pp. 917–925.
ICMLICML-2016-LiZZ #memory management
Learning to Generate with Memory (CL, JZ0, BZ0), pp. 1177–1186.
ICMLICML-2016-LouizosW #matrix #performance
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors (CL, MW), pp. 1708–1716.
ICMLICML-2016-MenschMTV #matrix #taxonomy
Dictionary Learning for Massive Matrix Factorization (AM, JM, BT, GV), pp. 1737–1746.
ICMLICML-2016-MnihBMGLHSK
Asynchronous Methods for Deep Reinforcement Learning (VM, APB, MM, AG, TPL, TH, DS, KK), pp. 1928–1937.
ICMLICML-2016-MussmannE
Learning and Inference via Maximum Inner Product Search (SM, SE), pp. 2587–2596.
ICMLICML-2016-NiepertAK #graph #network
Learning Convolutional Neural Networks for Graphs (MN, MA, KK), pp. 2014–2023.
ICMLICML-2016-OswalCRRN #network #similarity
Representational Similarity Learning with Application to Brain Networks (UO, CRC, MALR, TTR, RDN), pp. 1041–1049.
ICMLICML-2016-Papakonstantinou #on the
On the Power and Limits of Distance-Based Learning (PAP, JX0, GY), pp. 2263–2271.
ICMLICML-2016-PatriniNNC #robust
Loss factorization, weakly supervised learning and label noise robustness (GP, FN, RN, MC), pp. 708–717.
ICMLICML-2016-RahmaniA #approach #composition #matrix #performance
A Subspace Learning Approach for High Dimensional Matrix Decomposition with Efficient Column/Row Sampling (MR, GKA), pp. 1206–1214.
ICMLICML-2016-ScheinZBW #composition
Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations (AS, MZ, DMB, HMW), pp. 2810–2819.
ICMLICML-2016-SchnabelSSCJ #evaluation #recommendation
Recommendations as Treatments: Debiasing Learning and Evaluation (TS, AS, AS, NC, TJ), pp. 1670–1679.
ICMLICML-2016-ShahamCDJNCK #approach
A Deep Learning Approach to Unsupervised Ensemble Learning (US, XC, OD, AJ, BN, JTC, YK), pp. 30–39.
ICMLICML-2016-ShahG #correlation
Pareto Frontier Learning with Expensive Correlated Objectives (AS, ZG), pp. 1919–1927.
ICMLICML-2016-SinglaTK #elicitation
Actively Learning Hemimetrics with Applications to Eliciting User Preferences (AS, ST, AK0), pp. 412–420.
ICMLICML-2016-SongGC #network #sequence
Factored Temporal Sigmoid Belief Networks for Sequence Learning (JS, ZG, LC), pp. 1272–1281.
ICMLICML-2016-SuLCC #modelling #statistics #visual notation
Nonlinear Statistical Learning with Truncated Gaussian Graphical Models (QS, XL, CC, LC), pp. 1948–1957.
ICMLICML-2016-SunVBB #predict
Learning to Filter with Predictive State Inference Machines (WS0, AV, BB, JAB), pp. 1197–1205.
ICMLICML-2016-SyrgkanisKS #algorithm #performance
Efficient Algorithms for Adversarial Contextual Learning (VS, AK, RES), pp. 2159–2168.
ICMLICML-2016-ThomasB #evaluation #policy
Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning (PST, EB), pp. 2139–2148.
ICMLICML-2016-UstinovskiyFGS
Meta-Gradient Boosted Decision Tree Model for Weight and Target Learning (YU, VF, GG, PS), pp. 2692–2701.
ICMLICML-2016-WangSHHLF #architecture #network
Dueling Network Architectures for Deep Reinforcement Learning (ZW0, TS, MH, HvH, ML, NdF), pp. 1995–2003.
ICMLICML-2016-XieZX #modelling
Diversity-Promoting Bayesian Learning of Latent Variable Models (PX, JZ0, EPX), pp. 59–68.
ICMLICML-2016-XuFZ #process
Learning Granger Causality for Hawkes Processes (HX, MF, HZ), pp. 1717–1726.
ICMLICML-2016-YangCS #graph
Revisiting Semi-Supervised Learning with Graph Embeddings (ZY, WWC, RS), pp. 40–48.
ICMLICML-2016-YangZJY #online
Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient (TY, LZ0, RJ, JY), pp. 449–457.
ICMLICML-2016-YaoK #performance #product line
Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity (QY, JTK), pp. 2645–2654.
ICMLICML-2016-YuL #multi #performance
Learning from Multiway Data: Simple and Efficient Tensor Regression (RY, YL0), pp. 373–381.
ICMLICML-2016-ZadehHS #geometry #metric
Geometric Mean Metric Learning (PZ, RH, SS), pp. 2464–2471.
ICMLICML-2016-ZarembaMJF #algorithm
Learning Simple Algorithms from Examples (WZ, TM, AJ, RF), pp. 421–429.
ICMLICML-2016-ZhaoPX #modelling
Learning Mixtures of Plackett-Luce Models (ZZ, PP, LX), pp. 2906–2914.
ICPRICPR-2016-AbdicFBARMS #approach #detection
Detecting road surface wetness from audio: A deep learning approach (IA, LF, DEB, WA, BR, EM, BWS), pp. 3458–3463.
ICPRICPR-2016-AfridiRS #framework #latency #named
L-CNN: Exploiting labeling latency in a CNN learning framework (MJA, AR, EMS), pp. 2156–2161.
ICPRICPR-2016-AgustssonTG
Regressor Basis Learning for anchored super-resolution (EA, RT, LVG), pp. 3850–3855.
ICPRICPR-2016-AhmedK #multi #taxonomy
Coupled multiple dictionary learning based on edge sharpness for single-image super-resolution (JA, RK), pp. 3838–3843.
ICPRICPR-2016-BalaziaS16a #recognition #robust
Learning robust features for gait recognition by Maximum Margin Criterion (MB, PS), pp. 901–906.
ICPRICPR-2016-BarddalGGBE #nearest neighbour
Overcoming feature drifts via dynamic feature weighted k-nearest neighbor learning (JPB, HMG, JG, AdSBJ, FE), pp. 2186–2191.
ICPRICPR-2016-BayramogluKH #classification #image #independence
Deep learning for magnification independent breast cancer histopathology image classification (NB, JK, JH), pp. 2440–2445.
ICPRICPR-2016-BorgaAL #image #segmentation
Semi-supervised learning of anatomical manifolds for atlas-based segmentation of medical images (MB, TA, ODL), pp. 3146–3149.
ICPRICPR-2016-CaoN #fine-grained #process #recognition
Exploring deep learning based solutions in fine grained activity recognition in the wild (SC, RN), pp. 384–389.
ICPRICPR-2016-CarbonneauGG #identification #multi #random #using
Witness identification in multiple instance learning using random subspaces (MAC, EG, GG), pp. 3639–3644.
ICPRICPR-2016-ChenWHF #detection #estimation
Deep learning for integrated hand detection and pose estimation (TYC, MYW, YHH, LCF), pp. 615–620.
ICPRICPR-2016-ChenZW #approach #network #summary #video
Wireless capsule endoscopy video summarization: A learning approach based on Siamese neural network and support vector machine (JC, YZ, YW0), pp. 1303–1308.
ICPRICPR-2016-DasguptaYO #sequence
Regularized dynamic Boltzmann machine with Delay Pruning for unsupervised learning of temporal sequences (SD, TY, TO), pp. 1201–1206.
ICPRICPR-2016-DevanneWDBBP #analysis
Learning shape variations of motion trajectories for gait analysis (MD, HW, MD, SB, ADB, PP), pp. 895–900.
ICPRICPR-2016-FanWH #adaptation #multi
Multi-stage multi-task feature learning via adaptive threshold (YF, YW, TZH), pp. 1666–1671.
ICPRICPR-2016-FengLL #effectiveness #using
Learning effective Gait features using LSTM (YF0, YL, JL), pp. 325–330.
ICPRICPR-2016-Forstner #modelling #semantics
A future for learning semantic models of man-made environments (WF), pp. 2475–2485.
ICPRICPR-2016-GhaderiA #network
Selective unsupervised feature learning with Convolutional Neural Network (S-CNN) (AG, VA), pp. 2486–2490.
ICPRICPR-2016-GonzalezVT #classification #invariant
Learning rotation invariant convolutional filters for texture classification (DM, MV, DT), pp. 2012–2017.
ICPRICPR-2016-GuoCL #multi
Multi-label learning with globAl densiTy fusiOn Mapping features (YG, FC, GL0), pp. 462–467.
ICPRICPR-2016-HoSSEA #approach #estimation #parametricity
A temporal deep learning approach for MR perfusion parameter estimation in stroke (KCH, FS, KVS, SES, CWA), pp. 1315–1320.
ICPRICPR-2016-HouXX0 #classification #graph
Semi-supervised learning competence of classifiers based on graph for dynamic classifier selection (CH, YX, ZX, JS0), pp. 3650–3654.
ICPRICPR-2016-HuangWLLBC #automation #clustering #estimation #parametricity
Ensemble-driven support vector clustering: From ensemble learning to automatic parameter estimation (DH, CDW, JHL, YL0, SB, YC), pp. 444–449.
ICPRICPR-2016-HuLL #named #representation #semantics #video
Video2vec: Learning semantic spatio-temporal embeddings for video representation (ShH, YL, BL), pp. 811–816.
ICPRICPR-2016-JafariKNSSWN #image #segmentation #using
Skin lesion segmentation in clinical images using deep learning (MHJ, NK, ENE, SS, SMRS, KRW, KN), pp. 337–342.
ICPRICPR-2016-JenckelBD #documentation #named #sequence
anyOCR: A sequence learning based OCR system for unlabeled historical documents (MJ, SSB, AD0), pp. 4035–4040.
ICPRICPR-2016-JiaoZ #multi #taxonomy #using
Multiple Instance Dictionary Learning using Functions of Multiple Instances (CJ, AZ), pp. 2688–2693.
ICPRICPR-2016-JohnKGNMI #modelling #performance #segmentation #using
Fast road scene segmentation using deep learning and scene-based models (VJ, KK, CG, HTN, SM, KI), pp. 3763–3768.
ICPRICPR-2016-KalraSRT #network #using
Learning opposites using neural networks (SK, AS, SR, HRT), pp. 1213–1218.
ICPRICPR-2016-KanehiraSH #multi #scalability
True-negative label selection for large-scale multi-label learning (AK, AS, TH), pp. 3673–3678.
ICPRICPR-2016-KaremF #concept #multi
Multiple Instance Learning with multiple positive and negative target concepts (AK, HF), pp. 474–479.
ICPRICPR-2016-KaurDCM #hybrid #image
Hybrid deep learning for Reflectance Confocal Microscopy skin images (PK, KJD, GOC, MCM), pp. 1466–1471.
ICPRICPR-2016-KawanishiDIMF #classification #robust
Misclassification tolerable learning for robust pedestrian orientation classification (YK, DD, II, HM, HF), pp. 486–491.
ICPRICPR-2016-KhanH #adaptation #polynomial #using
Adapting instance weights for unsupervised domain adaptation using quadratic mutual information and subspace learning (MNAK, DRH), pp. 1560–1565.
ICPRICPR-2016-KhodabandehMVMP #segmentation #video
Unsupervised learning of supervoxel embeddings for video Segmentation (MK, SM, AV, NM, EMP, SS, GM), pp. 2392–2397.
ICPRICPR-2016-Kobayashi #data-driven #image #similarity
Learning data-driven image similarity measure (TK), pp. 3679–3684.
ICPRICPR-2016-LangenkamperN #architecture #classification #detection #online #realtime
COATL - a learning architecture for online real-time detection and classification assistance for environmental data (DL, TWN), pp. 597–602.
ICPRICPR-2016-LiangSWMWSG #image #optimisation #performance #precise #retrieval #similarity
Optimizing top precision performance measure of content-based image retrieval by learning similarity function (RZL, LS, HW, JM, JJYW, QS, YG), pp. 2954–2958.
ICPRICPR-2016-LiaoQL #image #multi
Semisupervised manifold learning for color transfer between multiview images (DL, YQ, ZNL), pp. 259–264.
ICPRICPR-2016-Liu16a #classification #multi #network #scalability
Hierarchical learning for large multi-class network classification (LL), pp. 2307–2312.
ICPRICPR-2016-LoogY #consistency #empirical #nondeterminism
An empirical investigation into the inconsistency of sequential active learning (ML, YY), pp. 210–215.
ICPRICPR-2016-MaoZCLHY16a #collaboration #recognition #taxonomy
Group and collaborative dictionary pair learning for face recognition (MM, ZZ, ZC, HL, XH, RY), pp. 4107–4111.
ICPRICPR-2016-MarkusPA #optimisation
Learning local descriptors by optimizing the keypoint-correspondence criterion (NM, ISP, JA), pp. 2380–2385.
ICPRICPR-2016-MoutafisLK #metric
Regression-based metric learning (PM, ML, IAK), pp. 2700–2705.
ICPRICPR-2016-NahaW16a #segmentation #using
Object figure-ground segmentation using zero-shot learning (SN, YW0), pp. 2842–2847.
ICPRICPR-2016-Nilsson #consistency #taxonomy
Sparse coding with unity range codes and label consistent discriminative dictionary learning (MN), pp. 3186–3191.
ICPRICPR-2016-NogueiraMCSS #image #semantics
Learning to semantically segment high-resolution remote sensing images (KN, MDM, JC, WRS, JAdS), pp. 3566–3571.
ICPRICPR-2016-OhY #algorithm #graph
Enhancing label inference algorithms considering vertex importance in graph-based semi-supervised learning (BO, JY), pp. 1671–1676.
ICPRICPR-2016-OrriteRM #distance #process #sequence #using
One-shot learning of temporal sequences using a distance dependent Chinese Restaurant Process (CO, MR, CM), pp. 2694–2699.
ICPRICPR-2016-PalCGCC #multi #using
Severity grading of psoriatic plaques using deep CNN based multi-task learning (AP, AC, UG, AC, RC), pp. 1478–1483.
ICPRICPR-2016-PassalisT #embedded #retrieval #word
Bag of Embedded Words learning for text retrieval (NP, AT), pp. 2416–2421.
ICPRICPR-2016-PengRP #network #recognition #using
Learning face recognition from limited training data using deep neural networks (XP, NKR, SP), pp. 1442–1447.
ICPRICPR-2016-PironkovDD #automation #multi #recognition #speech
Speaker-aware Multi-Task Learning for automatic speech recognition (GP, SD, TD), pp. 2900–2905.
ICPRICPR-2016-QianCKNM
Deep structured-output regression learning for computational color constancy (YQ, KC0, JKK, JN, JM), pp. 1899–1904.
ICPRICPR-2016-QuachtranHS #detection #using
Detection of Intracranial Hypertension using Deep Learning (BQ, RBH, FS), pp. 2491–2496.
ICPRICPR-2016-QuLFT #effectiveness #retrieval
Improving PGF retrieval effectiveness with active learning (JQ, XL, SF, ZT), pp. 1125–1130.
ICPRICPR-2016-RaytchevKKTK
Ensemble-based local learning for high-dimensional data regression (BR, YK, MK, TT, KK), pp. 2640–2645.
ICPRICPR-2016-RedkoB #kernel
Kernel alignment for unsupervised transfer learning (IR, YB), pp. 525–530.
ICPRICPR-2016-RotaSCP #analysis #education #forensics #image #question #student
Bad teacher or unruly student: Can deep learning say something in Image Forensics analysis? (PR, ES, VC, CP), pp. 2503–2508.
ICPRICPR-2016-RoyTL #network
Context-regularized learning of fully convolutional networks for scene labeling (AR, ST, LJL), pp. 3751–3756.
ICPRICPR-2016-Saha0PV #problem #visual notation
Transfer learning for rare cancer problems via Discriminative Sparse Gaussian Graphical model (BS, SG0, DQP, SV), pp. 537–542.
ICPRICPR-2016-SaikiaSSKG #analysis #kernel #multi #using
Multiple kernel learning using data envelopment analysis and feature vector selection and projection (GS, SS, VVS, RDK, PG), pp. 520–524.
ICPRICPR-2016-ShankarDG #network
Reinforcement Learning via Recurrent Convolutional Neural Networks (TS, SKD, PG), pp. 2592–2597.
ICPRICPR-2016-ShwetaE0B #architecture #identification #interactive
A deep learning architecture for protein-protein Interaction Article identification (S, AE, SS0, PB), pp. 3128–3133.
ICPRICPR-2016-SoleymaniGF
Loss factors for learning Boosting ensembles from imbalanced data (RS, EG, GF), pp. 204–209.
ICPRICPR-2016-SousaB #consistency
Constrained Local and Global Consistency for semi-supervised learning (CARdS, GEAPAB), pp. 1689–1694.
ICPRICPR-2016-SouzaSC #comprehension #semantics
Building semantic understanding beyond deep learning from sound and vision (FDMdS, SS, GCC), pp. 2097–2102.
ICPRICPR-2016-SunBTTH #detection #locality #using
Tattoo detection and localization using region-based deep learning (ZS, JB, PT, MT, AH), pp. 3055–3060.
ICPRICPR-2016-SunHLK #multi #network #recognition
Multiple Instance Learning Convolutional Neural Networks for object recognition (MS, TXH, MCL, AKR), pp. 3270–3275.
ICPRICPR-2016-SvobodaMB #recognition
Palmprint recognition via discriminative index learning (JS, JM, MMB), pp. 4232–4237.
ICPRICPR-2016-TairaTO #robust #synthesis
Robust feature matching by learning descriptor covariance with viewpoint synthesis (HT, AT, MO), pp. 1953–1958.
ICPRICPR-2016-TounsiMA #framework #recognition #taxonomy
Supervised dictionary learning in BoF framework for Scene Character recognition (MT, IM, AMA), pp. 3987–3992.
ICPRICPR-2016-Triantafyllidou #detection #incremental #network
Face detection based on deep convolutional neural networks exploiting incremental facial part learning (DT, AT), pp. 3560–3565.
ICPRICPR-2016-TzelepiT #image #retrieval
Exploiting supervised learning for finetuning deep CNNs in content based image retrieval (MT, AT), pp. 2918–2923.
ICPRICPR-2016-UlmB
Learning tubes (MU, NB), pp. 3655–3660.
ICPRICPR-2016-WangHG #classification #novel
A novel fingerprint classification method based on deep learning (RW, CH, TG), pp. 931–936.
ICPRICPR-2016-WangLLCL #visual notation
Visual tracking via sparsity pattern learning (YW, YL0, ZL, LFC, HL), pp. 2716–2721.
ICPRICPR-2016-WangZWGSH #identification #metric #similarity
Contextual Similarity Regularized Metric Learning for person re-identification (JW0, JZ, ZW, CG, NS, RH0), pp. 2048–2053.
ICPRICPR-2016-WeiLSKM #taxonomy
Joint learning dictionary and discriminative features for high dimensional data (XW, YL, HS, MK, YLM), pp. 366–371.
ICPRICPR-2016-WichtFH #keyword
Deep learning features for handwritten keyword spotting (BW, AF0, JH), pp. 3434–3439.
ICPRICPR-2016-WuWJ #multi #recognition
Multiple Facial Action Unit recognition by learning joint features and label relations (SW, SW, QJ), pp. 2246–2251.
ICPRICPR-2016-XueB #multi
Multi-task learning for one-class SVM with additional new features (YX, PB), pp. 1571–1576.
ICPRICPR-2016-XuSARS #multi #recognition #retrieval #taxonomy
Multi-Paced Dictionary Learning for cross-domain retrieval and recognition (DX0, JS, XAP, ER0, NS), pp. 3228–3233.
ICPRICPR-2016-XuT #3d #network
Beam search for learning a deep Convolutional Neural Network of 3D shapes (XX, ST), pp. 3506–3511.
ICPRICPR-2016-YangJPL #image #taxonomy
Enhancement of Low Light Level Images with coupled dictionary learning (JY, XJ, CP, CLL), pp. 751–756.
ICPRICPR-2016-YangL #nondeterminism #using
Active learning using uncertainty information (YY, ML), pp. 2646–2651.
ICPRICPR-2016-ZhaoZWJ #multi
Multilingual articulatory features augmentation learning (YZ, RZ, XW0, QJ), pp. 2895–2899.
ICPRICPR-2016-ZhengYYY #feature model #robust
Robust unsupervised feature selection by nonnegative sparse subspace learning (WZ, HY, JY0, JY), pp. 3615–3620.
ICPRICPR-2016-ZhuWLZ #gender #lightweight #network #recognition
Learning a lightweight deep convolutional network for joint age and gender recognition (LZ, KW, LL, LZ0), pp. 3282–3287.
KDDKDD-2016-BorisyukKSZ #documentation #framework #modelling #named #query
CaSMoS: A Framework for Learning Candidate Selection Models over Structured Queries and Documents (FB, KK, DS, BZ), pp. 441–450.
KDDKDD-2016-ChangZTYCHH #network #streaming
Positive-Unlabeled Learning in Streaming Networks (SC, YZ0, JT, DY, YC, MAHJ, TSH), pp. 755–764.
KDDKDD-2016-ChoiBSCTBTS #concept #multi #representation
Multi-layer Representation Learning for Medical Concepts (EC, MTB, ES, CC, MT, JB, JTS, JS), pp. 1495–1504.
KDDKDD-2016-FeiW0 #cumulative #information management
Learning Cumulatively to Become More Knowledgeable (GF, SW, BL0), pp. 1565–1574.
KDDKDD-2016-Freitas #composition #network
Learning to Learn and Compositionality with Deep Recurrent Neural Networks: Learning to Learn and Compositionality (NdF), p. 3.
KDDKDD-2016-GroverL #named #network #scalability
node2vec: Scalable Feature Learning for Networks (AG, JL), pp. 855–864.
KDDKDD-2016-Herbrich #modelling #scalability
Learning Sparse Models at Scale (RH), p. 407.
KDDKDD-2016-HuoNH #effectiveness #metric #robust #using
Robust and Effective Metric Learning Using Capped Trace Norm: Metric Learning via Capped Trace Norm (ZH, FN, HH), pp. 1605–1614.
KDDKDD-2016-LiGHZ #recommendation
Point-of-Interest Recommendations: Learning Potential Check-ins from Friends (HL, YG, RH, HZ), pp. 975–984.
KDDKDD-2016-LiMLFDYLQ #big data #data analysis #performance #scalability #taxonomy
Scalable Fast Rank-1 Dictionary Learning for fMRI Big Data Analysis (XL0, MM, BL, MSF, ID, JY, TL, SQ), pp. 511–519.
KDDKDD-2016-LinXBJZ #feature model #interactive #multi
Multi-Task Feature Interaction Learning (KL, JX, IMB, SJ, JZ), pp. 1735–1744.
KDDKDD-2016-LiWYR #analysis #multi
A Multi-Task Learning Formulation for Survival Analysis (YL, JW0, JY, CKR), pp. 1715–1724.
KDDKDD-2016-LynchAA #image #multimodal #rank #scalability #semantics #visual notation
Images Don't Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to Rank (CL, KA, JA), pp. 541–548.
KDDKDD-2016-NingMRR #modelling #multi
Modeling Precursors for Event Forecasting via Nested Multi-Instance Learning (YN, SM, HR, NR), pp. 1095–1104.
KDDKDD-2016-PetitjeanW #modelling #scalability #visual notation
Scalable Learning of Graphical Models (FP, GIW), pp. 2131–2132.
KDDKDD-2016-ReddyLBJ #bound #scheduling
Unbounded Human Learning: Optimal Scheduling for Spaced Repetition (SR, IL, SB, TJ), pp. 1815–1824.
KDDKDD-2016-Schneider #embedded #optimisation
Bayesian Optimization and Embedded Learning Systems (JS), p. 413.
KDDKDD-2016-XuT0 #multi #robust
Robust Extreme Multi-label Learning (CX0, DT, CX0), pp. 1275–1284.
KDDKDD-2016-ZhaiCZZ #named #network #online
DeepIntent: Learning Attentions for Online Advertising with Recurrent Neural Networks (SZ, KhC, RZ, Z(Z), pp. 1295–1304.
KDDKDD-2016-ZhangYS #online #symmetry
Online Asymmetric Active Learning with Imbalanced Data (XZ, TY, PS), pp. 2055–2064.
KDDKDD-2016-ZhangZL #ambiguity
Partial Label Learning via Feature-Aware Disambiguation (MLZ, BBZ, XYL), pp. 1335–1344.
KDDKDD-2016-ZhangZWX
Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising (WZ0, TZ, JW0, JX), pp. 665–674.
KDDKDD-2016-ZhaoYCLR #multi
Hierarchical Incomplete Multi-source Feature Learning for Spatiotemporal Event Forecasting (LZ0, JY, FC0, CTL, NR), pp. 2085–2094.
KDDKDD-2016-ZhengYC #invariant #performance #taxonomy
Efficient Shift-Invariant Dictionary Learning (GZ, YY, JGC), pp. 2095–2104.
MoDELSMoDELS-2016-BatotS #framework #testing #unification
A generic framework for model-set selection for the unification of testing and learning MDE tasks (EB, HAS), pp. 374–384.
PLDIPLDI-2016-HeuleS0A #automation #set #synthesis
Stratified synthesis: automatically learning the x86-64 instruction set (SH, ES, RS0, AA), pp. 237–250.
PLDIPLDI-2016-ZhuPJ #automation #specification
Automatically learning shape specifications (HZ0, GP, SJ), pp. 491–507.
POPLPOPL-2016-0001NMR #invariant #using
Learning invariants using decision trees and implication counterexamples (PG0, DN, PM, DR), pp. 499–512.
POPLPOPL-2016-LongR #automation #generative
Automatic patch generation by learning correct code (FL, MR), pp. 298–312.
POPLPOPL-2016-RaychevBVK #semistructured data #source code
Learning programs from noisy data (VR, PB, MTV, AK0), pp. 761–774.
SASSAS-2016-HeoOY #clustering #static analysis
Learning a Variable-Clustering Strategy for Octagon from Labeled Data Generated by a Static Analysis (KH, HO, HY), pp. 237–256.
ASEASE-2016-ChenCXX #retrieval
Learning a dual-language vector space for domain-specific cross-lingual question retrieval (GC, CC, ZX, BX), pp. 744–755.
ASEASE-2016-KrishnaMF #automation
Too much automation? the bellwether effect and its implications for transfer learning (RK, TM, WF), pp. 122–131.
ASEASE-2016-QiJZWC #estimation #obfuscation #privacy #subclass
Privacy preserving via interval covering based subclass division and manifold learning based bi-directional obfuscation for effort estimation (FQ, XYJ, XZ, FW, LC), pp. 75–86.
ASEASE-2016-WhiteTVP #clone detection #detection
Deep learning code fragments for code clone detection (MW, MT, CV, DP), pp. 87–98.
FSEFSE-2016-BusjaegerX #case study #industrial
Learning for test prioritization: an industrial case study (BB, TX), pp. 975–980.
FSEFSE-2016-GuZZK #api
Deep API learning (XG, HZ0, DZ, SK0), pp. 631–642.
FSEFSE-2016-NguyenHCNMRND #api #fine-grained #recommendation #statistics #using
API code recommendation using statistical learning from fine-grained changes (ATN0, MH, MC, HAN, LM, ER, TNN, DD), pp. 511–522.
ICSE-2016-NguyenPVN #api #approach #bytecode #statistics
Learning API usages from bytecode: a statistical approach (TTN, HVP, PMV, TTN), pp. 416–427.
ICSE-2016-WangLT #automation #fault #predict #semantics
Automatically learning semantic features for defect prediction (SW0, TL, LT0), pp. 297–308.
CASECASE-2016-LangsfeldKKG #modelling #online
Robotic bimanual cleaning of deformable objects with online learning of part and tool models (JDL, AMK, KNK, SKG), pp. 626–632.
CASECASE-2016-LaskeyLCGHPDG #using
Robot grasping in clutter: Using a hierarchy of supervisors for learning from demonstrations (ML, JL, CC, DVG, WYSH, FTP, ADD, KG), pp. 827–834.
CASECASE-2016-LiuS #kernel #online #recognition #taxonomy
Online kernel dictionary learning for object recognition (HL0, FS), pp. 268–273.
CAVCAV-2016-Fiterau-Brostean #implementation #model checking
Combining Model Learning and Model Checking to Analyze TCP Implementations (PFB, RJ, FWV), pp. 454–471.
CAVCAV-2016-SantolucitoZP #automation #probability
Probabilistic Automated Language Learning for Configuration Files (MS, EZ, RP), pp. 80–87.
CSLCSL-2016-Silva #algebra
Coalgebraic Learning (AS0), p. 1.
ICTSSICTSS-2016-ReichstallerEKR #testing #using
Risk-Based Interoperability Testing Using Reinforcement Learning (AR, BE, AK, WR, MG), pp. 52–69.
ECSAECSA-2015-KiwelekarW #architecture
Learning Objectives for a Course on Software Architecture (AWK, HSW), pp. 169–180.
DRRDRR-2015-FuLLQT #diagrams #multi #retrieval
A diagram retrieval method with multi-label learning (SF, XL, LL, JQ, ZT).
HTHT-2015-KirchnerR #collaboration #in the cloud
Collaborative Learning in the Cloud: A Cross-Cultural Perspective of Collaboration (KK, LR), pp. 333–336.
HTHT-2015-MishraDBS #analysis #incremental #sentiment
Sentiment Analysis with Incremental Human-in-the-Loop Learning and Lexical Resource Customization (SM, JD, JB, ES), pp. 323–325.
JCDLJCDL-2015-KananZMF #big data #problem #summary
Big Data Text Summarization for Events: A Problem Based Learning Course (TK, XZ, MM, EAF), pp. 87–90.
SIGMODSIGMOD-2015-KumarNP #linear #modelling #normalisation
Learning Generalized Linear Models Over Normalized Data (AK, JFN, JMP), pp. 1969–1984.
VLDBVLDB-2015-QianGJ #adaptation #comparison
Learning User Preferences By Adaptive Pairwise Comparison (LQ, JG, HVJ), pp. 1322–1333.
EDMEDM-2015-BergnerKP #analysis #challenge
Methodological Challenges in the Analysis of MOOC Data for Exploring the Relationship between Discussion Forum Views and Learning Outcomes (YB, DK, DEP), pp. 234–241.
EDMEDM-2015-BhatnagarDWLDLC #analysis
An Analysis of Peer-submitted and Peer-reviewed Answer Rationales in a Web-based Peer Instruction Based Learning Environment (SB, MCD, CW, NL, MD, KL, ESC), pp. 456–459.
EDMEDM-2015-BumbacherSWB #behaviour #comprehension #concept #development #how #physics
Learning Environments and Inquiry Behaviors in Science Inquiry Learning: How Their Interplay Affects the Development of Conceptual Understanding in Physics (EB, SS, MW, PB), pp. 61–68.
EDMEDM-2015-ChandrasekaranK
Learning Instructor Intervention from MOOC Forums: Early Results and Issues (MKC, MYK, BCYT, KR), pp. 218–225.
EDMEDM-2015-ChenBD #detection
Video-Based Affect Detection in Noninteractive Learning Environments (YC, NB, SKD), pp. 440–443.
EDMEDM-2015-DoroudiHAB #comprehension #how #induction #refinement #robust #towards
Towards Understanding How to Leverage Sense-making, Induction/Refinement and Fluency to Improve Robust Learning (SD, KH, VA, EB), pp. 376–379.
EDMEDM-2015-Fancsali #algebra #behaviour #modelling #using #visual notation
Confounding Carelessness? Exploring Causal Relationships Between Carelessness, Affect, Behavior, and Learning in Cognitive Tutor Algebra Using Graphical Causal Models (SF), pp. 508–511.
EDMEDM-2015-JugoKS #optimisation #tool support #visual notation
Integrating a Web-based ITS with DM tools for Providing Learning Path Optimization and Visual Analytics (IJ, BK, VS), pp. 574–575.
EDMEDM-2015-KeshtkarCKC #interactive #student
Analyzing Students' Interaction Based on Their Response to Determine Their Learning Outcomes (FK, JC, BK, AC), pp. 588–589.
EDMEDM-2015-LewkowZRE #education #framework #platform #scalability #streaming #towards
Learning Analytics Platform. Towards an Open Scalable Streaming Solution for Education (NL, NLZ, MR, AE), pp. 460–463.
EDMEDM-2015-LiuK #clustering #fault #student
Variations in Learning Rate: Student Clustering Based on Systematic Residual Error Patterns Across Practice Opportunities (RL0, KRK), pp. 420–423.
EDMEDM-2015-MacLellanLK #modelling #student
Accounting for Slipping and Other False Negatives in Logistic Models of Student Learning (CJM, RL0, KRK), pp. 53–60.
EDMEDM-2015-MostowGEG #automation #identification #word
Automatic Identification of Nutritious Contexts for Learning Vocabulary Words (JM, DG, RE, RG), pp. 266–273.
EDMEDM-2015-Olivares-Rodriguez #mining #student #word
Learning the Creative Potential of Students by Mining a Word Association Task (COR, MG), pp. 400–403.
EDMEDM-2015-OlsenAR #collaboration #performance #predict #student
Predicting Student Performance In a Collaborative Learning Environment (JKO, VA, NR), pp. 211–217.
EDMEDM-2015-Ostrow #adaptation #motivation #student
Enhancing Student Motivation and Learning Within Adaptive Tutors (KO), pp. 668–670.
EDMEDM-2015-Pedro #student
Assessing the Roles of Student Engagement and Academic Emotions within Middle School Computer-Based Learning in College-Going Pathways (MOSP), pp. 656–658.
EDMEDM-2015-Pelanek15b #modelling #question #student
Modeling Student Learning: Binary or Continuous Skill? (RP), pp. 560–561.
EDMEDM-2015-Rasanen #education
Educational Neuroscience as a Tool to Understand Learning and Learning Disabilities in Mathematics (PR), p. 7.
EDMEDM-2015-Rau #equation #how #why
Why Do the Rich Get Richer? A Structural Equation Model to Test How Spatial Skills Affect Learning with Representations (MAR), pp. 350–357.
EDMEDM-2015-RitterF
Carnegie Learning's Cognitive Tutor (SR, SF), pp. 633–634.
EDMEDM-2015-RoweBA #game studies
Strategic Game Moves Mediate Implicit Science Learning (ER, RSB, JAC), pp. 432–435.
EDMEDM-2015-SiemensBG #graph
Personal Knowledge/Learning Graph (GS, RSB, DG), p. 5.
EDMEDM-2015-Streeter #modelling
Mixture Modeling of Individual Learning Curves (MJS), pp. 45–52.
EDMEDM-2015-Tibbles #data mining #mining
Exploring the Impact of Spacing in Mathematics Learning through Data Mining (RT), pp. 590–591.
EDMEDM-2015-Truong #adaptation
Integrating Learning Styles into Adaptive e-Learning System (HMT), pp. 645–647.
EDMEDM-2015-VossSMS #approach #dataset #matrix
A Transfer Learning Approach for Applying Matrix Factorization to Small ITS Datasets (LV, CS, CM, LST), pp. 372–375.
EDMEDM-2015-WangYWKR #behaviour #how #student
Investigating How Student's Cognitive Behavior in MOOC Discussion Forum Affect Learning Gains (XW0, DY, MW, KRK, CPR), pp. 226–233.
EDMEDM-2015-YeKSB #behaviour #multi #process #sequence
Learning Behavior Characterization with Multi-Feature, Hierarchical Activity Sequences (CY, JSK, JRS, GB), pp. 380–383.
EDMEDM-2015-ZhengVP #composition #performance
The Impact of Small Learning Group Composition on Drop-Out Rate and Learning Performance in a MOOC (ZZ, TV, NP), pp. 500–503.
ITiCSEITiCSE-2015-AlshammariAH #adaptation #education #security
The Impact of Learning Style Adaptivity in Teaching Computer Security (MA, RA, RJH), pp. 135–140.
ITiCSEITiCSE-2015-Annamaa #ide #programming #python
Thonny, : a Python IDE for Learning Programming (AA), p. 343.
ITiCSEITiCSE-2015-Cukierman #predict #process #student
Predicting Success in University First Year Computing Science Courses: The Role of Student Participation in Reflective Learning Activities and in I-clicker Activities (DC), pp. 248–253.
ITiCSEITiCSE-2015-Hamilton #education
Learning and Teaching Computing Sustainability (MH), p. 338.
ITiCSEITiCSE-2015-Harms #community #source code
Department Programs to Encourage and Support Service Learning and Community Engagement (DEH), p. 330.
ITiCSEITiCSE-2015-MartinezGB #comparison #concept #framework #multi #platform #programming
A Comparison of Preschool and Elementary School Children Learning Computer Science Concepts through a Multilanguage Robot Programming Platform (MCM, MJG, LB), pp. 159–164.
ITiCSEITiCSE-2015-QuinsonO #education #programming
A Teaching System to Learn Programming: the Programmer’s Learning Machine (MQ, GO), pp. 260–265.
ITiCSEITiCSE-2015-SantosSFN #agile #development #framework #mobile
Combining Challenge-Based Learning and Scrum Framework for Mobile Application Development (ARS, AS, PF, MN), pp. 189–194.
ITiCSEITiCSE-2015-SettleLS #community
A Computer Science Linked-courses Learning Community (AS, JL, TS), pp. 123–128.
ITiCSEITiCSE-2015-TarmazdiVSFF #using #visualisation
Using Learning Analytics to Visualise Computer Science Teamwork (HT, RV, CS, KEF, NJGF), pp. 165–170.
ITiCSEITiCSE-2015-Tudor #optimisation #query #xml
Virtual Learning Laboratory about Query Optimization against XML Data (LNT), p. 348.
SIGITESIGITE-2015-BradyWGAW #low cost #programmable #smarttech
The CCL-Parallax Programmable Badge: Learning with Low-Cost, Communicative Wearable Computers (CEB, DW, KG, GA, UW), pp. 139–144.
SIGITESIGITE-2015-Miller #evaluation #usability
Usability Evaluation: Learning When Method Findings Converge-And When They Don’t (CSM), pp. 167–172.
SIGITESIGITE-2015-MillerSL #object-oriented #programming #python #testing #towards
Learning Object-Oriented Programming in Python: Towards an Inventory of Difficulties and Testing Pitfalls (CSM, AS, JL), pp. 59–64.
SIGITESIGITE-2015-NicolaiNHW #education #industrial
Experiential Learning Business/Industry and Education Wants and Needs (BN, DN, CHJ, CW), pp. 95–96.
SIGITESIGITE-2015-SettleLS #community #development
Evaluating a Linked-courses Learning Community for Development Majors (AS, JL, TS), pp. 127–132.
ICSMEICSME-2015-CorleyDK #feature model #using
Exploring the use of deep learning for feature location (CSC, KD, NAK), pp. 556–560.
MSRMSR-2015-WhiteVVP #repository #towards
Toward Deep Learning Software Repositories (MW, CV, MLV, DP), pp. 334–345.
LATALATA-2015-Yoshinaka #boolean grammar #grammar inference
Learning Conjunctive Grammars and Contextual Binary Feature Grammars (RY), pp. 623–635.
SEFMSEFM-2015-Muhlberg0DLP #source code #verification
Learning Assertions to Verify Linked-List Programs (JTM, DHW, MD, GL, FP), pp. 37–52.
ICFPICFP-2015-ZhuNJ #refinement
Learning refinement types (HZ, AVN, SJ), pp. 400–411.
AIIDEAIIDE-2015-UriarteO #automation #game studies #modelling
Automatic Learning of Combat Models for RTS Games (AU, SO), pp. 212–219.
CHI-PLAYCHI-PLAY-2015-CakirCAL #game studies
An Optical Brain Imaging Study on the Improvements in Mathematical Fluency from Game-based Learning (MPÇ, NAÇ, HA, FJL), pp. 209–219.
CHI-PLAYCHI-PLAY-2015-EagleRHBBAE #interactive #network
Measuring Implicit Science Learning with Networks of Player-Game Interactions (ME, ER, DH, RB, TB, JAC, TE), pp. 499–504.
CHI-PLAYCHI-PLAY-2015-HarpsteadA #analysis #design #education #empirical #game studies #using
Using Empirical Learning Curve Analysis to Inform Design in an Educational Game (EH, VA), pp. 197–207.
CoGCIG-2015-DannZT #approach
An improved approach to reinforcement learning in Computer Go (MD, FZ, JT), pp. 169–176.
CoGCIG-2015-DobreL #game studies #mining #online
Online learning and mining human play in complex games (MSD, AL), pp. 60–67.
CoGCIG-2015-GlavinM #clustering #game studies
Learning to shoot in first person shooter games by stabilizing actions and clustering rewards for reinforcement learning (FGG, MGM), pp. 344–351.
CoGCIG-2015-HuangW
Learning overtaking and blocking skills in simulated car racing (HHH, TW), pp. 439–445.
CoGCIG-2015-IvanovoRZL #monte carlo
Combining Monte Carlo tree search and apprenticeship learning for capture the flag (JI, WLR, FZ, XL0), pp. 154–161.
CoGCIG-2015-KamekoMT #game studies #generative
Learning a game commentary generator with grounded move expressions (HK, SM, YT), pp. 177–184.
CoGCIG-2015-NetoJ #automation #elicitation #named
ACE-RL-Checkers: Improving automatic case elicitation through knowledge obtained by reinforcement learning in player agents (HCN, RMdSJ), pp. 328–335.
CoGCIG-2015-QuiterioPM #approach #geometry
A reinforcement learning approach for the circle agent of geometry friends (JQ, RP, FSM), pp. 423–430.
CoGCIG-2015-Yao #game studies #speech
Keynote speech I: Co-evolutionary learning in game-playing (XY0), p. 16.
FDGFDG-2015-KaoH15a #game studies #named
Mazzy: A STEM Learning Game (DK, DFH).
FDGFDG-2015-KaoH15b #game studies #using
Exploring the Construction, Play, Use of Virtual Identities in a STEM Learning Game (DK, DFH).
FDGFDG-2015-PackardO #behaviour #metric #similarity
Learning Behavior form Demonstration in Minecraft via Symbolic Similarity Measures (BP, SO).
FDGFDG-2015-Pirker #collaboration
Learning in Collaborative and Motivational Virtual Environments (JP).
FDGFDG-2015-ShakerAS #modelling
Active Learning for Player Modeling (NS, MAZ, MS).
FDGFDG-2015-SummervilleBMJ #data-driven #game studies #generative
The Learning of Zelda: Data-Driven Level Generation for Action Role Playing Games (AS, MB, MM, AJ).
CoGVS-Games-2015-AsadipourDC #approach #game studies
A Game-Based Training Approach to Enhance Human Hand Motor Learning and Control Abilities (AA0, KD, AC), pp. 1–6.
CoGVS-Games-2015-DiazDHS #development #game studies #multi #online #using #video
Explicit Fun, Implicit Learning in Multiplayer Online Battle Arenas: Methodological Proposal for Studying the Development of Cognitive Skills Using Commercial Video Games (CMCD, BD, HH, JWS), pp. 1–3.
CoGVS-Games-2015-PanzoliPL #communication #game studies
Communication and Knowledge Sharing in an Immersive Learning Game (DP, CPL, PL), pp. 1–8.
CoGVS-Games-2015-YohannisP #algorithm #gamification #sorting #visualisation
Sort Attack: Visualization and Gamification of Sorting Algorithm Learning (AY, YP), pp. 1–8.
CHICHI-2015-BerardR #assessment #human-computer #similarity #towards
The Transfer of Learning as HCI Similarity: Towards an Objective Assessment of the Sensory-Motor Basis of Naturalness (FB, ARC), pp. 1315–1324.
CHICHI-2015-DavisK #student
Investigating High School Students’ Perceptions of Digital Badges in Afterschool Learning (KD, EK), pp. 4043–4046.
CHICHI-2015-KardanC #adaptation #evaluation #interactive #simulation
Providing Adaptive Support in an Interactive Simulation for Learning: An Experimental Evaluation (SK, CC), pp. 3671–3680.
CHICHI-2015-Noble #self
Resilience Ex Machina: Learning a Complex Medical Device for Haemodialysis Self-Treatment (PJN), pp. 4147–4150.
CHICHI-2015-NoroozMJMF #approach #named #smarttech #visualisation
BodyVis: A New Approach to Body Learning Through Wearable Sensing and Visualization (LN, MLM, AJ, BM, JEF), pp. 1025–1034.
CHICHI-2015-ShovmanBSS #3d #interface
Twist and Learn: Interface Learning in 3DOF Exploration of 3D Scatterplots (MMS, JLB, AS, KCSB), pp. 313–316.
CHICHI-2015-StrohmayerCB #people
Exploring Learning Ecologies among People Experiencing Homelessness (AS, RC, MB), pp. 2275–2284.
CHICHI-2015-Walther-FranksS #design #game studies
Robots, Pancakes, and Computer Games: Designing Serious Games for Robot Imitation Learning (BWF, JS, PS, AH, MB, RM), pp. 3623–3632.
CHICHI-2015-YannierKH #effectiveness #game studies #physics #question #tablet
Learning from Mixed-Reality Games: Is Shaking a Tablet as Effective as Physical Observation? (NY, KRK, SEH), pp. 1045–1054.
CSCWCSCW-2015-CoetzeeLFHH #interactive #scalability
Structuring Interactions for Large-Scale Synchronous Peer Learning (DC, SL, AF, BH, MAH), pp. 1139–1152.
CSCWCSCW-2015-DornSS #collaboration
Piloting TrACE: Exploring Spatiotemporal Anchored Collaboration in Asynchronous Learning (BD, LBS, AS), pp. 393–403.
CSCWCSCW-2015-JiaWXRC #behaviour #online #privacy #process
Risk-taking as a Learning Process for Shaping Teen’s Online Information Privacy Behaviors (HJ, PJW, HX, MBR, JMC), pp. 583–599.
HCIDHM-HM-2015-NishimuraK #case study
A Study on Learning Effects of Marking with Highlighter Pen (HN, NK), pp. 357–367.
HCIDUXU-DD-2015-KremerL #design #experience #research #user interface
Learning from Experience Oriented Disciplines for User Experience Design — A Research Agenda (SK, UL), pp. 306–314.
HCIDUXU-IXD-2015-BorgesonFKTR #energy #visualisation
Learning from Hourly Household Energy Consumption: Extracting, Visualizing and Interpreting Household Smart Meter Data (SB, JAF, JK, CWT, RR), pp. 337–345.
HCIDUXU-IXD-2015-BorumBB #design #lessons learnt
Designing with Young Children: Lessons Learned from a Co-creation of a Technology-Enhanced Playful Learning Environment (NB, EPB, ALB), pp. 142–152.
HCIDUXU-IXD-2015-Celi #experience #modelling #risk management #user interface
Application of Dashboards and Scorecards for Learning Models IT Risk Management: A User Experience (EC), pp. 153–165.
HCIDUXU-UI-2015-BeltranUPSSSPCA #design #game studies
Inclusive Gaming Creation by Design in Formal Learning Environments: “Girly-Girls” User Group in No One Left Behind (MEB, YU, AP, CS, WS, BS, SdlRP, MFCU, MTA), pp. 153–161.
HCIHCI-DE-2015-BakkeB #developer #proximity
The Closer the Better: Effects of Developer-User Proximity for Mutual Learning (SB, TB), pp. 14–26.
HCIHCI-IT-2015-TadayonMGRZLGP #case study #interactive
Interactive Motor Learning with the Autonomous Training Assistant: A Case Study (RT, TLM, MG, PMRF, JZ, ML, MG, SP), pp. 495–506.
HCIHIMI-IKC-2015-AraiTA #development
Development of a Learning Support System for Class Structure Mapping Based on Viewpoint (TA, TT, TA), pp. 285–293.
HCIHIMI-IKC-2015-HasegawaD #approach #framework #platform #ubiquitous
A Ubiquitous Lecture Archive Learning Platform with Note-Centered Approach (SH, JD), pp. 294–303.
HCIHIMI-IKC-2015-HayashiH #analysis #concept #process
Analysis of the Relationship Between Metacognitive Ability and Learning Activity with Kit-Build Concept Map (YH, TH), pp. 304–312.
HCIHIMI-IKC-2015-Iwata #difference
Method to Generate an Operation Learning Support System by Shortcut Key Differences in Similar Software (HI), pp. 332–340.
HCIHIMI-IKC-2015-KimitaMMNIS #education
Learning State Model for Value Co-Creative Education Services (KK, KM, SM, YN, TI, YS), pp. 341–349.
HCIHIMI-IKC-2015-WatanabeTA #abstraction #development #source code
Development of a Learning Support System for Reading Source Code by Stepwise Abstraction (KW, TT, TA), pp. 387–394.
HCIHIMI-IKD-2015-WinterSTMCSVS #question #student
Learning to Manage NextGen Environments: Do Student Controllers Prefer to Use Datalink or Voice? (AW, JS, YT, AM, SC, KS, KPLV, TZS), pp. 661–667.
HCILCT-2015-BoonbrahmKB #artificial reality #student #using
Using Augmented Reality Technology in Assisting English Learning for Primary School Students (SB, CK, PB), pp. 24–32.
HCILCT-2015-DuA #artificial reality #design #evaluation
Design and Evaluation of a Learning Assistant System with Optical Head-Mounted Display (OHMD) (XD, AA), pp. 75–86.
HCILCT-2015-FonsecaRVG #3d #education
From Formal to Informal 3D Learning. Assesment of Users in the Education (DF, ER, FV, ODG), pp. 460–469.
HCILCT-2015-GoelMTPSYD #collaboration #named #student
CATALYST: Technology-Assisted Collaborative and Experiential Learning for School Students (VG, UM, ST, RMP, KS, KY, OD), pp. 482–491.
HCILCT-2015-GonzalezHGS #interactive #student #tool support
Exploring Student Interactions: Learning Analytics Tools for Student Tracking (MÁCG, ÁHG, FJGP, MLSE), pp. 50–61.
HCILCT-2015-HoffmannPLSMJ #student
Enhancing the Learning Success of Engineering Students by Virtual Experiments (MH, LP, LL, KS, TM, SJ), pp. 394–405.
HCILCT-2015-KimAKW #game studies
H-Treasure Hunt: A Location and Object-Based Serious Game for Cultural Heritage Learning at a Historic Site (HK, SA, SK, WW), pp. 561–572.
HCILCT-2015-KimCD #artificial reality #simulation
The Learning Effect of Augmented Reality Training in a Computer-Based Simulation Environment (JHK, TC, WD), pp. 406–414.
HCILCT-2015-KlemkeKLS #education #game studies #mobile #multi
Transferring an Educational Board Game to a Multi-user Mobile Learning Game to Increase Shared Situational Awareness (RK, SK, HL, MS), pp. 583–594.
HCILCT-2015-LambropoulosMFK #design #experience #ontology
Ontological Design to Support Cognitive Plasticity for Creative Immersive Experience in Computer Aided Learning (NL, IM, HMF, IAK), pp. 261–270.
HCILCT-2015-OrehovackiB #game studies #programming #quality
Inspecting Quality of Games Designed for Learning Programming (TO, SB), pp. 620–631.
HCILCT-2015-RodriguezOD #hybrid #recommendation #repository #student
A Student-Centered Hybrid Recommender System to Provide Relevant Learning Objects from Repositories (PAR, DAO, NDD), pp. 291–300.
HCILCT-2015-ShimizuO #design #implementation #novel #word
Design and Implementation of Novel Word Learning System “Überall” (RS, KO), pp. 148–159.
HCILCT-2015-TamuraTHN #generative #wiki
Generating Quizzes for History Learning Based on Wikipedia Articles (YT, YT, YH, YIN), pp. 337–346.
HCILCT-2015-VielRTP #design #interactive #multi
Design Solutions for Interactive Multi-video Multimedia Learning Objects (CCV, KRHR, CACT, MdGCP), pp. 160–171.
HCILCT-2015-YusoffK #design #game studies #interactive #persuasion
Game Rhetoric: Interaction Design Model of Persuasive Learning for Serious Games (ZY, AK), pp. 644–654.
ICEISICEIS-v1-2015-PecliGPMFTTDFCG #predict #problem #reduction
Dimensionality Reduction for Supervised Learning in Link Prediction Problems (AP, BG, CCP, CM, FF, FT, JT, MVD, SF, MCC, RRG), pp. 295–302.
ICEISICEIS-v1-2015-RibeiroTWBE
A Learning Model for Intelligent Agents Applied to Poultry Farming (RR, MT, ALW, APB, FE), pp. 495–503.
ICEISICEIS-v1-2015-SouzaBGBE #online
Applying Ensemble-based Online Learning Techniques on Crime Forecasting (AJdS, APB, HMG, JPB, FE), pp. 17–24.
CIKMCIKM-2015-BizidNBFD #identification #microblog #sequence
Identification of Microblogs Prominent Users during Events by Learning Temporal Sequences of Features (IB, NN, PB, SF, AD), pp. 1715–1718.
CIKMCIKM-2015-BuchA #approximate #string #using
Approximate String Matching by End-Users using Active Learning (LB, AA0), pp. 93–102.
CIKMCIKM-2015-CaoLX #graph #named
GraRep: Learning Graph Representations with Global Structural Information (SC, WL0, QX), pp. 891–900.
CIKMCIKM-2015-HaoZHM #data type #online #similarity
Learning Relative Similarity from Data Streams: Active Online Learning Approaches (SH, PZ, SCHH, CM), pp. 1181–1190.
CIKMCIKM-2015-HeLJ0 #graph
Learning to Represent Knowledge Graphs with Gaussian Embedding (SH, KL0, GJ, JZ0), pp. 623–632.
CIKMCIKM-2015-HongWW #classification #clustering
Clustering-based Active Learning on Sensor Type Classification in Buildings (DH, HW, KW), pp. 363–372.
CIKMCIKM-2015-JinLZHH #distributed #multi #online
Collaborating between Local and Global Learning for Distributed Online Multiple Tasks (XJ0, PL0, FZ, JH, QH), pp. 113–122.
CIKMCIKM-2015-JinZPDLH #classification #multi #semantics
Heterogeneous Multi-task Semantic Feature Learning for Classification (XJ0, FZ, SJP, CD, PL0, QH), pp. 1847–1850.
CIKMCIKM-2015-KangLHWNXP #rank #similarity
Cross-Modal Similarity Learning: A Low Rank Bilinear Formulation (CK, SL, YH, JW, WN, SX, CP), pp. 1251–1260.
CIKMCIKM-2015-KholghiSZN #case study #information management #query
External Knowledge and Query Strategies in Active Learning: a Study in Clinical Information Extraction (MK, LS, GZ, ANN), pp. 143–152.
CIKMCIKM-2015-LiuTL #matrix #multi #named #scalability
MF-Tree: Matrix Factorization Tree for Large Multi-Class Learning (LL, PNT, XL), pp. 881–890.
CIKMCIKM-2015-MetrikovPA #crowdsourcing #integration #rank
Aggregation of Crowdsourced Ordinal Assessments and Integration with Learning to Rank: A Latent Trait Model (PM, VP, JAA), pp. 1391–1400.
CIKMCIKM-2015-MishraH #clustering #multi #using
Learning Task Grouping using Supervised Task Space Partitioning in Lifelong Multitask Learning (MM, JH), pp. 1091–1100.
CIKMCIKM-2015-MunozTG #approach #ranking
A Soft Computing Approach for Learning to Aggregate Rankings (JAVM, RdST, MAG), pp. 83–92.
CIKMCIKM-2015-ShuL #adaptation
Transductive Domain Adaptation with Affinity Learning (LS, LJL), pp. 1903–1906.
CIKMCIKM-2015-TranNKGA #adaptation #rank #summary #timeline
Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events (TT0, CN, NK, UG, AA), pp. 1201–1210.
CIKMCIKM-2015-WangSL #distance #summary #using
Update Summarization using Semi-Supervised Learning Based on Hellinger Distance (DW0, SS, TL0), pp. 1907–1910.
CIKMCIKM-2015-WanLKYGCH #classification #network
Classification with Active Learning and Meta-Paths in Heterogeneous Information Networks (CW, XL, BK, XY, QG, DWLC, JH0), pp. 443–452.
CIKMCIKM-2015-YeZMJZ #approach #consistency #multi #privacy #rank
Rank Consistency based Multi-View Learning: A Privacy-Preserving Approach (HJY, DCZ, YM, YJ0, ZHZ), pp. 991–1000.
CIKMCIKM-2015-YinWW #clustering #multi
Incomplete Multi-view Clustering via Subspace Learning (QY, SW, LW0), pp. 383–392.
CIKMCIKM-2015-ZenginC #documentation #topic
Learning User Preferences for Topically Similar Documents (MZ, BC), pp. 1795–1798.
CIKMCIKM-2015-ZhangJRXCY #graph #modelling #query
Learning Entity Types from Query Logs via Graph-Based Modeling (JZ, LJ, AR, SX, YC, PSY), pp. 603–612.
ECIRECIR-2015-HuynhHR #analysis #sentiment #strict
Learning Higher-Level Features with Convolutional Restricted Boltzmann Machines for Sentiment Analysis (TH, YH, SMR), pp. 447–452.
ECIRECIR-2015-NicosiaBM #rank
Learning to Rank Aggregated Answers for Crossword Puzzles (MN, GB, AM), pp. 556–561.
ECIRECIR-2015-PasinatoMZ #elicitation #rating
Active Learning Applied to Rating Elicitation for Incentive Purposes (MBP, CEM, GZ), pp. 291–302.
ECIRECIR-2015-PelejaM #retrieval #sentiment
Learning Sentiment Based Ranked-Lexicons for Opinion Retrieval (FP, JM), pp. 435–440.
ICMLICML-2015-AmidU #multi
Multiview Triplet Embedding: Learning Attributes in Multiple Maps (EA, AU), pp. 1472–1480.
ICMLICML-2015-BachHBG #performance
Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs (SHB, BH, JLBG, LG), pp. 381–390.
ICMLICML-2015-Bou-AmmarTE #policy #sublinear
Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret (HBA, RT, EE), pp. 2361–2369.
ICMLICML-2015-ChangKADL #education
Learning to Search Better than Your Teacher (KWC, AK, AA, HDI, JL), pp. 2058–2066.
ICMLICML-2015-ChenSYU #modelling
Learning Deep Structured Models (LCC, AGS, ALY, RU), pp. 1785–1794.
ICMLICML-2015-CilibertoMPR #multi
Convex Learning of Multiple Tasks and their Structure (CC, YM, TAP, LR), pp. 1548–1557.
ICMLICML-2015-CohenH #online
Following the Perturbed Leader for Online Structured Learning (AC, TH), pp. 1034–1042.
ICMLICML-2015-DanielyGS #adaptation #online
Strongly Adaptive Online Learning (AD, AG, SSS), pp. 1405–1411.
ICMLICML-2015-FetayaU #invariant
Learning Local Invariant Mahalanobis Distances (EF, SU), pp. 162–168.
ICMLICML-2015-GarberHM #online
Online Learning of Eigenvectors (DG, EH, TM), pp. 560–568.
ICMLICML-2015-GuptaAGN #precise
Deep Learning with Limited Numerical Precision (SG, AA, KG, PN), pp. 1737–1746.
ICMLICML-2015-HallakSMM #modelling
Off-policy Model-based Learning under Unknown Factored Dynamics (AH, FS, TAM, SM), pp. 711–719.
ICMLICML-2015-Hernandez-Lobato15b #network #probability #scalability
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks (JMHL, RA), pp. 1861–1869.
ICMLICML-2015-HockingRB #detection #named #segmentation
PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data (TH, GR, GB), pp. 324–332.
ICMLICML-2015-HongYKH #network #online
Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network (SH, TY, SK, BH), pp. 597–606.
ICMLICML-2015-HsiehND #matrix
PU Learning for Matrix Completion (CJH, NN, ISD), pp. 2445–2453.
ICMLICML-2015-HuangWSLC #classification #image #metric #set #symmetry
Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification (ZH, RW, SS, XL, XC), pp. 720–729.
ICMLICML-2015-JerniteRS #approach #markov #modelling #performance #random
A Fast Variational Approach for Learning Markov Random Field Language Models (YJ, AMR, DS), pp. 2209–2217.
ICMLICML-2015-JiangKS #abstraction #modelling
Abstraction Selection in Model-based Reinforcement Learning (NJ, AK, SS), pp. 179–188.
ICMLICML-2015-Kandemir #process #symmetry
Asymmetric Transfer Learning with Deep Gaussian Processes (MK), pp. 730–738.
ICMLICML-2015-KvetonSWA #rank
Cascading Bandits: Learning to Rank in the Cascade Model (BK, CS, ZW, AA), pp. 767–776.
ICMLICML-2015-LakshmananOR #bound
Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning (KL, RO, DR), pp. 524–532.
ICMLICML-2015-LeC #metric #using
Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations (TL, MC), pp. 2002–2011.
ICMLICML-2015-LiuY #graph #predict
Bipartite Edge Prediction via Transductive Learning over Product Graphs (HL, YY), pp. 1880–1888.
ICMLICML-2015-LondonHG #approximate
The Benefits of Learning with Strongly Convex Approximate Inference (BL, BH, LG), pp. 410–418.
ICMLICML-2015-LongC0J #adaptation #network
Learning Transferable Features with Deep Adaptation Networks (ML, YC, JW, MJ), pp. 97–105.
ICMLICML-2015-Lopez-PazMST #towards
Towards a Learning Theory of Cause-Effect Inference (DLP, KM, BS, IT), pp. 1452–1461.
ICMLICML-2015-MaclaurinDA #optimisation
Gradient-based Hyperparameter Optimization through Reversible Learning (DM, DKD, RPA), pp. 2113–2122.
ICMLICML-2015-MarietS #algorithm #fixpoint #process
Fixed-point algorithms for learning determinantal point processes (ZM, SS), pp. 2389–2397.
ICMLICML-2015-MenonROW #estimation
Learning from Corrupted Binary Labels via Class-Probability Estimation (AKM, BvR, CSO, BW), pp. 125–134.
ICMLICML-2015-PerrotH #analysis #metric
A Theoretical Analysis of Metric Hypothesis Transfer Learning (MP, AH), pp. 1708–1717.
ICMLICML-2015-PhamRFA #multi #novel
Multi-instance multi-label learning in the presence of novel class instances (ATP, RR, XZF, JPA), pp. 2427–2435.
ICMLICML-2015-PiechHNPSG #feedback #student
Learning Program Embeddings to Propagate Feedback on Student Code (CP, JH, AN, MP, MS, LJG), pp. 1093–1102.
ICMLICML-2015-PlessisNS
Convex Formulation for Learning from Positive and Unlabeled Data (MCdP, GN, MS), pp. 1386–1394.
ICMLICML-2015-Romera-ParedesT #approach
An embarrassingly simple approach to zero-shot learning (BRP, PHST), pp. 2152–2161.
ICMLICML-2015-SerrurierP #evaluation
Entropy evaluation based on confidence intervals of frequency estimates : Application to the learning of decision trees (MS, HP), pp. 1576–1584.
ICMLICML-2015-SibonyCJ #ranking #statistics
MRA-based Statistical Learning from Incomplete Rankings (ES, SC, JJ), pp. 1432–1441.
ICMLICML-2015-Sohl-DicksteinW #using
Deep Unsupervised Learning using Nonequilibrium Thermodynamics (JSD, EAW, NM, SG), pp. 2256–2265.
ICMLICML-2015-SrivastavaMS #using #video
Unsupervised Learning of Video Representations using LSTMs (NS, EM, RS), pp. 843–852.
ICMLICML-2015-SteinhardtL15a #modelling #predict
Learning Fast-Mixing Models for Structured Prediction (JS, PL), pp. 1063–1072.
ICMLICML-2015-SwaminathanJ #feedback
Counterfactual Risk Minimization: Learning from Logged Bandit Feedback (AS, TJ), pp. 814–823.
ICMLICML-2015-TangSX #network
Learning Scale-Free Networks by Dynamic Node Specific Degree Prior (QT, SS, JX), pp. 2247–2255.
ICMLICML-2015-TewariC #bound #documentation #fault #matter #question #rank
Generalization error bounds for learning to rank: Does the length of document lists matter? (AT, SC), pp. 315–323.
ICMLICML-2015-VanseijenS
A Deeper Look at Planning as Learning from Replay (HV, RS), pp. 2314–2322.
ICMLICML-2015-WangALB #multi #on the #representation
On Deep Multi-View Representation Learning (WW, RA, KL, JAB), pp. 1083–1092.
ICMLICML-2015-WangWLCW #multi #segmentation
Multi-Task Learning for Subspace Segmentation (YW, DPW, QL, WC, IJW), pp. 1209–1217.
ICMLICML-2015-WangY #matrix #multi
Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices (JW, JY), pp. 1747–1756.
ICMLICML-2015-WeiIB #set
Submodularity in Data Subset Selection and Active Learning (KW, RKI, JAB), pp. 1954–1963.
ICMLICML-2015-WeissN #alias
Learning Parametric-Output HMMs with Two Aliased States (RW, BN), pp. 635–644.
ICMLICML-2015-WenKA #combinator #performance #scalability
Efficient Learning in Large-Scale Combinatorial Semi-Bandits (ZW, BK, AA), pp. 1113–1122.
ICMLICML-2015-WuS #algorithm #modelling #online
An Online Learning Algorithm for Bilinear Models (YW, SS), pp. 890–898.
ICMLICML-2015-YogatamaFDS #word
Learning Word Representations with Hierarchical Sparse Coding (DY, MF, CD, NAS), pp. 87–96.
ICMLICML-2015-YuB
Learning Submodular Losses with the Lovasz Hinge (JY, MBB), pp. 1623–1631.
ICMLICML-2015-YuCL #multi #online #rank
Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams (RY, DC, YL), pp. 238–247.
KDDKDD-2015-ChakrabortyBSPY #classification #framework #named #novel
BatchRank: A Novel Batch Mode Active Learning Framework for Hierarchical Classification (SC, VNB, ARS, SP, JY), pp. 99–108.
KDDKDD-2015-DuS #adaptation #feature model
Unsupervised Feature Selection with Adaptive Structure Learning (LD, YDS), pp. 209–218.
KDDKDD-2015-GaoYCH #integration #multi #visual notation
Anatomical Annotations for Drosophila Gene Expression Patterns via Multi-Dimensional Visual Descriptors Integration: Multi-Dimensional Feature Learning (HG, LY, WC, HH), pp. 339–348.
KDDKDD-2015-GleichM #algorithm #graph #using
Using Local Spectral Methods to Robustify Graph-Based Learning Algorithms (DFG, MWM), pp. 359–368.
KDDKDD-2015-HanZ #multi
Learning Tree Structure in Multi-Task Learning (LH, YZ), pp. 397–406.
KDDKDD-2015-JohanssonD #geometry #graph #similarity #using
Learning with Similarity Functions on Graphs using Matchings of Geometric Embeddings (FDJ, DPD), pp. 467–476.
KDDKDD-2015-LanH #complexity #multi
Reducing the Unlabeled Sample Complexity of Semi-Supervised Multi-View Learning (CL, JH), pp. 627–634.
KDDKDD-2015-MaoWGS #graph #reduction
Dimensionality Reduction Via Graph Structure Learning (QM, LW, SG, YS), pp. 765–774.
KDDKDD-2015-NairRKBSKHD #detection #monitoring
Learning a Hierarchical Monitoring System for Detecting and Diagnosing Service Issues (VN, AR, SK, VB, SS, SSK, SH, SD), pp. 2029–2038.
KDDKDD-2015-Papagiannopoulou #multi
Discovering and Exploiting Deterministic Label Relationships in Multi-Label Learning (CP, GT, IT), pp. 915–924.
KDDKDD-2015-RiondatoU15a #algorithm #statistics
VC-Dimension and Rademacher Averages: From Statistical Learning Theory to Sampling Algorithms (MR, EU), pp. 2321–2322.
KDDKDD-2015-SunAYMMBY #classification
Transfer Learning for Bilingual Content Classification (QS, MSA, BY, CM, VM, AB, JY), pp. 2147–2156.
KDDKDD-2015-TanSZ0 #transitive
Transitive Transfer Learning (BT, YS, EZ, QY), pp. 1155–1164.
KDDKDD-2015-VeeriahDQ #architecture #predict
Deep Learning Architecture with Dynamically Programmed Layers for Brain Connectome Prediction (VV, RD, GJQ), pp. 1205–1214.
KDDKDD-2015-WangWY #collaboration #recommendation
Collaborative Deep Learning for Recommender Systems (HW, NW, DYY), pp. 1235–1244.
KDDKDD-2015-XuSB #predict
Longitudinal LASSO: Jointly Learning Features and Temporal Contingency for Outcome Prediction (TX, JS, JB), pp. 1345–1354.
KDDKDD-2015-YangH #multi
Model Multiple Heterogeneity via Hierarchical Multi-Latent Space Learning (PY, JH), pp. 1375–1384.
KDDKDD-2015-YangSJWDY #visual notation
Structural Graphical Lasso for Learning Mouse Brain Connectivity (SY, QS, SJ, PW, ID, JY), pp. 1385–1394.
KDDKDD-2015-YanRHC #distributed #modelling #optimisation #performance #scalability
Performance Modeling and Scalability Optimization of Distributed Deep Learning Systems (FY, OR, YH, TMC), pp. 1355–1364.
KDDKDD-2015-ZhangLZSKYJ #analysis #biology #image #modelling #multi
Deep Model Based Transfer and Multi-Task Learning for Biological Image Analysis (WZ, RL, TZ, QS, SK, JY, SJ), pp. 1475–1484.
KDDKDD-2015-ZhaoSYCLR #multi
Multi-Task Learning for Spatio-Temporal Event Forecasting (LZ, QS, JY, FC, CTL, NR), pp. 1503–1512.
MLDMMLDM-2015-Chou #data-driven #geometry
Data Driven Geometry for Learning (EPC), pp. 395–402.
MLDMMLDM-2015-DhulekarNOY #graph #mining #predict
Seizure Prediction by Graph Mining, Transfer Learning, and Transformation Learning (ND, SN, BO, BY), pp. 32–52.
MLDMMLDM-2015-FerrerSR #approximate #distance #edit distance #graph #heuristic
Learning Heuristics to Reduce the Overestimation of Bipartite Graph Edit Distance Approximation (MF, FS, KR), pp. 17–31.
MLDMMLDM-2015-GovadaJMS #approach #hybrid #induction #using
Hybrid Approach for Inductive Semi Supervised Learning Using Label Propagation and Support Vector Machine (AG, PJ, SM, SKS), pp. 199–213.
MLDMMLDM-2015-MoldovanM #data mining #mining #performance #using
Learning the Relationship Between Corporate Governance and Company Performance Using Data Mining (DM, SM), pp. 368–381.
RecSysRecSys-2015-AlmahairiKCC #collaboration #distributed
Learning Distributed Representations from Reviews for Collaborative Filtering (AA, KK, KC, ACC), pp. 147–154.
SEKESEKE-2015-AffonsoLON #adaptation #framework #self
A Framework Based on Learning Techniques for Decision-making in Self-adaptive Software (FJA, GL, RAPO, EYN), pp. 24–29.
SEKESEKE-2015-GoswamiWS #performance #using
Using Learning Styles of Software Professionals to Improve their Inspection Team Performance (AG, GSW, AS), pp. 680–685.
SEKESEKE-2015-LiuXC #recommendation
Context-aware Recommendation System with Anonymous User Profile Learning (YL, YX, MC), pp. 93–98.
SEKESEKE-2015-Murillo-MoreraJ #algorithm #approach #framework #predict #search-based #using
A Software Defect-Proneness Prediction Framework: A new approach using genetic algorithms to generate learning schemes (JMM, MJ), pp. 445–450.
SEKESEKE-2015-SampaioMLM #adaptation #approach #research
Reflecting, adapting and learning in small software organizations: an action research approach (SS, MM, AL, HPM), pp. 46–50.
SEKESEKE-2015-TironiMRM #approach #identification
An approach to identify relevant subjects for supporting the Learning Scheme creation task (HT, ALAM, SSR, AM), pp. 506–511.
SEKESEKE-2015-WanderleyP #detection #folksonomy
Learning Folksonomies for Trend Detection in Task-Oriented Dialogues (GW, ECP), pp. 483–488.
SEKESEKE-2015-ZegarraCW #graph #visualisation
Facilitating Peer Learning and Knowledge Sharing in STEM Courses via Pattern Based Graph Visualization (EZ, SKC, JW), pp. 284–289.
SIGIRSIGIR-2015-Arora
Promoting User Engagement and Learning in Amorphous Search Tasks (PA), p. 1051.
SIGIRSIGIR-2015-CormackG #multi #overview #perspective
Multi-Faceted Recall of Continuous Active Learning for Technology-Assisted Review (GVC, MRG), pp. 763–766.
SIGIRSIGIR-2015-FoleyBJ #web
Learning to Extract Local Events from the Web (JF, MB, VJ), pp. 423–432.
SIGIRSIGIR-2015-HarveyHE #query
Learning by Example: Training Users with High-quality Query Suggestions (MH, CH, DE), pp. 133–142.
SIGIRSIGIR-2015-Li15a #information retrieval
Transfer Learning for Information Retrieval (PL), p. 1061.
SIGIRSIGIR-2015-LiuW #collaboration
Learning Context-aware Latent Representations for Context-aware Collaborative Filtering (XL, WW), pp. 887–890.
SIGIRSIGIR-2015-MehrotraY #query #rank #using
Representative & Informative Query Selection for Learning to Rank using Submodular Functions (RM, EY), pp. 545–554.
SIGIRSIGIR-2015-SeverynM #network #rank
Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks (AS, AM), pp. 373–382.
SIGIRSIGIR-2015-SongNZAC #multi #network #predict #social #volunteer
Multiple Social Network Learning and Its Application in Volunteerism Tendency Prediction (XS, LN, LZ, MA, TSC), pp. 213–222.
SIGIRSIGIR-2015-SpinaPR #microblog
Active Learning for Entity Filtering in Microblog Streams (DS, MHP, MdR), pp. 975–978.
SIGIRSIGIR-2015-WangGLXWC #recommendation #representation
Learning Hierarchical Representation Model for NextBasket Recommendation (PW, JG, YL, JX, SW, XC), pp. 403–412.
SIGIRSIGIR-2015-WangLWZZ #named
LBMCH: Learning Bridging Mapping for Cross-modal Hashing (YW, XL, LW, WZ, QZ), pp. 999–1002.
SIGIRSIGIR-2015-XiaXLGC #evaluation #metric #optimisation
Learning Maximal Marginal Relevance Model via Directly Optimizing Diversity Evaluation Measures (LX, JX, YL, JG, XC), pp. 113–122.
SIGIRSIGIR-2015-ZamaniMS #adaptation #evaluation #multi
Adaptive User Engagement Evaluation via Multi-task Learning (HZ, PM, AS), pp. 1011–1014.
SIGIRSIGIR-2015-ZhengC #distributed
Learning to Reweight Terms with Distributed Representations (GZ, JC), pp. 575–584.
SKYSKY-2015-Oliveira #using
Learning the Meaning of Language and using It Creatively (HGO), p. 3.
OOPSLAOOPSLA-2015-OhYY #adaptation #optimisation #program analysis
Learning a strategy for adapting a program analysis via bayesian optimisation (HO, HY, KY), pp. 572–588.
PLATEAUPLATEAU-2015-KabacVC #developer #evaluation #tool support #usability
An evaluation of the DiaSuite toolset by professional developers: learning cost and usability (MK, NV, CC), pp. 9–16.
ASEASE-2015-LamNNN #debugging #information retrieval #locality
Combining Deep Learning with Information Retrieval to Localize Buggy Files for Bug Reports (N) (ANL, ATN, HAN, TNN), pp. 476–481.
ASEASE-2015-OdaFNHSTN #pseudo #source code #statistics #using
Learning to Generate Pseudo-Code from Source Code Using Statistical Machine Translation (T) (YO, HF, GN, HH, SS, TT, SN), pp. 574–584.
ASEASE-2015-ZhangGBC #configuration management #fourier #performance #predict
Performance Prediction of Configurable Software Systems by Fourier Learning (T) (YZ, JG, EB, KC), pp. 365–373.
ASEASE-2015-ZouYLM0 #rank #retrieval
Learning to Rank for Question-Oriented Software Text Retrieval (T) (YZ, TY, YL, JM, LZ), pp. 1–11.
ESEC-FSEESEC-FSE-2015-JingWDQX #fault #metric #predict #representation
Heterogeneous cross-company defect prediction by unified metric representation and CCA-based transfer learning (XYJ, FW, XD, FQ, BX), pp. 496–507.
ESEC-FSEESEC-FSE-2015-SunXLLQ #abstraction #named #testing #validation
TLV: abstraction through testing, learning, and validation (JS, HX, YL, SWL, SQ), pp. 698–709.
ICSEICSE-v1-2015-FilieriGL #adaptation #lightweight #modelling #performance #probability
Lightweight Adaptive Filtering for Efficient Learning and Updating of Probabilistic Models (AF, LG, AL), pp. 200–211.
ICSEICSE-v1-2015-JiaCHP #combinator #generative #interactive #testing #using
Learning Combinatorial Interaction Test Generation Strategies Using Hyperheuristic Search (YJ, MBC, MH, JP), pp. 540–550.
ICSEICSE-v1-2015-ZhuHFZLZ #developer
Learning to Log: Helping Developers Make Informed Logging Decisions (JZ, PH, QF, HZ, MRL, DZ), pp. 415–425.
ICSEICSE-v2-2015-Hanakawa #contest #motivation #re-engineering #student
Contest Based Learning with Blending Software Engineering and Business Management: For Students’ High Motivation and High Practice Ability (NH), pp. 360–369.
ICSEICSE-v2-2015-Honsel #evolution #mining #simulation #statistics
Statistical Learning and Software Mining for Agent Based Simulation of Software Evolution (VH), pp. 863–866.
ICSEICSE-v2-2015-JankeBW #education #object-oriented #programming #question
Does Outside-In Teaching Improve the Learning of Object-Oriented Programming? (EJ, PB, SW), pp. 408–417.
ICSEICSE-v2-2015-Jazayeri #case study #experience #programming
Combining Mastery Learning with Project-Based Learning in a First Programming Course: An Experience Report (MJ), pp. 315–318.
ICSEICSE-v2-2015-MonsalveLW #education #game studies
Transparently Teaching in the Context of Game-based Learning: the Case of SimulES-W (ESM, JCSdPL, VMBW), pp. 343–352.
ICSEICSE-v2-2015-PaasivaaraBLDSH #agile #re-engineering #using
Learning Global Agile Software Engineering Using Same-Site and Cross-Site Teams (MP, KB, CL, DED, JS, FH, PC, AY, VI), pp. 285–294.
ICSEICSE-v2-2015-SedelmaierL #education #induction #re-engineering
Active and Inductive Learning in Software Engineering Education (YS, DL), pp. 418–427.
ICSEICSE-v2-2015-WilkinsG #design #student
Drawing Insight from Student Perceptions of Reflective Design Learning (TVW, JCG), pp. 253–262.
SACSAC-2015-BarrosCMP #education #repository #reuse #using
Integrating educational repositories to improve the reuse of learning objects (HB, EC, JM, RP), pp. 270–272.
SACSAC-2015-Brefeld #multi
Multi-view learning with dependent views (UB), pp. 865–870.
SACSAC-2015-GomesBE #classification #data type
Pairwise combination of classifiers for ensemble learning on data streams (HMG, JPB, FE), pp. 941–946.
SACSAC-2015-LabibPCG #approach #development #product line #reuse
Enforcing reuse and customization in the development of learning objects: a product line approach (AEL, MCP, JHC, AG), pp. 261–263.
SACSAC-2015-OmatuYI #classification #smell
Smell classification of wines by the learning vector quantization method (SO, MY, YI), pp. 195–200.
SACSAC-2015-PaivaBSIJ #behaviour #recommendation #student
Improving pedagogical recommendations by classifying students according to their interactional behavior in a gamified learning environment (ROAP, IIB, APdS, SI, PAJ), pp. 233–238.
SACSAC-2015-PedroLPVI #case study #gamification #women
Does gamification work for boys and girls?: An exploratory study with a virtual learning environment (LZP, AMZL, BGP, JV, SI), pp. 214–219.
SACSAC-2015-Pesare #social
Smart learning environments for social learning (EP), pp. 273–274.
SACSAC-2015-ReadPB #data type
Deep learning in partially-labeled data streams (JR, FPC, AB), pp. 954–959.
SACSAC-2015-ReddySC #approach #aspect-oriented #incremental #performance #weaving
Incremental aspect weaving: an approach for faster AOP learning (YRR, AS, MC), pp. 1480–1485.
SACSAC-2015-RegoMP #approach #detection #folksonomy
A supervised learning approach to detect subsumption relations between tags in folksonomies (ASdCR, LBM, CESP), pp. 409–415.
SACSAC-2015-StracciaM #concept #estimation #fuzzy #named #owl #probability #using
pFOIL-DL: learning (fuzzy) EL concept descriptions from crisp OWL data using a probabilistic ensemble estimation (US, MM), pp. 345–352.
SACSAC-2015-SugiyamaS #multi
Meta-strategy for cooperative tasks with learning of environments in multi-agent continuous tasks (AS, TS), pp. 494–500.
SACSAC-2015-WanderleyP #folksonomy
Learning folksonomies from task-oriented dialogues (GMPW, ECP), pp. 360–367.
CASECASE-2015-ChenXZCL #effectiveness #multi #optimisation #simulation
An effective learning procedure for multi-fidelity simulation optimization with ordinal transformation (RC, JX, SZ, CHC, LHL), pp. 702–707.
CASECASE-2015-LiX #energy #multi
A multi-grid reinforcement learning method for energy conservation and comfort of HVAC in buildings (BL, LX), pp. 444–449.
CASECASE-2015-ParisACAR #behaviour #markov #smarttech #using
Using Hidden Semi-Markov Model for learning behavior in smarthomes (AP, SA, NC, AEA, NR), pp. 752–757.
CASECASE-2015-SuWCRT #adaptation #fuzzy
Adaptive PD fuzzy control with dynamic learning rate for two-wheeled balancing six degrees of freedom robotic arm (SFS, KJW, MCC, IJR, CCT), pp. 1258–1261.
CASECASE-2015-ZhangWZZ #automaton #optimisation #performance
Incorporation of ordinal optimization into learning automata for high learning efficiency (JZ, CW, DZ, MZ), pp. 1206–1211.
CGOCGO-2015-McAfeeO #framework #generative #multi #named
EMEURO: a framework for generating multi-purpose accelerators via deep learning (LCM, KO), pp. 125–135.
DATEDATE-2015-ChenKXMLYVSCY #algorithm #array
Technology-design co-optimization of resistive cross-point array for accelerating learning algorithms on chip (PYC, DK, ZX, AM, BL, JY, SBKV, JsS, YC, SY), pp. 854–859.
DATEDATE-2015-ChenM #distributed #manycore #optimisation #performance
Distributed reinforcement learning for power limited many-core system performance optimization (ZC, DM), pp. 1521–1526.
DATEDATE-2015-KanounS #big data #concept #data type #detection #online #scheduling #streaming
Big-data streaming applications scheduling with online learning and concept drift detection (KK, MvdS), pp. 1547–1550.
DATEDATE-2015-RenTB #detection #statistics
Detection of illegitimate access to JTAG via statistical learning in chip (XR, VGT, RD(B), pp. 109–114.
STOCSTOC-2015-BarakKS #composition #taxonomy
Dictionary Learning and Tensor Decomposition via the Sum-of-Squares Method (BB, JAK, DS), pp. 143–151.
STOCSTOC-2015-Bresler #graph #modelling
Efficiently Learning Ising Models on Arbitrary Graphs (GB), pp. 771–782.
STOCSTOC-2015-GeHK
Learning Mixtures of Gaussians in High Dimensions (RG, QH, SMK), pp. 761–770.
STOCSTOC-2015-HardtP #bound
Tight Bounds for Learning a Mixture of Two Gaussians (MH, EP), pp. 753–760.
STOCSTOC-2015-LiRSS #statistics
Learning Arbitrary Statistical Mixtures of Discrete Distributions (JL, YR, LJS, CS), pp. 743–752.
CAVCAV-2015-BrazdilCCFK #markov #process
Counterexample Explanation by Learning Small Strategies in Markov Decision Processes (TB, KC, MC, AF, JK), pp. 158–177.
CAVCAV-2015-GehrDV #commutative #specification
Learning Commutativity Specifications (TG, DD, MTV), pp. 307–323.
CAVCAV-2015-IsbernerHS #automaton #framework #open source
The Open-Source LearnLib — A Framework for Active Automata Learning (MI, FH, BS), pp. 487–495.
CAVCAV-2015-Saha0M #named
Alchemist: Learning Guarded Affine Functions (SS, PG, PM), pp. 440–446.
ICLPICLP-2015-MartinezRIAT #modelling #probability
Learning Probabilistic Action Models from Interpretation Transitions (DM, TR, KI, GA, CT), pp. 114–127.
ICLPICLP-J-2015-LawRB #constraints #programming #set
Learning weak constraints in answer set programming (ML, AR, KB), pp. 511–525.
ICSTSAT-2015-TuHJ #named #reasoning #satisfiability
QELL: QBF Reasoning with Extended Clause Learning and Levelized SAT Solving (KHT, TCH, JHRJ), pp. 343–359.
DRRDRR-2014-CartonLC #interactive #named
LearnPos: a new tool for interactive learning positioning (CC, AL, BC), p. ?–12.
DRRDRR-2014-TaoTX #documentation #random #using
Document page structure learning for fixed-layout e-books using conditional random fields (XT, ZT, CX), p. ?–9.
HTHT-2014-AbbasiTL #scalability #using
Scalable learning of users’ preferences using networked data (MAA, JT, HL), pp. 4–12.
JCDLJCDL-2014-BarrioSGG #framework #named
REEL: A Relation Extraction Learning framework (PB, GS, HG, LG), pp. 455–456.
JCDLJCDL-2014-ChakrabortyKGGM #approach #predict #towards
Towards a stratified learning approach to predict future citation counts (TC, SK, PG, NG, AM), pp. 351–360.
VLDBVLDB-2014-ZouJLGWX #framework #named #platform
Mariana: Tencent Deep Learning Platform and its Applications (YZ, XJ, YL, ZG, EW, BX), pp. 1772–1777.
VLDBVLDB-2015-MozafariSFJM14 #dataset #scalability
Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning (BM, PS, MJF, MIJ, SM), pp. 125–136.
CSEETCSEET-2014-Ackerman #re-engineering
An active learning module for an introduction to software engineering course (AFA), pp. 190–191.
CSEETCSEET-2014-BoeschS #automation
Automated mentor assignment in blended learning environments (CB, KS), pp. 94–98.
CSEETCSEET-2014-Ding #re-engineering #self
Self-guided learning environment for undergraduate software engineering (JD), pp. 188–189.
CSEETCSEET-2014-FranklBK #development
Learning and working together as prerequisites for the development of high-quality software (GF, SB, BK), pp. 154–157.
CSEETCSEET-2014-KroppMMZ #agile #collaboration #education
Teaching and learning agile collaboration (MK, AM, MM, CGZ), pp. 139–148.
CSEETCSEET-2014-PotterSDW #game studies #named
InspectorX: A game for software inspection training and learning (HP, MS, LD, VW), pp. 55–64.
CSEETCSEET-2014-YamadaIWKFYOKT #development #education #effectiveness
The impacts of personal characteristic on educational effectiveness in controlled-project based learning on software intensive systems development (YY, SI, HW, KK, YF, SY, MO, TK, MT), pp. 119–128.
EDMEDM-2014-AdjeiSHPBK
Refining Learning Maps with Data Fitting Techniques: Searching for Better Fitting Learning Maps (SA, DS, NTH, ZAP, AB, NK), pp. 413–414.
EDMEDM-2014-ColvinCLFP
Comparing Learning in a MOOC and a Blended, On-Campus Course (KFC, JC, AL, CF, DEP), pp. 343–344.
EDMEDM-2014-Fancsali #algebra #behaviour #modelling
Causal Discovery with Models: Behavior, Affect, and Learning in Cognitive Tutor Algebra (SF), pp. 28–35.
EDMEDM-2014-ForsythGPMS #predict
Discovering Theoretically Grounded Predictors of Shallow vs. Deep- level Learning (CF, ACG, PIPJ, KKM, BS), pp. 229–232.
EDMEDM-2014-GrafsgaardWBWL #data type #multimodal #predict #tutorial
Predicting Learning and Affect from Multimodal Data Streams in Task-Oriented Tutorial Dialogue (JFG, JBW, KEB, ENW, JCL), pp. 122–129.
EDMEDM-2014-KaserKG #analysis #parametricity #predict
Different parameters - same prediction: An analysis of learning curves (TK, KRK, MHG), pp. 52–59.
EDMEDM-2014-KhajahWLM #difference #modelling #predict
Integrating latent-factor and knowledge-tracing models to predict individual differences in learning (MK, RW, RVL, MM), pp. 99–106.
EDMEDM-2014-KimPSJ #comparison #linear #online #predict #student #using
Predicting students' learning achievement by using online learning patterns in blended learning environments: Comparison of two cases on linear and non-linear model (JK, YP, JS, IHJ), pp. 407–408.
EDMEDM-2014-LanSB #matrix #personalisation
Quantized Matrix Completion for Personalized Learning (ASL, CS, RGB), pp. 280–283.
EDMEDM-2014-LeeLP #approach #behaviour #data-driven #education #game studies
Learning Individual Behavior in an Educational Game: A Data-Driven Approach (SJL, YEL, ZP), pp. 114–121.
EDMEDM-2014-LiuMBP #multi
Trading Off Scientific Knowledge and User Learning with Multi-Armed Bandits (YEL, TM, EB, ZP), pp. 161–168.
EDMEDM-2014-MavrikisSPZ #adaptation #visualisation
Indicator Visualization for Adaptive Exploratory Learning Environments (MM, SGS, AP, ZZ), pp. 377–378.
EDMEDM-2014-MorganBR #analysis #fault #validation
Error Analysis as a Validation of Learning Progressions (BM, WB, VR), pp. 245–248.
EDMEDM-2014-NetoBGCWC #challenge #framework #multi #online #platform #student
Challenges on adopting BKT to model student knowledge in multi-context online learning platform (WLDMN, EB, FG, LC, NLW, PC), pp. 339–340.
EDMEDM-2014-PechenizkiyT #education
Learning to Teach like a Bandit (MP, PAT), pp. 381–382.
EDMEDM-2014-RoweBAKH #automation #detection
Building Automated Detectors of Gameplay Strategies to Measure Implicit Science Learning (ER, RSB, JAC, EK, WJH), pp. 337–338.
EDMEDM-2014-SantosMP #collaboration #mining #student
Mining students' strategies to enable collaborative learning (SGS, MM, AP), pp. 397–398.
EDMEDM-2014-Schneider #collaboration #detection #multimodal #towards
Toward Collaboration Sensing: Multimodal Detection of the Chameleon Effect in Collaborative Learning Settings (BS), pp. 435–437.
EDMEDM-2014-ShuQF #data mining #education #experience #mining #student
Educational Data Mining and Analyzing of Student Learning Outcomes from the Perspective of Learning Experience (ZS, QFQ, LQF), pp. 359–360.
EDMEDM-2014-SnowJVDM #named
Entropy: A Stealth Measure of Agency in Learning Environments (ELS, MEJ, LKV, JD, DSM), pp. 241–244.
EDMEDM-2014-SnowVM #analysis
Tracking Choices: Computational Analysis of Learning Trajectories (ELS, LKV, DSM), pp. 316–319.
EDMEDM-2014-SunYIS #recursion
Alternating Recursive Method for Q-matrix Learning (YS, SY, SI, YS), pp. 14–20.
EDMEDM-2014-VelasquezGMM #online #performance
Learning Aid Use Patterns and Their Impact on Exam Performance in Online Developmental Mathematics (NFV, IMG, TM, JM), pp. 379–380.
EDMEDM-2014-Wang #motivation
MOOC Leaner Motivation and Learning Pattern Discovery (YW), pp. 452–454.
EDMEDM-2014-WorsleyB #multimodal #using
Using Multimodal Learning Analytics to Study Learning Mechanisms (MW, PB), pp. 431–432.
EDMEDM-2014-YeKB #identification #mining #multi #process
Mining and Identifying Relationships Among Sequential Patterns in Multi-Feature, Hierarchical Learning Activity Data (CY, JSK, GB), pp. 389–390.
EDMEDM-2014-ZhengP #algorithm #using
Dynamic Re-Composition of Learning Groups Using PSO-Based Algorithms (ZZ, NP), pp. 357–358.
ITiCSEITiCSE-2014-BerryK #game studies #programming
The state of play: a notional machine for learning programming (MB, MK), pp. 21–26.
ITiCSEITiCSE-2014-EckerdalKTNSM #education
Teaching and learning with MOOCs: computing academics’ perspectives and engagement (AE, PK, NT, AN, JS, LM), pp. 9–14.
ITiCSEITiCSE-2014-EllisH #open source #re-engineering
Structuring software engineering learning within open source software participation (HJCE, GWH), p. 326.
ITiCSEITiCSE-2014-EllisJBPHD
Learning within a professional environment: shared ownership of an HFOSS project (HJCE, SJ, DB, LP, GWH, JD), p. 337.
ITiCSEITiCSE-2014-FalknerVF #identification #self
Identifying computer science self-regulated learning strategies (KF, RV, NJGF), pp. 291–296.
ITiCSEITiCSE-2014-GroverCP
Assessing computational learning in K-12 (SG, SC, RP), pp. 57–62.
ITiCSEITiCSE-2014-Hidalgo-CespedesRL #concept #design #game studies #programming #video
Playing with metaphors: a methodology to design video games for learning abstract programming concepts (JHC, GMR, VLV), p. 348.
ITiCSEITiCSE-2014-Hijon-NeiraVPC #experience #game studies #programming
Game programming for improving learning experience (RBHN, JÁVI, CPR, LC), pp. 225–230.
ITiCSEITiCSE-2014-Jasute #education #geometry #interactive #visualisation
An interactive visualization method of constructionist teaching and learning of geometry (EJ), p. 349.
ITiCSEITiCSE-2014-KothiyalMI #question #scalability
Think-pair-share in a large CS1 class: does learning really happen? (AK, SM, SI), pp. 51–56.
ITiCSEITiCSE-2014-Marcos-Abed #case study #effectiveness #programming
Learning computer programming: a study of the effectiveness of a COAC# (JMA), p. 333.
ITiCSEITiCSE-2014-MedinaSGG #student #using
Learning outcomes using objectives with computer science students (JAM, JJS, EGL, AGC), p. 339.
ITiCSEITiCSE-2014-PirkerRG #education #student
Motivational active learning: engaging university students in computer science education (JP, MRS, CG), pp. 297–302.
ITiCSEITiCSE-2014-PriorCL #case study #experience
Things coming together: learning experiences in a software studio (JP, AC, JL), pp. 129–134.
ITiCSEITiCSE-2014-Rogers #question
New technology, new learning? (YR), p. 1.
ITiCSEITiCSE-2014-TaubBA #physics
The effect of computer science on the learning of computational physics (RT, MBA, MA), p. 352.
ITiCSEITiCSE-2014-Urquiza-FuentesCHMH #framework #platform #social #student #video
A social platform supporting learning through video creation by students (JUF, JC, IH, EM, PAH), p. 330.
ITiCSEITiCSE-2014-Verwaal
Team based learning in theoretical computer science (NV), p. 331.
ITiCSEITiCSE-2014-WartVP #design #problem #social
Apps for social justice: motivating computer science learning with design and real-world problem solving (SVW, SV, TSP), pp. 123–128.
ITiCSEITiCSE-WGR-2014-BrusilovskyEKMB #education
Increasing Adoption of Smart Learning Content for Computer Science Education (PB, SHE, ANK, LM, LB, DB, PI, RP, TS, SAS, JUF, AV, MW), pp. 31–57.
SIGITESIGITE-2014-EllisJBPHD
Learning within a professional environment: shared ownership of an HFOSS project (HJCE, SJ, DB, LP, GWH, JD), pp. 95–100.
SIGITESIGITE-2014-RytikovaB #personalisation
A methodology for personalized competency-based learning in undergraduate courses (IR, MB), pp. 81–86.
SIGITESIGITE-2014-TsangGA #java #programming language #question #student
The practical application of LEGO® MINDSTORMS® robotics kits: does it enhance undergraduate computing students’ engagement in learning the Java programming language? (ET, CG, MA), pp. 121–126.
SANERCSMR-WCRE-2014-XiaFLCW #behaviour #multi #towards
Towards more accurate multi-label software behavior learning (XX, YF, DL, ZC, XW), pp. 134–143.
ICPCICPC-2014-KaulgudAMT #comprehension
Comprehension support during knowledge transitions: learning from field (VSK, KMA, JM, GT), pp. 205–206.
ICSMEICSME-2014-BinkleyL #information retrieval #rank
Learning to Rank Improves IR in SE (DB, DJL), pp. 441–445.
ICSMEICSME-2014-XuanM #fault #locality #metric #multi #ranking
Learning to Combine Multiple Ranking Metrics for Fault Localization (JX, MM), pp. 191–200.
ICALPICALP-v1-2014-Volkovich #bound #on the
On Learning, Lower Bounds and (un)Keeping Promises (IV), pp. 1027–1038.
ICALPICALP-v2-2014-DamsHK #network
Jamming-Resistant Learning in Wireless Networks (JD, MH, TK), pp. 447–458.
LATALATA-2014-LaurenceLNST #transducer
Learning Sequential Tree-to-Word Transducers (GL, AL, JN, SS, MT), pp. 490–502.
FMFM-2014-LinH #composition #concurrent #model checking #synthesis
Compositional Synthesis of Concurrent Systems through Causal Model Checking and Learning (SWL, PAH), pp. 416–431.
SEFMSEFM-2014-CasselHJS #finite #state machine
Learning Extended Finite State Machines (SC, FH, BJ, BS), pp. 250–264.
AIIDEAIIDE-2014-RoweML #approach #composition #experience #interactive #optimisation
Optimizing Player Experience in Interactive Narrative Planning: A Modular Reinforcement Learning Approach (JPR, BWM, JCL).
AIIDEAIIDE-2014-YoungH #game studies
Learning Micro-Management Skills in RTS Games by Imitating Experts (JY, NH).
CHI-PLAYCHI-PLAY-2014-BarataGJG #experience #game studies #performance #student
Relating gaming habits with student performance in a gamified learning experience (GB, SG, JAJ, DJVG), pp. 17–25.
CHI-PLAYCHI-PLAY-2014-GeurtsAKI #visual notation
Playfully learning visual perspective taking skills with sifteo cubes (LG, VVA, KVK, RI), pp. 107–113.
CHI-PLAYCHI-PLAY-2014-LinehanBKMR #challenge #game studies
Learning curves: analysing pace and challenge in four successful puzzle games (CL, GB, BK, ZHM, BR), pp. 181–190.
CHI-PLAYCHI-PLAY-2014-Melonio #co-evolution #design
Gamified co-design with cooperative learning at school (AM), pp. 295–298.
CoGCIG-2014-BallingerL #robust
Learning robust build-orders from previous opponents with coevolution (CAB, SJL), pp. 1–8.
CoGCIG-2014-IvanovicZLR
Reinforcement learning to control a commander for capture the flag (JI, FZ, XL0, JRV), pp. 1–8.
CoGCIG-2014-KimK #game studies #realtime #recommendation
Learning to recommend game contents for real-time strategy gamers (HTK, KJK), pp. 1–8.
CoGCIG-2014-OhCK #game studies
Imitation learning for combat system in RTS games with application to starcraft (ISO, HCC, KJK), pp. 1–2.
CoGCIG-2014-ParkK #game studies #using
Learning to play fighting game using massive play data (HSP, KJK), pp. 1–2.
CoGCIG-2014-SzubertJ #difference #game studies #network
Temporal difference learning of N-tuple networks for the game 2048 (MGS, WJ), pp. 1–8.
CoGCIG-2014-ThillBKK #difference #game studies
Temporal difference learning with eligibility traces for the game connect four (MT, SB, PK, WK), pp. 1–8.
DiGRADiGRA-2014-Marklund #comprehension #game studies
Out of Context - Understanding the Practicalities of Learning Games (BM).
FDGFDG-2014-BroeckhovenT #aspect-oriented #game studies #specification #using
Specifying the pedagogical aspects of narrative-based digital learning games using annotations (FVB, ODT).
FDGFDG-2014-RoweLML #design #education #game studies
Play in the museum: Designing game-based learning environments for informal education settings (JPR, EVL, BWM, JCL).
FDGFDG-2014-TomaiF #adaptation #behaviour #using
Adapting in-game agent behavior by observation of players using learning behavior trees (ET, RF).
FDGFDG-2014-ZookFR #automation #game studies #parametricity
Automatic playtesting for game parameter tuning via active learning (AZ, EF, MOR).
CoGVS-Games-2014-NinausKFNW #using
The Potential Use of Neurophysiological Signals for Learning Analytics (MN, SEK, EVCF, CN, GW), pp. 1–5.
CoGVS-Games-2014-Schmidt
Evaluating Digital Applications for Language Learning: Outcomes and Insights (IS), p. 1.
CoGVS-Games-2014-Thong #education #game studies
Situated Learning with Role-Playing Games to Improve Transfer of Learning in Tertiary Education Classrooms (LPT), pp. 1–5.
CHICHI-2014-DontchevaMBG #crowdsourcing #performance
Combining crowdsourcing and learning to improve engagement and performance (MD, RRM, JRB, EMG), pp. 3379–3388.
CHICHI-2014-DunwellFPHALS #approach #game studies #safety
A game-based learning approach to road safety: the code of everand (ID, SdF, PP, MH, SA, PL, CDS), pp. 3389–3398.
CHICHI-2014-GreenbergG #online
Learning to fail: experiencing public failure online through crowdfunding (MDG, EG), pp. 581–590.
CHICHI-2014-KovacsM
Smart subtitles for vocabulary learning (GK, RCM), pp. 853–862.
CHICHI-2014-MentisCS
Learning to see the body: supporting instructional practices in laparoscopic surgical procedures (HMM, AC, SDS), pp. 2113–2122.
CHICHI-2014-MonserratLZC #interactive
L.IVE: an integrated interactive video-based learning environment (TJKPM, YL, SZ, XC), pp. 3399–3402.
CHICHI-2014-Ruggiero #game studies #named #persuasion #student #towards #video
Spent: changing students’ affective learning toward homelessness through persuasive video game play (DNR), pp. 3423–3432.
CSCWCSCW-2014-MillerZGG #collaboration #people #research
Pair research: matching people for collaboration, learning, and productivity (RCM, HZ, EG, EG), pp. 1043–1048.
CSCWCSCW-2014-YuAKK #comparison #quality #social
A comparison of social, learning, and financial strategies on crowd engagement and output quality (LY, PA, AK, RK), pp. 967–978.
CSCWCSCW-2014-ZhuDKK #assessment #performance
Reviewing versus doing: learning and performance in crowd assessment (HZ, SPD, REK, AK), pp. 1445–1455.
HCIDUXU-DI-2014-ShafiqICRAAR #analysis #case study #smarttech #usability #user satisfaction #what
To What Extent System Usability Effects User Satisfaction: A Case Study of Smart Phone Features Analysis for Learning of Novice (MS, MI, JGC, ZR, MA, WA, SR), pp. 346–357.
HCIDUXU-DI-2014-Souto #design #experience #interactive #user interface #visualisation
Interactive Visualizations in Learning Mathematics: Implications for Information Design and User Experience (VTS), pp. 472–480.
HCIDUXU-ELAS-2014-KarlinPC #experience #online #user interface
Pumping Up the Citizen Muscle Bootcamp: Improving User Experience in Online Learning (BK, BP, AC), pp. 562–573.
HCIDUXU-ELAS-2014-Martins #industrial #prototype
Prototyping in a Learning Environment — Digital Publishing Projects from the Escola Superior de Desenho Industrial (MAFM), pp. 195–206.
HCIDUXU-ELAS-2014-MedeirosJG #memory management #named #student
Logograms: Memory Aids for Learning, and an Example with Hearing-Impaired Students (LM, MBJ, LVG), pp. 207–216.
HCIDUXU-ELAS-2014-MustafaMMAAMEBK #development #interface #multi
Rural Area Development through Multi-interface Technology and Virtual Learning System (FuM, AM, SM, SA, UA, SM, HE, TAB, MFK), pp. 442–451.
HCIHCI-AIMT-2014-AlkhashramiAA #design #interface
Human Factors in the Design of Arabic-Language Interfaces in Assistive Technologies for Learning Difficulties (SA, HA, AAW), pp. 362–369.
HCIHCI-AIMT-2014-MikamiM #3d #effectiveness
Effectiveness of Virtual Hands in 3D Learning Material (TM, SM), pp. 93–101.
HCIHCI-AIMT-2014-YanikTMMBGW #gesture
A Method for Lifelong Gesture Learning Based on Growing Neural Gas (PMY, AT, JM, JM, JOB, KEG, IDW), pp. 191–202.
HCIHCI-AS-2014-SchwallerKAL #feedback #gesture #visual notation
Improving In-game Gesture Learning with Visual Feedback (MS, JK, LA, DL), pp. 643–653.
HCIHCI-TMT-2014-MatsumotoKKA #adaptation #automation #delivery #student #word
Evaluating an Automatic Adaptive Delivery Method of English Words Learning Contents for University Students in Science and Technology (SM, TK, TK, MA), pp. 510–520.
HCIHCI-TMT-2014-MorDHF #education #human-computer #online
Teaching and Learning HCI Online (EM, MGD, EH, NF), pp. 230–241.
HCIHCI-TMT-2014-SilvaCP #education #human-computer #interactive
Studio-Based Learning as a Natural Fit to Teaching Human-Computer Interaction (PAS, MEC, BJP), pp. 251–258.
HCIHCI-TMT-2014-YajimaTS #collaboration
Proposal of Collaborative Learning Support Method in Risk Communications (HY, NT, RS), pp. 457–465.
HCIHIMI-AS-2014-AraiKTKA #comprehension #development #source code
Development of a Learning Support System for Source Code Reading Comprehension (TA, HK, TT, YK, TA), pp. 12–19.
HCIHIMI-AS-2014-HirashimaYH #problem #word
Triplet Structure Model of Arithmetical Word Problems for Learning by Problem-Posing (TH, SY, YH), pp. 42–50.
HCIHIMI-AS-2014-HirokawaFSY #mindmap
Learning Winespeak from Mind Map of Wine Blogs (SH, BF, TS, CY), pp. 383–393.
HCIHIMI-AS-2014-MatsuiHKA #behaviour #case study #education
A Study on Exploration of Relationships between Behaviors and Mental States of Learners for Value Co-creative Education and Learning Environment (TM, YH, KK, TA), pp. 69–79.
HCIHIMI-AS-2014-MikamiT #music #performance
A Music Search System for Expressive Music Performance Learning (TM, KT), pp. 80–89.
HCIHIMI-AS-2014-UeiFKNKS #design #education #evaluation
Learning Effect Evaluation of an Educational Tool for Product-Service System Design Based on Learner Viewpoints (KU, TF, AK, YN, KK, YS), pp. 643–652.
HCIHIMI-AS-2014-YamaguchiTT #process #visualisation
Visualizing Mental Learning Processes with Invisible Mazes for Continuous Learning (TY, KT, KT), pp. 137–148.
HCIHIMI-DE-2014-LinKT #analysis #collaboration #design
A Learning Method for Product Analysis in Product Design — Learning Method of Product Analysis Utilizing Collaborative Learning and a List of Analysis Items (HL, HK, TT), pp. 503–513.
HCILCT-NLE-2014-KaprosP
Empowering L&D Managers through Customisation of Inline Learning Analytics (EK, NP), pp. 282–291.
HCILCT-NLE-2014-Kim #feedback #self #simulation
Simulation Training in Self-Regulated Learning: Investigating the Effects of Dual Feedback on Dynamic Decision-Making Tasks (JHK), pp. 419–428.
HCILCT-NLE-2014-Milde #editing #html #online
An HTML5-Based Online Editor for Creating Annotated Learning Videos (JTM), pp. 172–179.
HCILCT-NLE-2014-MorGHH #assessment #design #tool support
Designing Learning Tools: The Case of a Competence Assessment Tool (EM, AEGR, EH, MAH), pp. 83–94.
HCILCT-NLE-2014-MoriT #development
Development of a Fieldwork Support System for Group Work in Project-Based Learning (MM, AT), pp. 429–440.
HCILCT-NLE-2014-Piki #collaboration #process #question
Learner Engagement in Computer-Supported Collaborative Learning Activities: Natural or Nurtured? (AP), pp. 107–118.
HCILCT-NLE-2014-TaraghiSES #classification #markov #multi
Markov Chain and Classification of Difficulty Levels Enhances the Learning Path in One Digit Multiplication (BT, AS, ME, MS), pp. 322–333.
HCILCT-NLE-2014-UlbrichtBFQ #component #interface #testing #usability
The Emotion Component on Usability Testing Human Computer Interface of an Inclusive Learning Management System (VRU, CHB, LF, SRPdQ), pp. 334–345.
HCILCT-NLE-2014-UzunosmanogluC #collaboration #online #paradigm
Examining an Online Collaboration Learning Environment with the Dual Eye-Tracking Paradigm: The Case of Virtual Math Teams (SDU, MPÇ), pp. 462–472.
HCILCT-NLE-2014-VasiliouIZ #case study #experience #multimodal #student
Measuring Students’ Flow Experience in a Multimodal Learning Environment: A Case Study (CV, AI, PZ), pp. 346–357.
HCILCT-NLE-2014-WangLC #online #student
Low-Achieving Students’ Perceptions of Online Language Learning: A Case of English Proficiency Threshold (ALW, YCL, SFC), pp. 250–258.
HCILCT-TRE-2014-Bharali #online #process
Enhancing Online Learning Activities for Groups in Flipped Classrooms (RB), pp. 269–276.
HCILCT-TRE-2014-BraunhoferEGR #mobile #recommendation
Context Dependent Preference Acquisition with Personality-Based Active Learning in Mobile Recommender Systems (MB, ME, MG, FR), pp. 105–116.
HCILCT-TRE-2014-Castro #case study #collaboration #named
Mosca — A Case Study on Collaborative Work — Combining Dimensions while Learning (SC), pp. 388–396.
HCILCT-TRE-2014-EradzeL #design #interactive
Interrelation between Pedagogical Design and Learning Interaction Patterns in different Virtual Learning Environments (ME, ML), pp. 23–32.
HCILCT-TRE-2014-Hayes14a #approach #development #game studies #simulation
An Approach to Holistic Development of Serious Games and Learning Simulations (ATH), pp. 42–49.
HCILCT-TRE-2014-HiramatsuIFS #development #using
Development of the Learning System for Outdoor Study Using Zeigarnik Effect (YH, AI, MF, FS), pp. 127–137.
HCILCT-TRE-2014-IkedaS
Dream Drill: A Bedtime Learning Application (AI, IS), pp. 138–145.
HCILCT-TRE-2014-IshikawaAKSTD #process #self #student
Sustaining Outside-of-Class CALL Activities by Means of a Student Self-Evaluation System in a University Blended Learning EFL Course (YI, RAY, MK, CS, YT, MD), pp. 146–154.
HCILCT-TRE-2014-MartinezMLLC #3d #interactive
Supporting Learning with 3D Interactive Applications in Early Years (ACM, MJMS, MLS, DCPL, MC), pp. 11–22.
HCILCT-TRE-2014-MartinWH #interactive #mobile
Sensor Based Interaction Mechanisms in Mobile Learning (KUM, MW, WH), pp. 165–172.
HCILCT-TRE-2014-OliveiraM #network #research
Digital Identity of Researchers and Their Personal Learning Network (NRO, LM), pp. 467–477.
HCILCT-TRE-2014-ShahoumianSZPH #education #simulation
Blended Simulation Based Medical Education: A Complex Learning/Training Opportunity (AS, MS, MZ, GP, JH), pp. 478–485.
HCILCT-TRE-2014-ShimizuO #effectiveness #question
Which Is More Effective for Learning German and Japanese Language, Paper or Digital? (RS, KO), pp. 309–318.
HCILCT-TRE-2014-SzklannyW #prototype
Prototyping M-Learning Course on the Basis of Puzzle Learning Methodology (KS, MW), pp. 215–226.
HCILCT-TRE-2014-YamaguchiSYNSM #collaboration #detection #distance
Posture and Face Detection with Dynamic Thumbnail Views for Collaborative Distance Learning (TY, HS, MY, YN, HS, TM), pp. 227–236.
ICEISICEIS-v2-2014-MahmoudBAG #approach
A New Approach Based on Learning Services to Generate Appropriate Learning Paths (CBM, FB, MHA, FG), pp. 643–646.
ICEISICEIS-v2-2014-OtonBGGB #metadata #using
Description of Accessible Learning Resources by Using Metadata (SO, CB, EG, AGC, RB), pp. 620–626.
ICEISICEIS-v2-2014-ZhengJL #hybrid #taxonomy #using
Cross-Sensor Iris Matching using Patch-based Hybrid Dictionary Learning (BRZ, DYJ, YHL), pp. 169–174.
ICEISICEIS-v3-2014-AzevedoF #case study #education #process #student
The Response Systems in the Student’s Learning/Teaching Process — A Case Study in a Portuguese School (PA, MJF), pp. 79–86.
CIKMCIKM-2014-DeBBGC #linear
Learning a Linear Influence Model from Transient Opinion Dynamics (AD, SB, PB, NG, SC), pp. 401–410.
CIKMCIKM-2014-DeveaudAMO #on the #rank
On the Importance of Venue-Dependent Features for Learning to Rank Contextual Suggestions (RD, MDA, CM, IO), pp. 1827–1830.
CIKMCIKM-2014-GoncalvesDCSZB #multi
Multi-task Sparse Structure Learning (ARG, PD, SC, VS, FJVZ, AB), pp. 451–460.
CIKMCIKM-2014-JinZXDLH #multi
Multi-task Multi-view Learning for Heterogeneous Tasks (XJ, FZ, HX, CD, PL, QH), pp. 441–450.
CIKMCIKM-2014-MaoWHO #classification #linear #multi
Nonlinear Classification via Linear SVMs and Multi-Task Learning (XM, OW, WH, PO), pp. 1955–1958.
CIKMCIKM-2014-PfeifferNB #network #probability #using
Active Exploration in Networks: Using Probabilistic Relationships for Learning and Inference (JJPI, JN, PNB), pp. 639–648.
CIKMCIKM-2014-PimplikarGBP
Learning to Propagate Rare Labels (RP, DG, DB, GRP), pp. 201–210.
CIKMCIKM-2014-ShiKBLH #named #recommendation
CARS2: Learning Context-aware Representations for Context-aware Recommendations (YS, AK, LB, ML, AH), pp. 291–300.
CIKMCIKM-2014-VinzamuriLR
Active Learning based Survival Regression for Censored Data (BV, YL, CKR), pp. 241–250.
CIKMCIKM-2014-WangMC #parametricity
Structure Learning via Parameter Learning (WYW, KM, WWC), pp. 1199–1208.
CIKMCIKM-2014-WuHPZCZ #feature model #multi
Exploring Features for Complicated Objects: Cross-View Feature Selection for Multi-Instance Learning (JW, ZH, SP, XZ, ZC, CZ), pp. 1699–1708.
CIKMCIKM-2014-XiePLW #framework #image #multi
A Cross-modal Multi-task Learning Framework for Image Annotation (LX, PP, YL, SW), pp. 431–440.
CIKMCIKM-2014-YangTZ #streaming
Active Learning for Streaming Networked Data (ZY, JT, YZ), pp. 1129–1138.
CIKMCIKM-2014-YuX #interactive #network #predict #scalability #social
Learning Interactions for Social Prediction in Large-scale Networks (XY, JX), pp. 161–170.
CIKMCIKM-2014-ZhongPXYM #adaptation #collaboration #recommendation
Adaptive Pairwise Preference Learning for Collaborative Recommendation with Implicit Feedbacks (HZ, WP, CX, ZY, ZM), pp. 1999–2002.
CIKMCIKM-2014-ZhuSY #information retrieval #taxonomy
Cross-Modality Submodular Dictionary Learning for Information Retrieval (FZ, LS, MY), pp. 1479–1488.
ECIRECIR-2014-BauerCRG #corpus #formal method #web
Learning a Theory of Marriage (and Other Relations) from a Web Corpus (SB, SC, LR, TG), pp. 591–597.
ECIRECIR-2014-BreussT #interactive #recommendation #social #social media
Learning from User Interactions for Recommending Content in Social Media (MB, MT), pp. 598–604.
ECIRECIR-2014-FiliceCCB #effectiveness #kernel #online
Effective Kernelized Online Learning in Language Processing Tasks (SF, GC, DC, RB), pp. 347–358.
ECIRECIR-2014-NainiA #feature model #rank
Exploiting Result Diversification Methods for Feature Selection in Learning to Rank (KDN, ISA), pp. 455–461.
ECIRECIR-2014-QiDCW #information management
Deep Learning for Character-Based Information Extraction (YQ, SGD, RC, JW), pp. 668–674.
ICMLICML-c1-2014-AroraBGM #bound
Provable Bounds for Learning Some Deep Representations (SA, AB, RG, TM), pp. 584–592.
ICMLICML-c1-2014-DenisGH #bound #matrix
Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning (FD, MG, AH), pp. 449–457.
ICMLICML-c1-2014-DickGS #markov #online #process #sequence
Online Learning in Markov Decision Processes with Changing Cost Sequences (TD, AG, CS), pp. 512–520.
ICMLICML-c1-2014-JainT #bound #independence
(Near) Dimension Independent Risk Bounds for Differentially Private Learning (PJ, AGT), pp. 476–484.
ICMLICML-c1-2014-LacosteMLL
Agnostic Bayesian Learning of Ensembles (AL, MM, FL, HL), pp. 611–619.
ICMLICML-c1-2014-LajugieBA #clustering #metric #problem
Large-Margin Metric Learning for Constrained Partitioning Problems (RL, FRB, SA), pp. 297–305.
ICMLICML-c1-2014-LuoS #online #towards
Towards Minimax Online Learning with Unknown Time Horizon (HL, RES), pp. 226–234.
ICMLICML-c1-2014-MohriM #algorithm #optimisation
Learning Theory and Algorithms for revenue optimization in second price auctions with reserve (MM, AMM), pp. 262–270.
ICMLICML-c1-2014-RooshenasL #interactive #network
Learning Sum-Product Networks with Direct and Indirect Variable Interactions (AR, DL), pp. 710–718.
ICMLICML-c1-2014-ShalitC #coordination #matrix #orthogonal
Coordinate-descent for learning orthogonal matrices through Givens rotations (US, GC), pp. 548–556.
ICMLICML-c1-2014-ShiZ #online
Online Bayesian Passive-Aggressive Learning (TS, JZ), pp. 378–386.
ICMLICML-c1-2014-SolomonRGB
Wasserstein Propagation for Semi-Supervised Learning (JS, RMR, LJG, AB), pp. 306–314.
ICMLICML-c1-2014-TandonR #graph
Learning Graphs with a Few Hubs (RT, PDR), pp. 602–610.
ICMLICML-c1-2014-Yu0KD #multi #scalability
Large-scale Multi-label Learning with Missing Labels (HFY, PJ, PK, ISD), pp. 593–601.
ICMLICML-c2-2014-AffandiFAT #kernel #parametricity #process
Learning the Parameters of Determinantal Point Process Kernels (RHA, EBF, RPA, BT), pp. 1224–1232.
ICMLICML-c2-2014-AminHK
Learning from Contagion (Without Timestamps) (KA, HH, MK), pp. 1845–1853.
ICMLICML-c2-2014-AndoniPV0 #network
Learning Polynomials with Neural Networks (AA, RP, GV, LZ), pp. 1908–1916.
ICMLICML-c2-2014-AziziAG #composition #network
Learning Modular Structures from Network Data and Node Variables (EA, EA, JEG), pp. 1440–1448.
ICMLICML-c2-2014-BalleHP #comparison #empirical #probability
Methods of Moments for Learning Stochastic Languages: Unified Presentation and Empirical Comparison (BB, WLH, JP), pp. 1386–1394.
ICMLICML-c2-2014-Bou-AmmarERT #multi #online #policy
Online Multi-Task Learning for Policy Gradient Methods (HBA, EE, PR, MET), pp. 1206–1214.
ICMLICML-c2-2014-BrunskillL
PAC-inspired Option Discovery in Lifelong Reinforcement Learning (EB, LL), pp. 316–324.
ICMLICML-c2-2014-Chen0 #big data #modelling #topic #using
Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data (ZC, BL), pp. 703–711.
ICMLICML-c2-2014-CohenW #commutative
Learning the Irreducible Representations of Commutative Lie Groups (TC, MW), pp. 1755–1763.
ICMLICML-c2-2014-DuLBS #information management #network
Influence Function Learning in Information Diffusion Networks (ND, YL, MFB, LS), pp. 2016–2024.
ICMLICML-c2-2014-FangCL #graph
Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness Probabilistically (YF, KCCC, HWL), pp. 406–414.
ICMLICML-c2-2014-GrandeWH #performance #process
Sample Efficient Reinforcement Learning with Gaussian Processes (RCG, TJW, JPH), pp. 1332–1340.
ICMLICML-c2-2014-HoangLJK #process
Nonmyopic ϵ-Bayes-Optimal Active Learning of Gaussian Processes (TNH, BKHL, PJ, MSK), pp. 739–747.
ICMLICML-c2-2014-HoulsbyHG #matrix #robust
Cold-start Active Learning with Robust Ordinal Matrix Factorization (NH, JMHL, ZG), pp. 766–774.
ICMLICML-c2-2014-JawanpuriaVN #feature model #kernel #multi #on the
On p-norm Path Following in Multiple Kernel Learning for Non-linear Feature Selection (PJ, MV, JSN), pp. 118–126.
ICMLICML-c2-2014-KricheneDB #convergence #on the
On the convergence of no-regret learning in selfish routing (WK, BD, AMB), pp. 163–171.
ICMLICML-c2-2014-LevineK #network #optimisation #policy
Learning Complex Neural Network Policies with Trajectory Optimization (SL, VK), pp. 829–837.
ICMLICML-c2-2014-LiG #classification #representation #semantics
Latent Semantic Representation Learning for Scene Classification (XL, YG), pp. 532–540.
ICMLICML-c2-2014-LimL #metric #performance #ranking
Efficient Learning of Mahalanobis Metrics for Ranking (DL, GRGL), pp. 1980–1988.
ICMLICML-c2-2014-LinK #constraints #performance #representation
Stable and Efficient Representation Learning with Nonnegativity Constraints (THL, HTK), pp. 1323–1331.
ICMLICML-c2-2014-LinYHY #distance
Geodesic Distance Function Learning via Heat Flow on Vector Fields (BL, JY, XH, JY), pp. 145–153.
ICMLICML-c2-2014-LiuD #problem #set
Learnability of the Superset Label Learning Problem (LPL, TGD), pp. 1629–1637.
ICMLICML-c2-2014-LiZ #higher-order #problem
High Order Regularization for Semi-Supervised Learning of Structured Output Problems (YL, RSZ), pp. 1368–1376.
ICMLICML-c2-2014-LiZ0 #multi
Bayesian Max-margin Multi-Task Learning with Data Augmentation (CL, JZ, JC), pp. 415–423.
ICMLICML-c2-2014-MengEH #modelling #visual notation
Learning Latent Variable Gaussian Graphical Models (ZM, BE, AOHI), pp. 1269–1277.
ICMLICML-c2-2014-MizrahiDF #linear #markov #parallel #random
Linear and Parallel Learning of Markov Random Fields (YDM, MD, NdF), pp. 199–207.
ICMLICML-c2-2014-MnihG #network
Neural Variational Inference and Learning in Belief Networks (AM, KG), pp. 1791–1799.
ICMLICML-c2-2014-NiuDPS #approximate #multi
Transductive Learning with Multi-class Volume Approximation (GN, BD, MCdP, MS), pp. 1377–1385.
ICMLICML-c2-2014-PandeyD #network
Learning by Stretching Deep Networks (GP, AD), pp. 1719–1727.
ICMLICML-c2-2014-PentinaL #bound
A PAC-Bayesian bound for Lifelong Learning (AP, CHL), pp. 991–999.
ICMLICML-c2-2014-QinLJ #optimisation
Sparse Reinforcement Learning via Convex Optimization (ZQ, WL, FJ), pp. 424–432.
ICMLICML-c2-2014-ReedSZL #interactive
Learning to Disentangle Factors of Variation with Manifold Interaction (SR, KS, YZ, HL), pp. 1431–1439.
ICMLICML-c2-2014-RippelGA #order
Learning Ordered Representations with Nested Dropout (OR, MAG, RPA), pp. 1746–1754.
ICMLICML-c2-2014-RodriguesPR #classification #multi #process
Gaussian Process Classification and Active Learning with Multiple Annotators (FR, FCP, BR), pp. 433–441.
ICMLICML-c2-2014-SantosZ
Learning Character-level Representations for Part-of-Speech Tagging (CNdS, BZ), pp. 1818–1826.
ICMLICML-c2-2014-SilvaKB
Active Learning of Parameterized Skills (BCdS, GK, AGB), pp. 1737–1745.
ICMLICML-c2-2014-SongGJMHD #locality #on the
On learning to localize objects with minimal supervision (HOS, RBG, SJ, JM, ZH, TD), pp. 1611–1619.
ICMLICML-c2-2014-SunIM #classification #linear
Learning Mixtures of Linear Classifiers (YS, SI, AM), pp. 721–729.
ICMLICML-c2-2014-SunM #geometry #statistics
An Information Geometry of Statistical Manifold Learning (KS, SMM), pp. 1–9.
ICMLICML-c2-2014-TrigeorgisBZS
A Deep Semi-NMF Model for Learning Hidden Representations (GT, KB, SZ, BWS), pp. 1692–1700.
ICMLICML-c2-2014-WangHS
Active Transfer Learning under Model Shift (XW, TKH, JS), pp. 1305–1313.
ICMLICML-c2-2014-WangNH #distance #metric #robust
Robust Distance Metric Learning via Simultaneous L1-Norm Minimization and Maximization (HW, FN, HH), pp. 1836–1844.
ICMLICML-c2-2014-WangSSMK #metric
Two-Stage Metric Learning (JW, KS, FS, SMM, AK), pp. 370–378.
ICMLICML-c2-2014-WenYG #nondeterminism #robust
Robust Learning under Uncertain Test Distributions: Relating Covariate Shift to Model Misspecification (JW, CNY, RG), pp. 631–639.
ICMLICML-c2-2014-WuCLY #behaviour #consistency #network #predict #social
Learning the Consistent Behavior of Common Users for Target Node Prediction across Social Networks (SHW, HHC, KHL, PSY), pp. 298–306.
ICPRICPR-2014-AkinM #detection #online
Online Learning and Detection with Part-Based, Circulant Structure (OA, KM), pp. 4229–4233.
ICPRICPR-2014-Al-HalahRS #metric #semantics #similarity #what
What to Transfer? High-Level Semantics in Transfer Metric Learning for Action Similarity (ZAH, LR, RS), pp. 2775–2780.
ICPRICPR-2014-Alvarez-MezaMC #adaptation #video
Correntropy-Based Adaptive Learning to Support Video Surveillance Systems (AMÁM, SMG, GCD), pp. 2590–2595.
ICPRICPR-2014-ArvanitopoulosBT #analysis
Laplacian Support Vector Analysis for Subspace Discriminative Learning (NA, DB, AT), pp. 1609–1614.
ICPRICPR-2014-BargiXP #adaptation #classification #infinity #online #segmentation #streaming
An Infinite Adaptive Online Learning Model for Segmentation and Classification of Streaming Data (AB, RYDX, MP), pp. 3440–3445.
ICPRICPR-2014-BertonL #graph
Graph Construction Based on Labeled Instances for Semi-supervised Learning (LB, AdAL), pp. 2477–2482.
ICPRICPR-2014-BouillonA #classification #evolution #gesture #online
Supervision Strategies for the Online Learning of an Evolving Classifier for Gesture Commands (MB, ÉA), pp. 2029–2034.
ICPRICPR-2014-CaiTF #recognition #taxonomy
Learning Pose Dictionary for Human Action Recognition (JxC, XT, GCF), pp. 381–386.
ICPRICPR-2014-CaoHS #approach #classification #kernel #multi
Optimization-Based Extreme Learning Machine with Multi-kernel Learning Approach for Classification (LlC, WbH, FS), pp. 3564–3569.
ICPRICPR-2014-ChengZHT #recognition
Semi-supervised Learning for RGB-D Object Recognition (YC, XZ, KH, TT), pp. 2377–2382.
ICPRICPR-2014-ChenK14a
Learning to Count with Back-propagated Information (KC, JKK), pp. 4672–4677.
ICPRICPR-2014-ChenZW #identification #metric
Relevance Metric Learning for Person Re-identification by Exploiting Global Similarities (JC, ZZ, YW), pp. 1657–1662.
ICPRICPR-2014-CheplyginaSTPLB #classification #multi
Classification of COPD with Multiple Instance Learning (VC, LS, DMJT, JJHP, ML, MdB), pp. 1508–1513.
ICPRICPR-2014-DengZS #recognition #speech
Linked Source and Target Domain Subspace Feature Transfer Learning — Exemplified by Speech Emotion Recognition (JD, ZZ, BWS), pp. 761–766.
ICPRICPR-2014-DuZCW #flexibility #linear #random
Learning Flexible Binary Code for Linear Projection Based Hashing with Random Forest (SD, WZ, SC, YW), pp. 2685–2690.
ICPRICPR-2014-FangZ #classification
Cross Domain Shared Subspace Learning for Unsupervised Transfer Classification (ZF, ZZ), pp. 3927–3932.
ICPRICPR-2014-FanSCD #framework #online #robust #taxonomy
A Unified Online Dictionary Learning Framework with Label Information for Robust Object Tracking (BF, JS, YC, YD), pp. 2311–2316.
ICPRICPR-2014-FiratCV #detection #representation
Representation Learning for Contextual Object and Region Detection in Remote Sensing (OF, GC, FTYV), pp. 3708–3713.
ICPRICPR-2014-FornoniC #naive bayes #recognition
Scene Recognition with Naive Bayes Non-linear Learning (MF, BC), pp. 3404–3409.
ICPRICPR-2014-GanSZ
An Extended Isomap for Manifold Topology Learning with SOINN Landmarks (QG, FS, JZ), pp. 1579–1584.
ICPRICPR-2014-GeDGC
Background Subtraction with Dynamic Noise Sampling and Complementary Learning (WG, YD, ZG, YC), pp. 2341–2346.
ICPRICPR-2014-GengWX #adaptation #estimation
Facial Age Estimation by Adaptive Label Distribution Learning (XG, QW, YX), pp. 4465–4470.
ICPRICPR-2014-GienTCL #fuzzy #multi #predict
Dual Fuzzy Hypergraph Regularized Multi-label Learning for Protein Subcellular Location Prediction (JG, YYT, CLPC, YL), pp. 512–516.
ICPRICPR-2014-GuoZLCZ #clustering #kernel #multi
Multiple Kernel Learning Based Multi-view Spectral Clustering (DG, JZ, XL, YC, CZ), pp. 3774–3779.
ICPRICPR-2014-HooKPC #comprehension #image #random
Enhanced Random Forest with Image/Patch-Level Learning for Image Understanding (WLH, TKK, YP, CSC), pp. 3434–3439.
ICPRICPR-2014-HouYW #adaptation #recognition #self
Domain Adaptive Self-Taught Learning for Heterogeneous Face Recognition (CAH, MCY, YCFW), pp. 3068–3073.
ICPRICPR-2014-HuDG #experience #online #recognition #visual notation
Online Regression of Grandmother-Cell Responses with Visual Experience Learning for Face Recognition (JH, WD, JG), pp. 4606–4611.
ICPRICPR-2014-JhuoL #detection #multi #video
Video Event Detection via Multi-modality Deep Learning (IHJ, DTL), pp. 666–671.
ICPRICPR-2014-KhoshrouCT #multi #video
Active Learning from Video Streams in a Multi-camera Scenario (SK, JSC, LFT), pp. 1248–1253.
ICPRICPR-2014-KrauseGDLF #fine-grained #recognition
Learning Features and Parts for Fine-Grained Recognition (JK, TG, JD, LJL, FFL), pp. 26–33.
ICPRICPR-2014-KumarG #documentation #keyword
Bayesian Active Learning for Keyword Spotting in Handwritten Documents (GK, VG), pp. 2041–2046.
ICPRICPR-2014-LeiSLCXP #metric #similarity
Humanoid Robot Imitation with Pose Similarity Metric Learning (JL, MS, ZNL, CC, XX, SP), pp. 4240–4245.
ICPRICPR-2014-LiuL0L #classification #image
Regularized Hierarchical Feature Learning with Non-negative Sparsity and Selectivity for Image Classification (BL, JL, XB, HL), pp. 4293–4298.
ICPRICPR-2014-LiuWCL #automation #category theory #image
Automatic Image Attribute Selection for Zero-Shot Learning of Object Categories (LL, AW, SC, BCL), pp. 2619–2624.
ICPRICPR-2014-LiuYHTH #recognition #visual notation
Semi-supervised Learning for Cross-Device Visual Location Recognition (PL, PY, KH, TT, HWH), pp. 2873–2878.
ICPRICPR-2014-LiuZC #identification #metric #multi #parametricity
Parametric Local Multi-modal Metric Learning for Person Re-identification (KL, ZCZ, AC), pp. 2578–2583.
ICPRICPR-2014-LuoJ #encoding #image #retrieval #semantics
Learning Semantic Binary Codes by Encoding Attributes for Image Retrieval (JL, ZJ), pp. 279–284.
ICPRICPR-2014-ManfrediGC #energy #graph #image #segmentation
Learning Graph Cut Energy Functions for Image Segmentation (MM, CG, RC), pp. 960–965.
ICPRICPR-2014-MarcaciniDHR #approach #clustering #documentation #metric
Privileged Information for Hierarchical Document Clustering: A Metric Learning Approach (RMM, MAD, ERH, SOR), pp. 3636–3641.
ICPRICPR-2014-NegrelPG #image #metric #performance #reduction #retrieval #using
Efficient Metric Learning Based Dimension Reduction Using Sparse Projectors for Image Near Duplicate Retrieval (RN, DP, PHG), pp. 738–743.
ICPRICPR-2014-NieJ #linear #using
Feature Learning Using Bayesian Linear Regression Model (SN, QJ), pp. 1502–1507.
ICPRICPR-2014-NieKZ #recognition #using
Periocular Recognition Using Unsupervised Convolutional RBM Feature Learning (LN, AK, SZ), pp. 399–404.
ICPRICPR-2014-NilufarP #detection #programming
Learning to Detect Contours with Dynamic Programming Snakes (SN, TJP), pp. 984–989.
ICPRICPR-2014-OHarneyMRCSCBF #kernel #multi #pseudo
Pseudo-Marginal Bayesian Multiple-Class Multiple-Kernel Learning for Neuroimaging Data (ADO, AM, KR, KC, ABS, AC, CB, MF), pp. 3185–3190.
ICPRICPR-2014-PatriciaTC #adaptation #multi #performance
Multi-source Adaptive Learning for Fast Control of Prosthetics Hand (NP, TT, BC), pp. 2769–2774.
ICPRICPR-2014-PengWQP #encoding #evaluation #recognition #taxonomy
A Joint Evaluation of Dictionary Learning and Feature Encoding for Action Recognition (XP, LW, YQ, QP), pp. 2607–2612.
ICPRICPR-2014-PhamKC #graph #image
Semi-supervised Learning on Bi-relational Graph for Image Annotation (HDP, KHK, SC), pp. 2465–2470.
ICPRICPR-2014-PillaiFR #classification #multi
Learning of Multilabel Classifiers (IP, GF, FR), pp. 3452–3456.
ICPRICPR-2014-RenYZH #classification #image #nearest neighbour
Learning Convolutional Nonlinear Features for K Nearest Neighbor Image Classification (WR, YY, JZ, KH), pp. 4358–4363.
ICPRICPR-2014-RiabchenkoKC #generative #modelling
Learning Generative Models of Object Parts from a Few Positive Examples (ER, JKK, KC), pp. 2287–2292.
ICPRICPR-2014-RozzaMP #graph #kernel #novel
A Novel Graph-Based Fisher Kernel Method for Semi-supervised Learning (AR, MM, AP), pp. 3786–3791.
ICPRICPR-2014-SaitoAFRSGC #using
Active Semi-supervised Learning Using Optimum-Path Forest (PTMS, WPA, AXF, PJdR, CTNS, JFG, MHdC), pp. 3798–3803.
ICPRICPR-2014-SatoKSK #classification #multi
Learning Multiple Complex Features Based on Classification Results (YS, KK, YS, MK), pp. 3369–3373.
ICPRICPR-2014-SavakisRP #difference #gesture #using
Gesture Control Using Active Difference Signatures and Sparse Learning (AES, RR, RWP), pp. 3969–3974.
ICPRICPR-2014-ShenHSGM #framework #interactive
Interactive Framework for Insect Tracking with Active Learning (MS, WH, PS, CGG, DM), pp. 2733–2738.
ICPRICPR-2014-StraehleKKH #multi #random
Multiple Instance Learning with Response-Optimized Random Forests (CNS, MK, UK, FAH), pp. 3768–3773.
ICPRICPR-2014-UmakanthanDFS #multi #process #representation #taxonomy
Multiple Instance Dictionary Learning for Activity Representation (SU, SD, CF, SS), pp. 1377–1382.
ICPRICPR-2014-VellankiDVP #parametricity
Nonparametric Discovery of Learning Patterns and Autism Subgroups from Therapeutic Data (PV, TVD, SV, DQP), pp. 1828–1833.
ICPRICPR-2014-WalhaDLGA #approach #image #taxonomy
Sparse Coding with a Coupled Dictionary Learning Approach for Textual Image Super-resolution (RW, FD, FL, CG, AMA), pp. 4459–4464.
ICPRICPR-2014-WangGJ #using
Learning with Hidden Information Using a Max-Margin Latent Variable Model (ZW, TG, QJ), pp. 1389–1394.
ICPRICPR-2014-WangWH #framework #multi #predict #risk management
A Multi-task Learning Framework for Joint Disease Risk Prediction and Comorbidity Discovery (XW, FW, JH), pp. 220–225.
ICPRICPR-2014-WangWJ
Learning with Hidden Information (ZW, XW, QJ), pp. 238–243.
ICPRICPR-2014-WangZWB #modelling
Learning Room Occupancy Patterns from Sparsely Recovered Light Transport Models (QW, XZ, MW, KLB), pp. 1987–1992.
ICPRICPR-2014-WanHA #image #recognition
Indoor Scene Recognition from RGB-D Images by Learning Scene Bases (SW, CH, JKA), pp. 3416–3421.
ICPRICPR-2014-WatanabeW #analysis #component #distance #metric #performance
Logistic Component Analysis for Fast Distance Metric Learning (KW, TW), pp. 1278–1282.
ICPRICPR-2014-WuJ #detection
Learning the Deep Features for Eye Detection in Uncontrolled Conditions (YW, QJ), pp. 455–459.
ICPRICPR-2014-WuLWHJ #multi
Multi-label Learning with Missing Labels (BW, ZL, SW, BGH, QJ), pp. 1964–1968.
ICPRICPR-2014-WuS #multi #recognition
Regularized Multi-view Multi-metric Learning for Action Recognition (XW, SKS), pp. 471–476.
ICPRICPR-2014-WuTS #3d #rank
Learning to Rank the Severity of Unrepaired Cleft Lip Nasal Deformity on 3D Mesh Data (JW, RT, LGS), pp. 460–464.
ICPRICPR-2014-XieUKG #incremental
Incremental Learning with Support Vector Data Description (WX, SU, SK, MG), pp. 3904–3909.
ICPRICPR-2014-XuS #network #using
Bayesian Network Structure Learning Using Causality (ZX, SNS), pp. 3546–3551.
ICPRICPR-2014-YangN #integration #multi
Semi-supervised Learning of Geospatial Objects through Multi-modal Data Integration (YY, SN), pp. 4062–4067.
ICPRICPR-2014-YangXWL #realtime
Real-Time Tracking via Deformable Structure Regression Learning (XY, QX, SW, PL), pp. 2179–2184.
ICPRICPR-2014-YangYH
Diversity-Based Ensemble with Sample Weight Learning (CY, XCY, HWH), pp. 1236–1241.
ICPRICPR-2014-YanSRLS #classification #interactive #multi
Evaluating Multi-task Learning for Multi-view Head-Pose Classification in Interactive Environments (YY, RS, ER, OL, NS), pp. 4182–4187.
ICPRICPR-2014-YiLLL #identification #metric
Deep Metric Learning for Person Re-identification (DY, ZL, SL, SZL), pp. 34–39.
ICPRICPR-2014-YinYPH #case study #classification
Shallow Classification or Deep Learning: An Experimental Study (XCY, CY, WYP, HWH), pp. 1904–1909.
ICPRICPR-2014-YooJKC #optimisation
Transfer Learning of Motion Patterns in Traffic Scene via Convex Optimization (YJY, HJ, SWK, JYC), pp. 4158–4163.
ICPRICPR-2014-ZenRS #distance #matrix #metric
Simultaneous Ground Metric Learning and Matrix Factorization with Earth Mover’s Distance (GZ, ER, NS), pp. 3690–3695.
ICPRICPR-2014-ZhangM14a #detection #multi
Simultaneous Detection of Multiple Facial Action Units via Hierarchical Task Structure Learning (XZ, MHM), pp. 1863–1868.
ICPRICPR-2014-ZhangQWL #classification #online
Object Classification in Traffic Scene Surveillance Based on Online Semi-supervised Active Learning (ZZ, JQ, YW, ML), pp. 3086–3091.
ICPRICPR-2014-ZhouIWBPKO #performance
Transfer Learning of a Temporal Bone Performance Model via Anatomical Feature Registration (YZ, II, SNRW, JB, PP, GK, SO), pp. 1916–1921.
ICPRICPR-2014-ZhuS #recognition #taxonomy
Correspondence-Free Dictionary Learning for Cross-View Action Recognition (FZ, LS), pp. 4525–4530.
ICPRICPR-2014-ZhuWYJ #modelling #multi #recognition #semantics
Multiple-Facial Action Unit Recognition by Shared Feature Learning and Semantic Relation Modeling (YZ, SW, LY, QJ), pp. 1663–1668.
KDDKDD-2014-Bengio #scalability
Scaling up deep learning (YB), p. 1966.
KDDKDD-2014-BensonRS #multi #network #scalability
Learning multifractal structure in large networks (ARB, CR, SS), pp. 1326–1335.
KDDKDD-2014-DalessandroCRPWP #online #scalability
Scalable hands-free transfer learning for online advertising (BD, DC, TR, CP, MHW, FJP), pp. 1573–1582.
KDDKDD-2014-GaddeAO #graph #using
Active semi-supervised learning using sampling theory for graph signals (AG, AA, AO), pp. 492–501.
KDDKDD-2014-GohR
Box drawings for learning with imbalanced data (STG, CR), pp. 333–342.
KDDKDD-2014-GongZFY #multi #performance
Efficient multi-task feature learning with calibration (PG, JZ, WF, JY), pp. 761–770.
KDDKDD-2014-GrabockaSWS
Learning time-series shapelets (JG, NS, MW, LST), pp. 392–401.
KDDKDD-2014-Kushnir #adaptation #kernel
Active-transductive learning with label-adapted kernels (DK), pp. 462–471.
KDDKDD-2014-LanSB #analysis
Time-varying learning and content analytics via sparse factor analysis (ASL, CS, RGB), pp. 452–461.
KDDKDD-2014-LiangRR #personalisation
Personalized search result diversification via structured learning (SL, ZR, MdR), pp. 751–760.
KDDKDD-2014-PerozziAS #named #online #social
DeepWalk: online learning of social representations (BP, RAR, SS), pp. 701–710.
KDDKDD-2014-PrabhuV #classification #multi #named #performance
FastXML: a fast, accurate and stable tree-classifier for extreme multi-label learning (YP, MV), pp. 263–272.
KDDKDD-2014-PurushothamMKO #feature model #higher-order #interactive #modelling
Factorized sparse learning models with interpretable high order feature interactions (SP, MRM, CCJK, RO), pp. 552–561.
KDDKDD-2014-QianHJPZ #approach #distance #metric #using
Distance metric learning using dropout: a structured regularization approach (QQ, JH, RJ, JP, SZ), pp. 323–332.
KDDKDD-2014-Salakhutdinov
Deep learning (RS), p. 1973.
KDDKDD-2014-ShaoAK #concept #data type #prototype
Prototype-based learning on concept-drifting data streams (JS, ZA, SK), pp. 412–421.
KDDKDD-2014-TayebiEGB #embedded #predict #using
Spatially embedded co-offence prediction using supervised learning (MAT, ME, UG, PLB), pp. 1789–1798.
KDDKDD-2014-VasishtDVK #classification #multi
Active learning for sparse bayesian multilabel classification (DV, ACD, MV, AK), pp. 472–481.
KDDKDD-2014-WangNH #adaptation #induction #scalability
Large-scale adaptive semi-supervised learning via unified inductive and transductive model (DW, FN, HH), pp. 482–491.
KDDKDD-2014-WangSE #collaboration #permutation
Active collaborative permutation learning (JW, NS, JE), pp. 502–511.
KDDKDD-2014-WangSW #modelling
Unsupervised learning of disease progression models (XW, DS, FW), pp. 85–94.
KDDKDD-2014-XuL #behaviour #problem
Product selection problem: improve market share by learning consumer behavior (SX, JCSL), pp. 851–860.
KDDKDD-2014-YangH #parametricity
Learning with dual heterogeneity: a nonparametric bayes model (HY, JH), pp. 582–590.
KDDKDD-2014-ZhangTMF #network
Supervised deep learning with auxiliary networks (JZ, GT, YM, WF), pp. 353–361.
KDDKDD-2014-ZhouC #adaptation #documentation #rank
Unifying learning to rank and domain adaptation: enabling cross-task document scoring (MZ, KCCC), pp. 781–790.
KDIRKDIR-2014-DistanteCVL #online #paradigm #plugin #topic
Enhancing Online Discussion Forums with a Topic-driven Navigational Paradigm — A Plugin for the Moodle Learning Management System (DD, LC, AV, ML), pp. 97–106.
KDIRKDIR-2014-SuciuICDP #word
Learning Good Opinions from Just Two Words Is Not Bad (DAS, VVI, ACC, MD, RP), pp. 233–241.
KEODKEOD-2014-KarkalasS #concept #modelling #student
Intelligent Student Support in the FLIP Learning System based on Student Initial Misconceptions and Student Modelling (SK, SGS), pp. 353–360.
KMISKMIS-2014-AtrashAM #collaboration
Supporting Organizational Learning with Collaborative Annotation (AA, MHA, CM), pp. 237–244.
KMISKMIS-2014-BartuskovaK #information management
Knowledge Management and Sharing in E-Learning — Hierarchical System for Managing Learning Resources (AB, OK), pp. 179–185.
KMISKMIS-2014-HisakaneS #visualisation
A Visualization System of Discussion Structure in Case Method Learning (DH, MS), pp. 126–132.
KRKR-2014-KonevLOW #lightweight #logic #ontology
Exact Learning of Lightweight Description Logic Ontologies (BK, CL, AO, FW).
KRKR-2014-Michael #predict
Simultaneous Learning and Prediction (LM).
MLDMMLDM-2014-BugaychenkoZ #diagrams #multi #pattern matching #pattern recognition #performance #recognition #using
Fast Pattern Recognition and Deep Learning Using Multi-Rooted Binary Decision Diagrams (DB, DZ), pp. 73–77.
MLDMMLDM-2014-KhasnabishSDS #detection #programming language #source code #using
Detecting Programming Language from Source Code Using Bayesian Learning Techniques (JNK, MS, JD, GS), pp. 513–522.
MLDMMLDM-2014-KuleshovB #data mining #mining
Manifold Learning in Data Mining Tasks (APK, AVB), pp. 119–133.
MLDMMLDM-2014-NeumannHRL #case study #experience
A Robot Waiter Learning from Experiences (BN, LH, PR, JL), pp. 285–299.
MLDMMLDM-2014-SandovalH #network #using
Learning of Natural Trading Strategies on Foreign Exchange High-Frequency Market Data Using Dynamic Bayesian Networks (JS, GH), pp. 408–421.
RecSysRecSys-2014-BhagatWIT #matrix #recommendation #using
Recommending with an agenda: active learning of private attributes using matrix factorization (SB, UW, SI, NT), pp. 65–72.
RecSysRecSys-2014-KrishnanPFG #bias #recommendation #social
A methodology for learning, analyzing, and mitigating social influence bias in recommender systems (SK, JP, MJF, KG), pp. 137–144.
RecSysRecSys-2014-SaveskiM #recommendation
Item cold-start recommendations: learning local collective embeddings (MS, AM), pp. 89–96.
SEKESEKE-2014-GaoKN #estimation #quality #ranking
Comparing Two Approaches for Adding Feature Ranking to Sampled Ensemble Learning for Software Quality Estimation (KG, TMK, AN), pp. 280–285.
SEKESEKE-2014-JuniorFJB #mobile #product line #towards
Towards the Establishment of a Software Product Line for Mobile Learning Applications (VFJ, NFDF, EAdOJ, EFB), pp. 678–683.
SEKESEKE-2014-SantosBSC #game studies #programming #semantics #source code
A Semantic Analyzer for Simple Games Source Codes to Programming Learning (ECOdS, GBB, VHVdS, EC), pp. 522–527.
SIGIRSIGIR-2014-CostaCS #modelling #ranking
Learning temporal-dependent ranking models (MC, FMC, MJS), pp. 757–766.
SIGIRSIGIR-2014-EfronWS #query
Learning sufficient queries for entity filtering (ME, CW, GS), pp. 1091–1094.
SIGIRSIGIR-2014-FangWYZ #information retrieval #modelling #named
VIRLab: a web-based virtual lab for learning and studying information retrieval models (HF, HW, PY, CZ), pp. 1249–1250.
SIGIRSIGIR-2014-JiangKCC #behaviour #query
Learning user reformulation behavior for query auto-completion (JYJ, YYK, PYC, PJC), pp. 445–454.
SIGIRSIGIR-2014-LengCL #image #random #retrieval #scalability
Random subspace for binary codes learning in large scale image retrieval (CL, JC, HL), pp. 1031–1034.
SIGIRSIGIR-2014-LiuL #probability #segmentation #word
Probabilistic ensemble learning for vietnamese word segmentation (WL, LL), pp. 931–934.
SIGIRSIGIR-2014-NiuLGCG #data analysis #rank #robust #what
What makes data robust: a data analysis in learning to rank (SN, YL, JG, XC, XG), pp. 1191–1194.
SIGIRSIGIR-2014-PanYMLNR #image
Click-through-based cross-view learning for image search (YP, TY, TM, HL, CWN, YR), pp. 717–726.
SIGIRSIGIR-2014-QiuCYLL #personalisation #ranking
Item group based pairwise preference learning for personalized ranking (SQ, JC, TY, CL, HL), pp. 1219–1222.
SIGIRSIGIR-2014-SokolovHR #query
Learning to translate queries for CLIR (AS, FH, SR), pp. 1179–1182.
SIGIRSIGIR-2014-SpinaGA #detection #monitoring #online #similarity #topic
Learning similarity functions for topic detection in online reputation monitoring (DS, JG, EA), pp. 527–536.
SIGIRSIGIR-2014-UstaAVOU #analysis #education #how #student
How k-12 students search for learning?: analysis of an educational search engine log (AU, ISA, IBV, RO, ÖU), pp. 1151–1154.
SIGIRSIGIR-2014-VulicZM #e-commerce #formal method
Learning to bridge colloquial and formal language applied to linking and search of E-Commerce data (IV, SZ, MFM), pp. 1195–1198.
SIGIRSIGIR-2014-WuMHR #image #personalisation
Learning to personalize trending image search suggestion (CCW, TM, WHH, YR), pp. 727–736.
SIGIRSIGIR-2014-YuWZTSZ #rank
Hashing with List-Wise learning to rank (ZY, FW, YZ, ST, JS, YZ), pp. 999–1002.
SIGIRSIGIR-2014-ZhuLGCN
Learning for search result diversification (YZ, YL, JG, XC, SN), pp. 293–302.
SIGIRSIGIR-2014-ZhuNG #adaptation #random #social
An adaptive teleportation random walk model for learning social tag relevance (XZ, WN, MG), pp. 223–232.
MODELSMoDELS-2014-BakiSCMF #model transformation
Learning Implicit and Explicit Control in Model Transformations by Example (IB, HAS, QC, PM, MF), pp. 636–652.
ASEASE-2014-NguyenNNN #api #approach #migration #mining #statistics
Statistical learning approach for mining API usage mappings for code migration (ATN, HAN, TTN, TNN), pp. 457–468.
FSEFSE-2014-AllamanisBBS
Learning natural coding conventions (MA, ETB, CB, CAS), pp. 281–293.
FSEFSE-2014-YeBL #debugging #rank #using
Learning to rank relevant files for bug reports using domain knowledge (XY, RCB, CL), pp. 689–699.
ICSEICSE-2014-HeWYZ #reasoning
Symbolic assume-guarantee reasoning through BDD learning (FH, BYW, LY, LZ), pp. 1071–1082.
ICSEICSE-2014-JingYZWL #fault #predict #taxonomy
Dictionary learning based software defect prediction (XYJ, SY, ZWZ, SSW, JL), pp. 414–423.
SACSAC-2014-ChallcoI #authoring #design #personalisation #towards
Towards a learning design authoring tool that generates personalized units of learning for CSCL (GCC, SI), pp. 778–780.
SACSAC-2014-DhanjalC #network
Learning reputation in an authorship network (CD, SC), pp. 1724–1726.
SACSAC-2014-LiWL #mobile #online #recognition
Online learning with mobile sensor data for user recognition (HGL, XW, ZL), pp. 64–70.
SACSAC-2014-PessinOUWMV #evolution #network #self
Self-localisation in indoor environments combining learning and evolution with wireless networks (GP, FSO, JU, DFW, RCM, PAV), pp. 661–666.
CASECASE-2014-HabibDBHP #android
Learning human-like facial expressions for Android Phillip K. Dick (AH, SKD, ICB, DH, DOP), pp. 1159–1165.
CASECASE-2014-HwangLW #adaptation
Adaptive reinforcement learning in box-pushing robots (KSH, JLL, WHW), pp. 1182–1187.
CASECASE-2014-MaDLZ #modelling #simulation
Modeling and simulation of product diffusion considering learning effect (KPM, XD, CFL, JZ), pp. 665–670.
CASECASE-2014-MahlerKLSMKPWFAG #process #using
Learning accurate kinematic control of cable-driven surgical robots using data cleaning and Gaussian Process Regression (JM, SK, ML, SS, AM, BK, SP, JW, MF, PA, KYG), pp. 532–539.
CASECASE-2014-MinakaisMW
Groundhog Day: Iterative learning for building temperature control (MM, SM, JTW), pp. 948–953.
CASECASE-2014-MurookaNNKOI #physics #scalability
Manipulation strategy learning for carrying large objects based on mapping from object physical property to object manipulation action in virtual environment (MM, SN, SN, YK, KO, MI), pp. 263–270.
DATEDATE-2014-HanKNV
A deep learning methodology to proliferate golden signoff timing (SSH, ABK, SN, ASV), pp. 1–6.
HPCAHPCA-2014-WonCGHS #network #online #power management
Up by their bootstraps: Online learning in Artificial Neural Networks for CMP uncore power management (JYW, XC, PG, JH, VS), pp. 308–319.
OSDIOSDI-2014-ChilimbiSAK #performance #scalability
Project Adam: Building an Efficient and Scalable Deep Learning Training System (TMC, YS, JA, KK), pp. 571–582.
PDPPDP-2014-FarahnakianLP #energy #using #virtual machine
Energy-Efficient Virtual Machines Consolidation in Cloud Data Centers Using Reinforcement Learning (FF, PL, JP), pp. 500–507.
STOCSTOC-2014-AwasthiBL #linear #locality #power of
The power of localization for efficiently learning linear separators with noise (PA, MFB, PML), pp. 449–458.
STOCSTOC-2014-Christiano #online #programming
Online local learning via semidefinite programming (PC), pp. 468–474.
STOCSTOC-2014-DanielyLS #complexity
From average case complexity to improper learning complexity (AD, NL, SSS), pp. 441–448.
TACASTACAS-2014-MalerM #regular expression #scalability
Learning Regular Languages over Large Alphabets (OM, IEM), pp. 485–499.
CAVCAV-2014-0001LMN #framework #invariant #named #robust
ICE: A Robust Framework for Learning Invariants (PG, CL, PM, DN), pp. 69–87.
CAVCAV-2014-HeizmannHP #analysis #source code #termination
Termination Analysis by Learning Terminating Programs (MH, JH, AP), pp. 797–813.
SMTSMT-2014-KorovinKS #towards
Towards Conflict-Driven Learning for Virtual Substitution (KK, MK, TS), p. 71.
ICDARICDAR-2013-AgarwalGC
Greedy Search for Active Learning of OCR (AA, RG, SC), pp. 837–841.
ICDARICDAR-2013-BougueliaBB #approach #classification #documentation
A Stream-Based Semi-supervised Active Learning Approach for Document Classification (MRB, YB, AB), pp. 611–615.
ICDARICDAR-2013-BouillonLAR #gesture #using
Using Confusion Reject to Improve (User and) System (Cross) Learning of Gesture Commands (MB, PL, ÉA, GR), pp. 1017–1021.
ICDARICDAR-2013-KasarBACP #detection #documentation #image #using
Learning to Detect Tables in Scanned Document Images Using Line Information (TK, PB, SA, CC, TP), pp. 1185–1189.
ICDARICDAR-2013-NguyenCBO #image #interactive
Interactive Knowledge Learning for Ancient Images (NVN, MC, AB, JMO), pp. 300–304.
ICDARICDAR-2013-PuriST #network
Bayesian Network Structure Learning and Inference Methods for Handwriting (MP, SNS, YT), pp. 1320–1324.
ICDARICDAR-2013-SchambachR #network #sequence
Stabilize Sequence Learning with Recurrent Neural Networks by Forced Alignment (MPS, SFR), pp. 1270–1274.
ICDARICDAR-2013-SuL #recognition
Discriminative Weighting and Subspace Learning for Ensemble Symbol Recognition (FS, TL), pp. 1088–1092.
ICDARICDAR-2013-SuTLDT #classification #documentation #image #representation
Self Learning Classification for Degraded Document Images by Sparse Representation (BS, ST, SL, TAD, CLT), pp. 155–159.
ICDARICDAR-2013-ZhouYL #performance #polynomial #recognition
GPU-Based Fast Training of Discriminative Learning Quadratic Discriminant Function for Handwritten Chinese Character Recognition (MKZ, FY, CLL), pp. 842–846.
ICDARICDAR-2013-Zhu0N #recognition
Sub-structure Learning Based Handwritten Chinese Text Recognition (YZ, JS, SN), pp. 295–299.
JCDLJCDL-2013-NockNB #geometry #library
Non-linear book manifolds: learning from associations the dynamic geometry of digital libraries (RN, FN, EB), pp. 313–322.
JCDLJCDL-2013-OkoyeSB #automation #generative #library #sequence
Automatic extraction of core learning goals and generation of pedagogical sequences through a collection of digital library resources (IO, TS, SB), pp. 67–76.
PODSPODS-2013-AbouziedAPHS #quantifier #query #verification
Learning and verifying quantified boolean queries by example (AA, DA, CHP, JMH, AS), pp. 49–60.
TPDLTPDL-2013-MajidiC #dependence #parsing
Committee-Based Active Learning for Dependency Parsing (SM, GRC), pp. 442–445.
VLDBVLDB-2013-BrunatoB #optimisation
Learning and Intelligent Optimization (LION): One Ring to Rule Them All (MB, RB), pp. 1176–1177.
VLDBVLDB-2013-Hoppe #automation #big data #ontology #web
Automatic ontology-based User Profile Learning from heterogeneous Web Resources in a Big Data Context (AH), pp. 1428–1433.
VLDBVLDB-2013-ZhouTWN #2d #named #predict #probability
R2-D2: a System to Support Probabilistic Path Prediction in Dynamic Environments via “Semi-Lazy” Learning (JZ, AKHT, WW, WSN), pp. 1366–1369.
CSEETCSEET-2013-ChimalakondaN #adaptation #education #personalisation #re-engineering #what
What makes it hard to teach software engineering to end users? some directions from adaptive and personalized learning (SC, KVN), pp. 324–328.
CSEETCSEET-2013-Georgas #composition #design #education #towards
Toward infusing modular and reflective design learning throughout the curriculum (JCG), pp. 274–278.
CSEETCSEET-2013-RibaudS #cost analysis #information management #problem
The cost of problem-based learning: An example in information systems engineering (VR, PS), pp. 259–263.
CSEETCSEET-2013-StejskalS #testing
Test-driven learning in high school computer science (RS, HPS), pp. 289–293.
ITiCSEITiCSE-2013-Alshaigy #development #education #interactive #programming language #python
Development of an interactive learning tool to teach python programming language (BA), p. 344.
ITiCSEITiCSE-2013-CalvoGII #content management #evaluation #heuristic
Are chats and forums accessible in e-learning systems?: a heuristic evaluation comparing four learning content management systems (RC, AG, BI, AI), p. 342.
ITiCSEITiCSE-2013-FernandesCB
A pilot project on non-conventional learning (SF, AC, LSB), p. 346.
ITiCSEITiCSE-2013-German
Jump-starting team-based learning in the computer science classroom (DAG), p. 323.
ITiCSEITiCSE-2013-GorlatovaSKKZ #research #scalability
Project-based learning within a large-scale interdisciplinary research effort (MG, JS, PRK, IK, GZ), pp. 207–212.
ITiCSEITiCSE-2013-HawthorneC #source code
ACM core IT learning outcomes for associate-degree programs (EKH, RDC), p. 357.
ITiCSEITiCSE-2013-JalilPWL #design #interactive #taxonomy
Design eye: an interactive learning environment based on the solo taxonomy (SAJ, BP, IW, ALR), pp. 22–27.
ITiCSEITiCSE-2013-JohnsonCH #contest #development #game studies
Learning elsewhere: tales from an extracurricular game development competition (CJ, AC, SH), pp. 70–75.
ITiCSEITiCSE-2013-MedinaPGR #data mining #education #mining #programming #using
Assistance in computer programming learning using educational data mining and learning analytics (CFM, JRPP, VMÁG, MdPPR), pp. 237–242.
ITiCSEITiCSE-2013-MellodgeR #arduino #case study #experience #framework #platform #student #using
Using the arduino platform to enhance student learning experiences (PM, IR), p. 338.
ITiCSEITiCSE-2013-Paule-RuizGPG #evaluation #framework #interactive
Voice interactive learning: a framework and evaluation (MPPR, VMÁG, JRPP, MRG), pp. 34–39.
ITiCSEITiCSE-2013-QianYGBT #authentication #mobile #network #security
Mobile device based authentic learning for computer network and security (KQ, MY, MG, PB, LT), p. 335.
ITiCSEITiCSE-2013-ReedZ #framework
A hierarchical framework for mapping and quantitatively assessing program and learning outcomes (JR, HZ), pp. 52–57.
ITiCSEITiCSE-2013-RowanD #mobile #overview #using
A systematic literature review on using mobile computing as a learning intervention (MR, JD), p. 339.
ITiCSEITiCSE-2013-Sanchez-Nielsen #multi #student
Producing multimedia pills to stimulate student learning and engagement (ESN), pp. 165–170.
ITiCSEITiCSE-2013-ScottG #programming #question
Implicit theories of programming aptitude as a barrier to learning to code: are they distinct from intelligence? (MJS, GG), p. 347.
ITiCSEITiCSE-2013-VihavainenVLP #student #using
Scaffolding students’ learning using test my code (AV, TV, ML, MP), pp. 117–122.
ITiCSEITiCSE-2013-Wildsmith #named
Kinetic: a learning environment within business (CW), p. 3.
SIGITESIGITE-2013-BrannockLN #authentication #case study #development #experience
Integrating authentic learning into a software development course: an experience report (EB, RL, NPN), pp. 195–200.
SIGITESIGITE-2013-HeinonenHLV #agile #re-engineering #using
Learning agile software engineering practices using coding dojo (KH, KH, ML, AV), pp. 97–102.
SIGITESIGITE-2013-PrestonRRZZ #education
New educational learning environments: riding the wave of change instead of having it crash upon us (JAP, HR, RHR, CZ, JZ), pp. 51–52.
SIGITESIGITE-2013-StokerAM #using #virtual machine
Using virtual machines to improve learning and save resources in an introductory IT course (GS, TA, PM), pp. 91–96.
SIGITESIGITE-2013-ZhangZ
Supporting adult learning: enablers, barriers, and services (CZ, GZ), pp. 151–152.
CSMRCSMR-2013-XiaLWYLS #algorithm #case study #comparative #debugging #predict
A Comparative Study of Supervised Learning Algorithms for Re-opened Bug Prediction (XX, DL, XW, XY, SL, JS), pp. 331–334.
DLTDLT-2013-BolligHLM #approach #automaton
A Fresh Approach to Learning Register Automata (BB, PH, ML, BM), pp. 118–130.
ICALPICALP-v2-2013-FuscoPP #performance
Learning a Ring Cheaply and Fast (EGF, AP, RP), pp. 557–568.
LATALATA-2013-BjorklundFK #automaton
MAT Learning of Universal Automata (JB, HF, AK), pp. 141–152.
AIIDEAIIDE-2013-ChenKS #detection #interactive
Learning Interrogation Strategies while Considering Deceptions in Detective Interactive Stories (GYC, ECCK, VWS).
AIIDEAIIDE-2013-LeeceJ #game studies #reasoning
Reinforcement Learning for Spatial Reasoning in Strategy Games (MAL, AJ).
CoGCIG-2013-BishopM #evaluation #online
Evolutionary reature evaluation for online Reinforcement Learning (JB, RM), pp. 1–8.
CoGCIG-2013-ParkK #case study #incremental #modelling
Opponent modeling with incremental active learning: A case study of Iterative Prisoner's Dilemma (HSP, KJK), pp. 1–2.
CoGCIG-2013-PerezSL #monte carlo #multi #online
Online and offline learning in multi-objective Monte Carlo Tree Search (DPL, SS, SML), pp. 1–8.
CoGCIG-2013-Schaul #game studies #interactive #modelling #video
A video game description language for model-based or interactive learning (TS), pp. 1–8.
CoGCIG-2013-SifaB #analysis #behaviour #game studies
Archetypical motion: Supervised game behavior learning with Archetypal Analysis (RS, CB), pp. 1–8.
CoGCIG-2013-WiensDP #behaviour #game studies #scalability
Creating large numbers of game AIs by learning behavior for cooperating units (SW, JD, SP), pp. 1–8.
DiGRADiGRA-2013-Wechselberger #game studies #question
Learning and Enjoyment in Serious Gaming - Contradiction or Complement? (UW).
FDGFDG-2013-BarendregtF #design #game studies #interactive #student
Course on interaction games and learning for interaction design students (WB, MvF), pp. 261–268.
FDGFDG-2013-LlansoGGGE #architecture #component #game studies
Tool-supported iterative learning of component-based software architecture for games (DL, MAGM, PPGM, PAGC, MSEN), pp. 376–379.
FDGFDG-2013-Marklund #development #game studies #on the
On the development of learning games (BBM), pp. 474–476.
CoGVS-Games-2013-CruzCMGB #research #roadmap #towards
Federation Technology and Virtual Worlds for Learning: Research Trends and Opportunities Towards Identity Federation (GC, ARC, PM0, RG, JB), pp. 1–4.
CoGVS-Games-2013-FeigenbaumF #case study #collaboration
Gameful Pedagogy and Collaborative Learning a Case Study of the Netsx Project (AF, AF), pp. 1–7.
CoGVS-Games-2013-FranzwaTJ #design #game studies #student
Serious Game Design: Motivating Students through a Balance of Fun and Learning (CF, YT, AJ), pp. 1–7.
CoGVS-Games-2013-Wilkinson #education #game studies
Affective Educational Games: Utilizing Emotions in Game-Based Learning (PW), pp. 1–8.
GT-VMTGT-VMT-2013-AlshanqitiHK #graph transformation
Learning Minimal and Maximal Rules from Observations of Graph Transformations (AMA, RH, TAK).
CHICHI-2013-AndersonB #gesture #performance
Learning and performance with gesture guides (FA, WFB), pp. 1109–1118.
CHICHI-2013-EdgeCW #named
SpatialEase: learning language through body motion (DE, KYC, MW), pp. 469–472.
CHICHI-2013-HarpsteadMA #data analysis #education #game studies
In search of learning: facilitating data analysis in educational games (EH, BAM, VA), pp. 79–88.
CHICHI-2013-RauARR #design #interactive #why
Why interactive learning environments can have it all: resolving design conflicts between competing goals (MAR, VA, NR, SR), pp. 109–118.
CHICHI-2013-SzafirM #adaptation #named #overview
ARTFul: adaptive review technology for flipped learning (DS, BM), pp. 1001–1010.
CSCWCSCW-2013-KowY #community
Media technologies and learning in the starcraft esport community (YMK, TY), pp. 387–398.
CSCWCSCW-2013-LinF #network
Opportunities via extended networks for teens’ informal learning (PL, SDF), pp. 1341–1352.
HCIDHM-HB-2013-NakamuraKOOHNAKMK #artificial reality #self #student #towards #using
The Relationship between Nursing Students’ Attitudes towards Learning and Effects of Self-learning System Using Kinect (MN, YK, JO, TO, ZH, AN, KA, NK, JM, MKP), pp. 111–116.
HCIDUXU-CXC-2013-ChoensawatSKH #education
Desirability of a Teaching and Learning Tool for Thai Dance Body Motion (WC, KS, CK, KH), pp. 171–179.
HCIDUXU-CXC-2013-MarchettiB #game studies
Setting Conditions for Learning: Mediated Play and Socio-material Dialogue (EM, EPB), pp. 238–246.
HCIDUXU-CXC-2013-MarcusPL #design #mobile #persuasion #user interface
The Learning Machine: Mobile UX Design That Combines Information Design with Persuasion Design (AM, YP, NL), pp. 247–256.
HCIDUXU-CXC-2013-MouraVCBSTLK #exclamation #game studies #how #mobile
Luz, Câmera, Libras!: How a Mobile Game Can Improve the Learning of Sign Languages (GdSM, LAV, AC, FB, DdS, JMXNT, CWML, JK), pp. 266–275.
HCIDUXU-WM-2013-SasajimaNKHHNTTM #ontology
CHARM Pad: Ontology-Based Tool for Learning Systematic Knowledge about Nursing (MS, SN, YK, AH, KH, AN, HT, YT, RM), pp. 560–567.
HCIDUXU-WM-2013-WilkinsonLC #experience #interactive
Exploring Prior Experience and the Effects of Age on Product Interaction and Learning (CRW, PL, PJC), pp. 457–466.
HCIHCI-AMTE-2013-AkiyoshiT #estimation #eye tracking #framework #interface #using
An Estimation Framework of a User Learning Curve on Web-Based Interface Using Eye Tracking Equipment (MA, HT), pp. 159–165.
HCIHCI-AS-2013-AndujarEGM
Evaluating Engagement Physiologically and Knowledge Retention Subjectively through Two Different Learning Techniques (MA, JIE, JEG, PM), pp. 335–342.
HCIHCI-AS-2013-EskildsenR #challenge #integration
Challenges for Contextualizing Language Learning — Supporting Cultural Integration (SE, MR), pp. 361–369.
HCIHCI-AS-2013-FrajhofACLLM #collaboration #framework #network #platform #social #student #usability
Usability of a Social Network as a Collaborative Learning Platform Tool for Medical Students (LF, ACCA, ATdSC, CJPdL, CAPdL, CRM), pp. 370–375.
HCIHCI-AS-2013-GotodaSMNM #process #realtime
A Server-Based System Supporting Motor Learning through Real-Time and Reflective Learning Activities (NG, YS, KM, KN, CM), pp. 84–93.
HCIHCI-AS-2013-HarunBON #using
Refining Rules Learning Using Evolutionary PD (AFH, SB, CO, NLMN), pp. 376–385.
HCIHCI-AS-2013-HuangC13a #education #interface #music #self #visualisation
Sound to Sight: The Effects of Self-generated Visualization on Music Sight-Singing as an Alternate Learning Interface for Music Education within a Web-Based Environment (YTH, CNC), pp. 386–390.
HCIHCI-AS-2013-LekkasGTMS #behaviour #component #experience #how #process
Personality and Emotion as Determinants of the Learning Experience: How Affective Behavior Interacts with Various Components of the Learning Process (ZL, PG, NT, CM, GS), pp. 418–427.
HCIHCI-AS-2013-LimaRSBSO #using
Innovation in Learning — The Use of Avatar for Sign Language (TL, MSR, TAS, AB, ES, HSdO), pp. 428–433.
HCIHCI-AS-2013-MajimaMSS
A Proposal of the New System Model for Nursing Skill Learning Based on Cognition and Technique (YM, YM, MS, MS), pp. 134–143.
HCIHCI-AS-2013-MatsumotoAK #development #email #using #word
Development of Push-Based English Words Learning System by Using E-Mail Service (SM, MA, TK), pp. 444–453.
HCIHCI-AS-2013-MbathaM #experience #named
E-learning: The Power Source of Transforming the Learning Experience in an ODL Landscape (BM, MM), pp. 454–463.
HCIHCI-AS-2013-NouriCZ #case study #collaboration #mobile #performance
Mobile Inquiry-Based Learning — A Study of Collaborative Scaffolding and Performance (JN, TCP, KZ), pp. 464–473.
HCIHCI-AS-2013-TakanoS
Nature Sound Ensemble Learning in Narrative-Episode Creation with Pictures (KT, SS), pp. 493–502.
HCIHCI-UC-2013-LinHW #using #visual notation
Establishing a Cognitive Map of Public Place for Blind and Visual Impaired by Using IVEO Hands-On Learning System (QWL, SLH, JLW), pp. 193–198.
HCIHCI-UC-2013-StarySF #interactive
Agility Based on Stakeholder Interaction — Blending Organizational Learning with Interactive BPM (CS, WS, AF), pp. 456–465.
HCIHIMI-D-2013-TakemoriYST #interactive #modelling #process
Modeling a Human’s Learning Processes to Support Continuous Learning on Human Computer Interaction (KT, TY, KS, KT), pp. 555–564.
HCIHIMI-HSM-2013-HiyamaOMESH #artificial reality
Augmented Reality System for Measuring and Learning Tacit Artisan Skills (AH, HO, MM, EE, MS, MH), pp. 85–91.
HCIHIMI-HSM-2013-SaitohI #detection #using #visualisation
Visualization of Anomaly Data Using Peculiarity Detection on Learning Vector Quantization (FS, SI), pp. 181–188.
HCIHIMI-LCCB-2013-Frederick-RecascinoLDKL #case study #game studies
Articulating an Experimental Model for the Study of Game-Based Learning (CFR, DL, SD, JPK, DL), pp. 25–32.
HCIHIMI-LCCB-2013-HallLS #assessment #evaluation #tool support
Psychophysiological Assessment Tools for Evaluation of Learning Technologies (RHH, NSL, HS), pp. 33–42.
HCIHIMI-LCCB-2013-HayashiON #collaboration #interactive
An Experimental Environment for Analyzing Collaborative Learning Interaction (YH, YO, YIN), pp. 43–52.
HCIHIMI-LCCB-2013-KanamoriTA #development #programming
Development of a Computer Programming Learning Support System Based on Reading Computer Program (HK, TT, TA), pp. 63–69.
HCIHIMI-LCCB-2013-NakajimaT #generative #online
New Potential of E-learning by Re-utilizing Open Content Online — TED NOTE: English Learning System as an Auto-assignment Generator (AN, KT), pp. 108–117.
HCIHIMI-LCCB-2013-YamamotoKYMH #online #problem
Learning by Problem-Posing with Online Connected Media Tablets (SY, TK, YY, KM, TH), pp. 165–174.
HCIOCSC-2013-Eustace #network
Building and Sustaining a Lifelong Adult Learning Network (KE), pp. 260–268.
HCIOCSC-2013-StieglitzES #behaviour #education #student
Influence of Monetary and Non-monetary Incentives on Students’ Behavior in Blended Learning Settings in Higher Education (SS, AE, MS), pp. 104–112.
EDOCEDOC-2013-Swenson #design
Designing for an Innovative Learning Organization (KDS), pp. 209–213.
ICEISICEIS-v2-2013-KalsingITN #incremental #legacy #mining #modelling #process #using
Evolutionary Learning of Business Process Models from Legacy Systems using Incremental Process Mining (ACK, CI, LHT, GSdN), pp. 58–69.
ICEISICEIS-v2-2013-MoreiraF #mobile
A Blended Mobile Learning Context Oriented Model in a Cloud Environment applied to a RE Course (FM, MJF), pp. 539–544.
ICEISICEIS-v2-2013-SantaN #framework #modelling #using
Modeling the Creation of a Learning Organization by using the Learning Organization Atlas Framework (MS, SN), pp. 278–285.
ICEISICEIS-v3-2013-VielMPT #how #interactive #multi #student
How are they Watching Me — Learning from Student Interactions with Multimedia Objects Captured from Classroom Presentations (CCV, ELM, MdGCP, CACT), pp. 5–16.
CIKMCIKM-2013-BaragliaMNS #named #predict
LearNext: learning to predict tourists movements (RB, CIM, FMN, FS), pp. 751–756.
CIKMCIKM-2013-CeccarelliLOPT #metric
Learning relatedness measures for entity linking (DC, CL, SO, RP, ST), pp. 139–148.
CIKMCIKM-2013-ChengCLWAC #data type #multi
Feedback-driven multiclass active learning for data streams (YC, ZC, LL, JW, AA, ANC), pp. 1311–1320.
CIKMCIKM-2013-ChenW #classification #scalability
Cost-sensitive learning for large-scale hierarchical classification (JC, DW), pp. 1351–1360.
CIKMCIKM-2013-FangZ #feature model #multi
Discriminative feature selection for multi-view cross-domain learning (ZF, Z(Z), pp. 1321–1330.
CIKMCIKM-2013-HashemiNB #approach #network #retrieval #topic
Expertise retrieval in bibliographic network: a topic dominance learning approach (SHH, MN, HB), pp. 1117–1126.
CIKMCIKM-2013-KamathC #predict #what
Spatio-temporal meme prediction: learning what hashtags will be popular where (KYK, JC), pp. 1341–1350.
ECIRECIR-2013-DangBC #information retrieval #rank
Two-Stage Learning to Rank for Information Retrieval (VD, MB, WBC), pp. 423–434.
ECIRECIR-2013-JuMJ #classification #rank
Learning to Rank from Structures in Hierarchical Text Classification (QJ, AM, RJ), pp. 183–194.
ECIRECIR-2013-NguyenTT #classification #rank #using
Folktale Classification Using Learning to Rank (DN, DT, MT), pp. 195–206.
ICMLICML-c1-2013-0005LSL #feature model #modelling #online
Online Feature Selection for Model-based Reinforcement Learning (TTN, ZL, TS, TYL), pp. 498–506.
ICMLICML-c1-2013-AbernethyAKD #problem #scalability
Large-Scale Bandit Problems and KWIK Learning (JA, KA, MK, MD), pp. 588–596.
ICMLICML-c1-2013-AfkanpourGSB #algorithm #kernel #multi #random #scalability
A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning (AA, AG, CS, MB), pp. 374–382.
ICMLICML-c1-2013-AnandkumarHJK #linear #network
Learning Linear Bayesian Networks with Latent Variables (AA, DH, AJ, SK), pp. 249–257.
ICMLICML-c1-2013-BalcanBEL #performance
Efficient Semi-supervised and Active Learning of Disjunctions (NB, CB, SE, YL), pp. 633–641.
ICMLICML-c1-2013-BootsG #approach
A Spectral Learning Approach to Range-Only SLAM (BB, GJG), pp. 19–26.
ICMLICML-c1-2013-ChenK #adaptation #optimisation
Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization (YC, AK), pp. 160–168.
ICMLICML-c1-2013-CotterSS
Learning Optimally Sparse Support Vector Machines (AC, SSS, NS), pp. 266–274.
ICMLICML-c1-2013-GiguereLMS #algorithm #approach #bound #predict
Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction (SG, FL, MM, KS), pp. 107–114.
ICMLICML-c1-2013-GolubCY
Learning an Internal Dynamics Model from Control Demonstration (MG, SC, BY), pp. 606–614.
ICMLICML-c1-2013-GonenSS #approach #performance
Efficient Active Learning of Halfspaces: an Aggressive Approach (AG, SS, SSS), pp. 480–488.
ICMLICML-c1-2013-GongGS #adaptation #invariant
Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation (BG, KG, FS), pp. 222–230.
ICMLICML-c1-2013-KadriGP #approach #kernel
A Generalized Kernel Approach to Structured Output Learning (HK, MG, PP), pp. 471–479.
ICMLICML-c1-2013-KarbasiSS
Iterative Learning and Denoising in Convolutional Neural Associative Memories (AK, AHS, AS), pp. 445–453.
ICMLICML-c1-2013-KumarB #bound #graph
Convex Relaxations for Learning Bounded-Treewidth Decomposable Graphs (KSSK, FRB), pp. 525–533.
ICMLICML-c1-2013-LiLSHD #generative #using
Learning Hash Functions Using Column Generation (XL, GL, CS, AvdH, ARD), pp. 142–150.
ICMLICML-c1-2013-LimLM #metric #robust
Robust Structural Metric Learning (DL, GRGL, BM), pp. 615–623.
ICMLICML-c1-2013-MaatenCTW
Learning with Marginalized Corrupted Features (LvdM, MC, ST, KQW), pp. 410–418.
ICMLICML-c1-2013-MaillardNOR #bound #representation
Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning (OAM, PN, RO, DR), pp. 543–551.
ICMLICML-c1-2013-RuvoloE #algorithm #named #performance
ELLA: An Efficient Lifelong Learning Algorithm (PR, EE), pp. 507–515.
ICMLICML-c1-2013-ZuluagaSKP #multi #optimisation
Active Learning for Multi-Objective Optimization (MZ, GS, AK, MP), pp. 462–470.
ICMLICML-c2-2013-GaneshapillaiGL
Learning Connections in Financial Time Series (GG, JVG, AL), pp. 109–117.
ICMLICML-c2-2013-GolovinSMY #ram #scalability
Large-Scale Learning with Less RAM via Randomization (DG, DS, HBM, MY), pp. 325–333.
ICMLICML-c2-2013-KrummenacherOB #multi
Ellipsoidal Multiple Instance Learning (GK, CSO, JMB), pp. 73–81.
ICMLICML-c2-2013-MaurerPR #multi
Sparse coding for multitask and transfer learning (AM, MP, BRP), pp. 343–351.
ICMLICML-c2-2013-MeentBWGW #markov #modelling
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data (JWvdM, JEB, FW, RLG, CW), pp. 361–369.
ICMLICML-c2-2013-MinhBM #framework #multi
A unifying framework for vector-valued manifold regularization and multi-view learning (HQM, LB, VM), pp. 100–108.
ICMLICML-c2-2013-RanganathWBX #adaptation #probability
An Adaptive Learning Rate for Stochastic Variational Inference (RR, CW, DMB, EPX), pp. 298–306.
ICMLICML-c2-2013-SohnZLL
Learning and Selecting Features Jointly with Point-wise Gated Boltzmann Machines (KS, GZ, CL, HL), pp. 217–225.
ICMLICML-c2-2013-Tran-DinhKC #framework #graph #matrix
A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions (QTD, ATK, VC), pp. 271–279.
ICMLICML-c2-2013-TranPV #multi
Thurstonian Boltzmann Machines: Learning from Multiple Inequalities (TT, DQP, SV), pp. 46–54.
ICMLICML-c2-2013-YangH #classification
Activized Learning with Uniform Classification Noise (LY, SH), pp. 370–378.
ICMLICML-c3-2013-0002T #kernel
Differentially Private Learning with Kernels (PJ, AT), pp. 118–126.
ICMLICML-c3-2013-AlmingolML #behaviour #multi
Learning Multiple Behaviors from Unlabeled Demonstrations in a Latent Controller Space (JA, LM, ML), pp. 136–144.
ICMLICML-c3-2013-BalasubramanianYL
Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations (KB, KY, GL), pp. 289–297.
ICMLICML-c3-2013-BalcanBM #ontology
Exploiting Ontology Structures and Unlabeled Data for Learning (NB, AB, YM), pp. 1112–1120.
ICMLICML-c3-2013-BellemareVB #recursion
Bayesian Learning of Recursively Factored Environments (MGB, JV, MB), pp. 1211–1219.
ICMLICML-c3-2013-BrechtelGD #incremental #performance #representation
Solving Continuous POMDPs: Value Iteration with Incremental Learning of an Efficient Space Representation (SB, TG, RD), pp. 370–378.
ICMLICML-c3-2013-ChattopadhyayFDPY
Joint Transfer and Batch-mode Active Learning (RC, WF, ID, SP, JY), pp. 253–261.
ICMLICML-c3-2013-Cheng #similarity
Riemannian Similarity Learning (LC), pp. 540–548.
ICMLICML-c3-2013-CoatesHWWCN #off the shelf
Deep learning with COTS HPC systems (AC, BH, TW, DJW, BCC, AYN), pp. 1337–1345.
ICMLICML-c3-2013-DalalyanHMS #modelling #programming
Learning Heteroscedastic Models by Convex Programming under Group Sparsity (ASD, MH, KM, JS), pp. 379–387.
ICMLICML-c3-2013-DimitrakakisT
ABC Reinforcement Learning (CD, NT), pp. 684–692.
ICMLICML-c3-2013-GensD #network
Learning the Structure of Sum-Product Networks (RG, PMD), pp. 873–880.
ICMLICML-c3-2013-GuptaPV #approach #multi #parametricity
Factorial Multi-Task Learning : A Bayesian Nonparametric Approach (SKG, DQP, SV), pp. 657–665.
ICMLICML-c3-2013-HockingRVB #detection #using
Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression (TH, GR, JPV, FRB), pp. 172–180.
ICMLICML-c3-2013-HoXV #on the #taxonomy
On A Nonlinear Generalization of Sparse Coding and Dictionary Learning (JH, YX, BCV), pp. 1480–1488.
ICMLICML-c3-2013-HuangS #markov #modelling
Spectral Learning of Hidden Markov Models from Dynamic and Static Data (TKH, JGS), pp. 630–638.
ICMLICML-c3-2013-JancsaryNR #predict
Learning Convex QP Relaxations for Structured Prediction (JJ, SN, CR), pp. 915–923.
ICMLICML-c3-2013-JoseGAV #kernel #performance #predict
Local Deep Kernel Learning for Efficient Non-linear SVM Prediction (CJ, PG, PA, MV), pp. 486–494.
ICMLICML-c3-2013-JoulaniGS #feedback #online
Online Learning under Delayed Feedback (PJ, AG, CS), pp. 1453–1461.
ICMLICML-c3-2013-JunZSR
Learning from Human-Generated Lists (KSJ, X(Z, BS, TTR), pp. 181–189.
ICMLICML-c3-2013-KarS0K #algorithm #on the #online
On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions (PK, BKS, PJ, HK), pp. 441–449.
ICMLICML-c3-2013-KontorovichNW #on the
On learning parametric-output HMMs (AK, BN, RW), pp. 702–710.
ICMLICML-c3-2013-KoppulaS #detection #process
Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and Anticipation (HSK, AS), pp. 792–800.
ICMLICML-c3-2013-KraehenbuehlK #convergence #parametricity #random
Parameter Learning and Convergent Inference for Dense Random Fields (PK, VK), pp. 513–521.
ICMLICML-c3-2013-KuzborskijO
Stability and Hypothesis Transfer Learning (IK, FO), pp. 942–950.
ICMLICML-c3-2013-LattimoreHS
The Sample-Complexity of General Reinforcement Learning (TL, MH, PS), pp. 28–36.
ICMLICML-c3-2013-MalioutovV
Exact Rule Learning via Boolean Compressed Sensing (DMM, KRV), pp. 765–773.
ICMLICML-c3-2013-MemisevicE #invariant #problem
Learning invariant features by harnessing the aperture problem (RM, GE), pp. 100–108.
ICMLICML-c3-2013-NiuJDHS #approach #novel
Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning (GN, WJ, BD, HH, MS), pp. 10–18.
ICMLICML-c3-2013-RamanJSS
Stable Coactive Learning via Perturbation (KR, TJ, PS, TS), pp. 837–845.
ICMLICML-c3-2013-Romera-ParedesABP #multi
Multilinear Multitask Learning (BRP, HA, NBB, MP), pp. 1444–1452.
ICMLICML-c3-2013-RossZYDB #policy #predict
Learning Policies for Contextual Submodular Prediction (SR, JZ, YY, DD, DB), pp. 1364–1372.
ICMLICML-c3-2013-SchaulZL
No more pesky learning rates (TS, SZ, YL), pp. 343–351.
ICMLICML-c3-2013-SilverNBWM #concurrent #interactive
Concurrent Reinforcement Learning from Customer Interactions (DS, LN, DB, SW, JM), pp. 924–932.
ICMLICML-c3-2013-SimsekliCY #matrix #modelling
Learning the β-Divergence in Tweedie Compound Poisson Matrix Factorization Models (US, ATC, YKY), pp. 1409–1417.
ICMLICML-c3-2013-SodomkaHLG #game studies #named #probability
Coco-Q: Learning in Stochastic Games with Side Payments (ES, EH, MLL, AG), pp. 1471–1479.
ICMLICML-c3-2013-SutskeverMDH #on the
On the importance of initialization and momentum in deep learning (IS, JM, GED, GEH), pp. 1139–1147.
ICMLICML-c3-2013-TarlowSCSZ #probability
Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning (DT, KS, LC, IS, RSZ), pp. 199–207.
ICMLICML-c3-2013-WangNH #robust #self
Robust and Discriminative Self-Taught Learning (HW, FN, HH), pp. 298–306.
ICMLICML-c3-2013-WangNH13a #clustering #multi
Multi-View Clustering and Feature Learning via Structured Sparsity (HW, FN, HH), pp. 352–360.
ICMLICML-c3-2013-WangWBLT #multi #taxonomy
Max-Margin Multiple-Instance Dictionary Learning (XW, BW, XB, WL, ZT), pp. 846–854.
ICMLICML-c3-2013-XuKHW #representation
Anytime Representation Learning (ZEX, MJK, GH, KQW), pp. 1076–1084.
ICMLICML-c3-2013-YangLZ #matrix #multi
Multi-Task Learning with Gaussian Matrix Generalized Inverse Gaussian Model (MY, YL, ZZ), pp. 423–431.
ICMLICML-c3-2013-YuLKJC
∝SVM for Learning with Label Proportions (FXY, DL, SK, TJ, SFC), pp. 504–512.
ICMLICML-c3-2013-ZemelWSPD
Learning Fair Representations (RSZ, YW, KS, TP, CD), pp. 325–333.
ICMLICML-c3-2013-ZhangHSL #multi #named
MILEAGE: Multiple Instance LEArning with Global Embedding (DZ, JH, LS, RDL), pp. 82–90.
ICMLICML-c3-2013-ZhangYJLH #bound #kernel #online
Online Kernel Learning with a Near Optimal Sparsity Bound (LZ, JY, RJ, ML, XH), pp. 621–629.
ICMLICML-c3-2013-ZhouZS #kernel #multi #process
Learning Triggering Kernels for Multi-dimensional Hawkes Processes (KZ, HZ, LS), pp. 1301–1309.
ICMLICML-c3-2013-ZweigW
Hierarchical Regularization Cascade for Joint Learning (AZ, DW), pp. 37–45.
KDDKDD-2013-BahadoriLX #performance #probability #process
Fast structure learning in generalized stochastic processes with latent factors (MTB, YL, EPX), pp. 284–292.
KDDKDD-2013-ChakrabartiH #scalability #social
Speeding up large-scale learning with a social prior (DC, RH), pp. 650–658.
KDDKDD-2013-ChenHKB #named
DTW-D: time series semi-supervised learning from a single example (YC, BH, EJK, GEAPAB), pp. 383–391.
KDDKDD-2013-DasMGW
Learning to question: leveraging user preferences for shopping advice (MD, GDFM, AG, IW), pp. 203–211.
KDDKDD-2013-FeiKSNMH #detection
Heat pump detection from coarse grained smart meter data with positive and unlabeled learning (HF, YK, SS, MRN, SKM, JH), pp. 1330–1338.
KDDKDD-2013-GeGLZ #estimation #multi
Multi-source deep learning for information trustworthiness estimation (LG, JG, XL, AZ), pp. 766–774.
KDDKDD-2013-GilpinED #algorithm #framework
Guided learning for role discovery (GLRD): framework, algorithms, and applications (SG, TER, IND), pp. 113–121.
KDDKDD-2013-HaoCZ0RK #towards
Towards never-ending learning from time series streams (YH, YC, JZ, BH, TR, EJK), pp. 874–882.
KDDKDD-2013-Howard
The business impact of deep learning (JH), p. 1135.
KDDKDD-2013-KongY #automation #classification #distance
Discriminant malware distance learning on structural information for automated malware classification (DK, GY), pp. 1357–1365.
KDDKDD-2013-KutzkovBBG #named
STRIP: stream learning of influence probabilities (KK, AB, FB, AG), pp. 275–283.
KDDKDD-2013-LinWHY #information management #modelling #social
Extracting social events for learning better information diffusion models (SL, FW, QH, PSY), pp. 365–373.
KDDKDD-2013-LiuFYX #recommendation
Learning geographical preferences for point-of-interest recommendation (BL, YF, ZY, HX), pp. 1043–1051.
KDDKDD-2013-MorenoNK #graph #modelling
Learning mixed kronecker product graph models with simulated method of moments (SM, JN, SK), pp. 1052–1060.
KDDKDD-2013-SutherlandPS #matrix #rank
Active learning and search on low-rank matrices (DJS, BP, JGS), pp. 212–220.
KDDKDD-2013-TanXGW #metric #modelling #optimisation #rank #ranking
Direct optimization of ranking measures for learning to rank models (MT, TX, LG, SW), pp. 856–864.
KDDKDD-2013-Vatsavai #approach #multi #using
Gaussian multiple instance learning approach for mapping the slums of the world using very high resolution imagery (RRV), pp. 1419–1426.
KDDKDD-2013-WangY #query
Querying discriminative and representative samples for batch mode active learning (ZW, JY), pp. 158–166.
KDDKDD-2013-Wright #data analysis #optimisation
Optimization in learning and data analysis (SJW), p. 3.
KDDKDD-2013-XiangYFWTY #multi #predict
Multi-source learning with block-wise missing data for Alzheimer’s disease prediction (SX, LY, WF, YW, PMT, JY), pp. 185–193.
KDDKDD-2013-ZhangHL #multi #named
MI2LS: multi-instance learning from multiple informationsources (DZ, JH, RDL), pp. 149–157.
KDDKDD-2013-ZhaoH #detection #online
Cost-sensitive online active learning with application to malicious URL detection (PZ, SCHH), pp. 919–927.
KDDKDD-2013-ZhaoYNG #framework #twitter
A transfer learning based framework of crowd-selection on twitter (ZZ, DY, WN, SG), pp. 1514–1517.
KDIRKDIR-KMIS-2013-AtrashAM #enterprise #semantics
A Semantic Model for Small and Medium-sized Enterprises to Support Organizational Learning (AA, MHA, CM), pp. 476–483.
KDIRKDIR-KMIS-2013-BerkaniN #collaboration #recommendation #semantics
Semantic Collaborative Filtering for Learning Objects Recommendation (LB, ON), pp. 52–63.
KDIRKDIR-KMIS-2013-Dessne
Learning in an Organisation — Exploring the Nature of Relationships (KD), pp. 496–501.
KDIRKDIR-KMIS-2013-Eardley #information management
Negotiated Work-based Learning and Organisational Learning — The Relationship between Individual and Organisational Knowledge Management (AE), pp. 1–5.
KDIRKDIR-KMIS-2013-NhungNCLT #approach #image #multi
A Multiple Instance Learning Approach to Image Annotation with Saliency Map (TPN, CTN, JC, HVL, TT), pp. 152–159.
KDIRKDIR-KMIS-2013-SaxenaBW #composition
A Cognitive Reference based Model for Learning Compositional Hierarchies with Whole-composite Tags (ABS, AB, AW), pp. 119–127.
KEODKEOD-2013-WohlgenanntBS #automation #evolution #ontology #prototype
A Prototype for Automating Ontology Learning and Ontology Evolution (GW, SB, MS), pp. 407–412.
MLDMMLDM-2013-BouillonAA #evolution #fuzzy #gesture #recognition
Decremental Learning of Evolving Fuzzy Inference Systems: Application to Handwritten Gesture Recognition (MB, ÉA, AA), pp. 115–129.
MLDMMLDM-2013-ElGibreenA #multi #product line
Multi Model Transfer Learning with RULES Family (HE, MSA), pp. 42–56.
MLDMMLDM-2013-KoharaS #self
Typhoon Damage Scale Forecasting with Self-Organizing Maps Trained by Selective Presentation Learning (KK, IS), pp. 16–26.
MLDMMLDM-2013-MaziluCGRHT #detection #predict
Feature Learning for Detection and Prediction of Freezing of Gait in Parkinson’s Disease (SM, AC, EG, DR, JMH, GT), pp. 144–158.
RecSysRecSys-2013-HuY #process #recommendation
Interview process learning for top-n recommendation (FH, YY), pp. 331–334.
RecSysRecSys-2013-KaratzoglouBS #rank #recommendation
Learning to rank for recommender systems (AK, LB, YS), pp. 493–494.
RecSysRecSys-2013-KucharK #case study #named #web #web service
GAIN: web service for user tracking and preference learning — a smart TV use case (JK, TK), pp. 467–468.
RecSysRecSys-2013-SharmaY #community #recommendation
Pairwise learning in recommendation: experiments with community recommendation on linkedin (AS, BY), pp. 193–200.
RecSysRecSys-2013-WestonYW #rank #recommendation #statistics
Learning to rank recommendations with the k-order statistic loss (JW, HY, RJW), pp. 245–248.
SEKESEKE-2013-BarbosaFNM #architecture #towards
Towards the Establishment of a Reference Architecture for Developing Learning Environments (EFB, MLF, EYN, JCM), pp. 350–355.
SIGIRSIGIR-2013-LimsopathamMO
Learning to combine representations for medical records search (NL, CM, IO), pp. 833–836.
SIGIRSIGIR-2013-Moschitti #kernel #rank #semantics
Kernel-based learning to rank with syntactic and semantic structures (AM), p. 1128.
SIGIRSIGIR-2013-Shokouhi #personalisation #query
Learning to personalize query auto-completion (MS), pp. 103–112.
SIGIRSIGIR-2013-WangHWZ0M #multimodal #search-based
Learning to name faces: a multimodal learning scheme for search-based face annotation (DW, SCHH, PW, JZ, YH, CM), pp. 443–452.
SIGIRSIGIR-2013-ZhangWYW #network #predict
Learning latent friendship propagation networks with interest awareness for link prediction (JZ, CW, PSY, JW), pp. 63–72.
OOPSLAOOPSLA-2013-ChoiNS #android #approximate #testing #user interface
Guided GUI testing of android apps with minimal restart and approximate learning (WC, GCN, KS), pp. 623–640.
POPLPOPL-2013-BotincanB #specification
Sigma*: symbolic learning of input-output specifications (MB, DB), pp. 443–456.
POPLPOPL-2013-DSilvaHK
Abstract conflict driven learning (VD, LH, DK), pp. 143–154.
SASSAS-2013-0001GHAN #concept #geometry #verification
Verification as Learning Geometric Concepts (RS, SG, BH, AA, AVN), pp. 388–411.
RERE-2013-ShiWL #evolution #predict
Learning from evolution history to predict future requirement changes (LS, QW, ML), pp. 135–144.
RERE-2013-SultanovH #requirements
Application of reinforcement learning to requirements engineering: requirements tracing (HS, JHH), pp. 52–61.
ASEASE-2013-DietrichCS #effectiveness #query #requirements #retrieval
Learning effective query transformations for enhanced requirements trace retrieval (TD, JCH, YS), pp. 586–591.
ASEASE-2013-GuoCASW #approach #performance #predict #statistics #variability
Variability-aware performance prediction: A statistical learning approach (JG, KC, SA, NS, AW), pp. 301–311.
ASEASE-2013-Xiao0LLS #named #type system
TzuYu: Learning stateful typestates (HX, JS, YL, SWL, CS), pp. 432–442.
ICSEICSE-2013-MengKM #named
LASE: locating and applying systematic edits by learning from examples (NM, MK, KSM), pp. 502–511.
ICSEICSE-2013-NamPK #fault
Transfer defect learning (JN, SJP, SK), pp. 382–391.
ICSEICSE-2013-SykesCMKRI #adaptation #modelling
Learning revised models for planning in adaptive systems (DS, DC, JM, JK, AR, KI), pp. 63–71.
ICSEICSE-2013-TillmannHXGB #education #game studies #interactive #programming #re-engineering
Teaching and learning programming and software engineering via interactive gaming (NT, JdH, TX, SG, JB), pp. 1117–1126.
SACSAC-2013-BlondelSU #classification #constraints #using
Learning non-linear classifiers with a sparsity constraint using L1 regularization (MB, KS, KU), pp. 167–169.
SACSAC-2013-FilhoB #mobile #requirements
A requirements catalog for mobile learning environments (NFDF, EFB), pp. 1266–1271.
SACSAC-2013-LinCLG #approach #data-driven #distributed #predict
Distributed dynamic data driven prediction based on reinforcement learning approach (SYL, KMC, CCL, NG), pp. 779–784.
SACSAC-2013-LommatzschKA #hybrid #modelling #recommendation #semantics
Learning hybrid recommender models for heterogeneous semantic data (AL, BK, SA), pp. 275–276.
SACSAC-2013-SeelandKP #graph #kernel
Model selection based product kernel learning for regression on graphs (MS, SK, BP), pp. 136–143.
CASECASE-2013-LiX #adaptation
Off-line learning based adaptive dispatching rule for semiconductor wafer fabrication facility (LL, HX), pp. 1028–1033.
CASECASE-2013-OFlahertyE #bound #sequence
Learning to locomote: Action sequences and switching boundaries (RO, ME), pp. 7–12.
STOCSTOC-2013-BrakerskiLPRS #fault
Classical hardness of learning with errors (ZB, AL, CP, OR, DS), pp. 575–584.
TACASTACAS-2013-ChenW #algorithm #library #named
BULL: A Library for Learning Algorithms of Boolean Functions (YFC, BYW), pp. 537–542.
TACASTACAS-2013-WhiteL #data type #evolution #identification #in memory #memory management
Identifying Dynamic Data Structures by Learning Evolving Patterns in Memory (DHW, GL), pp. 354–369.
CAVCAV-2013-0001LMN #data type #invariant #linear #quantifier
Learning Universally Quantified Invariants of Linear Data Structures (PG, CL, PM, DN), pp. 813–829.
CAVCAV-2013-ChagantyLNR #relational #smt #using
Combining Relational Learning with SMT Solvers Using CEGAR (ATC, AL, AVN, SKR), pp. 447–462.
ISSTAISSTA-2013-HowarGR #analysis #generative #hybrid #interface
Hybrid learning: interface generation through static, dynamic, and symbolic analysis (FH, DG, ZR), pp. 268–279.
ISSTAISSTA-2013-TrippWG #approach #security #testing #web
Finding your way in the testing jungle: a learning approach to web security testing (OT, OW, LG), pp. 347–357.
ICSTSAT-2013-Johannsen #exponential #proving
Exponential Separations in a Hierarchy of Clause Learning Proof Systems (JJ), pp. 40–51.
ICSTSAT-2013-LonsingEG #performance #pseudo #quantifier
Efficient Clause Learning for Quantified Boolean Formulas via QBF Pseudo Unit Propagation (FL, UE, AVG), pp. 100–115.
CBSECBSE-2012-AbateCTZ #component #future of #repository
Learning from the future of component repositories (PA, RDC, RT, SZ), pp. 51–60.
DocEngDocEng-2012-MoulderM #how #layout
Learning how to trade off aesthetic criteria in layout (PM, KM), pp. 33–36.
HTHT-2012-SchofeggerKSG #behaviour #social
Learning user characteristics from social tagging behavior (KS, CK, PS, MG), pp. 207–212.
JCDLJCDL-2012-NewmanNHB #topic
Learning topics and related passages in books (DN, YN, KH, AB), pp. 195–198.
SIGMODSIGMOD-2012-AbiteboulAMS #xml
Auto-completion learning for XML (SA, YA, TM, PS), pp. 669–672.
VLDBVLDB-2012-IseleB #programming #search-based #using
Learning Expressive Linkage Rules using Genetic Programming (RI, CB), pp. 1638–1649.
VLDBVLDB-2012-KanagalAPJYP #behaviour #recommendation #taxonomy #using
Supercharging Recommender Systems using Taxonomies for Learning User Purchase Behavior (BK, AA, SP, VJ, JY, LGP), pp. 956–967.
VLDBVLDB-2012-SinghG #semantics #string
Learning Semantic String Transformations from Examples (RS, SG), pp. 740–751.
CSEETCSEET-2012-AroraG #collaboration #programming #source code
Learning to Write Programs with Others: Collaborative Quadruple Programming (RA, SG), pp. 32–41.
CSEETCSEET-2012-TillmannHXB #education #game studies #named #social
Pex4Fun: Teaching and Learning Computer Science via Social Gaming (NT, JdH, TX, JB), pp. 90–91.
ITiCSEITiCSE-2012-AsadB #aspect-oriented #concept #image
Are children capable of learning image processing concepts?: cognitive and affective aspects (KA, MB), pp. 227–231.
ITiCSEITiCSE-2012-BaghdadiAR #case study #distance #safety #tool support
Applying advanced technology tools in distance learning: case study: traffic data and road safety (MB, KA, JR), p. 389.
ITiCSEITiCSE-2012-BoyceCPCB #behaviour #game studies
Maximizing learning and guiding behavior in free play user generated content environments (AKB, AC, SP, DC, TB), pp. 10–15.
ITiCSEITiCSE-2012-CamaraPV #collaboration #evaluation #framework #programming
Evaluation of a collaborative instructional framework for programming learning (LMSC, MPV, JÁVI), pp. 162–167.
ITiCSEITiCSE-2012-ChristensenC
Lectures abandoned: active learning by active seminars (HBC, AVC), pp. 16–21.
ITiCSEITiCSE-2012-GomesSM #behaviour #case study #student #towards
A study on students’ behaviours and attitudes towards learning to program (AJG, ÁNS, AJM), pp. 132–137.
ITiCSEITiCSE-2012-GovenderG #object-oriented #programming #student
Are students learning object oriented programming in an object oriented programming course?: student voices (DWG, IG), p. 395.
ITiCSEITiCSE-2012-HamadaN
A learning tool for MP3 audio compression (MH, HN), p. 397.
ITiCSEITiCSE-2012-HiltonJ #array #education #on the #testing
On teaching arrays with test-driven learning in WebIDE (MH, DSJ), pp. 93–98.
ITiCSEITiCSE-2012-KrausePR
Formal learning groups in an introductory CS course: a qualitative exploration (JK, IP, CR), pp. 315–320.
ITiCSEITiCSE-2012-Luxton-ReillyDPS #how #process #student
Activities, affordances and attitude: how student-generated questions assist learning (ALR, PD, BP, RS), pp. 4–9.
ITiCSEITiCSE-2012-MalekoHD #case study #experience #mobile #programming #social
Novices’ perceptions and experiences of a mobile social learning environment for learning of programming (MM, MH, DJD), pp. 285–290.
ITiCSEITiCSE-2012-MehtaKP #algorithm #network
Forming project groups while learning about matching and network flows in algorithms (DPM, TMK, IP), pp. 40–45.
ITiCSEITiCSE-2012-MussaiL #animation #concept #object-oriented
An animation as an illustrate tool for learning concepts in oop (YM, NL), p. 386.
ITiCSEITiCSE-2012-MyllymakiH #case study
Choosing a study mode in blended learning (MM, IH), pp. 291–296.
ITiCSEITiCSE-2012-Sudol-DeLyserSC #comprehension #problem
Code comprehension problems as learning events (LASD, MS, SC), pp. 81–86.
ITiCSEITiCSE-2012-Velazquez-Iturbide #algorithm #approach #refinement
Refinement of an experimental approach tocomputer-based, active learning of greedy algorithms (JÁVI), pp. 46–51.
SIGITESIGITE-2012-Al-NoryI #design #education #information management
Learning by design: making the case for a teaching strategy to teach information systems courses (MTAN, DAI), pp. 37–42.
SIGITESIGITE-2012-ElnagarA #delivery #effectiveness #programming
A modified team-based learning methodology for effective delivery of an introductory programming course (AE, MA), pp. 177–182.
SIGITESIGITE-2012-Farag #online #programming
Comparing achievement of intended learning outcomes in online programming classes with blended offerings (WF), pp. 25–30.
SIGITESIGITE-2012-Kawash #problem #student
Engaging students by intertwining puzzle-based and problem-based learning (JK), pp. 227–232.
SIGITESIGITE-2012-SabinSR #challenge #interactive #online
Interactive learning online: challenges and opportunities (MS, AS, BR), pp. 201–202.
SIGITESIGITE-2012-SeolSK #mobile #student #towards #using
Use of a mobile application to promote scientific discovery learning: students’ perceptions towards and practical adoption of a mobile application (SS, AS, PK), pp. 121–126.
SIGITESIGITE-2012-Settle #education #student
Turning the tables: learning from students about teaching CS1 (AS), pp. 133–138.
SIGITESIGITE-2012-ShaikhK #delphi #education #identification #metric
Identifying measures to foster teachers’ competence for personal learning environment conceived teaching scenarios: a delphi study (ZAS, SAK), pp. 127–132.
DLTDLT-2012-BoiretLN
Learning Rational Functions (AB, AL, JN), pp. 273–283.
LATALATA-2012-GeilkeZ #algorithm #pattern matching #polynomial
Polynomial-Time Algorithms for Learning Typed Pattern Languages (MG, SZ), pp. 277–288.
LATALATA-2012-Yoshinaka #context-free grammar #integration
Integration of the Dual Approaches in the Distributional Learning of Context-Free Grammars (RY), pp. 538–550.
FMFM-2012-AartsHKOV #abstraction #automaton #refinement
Automata Learning through Counterexample Guided Abstraction Refinement (FA, FH, HK, PO, FWV), pp. 10–27.
AIIDEAIIDE-2012-YoungH #game studies #realtime
Evolutionary Learning of Goal Priorities in a Real-Time Strategy Game (JY, NH).
CoGCIG-2012-GemineSFE #game studies #realtime
Imitative learning for real-time strategy games (QG, FS, RF, DE), pp. 424–429.
CoGCIG-2012-PenaOPL #evolution #game studies
Learning and evolving combat game controllers (LP, SO, JMPS, SML), pp. 195–202.
CoGCIG-2012-RunarssonL #difference #game studies
Imitating play from game trajectories: Temporal difference learning versus preference learning (TPR, SML), pp. 79–82.
CoGCIG-2012-SwansonEJ #composition #corpus #visual notation
Learning visual composition preferences from an annotated corpus generated through gameplay (RS, DE, AJ), pp. 363–370.
CoGCIG-2012-TastanCS #game studies
Learning to intercept opponents in first person shooter games (BT, YC, GS), pp. 100–107.
CoGCIG-2012-WenderW #game studies #realtime
Applying reinforcement learning to small scale combat in the real-time strategy game StarCraft: Broodwar (SW, IDW), pp. 402–408.
CoGCIG-2012-WittkampBHW #realtime
Noise tolerance for real-time evolutionary learning of cooperative predator-prey strategies (MW, LB, PH, RLW), pp. 25–32.
FDGFDG-2012-PettitH #policy #simulation
Evolutionary learning of policies for MCTS simulations (JP, DPH), pp. 212–219.
FDGFDG-2012-TuitePFRJLAT #game studies #named #online #social
Picard: a creative and social online flashcard learning game (KT, TP, SBF, TR, AJ, YEL, EA, SLT), pp. 231–234.
CoGVS-Games-2012-BachvarovaBPPR #effectiveness #game studies
Measuring the Effectiveness of Learning with Serious Games in Corporate Training (YB, SB, BvdP, MP, IR), pp. 221–232.
CoGVS-Games-2012-CuratelliM #design #education #tool support
Design Criteria for Educational Tools to Overcome Mathematics Learning Difficulties (FC, CM), pp. 92–102.
CoGVS-Games-2012-FreitasEOKNPRUS #game studies
Hot Issues in Game Enhanced Learning: The GEL Viewpoint (SdF, JE, MO, KK, MN, MP, MR, MU, IAS), pp. 25–31.
CoGVS-Games-2012-FreitasKNOPRS #game studies #named
GEL: Exploring Game Enhanced Learning (SdF, KK, MN, MO, MP, MR, IAS), pp. 289–292.
CoGVS-Games-2012-HulusicP #framework #quote
“LeFCA”: Learning Framework for Children with Autism (VH, NP), pp. 4–16.
CoGVS-Games-2012-Lombardi #game studies
Not-so-Serious Games for Language Learning. Now with 99, 9% More Humour on Top (IL), pp. 148–158.
CoGVS-Games-2012-NeyEE #education #game studies #matrix
Paving the Way to Game Based Learning: A Question Matrix for Teacher Reflection (MN, VE, JE), pp. 17–24.
CoGVS-Games-2012-ObikweluR #framework #game studies
The Serious Game Constructivist Framework for Children's Learning (CO, JCR), pp. 32–37.
CoGVS-Games-2012-PereiraBPPBKK #game studies #roadmap #social
Serious Games for Personal and Social Learning & Ethics: Status and Trends (GDGP, AB, RP, AP, FB, MK, RK), pp. 53–65.
CoGVS-Games-2012-PerezA12a #game studies #social
Learning with your Friend's Data: Game Entity Social Mapping in Serious Games (AMP, OA), pp. 299–300.
CoGVS-Games-2012-Serrano-LagunaTMF #assessment #game studies #student
Tracing a Little for Big Improvements: Application of Learning Analytics and Videogames for Student Assessment (ÁSL, JT, PMG, BFM), pp. 203–209.
CHICHI-2012-ChinF #difference #health
Age differences in exploratory learning from a health information website (JC, WTF), pp. 3031–3040.
CHICHI-2012-DongDJKNA #game studies
Discovery-based games for learning software (TD, MD, DJ, KK, MWN, MSA), pp. 2083–2086.
CHICHI-2012-JainB #performance
User learning and performance with bezel menus (MJ, RB), pp. 2221–2230.
CHICHI-2012-OganFMDMC #exclamation #interactive #quote #social
“Oh dear Stacy!”: social interaction, elaboration, and learning with teachable agents (AO, SLF, EM, CD, NM, JC), pp. 39–48.
CHICHI-2012-ParkC12a #adaptation #deployment #design
Adaptation as design: learning from an EMR deployment study (SYP, YC), pp. 2097–2106.
CHICHI-2012-VitakIDEG
Gaze-augmented think-aloud as an aid to learning (SAV, JEI, ATD, SE, AKG), pp. 2991–3000.
CHICHI-2012-XuBRTM #communication #how #towards
Learning how to feel again: towards affective workplace presence and communication technologies (AX, JTB, EGR, TT, WvM), pp. 839–848.
CSCWCSCW-2012-HemphillO #adaptation #community #gender #overview
Learning the lingo?: gender, prestige and linguistic adaptation in review communities (LH, JO), pp. 305–314.
CSCWCSCW-2012-RzeszotarskiK #predict #wiki #word
Learning from history: predicting reverted work at the word level in wikipedia (JMR, AK), pp. 437–440.
CSCWCSCW-2012-SarcevicPWSBA #coordination #distributed
“Beacons of hope” in decentralized coordination: learning from on-the-ground medical twitterers during the 2010 Haiti earthquake (AS, LP, JW, KS, MB, KMA), pp. 47–56.
ICEISICEIS-J-2012-RibeiroFBKE #algorithm #approach #markov #process
Combining Learning Algorithms: An Approach to Markov Decision Processes (RR, FF, MACB, ALK, FE), pp. 172–188.
ICEISICEIS-v1-2012-RibeiroFBBDKE #algorithm #approach
Unified Algorithm to Improve Reinforcement Learning in Dynamic Environments — An Instance-based Approach (RR, FF, MACB, APB, OBD, ALK, FE), pp. 229–238.
CIKMCIKM-2012-AgarwalRSMLGF #rank #robust
Learning to rank for robust question answering (AA, HR, KS, PM, RDL, DG, JF), pp. 833–842.
CIKMCIKM-2012-AnHS #ontology #web
Learning to discover complex mappings from web forms to ontologies (YA, XH, IYS), pp. 1253–1262.
CIKMCIKM-2012-CaiZ #injection #rank
Variance maximization via noise injection for active sampling in learning to rank (WC, YZ), pp. 1809–1813.
CIKMCIKM-2012-ChaliHI #performance
Improving the performance of the reinforcement learning model for answering complex questions (YC, SAH, KI), pp. 2499–2502.
CIKMCIKM-2012-ChengZXAC #classification #on the
On active learning in hierarchical classification (YC, KZ, YX, AA, ANC), pp. 2467–2470.
CIKMCIKM-2012-Cohen #metric #random #similarity
Learning similarity measures based on random walks (WWC), p. 3.
CIKMCIKM-2012-FangS #approach #feedback #recommendation
A latent pairwise preference learning approach for recommendation from implicit feedback (YF, LS), pp. 2567–2570.
CIKMCIKM-2012-GuoMCJ #recommendation #social
Learning to recommend with social relation ensemble (LG, JM, ZC, HJ), pp. 2599–2602.
CIKMCIKM-2012-KanhabuaN #query #rank
Learning to rank search results for time-sensitive queries (NK, KN), pp. 2463–2466.
CIKMCIKM-2012-LiBCH #clustering #relational
Relational co-clustering via manifold ensemble learning (PL, JB, CC, ZH), pp. 1687–1691.
CIKMCIKM-2012-LuZZX #image #scalability #semantics #set
Semantic context learning with large-scale weakly-labeled image set (YL, WZ, KZ, XX), pp. 1859–1863.
CIKMCIKM-2012-MacdonaldSO #on the #query #rank
On the usefulness of query features for learning to rank (CM, RLTS, IO), pp. 2559–2562.
CIKMCIKM-2012-MetzgerSHS #interactive
LUKe and MIKe: learning from user knowledge and managing interactive knowledge extraction (SM, MS, KH, RS), pp. 2671–2673.
CIKMCIKM-2012-MorenoSRS #multi #named
TALMUD: transfer learning for multiple domains (OM, BS, LR, GS), pp. 425–434.
CIKMCIKM-2012-NegahbanRG #multi #performance #scalability #statistics #using
Scaling multiple-source entity resolution using statistically efficient transfer learning (SN, BIPR, JG), pp. 2224–2228.
CIKMCIKM-2012-QuanzH #generative #multi #named
CoNet: feature generation for multi-view semi-supervised learning with partially observed views (BQ, JH), pp. 1273–1282.
CIKMCIKM-2012-RamanSGB #algorithm #towards
Learning from mistakes: towards a correctable learning algorithm (KR, KMS, RGB, CJCB), pp. 1930–1934.
CIKMCIKM-2012-RenCJ #topic
Topic based pose relevance learning in dance archives (RR, JPC, JMJ), pp. 2571–2574.
CIKMCIKM-2012-ShangJLW
Learning spectral embedding via iterative eigenvalue thresholding (FS, LCJ, YL, FW), pp. 1507–1511.
CIKMCIKM-2012-SunG
Active learning for relation type extension with local and global data views (AS, RG), pp. 1105–1112.
CIKMCIKM-2012-SunSL #multi #performance #query
Fast multi-task learning for query spelling correction (XS, AS, PL), pp. 285–294.
CIKMCIKM-2012-SunWGM #hybrid #rank #recommendation
Learning to rank for hybrid recommendation (JS, SW, BJG, JM), pp. 2239–2242.
CIKMCIKM-2012-VolkovsLZ #rank
Learning to rank by aggregating expert preferences (MV, HL, RSZ), pp. 843–851.
CIKMCIKM-2012-WangC #predict #word
Learning to predict the cost-per-click for your ad words (CJW, HHC), pp. 2291–2294.
CIKMCIKM-2012-WangH0 #framework #image #mining #web
A unified learning framework for auto face annotation by mining web facial images (DW, SCHH, YH), pp. 1392–1401.
CIKMCIKM-2012-WangWYHDC #framework #modelling #novel
A novel local patch framework for fixing supervised learning models (YW, BW, JY, YH, ZHD, ZC), pp. 1233–1242.
CIKMCIKM-2012-WangXY
Importance weighted passive learning (SW, XX, YY), pp. 2243–2246.
CIKMCIKM-2012-YangTKZLDLW #mining #network
Mining competitive relationships by learning across heterogeneous networks (YY, JT, JK, YZ, JL, YD, TL, LW), pp. 1432–1441.
CIKMCIKM-2012-YaoS #relational #ubiquitous
Exploiting latent relevance for relational learning of ubiquitous things (LY, QZS), pp. 1547–1551.
CIKMCIKM-2012-ZhangHLL #rank #realtime #twitter
Query-biased learning to rank for real-time twitter search (XZ, BH, TL, BL), pp. 1915–1919.
CIKMCIKM-2012-ZhangWW #framework #interactive #ontology
An interaction framework of service-oriented ontology learning (JZ, YW, HW), pp. 2303–2306.
CIKMCIKM-2012-ZhouLZ #community #quality
Joint relevance and answer quality learning for question routing in community QA (GZ, KL, JZ), pp. 1492–1496.
CIKMCIKM-2012-ZhouZ #debugging #rank
Learning to rank duplicate bug reports (JZ, HZ), pp. 852–861.
ECIRECIR-2012-Lubell-DoughtieH #feedback #rank
Learning to Rank from Relevance Feedback for e-Discovery (PLD, KH), pp. 535–539.
ECIRECIR-2012-LungleyKS #adaptation #domain model #interactive #modelling #web
Learning Adaptive Domain Models from Click Data to Bootstrap Interactive Web Search (DL, UK, DS), pp. 527–530.
ICMLICML-2012-AzarMK #complexity #generative #on the
On the Sample Complexity of Reinforcement Learning with a Generative Model (MGA, RM, BK), p. 222.
ICMLICML-2012-AzimiFFBH #coordination
Batch Active Learning via Coordinated Matching (JA, AF, XZF, GB, BH), p. 44.
ICMLICML-2012-BalleQC #modelling #optimisation
Local Loss Optimization in Operator Models: A New Insight into Spectral Learning (BB, AQ, XC), p. 236.
ICMLICML-2012-BelletHS #classification #linear #similarity
Similarity Learning for Provably Accurate Sparse Linear Classification (AB, AH, MS), p. 193.
ICMLICML-2012-BonillaR #probability #prototype
Discriminative Probabilistic Prototype Learning (EVB, ARK), p. 155.
ICMLICML-2012-BronsteinSS #modelling #performance
Learning Efficient Structured Sparse Models (AMB, PS, GS), p. 33.
ICMLICML-2012-ChambersJ
Learning the Central Events and Participants in Unlabeled Text (NC, DJ), p. 3.
ICMLICML-2012-CharlinZB #problem
Active Learning for Matching Problems (LC, RSZ, CB), p. 23.
ICMLICML-2012-DekelTA #adaptation #online #policy
Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret (OD, AT, RA), p. 227.
ICMLICML-2012-DuanXT #adaptation
Learning with Augmented Features for Heterogeneous Domain Adaptation (LD, DX, IWT), p. 89.
ICMLICML-2012-DundarAQR #modelling #online
Bayesian Nonexhaustive Learning for Online Discovery and Modeling of Emerging Classes (MD, FA, AQ, BR), p. 18.
ICMLICML-2012-EbanBSG #online #predict #sequence
Learning the Experts for Online Sequence Prediction (EE, AB, SSS, AG), p. 38.
ICMLICML-2012-FarabetCNL #multi #parsing
Scene parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers (CF, CC, LN, YL), p. 241.
ICMLICML-2012-GeistSLG #approach #difference
A Dantzig Selector Approach to Temporal Difference Learning (MG, BS, AL, MG), p. 49.
ICMLICML-2012-Gonen #kernel #multi #performance
Bayesian Efficient Multiple Kernel Learning (MG), p. 17.
ICMLICML-2012-GongZM #multi #robust
Robust Multiple Manifold Structure Learning (DG, XZ, GGM), p. 7.
ICMLICML-2012-GoodfellowCB #scalability
Large-Scale Feature Learning With Spike-and-Slab Sparse Coding (IJG, ACC, YB), p. 180.
ICMLICML-2012-GuoX #classification #multi
Cross Language Text Classification via Subspace Co-regularized Multi-view Learning (YG, MX), p. 120.
ICMLICML-2012-HanLC #modelling #multi
Cross-Domain Multitask Learning with Latent Probit Models (SH, XL, LC), p. 51.
ICMLICML-2012-HazanK #online
Projection-free Online Learning (EH, SK), p. 239.
ICMLICML-2012-HoiWZ
Exact Soft Confidence-Weighted Learning (SCHH, JW, PZ), p. 19.
ICMLICML-2012-HoiWZJW #algorithm #bound #kernel #online #performance #scalability
Fast Bounded Online Gradient Descent Algorithms for Scalable Kernel-Based Online Learning (SCHH, JW, PZ, RJ, PW), p. 141.
ICMLICML-2012-Honorio #convergence #modelling #optimisation #probability
Convergence Rates of Biased Stochastic Optimization for Learning Sparse Ising Models (JH), p. 144.
ICMLICML-2012-JalaliS #dependence #graph
Learning the Dependence Graph of Time Series with Latent Factors (AJ, SS), p. 83.
ICMLICML-2012-JawanpuriaN
A Convex Feature Learning Formulation for Latent Task Structure Discovery (PJ, JSN), p. 199.
ICMLICML-2012-JiangLS #3d #using
Learning Object Arrangements in 3D Scenes using Human Context (YJ, ML, AS), p. 119.
ICMLICML-2012-JiYLJH #algorithm #bound #fault
A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound (MJ, TY, BL, RJ, JH), p. 110.
ICMLICML-2012-KalakrishnanRPS #policy
Learning Force Control Policies for Compliant Robotic Manipulation (MK, LR, PP, SS), p. 10.
ICMLICML-2012-KarbasiIM #rank
Comparison-Based Learning with Rank Nets (AK, SI, LM), p. 161.
ICMLICML-2012-KumarD #multi
Learning Task Grouping and Overlap in Multi-task Learning (AK, HDI), p. 224.
ICMLICML-2012-KumarNKD #classification #framework #kernel #multi
A Binary Classification Framework for Two-Stage Multiple Kernel Learning (AK, ANM, KK, HDI), p. 173.
ICMLICML-2012-KumarPK #modelling #nondeterminism
Modeling Latent Variable Uncertainty for Loss-based Learning (MPK, BP, DK), p. 29.
ICMLICML-2012-LanctotGBB #game studies
No-Regret Learning in Extensive-Form Games with Imperfect Recall (ML, RGG, NB, MB), p. 135.
ICMLICML-2012-LeRMDCCDN #scalability #using
Building high-level features using large scale unsupervised learning (QVL, MR, RM, MD, GC, KC, JD, AYN), p. 69.
ICMLICML-2012-LinXWZ
Total Variation and Euler’s Elastica for Supervised Learning (TL, HX, LW, HZ), p. 82.
ICMLICML-2012-LouH
Structured Learning from Partial Annotations (XL, FAH), p. 52.
ICMLICML-2012-MakinoT #parametricity
Apprenticeship Learning for Model Parameters of Partially Observable Environments (TM, JT), p. 117.
ICMLICML-2012-MatuszekFZBF
A Joint Model of Language and Perception for Grounded Attribute Learning (CM, NF, LSZ, LB, DF), p. 186.
ICMLICML-2012-Memisevic #multi #on the
On multi-view feature learning (RM), p. 140.
ICMLICML-2012-MnihH #image #semistructured data
Learning to Label Aerial Images from Noisy Data (VM, GEH), p. 31.
ICMLICML-2012-MohamedHG
Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning (SM, KAH, ZG), p. 91.
ICMLICML-2012-NiuDYS #metric
Information-theoretic Semi-supervised Metric Learning via Entropy Regularization (GN, BD, MY, MS), p. 136.
ICMLICML-2012-Painter-WakefieldP #algorithm
Greedy Algorithms for Sparse Reinforcement Learning (CPW, RP), p. 114.
ICMLICML-2012-PassosRWD #flexibility #modelling #multi
Flexible Modeling of Latent Task Structures in Multitask Learning (AP, PR, JW, HDI), p. 167.
ICMLICML-2012-PeharzP #network
Exact Maximum Margin Structure Learning of Bayesian Networks (RP, FP), p. 102.
ICMLICML-2012-PiresS #estimation #linear #statistics
Statistical linear estimation with penalized estimators: an application to reinforcement learning (BAP, CS), p. 228.
ICMLICML-2012-PlessisS
Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching (MCdP, MS), p. 159.
ICMLICML-2012-PrasseSLS #email #identification #regular expression
Learning to Identify Regular Expressions that Describe Email Campaigns (PP, CS, NL, TS), p. 146.
ICMLICML-2012-RossB #identification #modelling
Agnostic System Identification for Model-Based Reinforcement Learning (SR, DB), p. 247.
ICMLICML-2012-SamdaniR #performance #predict
Efficient Decomposed Learning for Structured Prediction (RS, DR), p. 200.
ICMLICML-2012-ScholkopfJPSZM #on the
On causal and anticausal learning (BS, DJ, JP, ES, KZ, JMM), p. 63.
ICMLICML-2012-ShiS #adaptation #clustering
Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation (YS, FS), p. 166.
ICMLICML-2012-ShivaswamyJ #online #predict
Online Structured Prediction via Coactive Learning (PS, TJ), p. 12.
ICMLICML-2012-SilvaKB
Learning Parameterized Skills (BCdS, GK, AGB), p. 187.
ICMLICML-2012-SohnL #invariant
Learning Invariant Representations with Local Transformations (KS, HL), p. 174.
ICMLICML-2012-WangWHL #monte carlo
Monte Carlo Bayesian Reinforcement Learning (YW, KSW, DH, WSL), p. 105.
ICMLICML-2012-XieHS #approach #automation #generative
Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting (NX, HH, MS), p. 139.
ICMLICML-2012-XuWC
The Greedy Miser: Learning under Test-time Budgets (ZEX, KQW, OC), p. 169.
ICMLICML-2012-YackleyL
Smoothness and Structure Learning by Proxy (BY, TL), p. 57.
ICMLICML-2012-YangMJZZ #kernel #multi #probability #programming
Multiple Kernel Learning from Noisy Labels by Stochastic Programming (TY, MM, RJ, LZ, YZ), p. 21.
ICMLICML-2012-ZhongK #clustering #flexibility #multi
Convex Multitask Learning with Flexible Task Clusters (WZ, JTYK), p. 66.
ICPRICPR-2012-AbeOD #image #rank
Recognizing surface qualities from natural images based on learning to rank (TA, TO, KD), pp. 3712–3715.
ICPRICPR-2012-AntoniukFH #markov #network
Learning Markov Networks by Analytic Center Cutting Plane Method (KA, VF, VH), pp. 2250–2253.
ICPRICPR-2012-BaccoucheMWGB #2d #invariant #recognition #representation #sequence
Sparse shift-invariant representation of local 2D patterns and sequence learning for human action recognition (MB, FM, CW, CG, AB), pp. 3823–3826.
ICPRICPR-2012-BaillyMPB #cost analysis
Learning global cost function for face alignment (KB, MM, PP, EB), pp. 1112–1115.
ICPRICPR-2012-BanerjeeN #kernel #multi #process #recognition #using
Pose based activity recognition using Multiple Kernel learning (PB, RN), pp. 445–448.
ICPRICPR-2012-CermanH #problem
Tracking with context as a semi-supervised learning and labeling problem (LC, VH), pp. 2124–2127.
ICPRICPR-2012-ChernoffLN #fault #metric
Metric learning by directly minimizing the k-NN training error (KC, ML, MN), pp. 1265–1268.
ICPRICPR-2012-DahmaneLDB #estimation #symmetry
Learning symmetrical model for head pose estimation (AD, SL, CD, IMB), pp. 3614–3617.
ICPRICPR-2012-DAmbrosioIS #re-engineering
A One-per-Class reconstruction rule for class imbalance learning (RD, GI, PS), pp. 1310–1313.
ICPRICPR-2012-DuanWLDC #named
K-CPD: Learning of overcomplete dictionaries for tensor sparse coding (GD, HW, ZL, JD, YWC), pp. 493–496.
ICPRICPR-2012-FangZ
I don’t know the label: Active learning with blind knowledge (MF, XZ), pp. 2238–2241.
ICPRICPR-2012-FiaschiKNH
Learning to count with regression forest and structured labels (LF, UK, RN, FAH), pp. 2685–2688.
ICPRICPR-2012-GhanemKFZ #automation #recognition
Context-aware learning for automatic sports highlight recognition (BG, MK, MF, TZ), pp. 1977–1980.
ICPRICPR-2012-GhoseMOMLFVCSM12a #3d #energy #framework #graph #probability #segmentation
Graph cut energy minimization in a probabilistic learning framework for 3D prostate segmentation in MRI (SG, JM, AO, RM, XL, JF, JCV, JC, DS, FM), pp. 125–128.
ICPRICPR-2012-GranaCBC #image #segmentation
Learning non-target items for interesting clothes segmentation in fashion images (CG, SC, DB, RC), pp. 3317–3320.
ICPRICPR-2012-GuK #online #visual notation
Grassmann manifold online learning and partial occlusion handling for visual object tracking under Bayesian formulation (IYHG, ZHK), pp. 1463–1466.
ICPRICPR-2012-GutmannH #architecture #feature model #image
Learning a selectivity-invariance-selectivity feature extraction architecture for images (MG, AH), pp. 918–921.
ICPRICPR-2012-HidoK #graph #similarity
Hash-based structural similarity for semi-supervised Learning on attribute graphs (SH, HK), pp. 3009–3012.
ICPRICPR-2012-HinoO #kernel #multi
An improved entropy-based multiple kernel learning (HH, TO), pp. 1189–1192.
ICPRICPR-2012-HiradeY #predict
Ensemble learning for change-point prediction (RH, TY), pp. 1860–1863.
ICPRICPR-2012-HuangLT #invariant #recognition
Learning modality-invariant features for heterogeneous face recognition (LH, JL, YPT), pp. 1683–1686.
ICPRICPR-2012-JinGYZ #algorithm #multi
Multi-label learning vector quantization algorithm (XBJ, GG, JY, DZ), pp. 2140–2143.
ICPRICPR-2012-JiS12a #3d #estimation #robust
Robust 3D human pose estimation via dual dictionaries learning (HJ, FS), pp. 3370–3373.
ICPRICPR-2012-KhanT #taxonomy
Stable discriminative dictionary learning via discriminative deviation (NK, MFT), pp. 3224–3227.
ICPRICPR-2012-KongW #clustering #multi
A multi-task learning strategy for unsupervised clustering via explicitly separating the commonality (SK, DW), pp. 771–774.
ICPRICPR-2012-KumarRS #predict
Learning to predict super resolution wavelet coefficients (NK, NKR, AS), pp. 3468–3471.
ICPRICPR-2012-KumarYD #classification #documentation #retrieval
Learning document structure for retrieval and classification (JK, PY, DSD), pp. 1558–1561.
ICPRICPR-2012-LeeKD #induction
Learning action symbols for hierarchical grammar induction (KL, TKK, YD), pp. 3778–3782.
ICPRICPR-2012-LiCHWM #3d #kernel #multi #recognition
3D facial expression recognition via multiple kernel learning of Multi-Scale Local Normal Patterns (HL, LC, DH, YW, JMM), pp. 2577–2580.
ICPRICPR-2012-LiHL #adaptation #multi #online #people
Online adaptive learning for multi-camera people counting (JL, LH, CL), pp. 3415–3418.
ICPRICPR-2012-LiLLL #distance #estimation #metric
Learning distance metric regression for facial age estimation (CL, QL, JL, HL), pp. 2327–2330.
ICPRICPR-2012-LinLZ #representation #taxonomy
Incoherent dictionary learning for sparse representation (TL, SL, HZ), pp. 1237–1240.
ICPRICPR-2012-LiPMH #classification #email #incremental #using
Business email classification using incremental subspace learning (ML, YP, RM, HYH), pp. 625–628.
ICPRICPR-2012-LiuCSTN #multi #performance #problem #recursion #scalability
Recursive NMF: Efficient label tree learning for large multi-class problems (LL, PMC, SS, PNT, AN), pp. 2148–2151.
ICPRICPR-2012-LiuL #analysis #detection #multi
Unsupervised multi-target trajectory detection, learning and analysis in complicated environments (HL, JL), pp. 3716–3720.
ICPRICPR-2012-LiuLWZ #linear
Locally linear embedding based example learning for pan-sharpening (QL, LL, YW, ZZ), pp. 1928–1931.
ICPRICPR-2012-LiuLYZ #composition #visual notation
Learning to describe color composition of visual objects (YL, YL, ZY, NZ), pp. 3337–3340.
ICPRICPR-2012-LiuML #multi
Training data recycling for multi-level learning (JL, SM, YL), pp. 2314–2318.
ICPRICPR-2012-LiuSW #recognition #taxonomy
Facial expression recognition based on discriminative dictionary learning (WL, CS, YW), pp. 1839–1842.
ICPRICPR-2012-LiVBB #clustering #using
Feature learning using Generalized Extreme Value distribution based K-means clustering (ZL, OV, HB, RB), pp. 1538–1541.
ICPRICPR-2012-LuLY #adaptation #classification #kernel
Adaptive kernel learning based on centered alignment for hierarchical classification (YL, JL, JY), pp. 569–572.
ICPRICPR-2012-MarcaciniCR #approach #clustering
An active learning approach to frequent itemset-based text clustering (RMM, GNC, SOR), pp. 3529–3532.
ICPRICPR-2012-MogelmoseTM #comparative #dataset #detection #evaluation
Learning to detect traffic signs: Comparative evaluation of synthetic and real-world datasets (AM, MMT, TBM), pp. 3452–3455.
ICPRICPR-2012-MoZW #classification
Enhancing cross-view object classification by feature-based transfer learning (YM, ZZ, YW), pp. 2218–2221.
ICPRICPR-2012-Nagy #web
Learning the characteristics of critical cells from web tables (GN), pp. 1554–1557.
ICPRICPR-2012-NamA #image
Learning human preferences to sharpen images (MN, NA), pp. 2173–2176.
ICPRICPR-2012-NayefAB
Learning feature weights of symbols, with application to symbol spotting (NN, MZA, TMB), pp. 2371–2374.
ICPRICPR-2012-Noh #analysis #classification #metric #nearest neighbour
χ2 Metric learning for nearest neighbor classification and its analysis (SN), pp. 991–995.
ICPRICPR-2012-PangHYQW #analysis #classification
Theoretical analysis of learning local anchors for classification (JP, QH, BY, LQ, DW), pp. 1803–1806.
ICPRICPR-2012-PanLS #kernel
Learning kernels from labels with ideal regularization (BP, JHL, LS), pp. 505–508.
ICPRICPR-2012-PourdamghaniRZ #estimation #graph #metric
Metric learning for graph based semi-supervised human pose estimation (NP, HRR, MZ), pp. 3386–3389.
ICPRICPR-2012-QinZCW #online
Matting-driven online learning of Hough forests for object tracking (TQ, BZ, TJC, HW), pp. 2488–2491.
ICPRICPR-2012-San-BiagioUCCCM #approach #classification #kernel #multi
A multiple kernel learning approach to multi-modal pedestrian classification (MSB, AU, MC, MC, UC, VM), pp. 2412–2415.
ICPRICPR-2012-SchauerteS #image #modelling #robust #web
Learning robust color name models from web images (BS, RS), pp. 3598–3601.
ICPRICPR-2012-SharmaHN #classification #detection #incremental #performance
Efficient incremental learning of boosted classifiers for object detection (PS, CH, RN), pp. 3248–3251.
ICPRICPR-2012-ShenMZ #analysis #graph #online
Unsupervised online learning trajectory analysis based on weighted directed graph (YS, ZM, JZ), pp. 1306–1309.
ICPRICPR-2012-SuLT #documentation #framework #image #markov #random #using
A learning framework for degraded document image binarization using Markov Random Field (BS, SL, CLT), pp. 3200–3203.
ICPRICPR-2012-SunBM
Unsupervised skeleton learning for manifold denoising (KS, EB, SMM), pp. 2719–2722.
ICPRICPR-2012-TabernikKBL #low level #statistics #visual notation
Learning statistically relevant edge structure improves low-level visual descriptors (DT, MK, MB, AL), pp. 1471–1474.
ICPRICPR-2012-TangS #independence #network #performance #testing #using
Efficient and accurate learning of Bayesian networks using chi-squared independence tests (YT, SNS), pp. 2723–2726.
ICPRICPR-2012-TiribuziPVR #detection #framework #kernel #multi
A Multiple Kernel Learning framework for detecting altered fingerprints (MT, MP, PV, ER), pp. 3402–3405.
ICPRICPR-2012-TuS #adaptation #classification
Dynamical ensemble learning with model-friendly classifiers for domain adaptation (WT, SS), pp. 1181–1184.
ICPRICPR-2012-VillamizarGSM #online #random #using
Online human-assisted learning using Random Ferns (MV, AG, AS, FMN), pp. 2821–2824.
ICPRICPR-2012-WangJ12b #network #process #recognition
Learning dynamic Bayesian network discriminatively for human activity recognition (XW, QJ), pp. 3553–3556.
ICPRICPR-2012-WangL12b #recognition #string
String-level learning of confidence transformation for Chinese handwritten text recognition (DHW, CLL), pp. 3208–3211.
ICPRICPR-2012-WeberBLS #segmentation
Unsupervised motion pattern learning for motion segmentation (MW, GB, ML, DS), pp. 202–205.
ICPRICPR-2012-XiaTWLL #categorisation
Object categorization based on hierarchical learning (TX, YYT, YW, HL, LL), pp. 1419–1422.
ICPRICPR-2012-YangLZC #image #multi #retrieval
Multi-view learning with batch mode active selection for image retrieval (WY, GL, LZ, EC), pp. 979–982.
ICPRICPR-2012-YanKMW #automation #game studies
Automatic annotation of court games with structured output learning (FY, JK, KM, DW), pp. 3577–3580.
ICPRICPR-2012-YanRLS #classification #multi
Active transfer learning for multi-view head-pose classification (YY, SR, OL, NS), pp. 1168–1171.
ICPRICPR-2012-YeD #predict
Learning features for predicting OCR accuracy (PY, DSD), pp. 3204–3207.
ICPRICPR-2012-ZhangHR #classification #gender
Hypergraph based semi-supervised learning for gender classification (ZZ, ERH, PR), pp. 1747–1750.
ICPRICPR-2012-ZhangZNH #multi #recognition
Joint dynamic sparse learning and its application to multi-view face recognition (HZ, YZ, NMN, TSH), pp. 1671–1674.
ICPRICPR-2012-ZhaoSS #predict
Importance-weighted label prediction for active learning with noisy annotations (LZ, GS, RS), pp. 3476–3479.
ICPRICPR-2012-ZhaoXY #network #speech
Unsupervised Tibetan speech features Learning based on Dynamic Bayesian Networks (YZ, XX, GY), pp. 2319–2322.
ICPRICPR-2012-ZhaoYXJ
A near-optimal non-myopic active learning method (YZ, GY, XX, QJ), pp. 1715–1718.
ICPRICPR-2012-ZhouWXZM #recognition
Learning weighted features for human action recognition (WZ, CW, BX, ZZ, LM), pp. 1160–1163.
ICPRICPR-2012-ZhuoCQYX #algorithm #classification #image #using
Image classification using HTM cortical learning algorithms (WZ, ZC, YQ, ZY, YX), pp. 2452–2455.
KDDKDD-2012-GongYZ #multi #robust
Robust multi-task feature learning (PG, JY, CZ), pp. 895–903.
KDDKDD-2012-HalawiDGK #constraints #scalability #word
Large-scale learning of word relatedness with constraints (GH, GD, EG, YK), pp. 1406–1414.
KDDKDD-2012-HoensC
Learning in non-stationary environments with class imbalance (TRH, NVC), pp. 168–176.
KDDKDD-2012-JainVV #kernel #multi #named
SPF-GMKL: generalized multiple kernel learning with a million kernels (AJ, SVNV, MV), pp. 750–758.
KDDKDD-2012-LiJPS #classification #multi
Multi-domain active learning for text classification (LL, XJ, SJP, JTS), pp. 1086–1094.
KDDKDD-2012-PatroDSWFK #approach #data-driven #how #modelling #network
The missing models: a data-driven approach for learning how networks grow (RP, GD, ES, HW, DF, CK), pp. 42–50.
KDDKDD-2012-RamanSJ #feedback #online
Online learning to diversify from implicit feedback (KR, PS, TJ), pp. 705–713.
KDDKDD-2012-SeelandKK #clustering #graph #kernel
A structural cluster kernel for learning on graphs (MS, AK, SK), pp. 516–524.
KDDKDD-2012-ShangJW
Semi-supervised learning with mixed knowledge information (FS, LCJ, FW), pp. 732–740.
KDDKDD-2012-ShenJ #recommendation #social
Learning personal + social latent factor model for social recommendation (YS, RJ), pp. 1303–1311.
KDDKDD-2012-SilvaC #matrix #online
Active learning for online bayesian matrix factorization (JGS, LC), pp. 325–333.
KDDKDD-2012-SindhwaniG #distributed #scalability #taxonomy
Large-scale distributed non-negative sparse coding and sparse dictionary learning (VS, AG), pp. 489–497.
KDDKDD-2012-TianZ
Learning from crowds in the presence of schools of thought (YT, JZ), pp. 226–234.
KDDKDD-2012-XiongJXC #dependence #metric #random
Random forests for metric learning with implicit pairwise position dependence (CX, DMJ, RX, JJC), pp. 958–966.
KDDKDD-2012-YuanWTNY #analysis #multi
Multi-source learning for joint analysis of incomplete multi-modality neuroimaging data (LY, YW, PMT, VAN, JY), pp. 1149–1157.
KDDKDD-2012-ZhangH #induction #multi
Inductive multi-task learning with multiple view data (JZ, JH), pp. 543–551.
KDDKDD-2012-ZhenY #multimodal #probability
A probabilistic model for multimodal hash function learning (YZ, DYY), pp. 940–948.
KDDKDD-2012-ZhouZ #collaboration
Learning binary codes for collaborative filtering (KZ, HZ), pp. 498–506.
KDIRKDIR-2012-AbdullinN #clustering #data type #framework
A Semi-supervised Learning Framework to Cluster Mixed Data Types (AA, ON), pp. 45–54.
KDIRKDIR-2012-BressoGDNS #3d #concept analysis #relational
Formal Concept Analysis for the Interpretation of Relational Learning Applied on 3D Protein-binding Sites (EB, RG, MDD, AN, MST), pp. 111–120.
KDIRKDIR-2012-IkebeKT #predict #smarttech #using
Friendship Prediction using Semi-supervised Learning of Latent Features in Smartphone Usage Data (YI, MK, HT), pp. 199–205.
KDIRKDIR-2012-LindnerH #constraints #maintenance #parsing #random
Parsing and Maintaining Bibliographic References — Semi-supervised Learning of Conditional Random Fields with Constraints (SL, WH), pp. 233–238.
KEODKEOD-2012-RuizHM #education #evaluation #ontology #quality
A New Proposal for Learning Objects Quality Evaluation in Learning Strategies based on Ontology for Education (LMGR, JMH, AMG), pp. 373–376.
KEODKEOD-2012-WohlgenanntWSS #ontology #web
Confidence Management for Learning Ontologies from Dynamic Web Sources (GW, AW, AS, MS), pp. 172–177.
KMISKMIS-2012-AkiyoshiSK #problem #towards
A Project Manager Skill-up Simulator Towards Problem Solving-based Learning (MA, MS, NK), pp. 190–195.
KMISKMIS-2012-AtkociunieneG #convergence
Strategic Management, Learning and Innovation — Convergence of Strategic Management, Organizational Learning and Innovation: The Case of Lithuanian Organizations (ZA, IG), pp. 243–246.
KMISKMIS-2012-HackerMHHM #collaboration
Management of Collaboration — Impacts of Virtualization to Learning & Knowledge (GH, MM, PH, GH, MM), pp. 235–239.
KMISKMIS-2012-HamadaAS #generative #using
A Generation Method of Reference Operation using Reinforcement Learning on Project Manager Skill-up Simulator (KH, MA, MS), pp. 15–20.
KMISKMIS-2012-HubwieserM #collaboration #education #network #ontology #social
A Social Network for Learning — Supporting Collaborative Learning based on the Ontology for Educational Knowledge (PH, AM), pp. 298–301.
KRKR-2012-BaralD #automation #how #programming #set
Solving Puzzles Described in English by Automated Translation to Answer Set Programming and Learning How to Do that Translation (CB, JD).
MLDMMLDM-2012-BouhamedMLR #heuristic #network
A New Learning Structure Heuristic of Bayesian Networks from Data (HB, AM, TL, AR), pp. 183–197.
MLDMMLDM-2012-HoaD
A New Learning Strategy of General BAMs (NTH, TDB), pp. 213–221.
MLDMMLDM-2012-PitelisT
Discriminant Subspace Learning Based on Support Vectors Machines (NP, AT), pp. 198–212.
MLDMMLDM-2012-ToussaintB #comparison #empirical
Proximity-Graph Instance-Based Learning, Support Vector Machines, and High Dimensionality: An Empirical Comparison (GTT, CB), pp. 222–236.
MLDMMLDM-2012-XuCG #concept #multi #using
Constructing Target Concept in Multiple Instance Learning Using Maximum Partial Entropy (TX, DKYC, IG), pp. 169–182.
RecSysRecSys-2012-DeDGM #difference #using
Local learning of item dissimilarity using content and link structure (AD, MSD, NG, PM), pp. 221–224.
RecSysRecSys-2012-Herbrich #distributed #online #realtime
Distributed, real-time bayesian learning in online services (RH), pp. 203–204.
RecSysRecSys-2012-KarimiFNS #matrix #recommendation
Exploiting the characteristics of matrix factorization for active learning in recommender systems (RK, CF, AN, LST), pp. 317–320.
RecSysRecSys-2012-SalimansPG #collaboration #ranking
Collaborative learning of preference rankings (TS, UP, TG), pp. 261–264.
RecSysRecSys-2012-ShiKBLOH #collaboration #named #rank
CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering (YS, AK, LB, ML, NO, AH), pp. 139–146.
SEKESEKE-2012-AlawawdehAL #adaptation #collaboration #named
CLAT: Collaborative Learning Adaptive Tutor (AMHA, CA, LL), pp. 747–752.
SEKESEKE-2012-El-SherifFE #concept #multi #network #social #using
Using Social Networks for Learning New Concepts in Multi-Agent Systems (SMES, BHF, AE), pp. 261–266.
SEKESEKE-2012-XavierOC #fuzzy #logic
Evolutionary Learning and Fuzzy Logic Applied to a Load Balancer (FCX, MGdO, CLdC), pp. 256–260.
SEKESEKE-2012-Zhang #bias #named
i2Learning: Perpetual Learning through Bias Shifting (DZ), pp. 249–255.
SIGIRSIGIR-2012-BilgicB #query
Active query selection for learning rankers (MB, PNB), pp. 1033–1034.
SIGIRSIGIR-2012-GaoWL #graph #information retrieval #mining #scalability
Large-scale graph mining and learning for information retrieval (BG, TW, TYL), pp. 1194–1195.
SIGIRSIGIR-2012-HongBAD #rank #social
Learning to rank social update streams (LH, RB, JA, BDD), pp. 651–660.
SIGIRSIGIR-2012-JiangWLAW #alias #approach #detection #similarity #string #towards
Towards alias detection without string similarity: an active learning based approach (LJ, JW, PL, NA, MW), pp. 1155–1156.
SIGIRSIGIR-2012-KanhabuaBN #retrieval
Learning to select a time-aware retrieval model (NK, KB, KN), pp. 1099–1100.
SIGIRSIGIR-2012-KovesiGA #categorisation #multi #online #performance
Fast on-line learning for multilingual categorization (MK, CG, MRA), pp. 1071–1072.
SIGIRSIGIR-2012-MacdonaldTO #online #predict #query #scheduling
Learning to predict response times for online query scheduling (CM, NT, IO), pp. 621–630.
SIGIRSIGIR-2012-MacdonaldTO12a #effectiveness #rank #safety
Effect of dynamic pruning safety on learning to rank effectiveness (CM, NT, IO), pp. 1051–1052.
SIGIRSIGIR-2012-NiuGLC #evaluation #rank #ranking
Top-k learning to rank: labeling, ranking and evaluation (SN, JG, YL, XC), pp. 751–760.
SIGIRSIGIR-2012-SeverynM #ranking #scalability
Structural relationships for large-scale learning of answer re-ranking (AS, AM), pp. 741–750.
SIGIRSIGIR-2012-ZhangWDH #detection #performance #reuse
Learning hash codes for efficient content reuse detection (QZ, YW, ZD, XH), pp. 405–414.
TOOLSTOOLS-EUROPE-2012-Sureka #component #debugging
Learning to Classify Bug Reports into Components (AS), pp. 288–303.
SASSAS-2012-GiannakopoulouRR #component #interface
Symbolic Learning of Component Interfaces (DG, ZR, VR), pp. 248–264.
REFSQREFSQ-2012-KnaussS #documentation #heuristic #requirements
Supporting Learning Organisations in Writing Better Requirements Documents Based on Heuristic Critiques (EK, KS), pp. 165–171.
ASEASE-2012-LuCC #fault #predict #reduction #using
Software defect prediction using semi-supervised learning with dimension reduction (HL, BC, MC), pp. 314–317.
ICSEICSE-2012-DagenaisR #api #traceability
Recovering traceability links between an API and its learning resources (BD, MPR), pp. 47–57.
ICSEICSE-2012-FengC #behaviour #multi
Multi-label software behavior learning (YF, ZC), pp. 1305–1308.
ICSEICSE-2012-GrechanikFX #automation #performance #problem #testing
Automatically finding performance problems with feedback-directed learning software testing (MG, CF, QX), pp. 156–166.
SACSAC-2012-MinervinidF #concept #logic #probability
Learning probabilistic Description logic concepts: under different Assumptions on missing knowledge (PM, Cd, NF), pp. 378–383.
SACSAC-2012-NunesCM #network #similarity #social
Resolving user identities over social networks through supervised learning and rich similarity features (AN, PC, BM), pp. 728–729.
SACSAC-2012-OongI #classification #fuzzy #multi #performance #testing
Multilayer Fuzzy ARTMAP: fast learning and fast testing for pattern classification (THO, NAMI), pp. 27–32.
CASECASE-2012-AnKP #modelling #process
Grasp motion learning with Gaussian Process Dynamic Models (BA, HK, FCP), pp. 1114–1119.
CASECASE-2012-YamamotoD #interface
Robot interface learning user-defined voice instructions (DY, MD), pp. 926–929.
DACDAC-2012-WardDP #automation #evaluation #named
PADE: a high-performance placer with automatic datapath extraction and evaluation through high dimensional data learning (SIW, DD, DZP), pp. 756–761.
DATEDATE-2012-MaricauJG #analysis #multi #reliability #using
Hierarchical analog circuit reliability analysis using multivariate nonlinear regression and active learning sample selection (EM, DdJ, GGEG), pp. 745–750.
FASEFASE-2012-AlrajehKRU #satisfiability #specification
Learning from Vacuously Satisfiable Scenario-Based Specifications (DA, JK, AR, SU), pp. 377–393.
STOCSTOC-2012-DaskalakisDS
Learning poisson binomial distributions (CD, ID, RAS), pp. 709–728.
TACASTACAS-2012-DSilvaHKT #analysis #bound
Numeric Bounds Analysis with Conflict-Driven Learning (VD, LH, DK, MT), pp. 48–63.
TACASTACAS-2012-MertenHSCJ #automaton
Demonstrating Learning of Register Automata (MM, FH, BS, SC, BJ), pp. 466–471.
CAVCAV-2012-ChenW #incremental
Learning Boolean Functions Incrementally (YFC, BYW), pp. 55–70.
CAVCAV-2012-LeeWY #algorithm #analysis #termination
Termination Analysis with Algorithmic Learning (WL, BYW, KY), pp. 88–104.
CSLCSL-2012-Berardid
Knowledge Spaces and the Completeness of Learning Strategies (SB, Ud), pp. 77–91.
ICSTICST-2012-SunSPR #cost analysis #named #reliability
CARIAL: Cost-Aware Software Reliability Improvement with Active Learning (BS, GS, AP, SR), pp. 360–369.
ICTSSICTSS-2012-Vaandrager #finite #state machine
Active Learning of Extended Finite State Machines (FWV), pp. 5–7.
LICSLICS-2012-KomuravelliPC #probability
Learning Probabilistic Systems from Tree Samples (AK, CSP, EMC), pp. 441–450.
ICSTSAT-2012-BonetB
An Improved Separation of Regular Resolution from Pool Resolution and Clause Learning (MLB, SRB), pp. 44–57.
ICSTSAT-2012-LaitinenJN
Conflict-Driven XOR-Clause Learning (TL, TAJ, IN), pp. 383–396.
CBSECBSE-2011-AletiM #component #deployment #optimisation
Component deployment optimisation with bayesian learning (AA, IM), pp. 11–20.
DocEngDocEng-2011-ChidlovskiiB #metric #network #recommendation #social
Local metric learning for tag recommendation in social networks (BC, AB), pp. 205–208.
ICDARICDAR-2011-CoatesCCSSWWN #detection #image #recognition
Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning (AC, BC, CC, SS, BS, TW, DJW, AYN), pp. 440–445.
ICDARICDAR-2011-KumarPD #classification #documentation #image #multi #using
Document Image Classification and Labeling Using Multiple Instance Learning (JK, JP, DSD), pp. 1059–1063.
ICDARICDAR-2011-ShaoWXZZ11a #multi
Multiple Instance Learning Based Method for Similar Handwritten Chinese Characters Discrimination (YS, CW, BX, RZ, YZ), pp. 1002–1006.
ICDARICDAR-2011-SuLZ #polynomial
Perceptron Learning of Modified Quadratic Discriminant Function (THS, CLL, XYZ), pp. 1007–1011.
ICDARICDAR-2011-TaoLJG #locality #recognition #using
Similar Handwritten Chinese Character Recognition Using Discriminative Locality Alignment Manifold Learning (DT, LL, LJ, YG), pp. 1012–1016.
ICDARICDAR-2011-VajdaJF #approach
A Semi-supervised Ensemble Learning Approach for Character Labeling with Minimal Human Effort (SV, AJ, GAF), pp. 259–263.
ICDARICDAR-2011-WangDL #recognition
MQDF Discriminative Learning Based Offline Handwritten Chinese Character Recognition (YW, XD, CL), pp. 1100–1104.
SIGMODSIGMOD-2011-GetoorM #modelling #relational #statistics
Learning statistical models from relational data (LG, LM), pp. 1195–1198.
CSEETCSEET-2011-ChimalakondaN #education #question #re-engineering
Can we make software engineering education better by applying learning theories? (SC, KVN), p. 561.
CSEETCSEET-2011-EngM #assessment #communication #experience #student
Continued assessment of students’ learning experience in an oral communication course at MIT for EECS majors (TLE, RM), pp. 439–443.
CSEETCSEET-2011-GalvaoARAFG #education #logic programming #process
A proposal for an educational system service to support teaching/learning process for logic programming (ERDG, RRdA, CMOR, SCA, FF, VCG), p. 556.
CSEETCSEET-2011-GimenesBB #distance #re-engineering #source code
International workshop on distance learning support for postgraduate programs in software engineering (e-gradSE) (IMdSG, LB, EFB), pp. 517–519.
CSEETCSEET-2011-HattoriBLL #game studies
Erase and rewind — Learning by replaying examples (LH, AB, ML, ML), p. 558.
CSEETCSEET-2011-HoskingSKJ #re-engineering #student
Learning at the elbows of experts: Technology roadmapping with Software Engineering students (JGH, PS, EK, NJ), pp. 139–148.
CSEETCSEET-2011-RichardsonRSPD #problem #quality #research
Educating software engineers of the future: Software quality research through problem-based learning (IR, LR, SBS, BP, YD), pp. 91–100.
CSEETCSEET-2011-TillmannHX #education #game studies #named #social
Pex4Fun: Teaching and learning computer science via social gaming (NT, JdH, TX), pp. 546–548.
CSEETCSEET-2011-TuTOBHKY
Turning real-world systems into verification-driven learning cases (ST, ST, SO, BB, BH, AK, ZY), pp. 129–138.
CSEETCSEET-2011-Virseda #education #re-engineering #semantics
A learning methodology based on semantic tableaux for software engineering education (RdVV), pp. 401–405.
ITiCSEITiCSE-2011-AnjorinGR #collaboration #framework #named #platform #web
CROKODIL: a platform supporting the collaborative management of web resources for learning purposes (MA, RDG, CR), p. 361.
ITiCSEITiCSE-2011-BowerM #comparison
Continual and explicit comparison to promote proactive facilitation during second computer language learning (MB, AM), pp. 218–222.
ITiCSEITiCSE-2011-BoyceCPCB #education #evaluation #game studies #how #motivation
Experimental evaluation of BeadLoom game: how adding game elements to an educational tool improves motivation and learning (AKB, AC, SP, DC, TB), pp. 243–247.
ITiCSEITiCSE-2011-CamachoM #programming
Facilitating learning dynamic programming through a previous introduction of exhaustive search (AC, AM), p. 355.
ITiCSEITiCSE-2011-ChanK #education #multi #question
Do educational software systems provide satisfactory learning opportunities for “multi-sensory learning” methodology? (PC, GK), p. 358.
ITiCSEITiCSE-2011-EllisH #named #student
Courseware: student learning via FOSS field trips (HJCE, GWH), p. 329.
ITiCSEITiCSE-2011-GarciaMGH #interface #unification
A system for usable unification of interfaces of learning objects in m-learning (EG, LdM, AGC, JRH), p. 347.
ITiCSEITiCSE-2011-Goldweber #process #turing machine
Two kinesthetic learning activities: turing machines and basic computer organization (MG), p. 335.
ITiCSEITiCSE-2011-Goldweber11a #social
Computing for the social good: a service learning project (MG), p. 379.
ITiCSEITiCSE-2011-HarrachA #collaboration #optimisation #process #recommendation #using
Optimizing collaborative learning processes by using recommendation systems (SH, MA), p. 389.
ITiCSEITiCSE-2011-Hijon-NeiraV11a #design
A first step mapping IMS learning design and Merlin-Mo (RHN, JÁVI), p. 365.
ITiCSEITiCSE-2011-HoverHR #collaboration
A collaborative linked learning space (KMH, MH, GR), p. 380.
ITiCSEITiCSE-2011-HoverHRM #collaboration #how #student
Evaluating how students would use a collaborative linked learning space (KMH, MH, GR, MM), pp. 88–92.
ITiCSEITiCSE-2011-KonertRGSB #ad hoc #community
Supporting peer learning with ad-hoc communities (JK, KR, SG, RS, RB), p. 393.
ITiCSEITiCSE-2011-LasserreS
Effects of team-based learning on a CS1 course (PL, CS), pp. 133–137.
ITiCSEITiCSE-2011-MothVB #named #syntax
SyntaxTrain: relieving the pain of learning syntax (ALAM, JV, MBA), p. 387.
ITiCSEITiCSE-2011-OliveiraMR #problem #programming
From concrete to abstract?: problem domain in the learning of introductory programming (OLO, AMM, NTR), pp. 173–177.
ITiCSEITiCSE-2011-PollockH #multi
Combining multiple pedagogies to boost learning and enthusiasm (LLP, TH), pp. 258–262.
ITiCSEITiCSE-2011-RussellMD #approach #student
A contextualized project-based approach for improving student engagement and learning in AI courses (IR, ZM, JD), p. 368.
ITiCSEITiCSE-2011-Sanchez-TorrubiaTT #algorithm #assessment #automation
GLMP for automatic assessment of DFS algorithm learning (MGST, CTB, GT), p. 351.
ITiCSEITiCSE-2011-ShuhidanHD #comprehension
Understanding novice programmer difficulties via guided learning (SMS, MH, DJD), pp. 213–217.
ITiCSEITiCSE-2011-VanoM #quote
“Computer science and nursery rhymes”: a learning path for the middle school (DDV, CM), pp. 238–242.
ITiCSEITiCSE-2011-WolzMS #process
Kinesthetic learning of computing via “off-beat” activities (UW, MM, MS), pp. 68–72.
SIGITESIGITE-2011-Cosgrove #low cost #network
Bringing together a low-cost networking learning environment (SRC), pp. 101–106.
SIGITESIGITE-2011-DavisJ #community #linux
Learning in the GNU/Linux community (DD, IJ), pp. 21–26.
SIGITESIGITE-2011-McReynolds #navigation #student
Impact of student training on the perceived ease of use and ease of navigation of a learning management system (KM), pp. 161–164.
SIGITESIGITE-2011-Mustafa #operating system #simulation #visualisation
Visualizing the modern operating system: simulation experiments supporting enhanced learning (BM), pp. 209–214.
SIGITESIGITE-2011-RenwickF #student
Learning styles of information technology students (JSR, CBF), pp. 313–314.
ICPCICPC-J-2009-Sanz-RodriguezDA11 #evaluation #reuse #usability
Metrics-based evaluation of learning object reusability (JSR, JMD, SSA), pp. 121–140.
DLTDLT-2011-Yoshinaka #concept #context-free grammar #towards
Towards Dual Approaches for Learning Context-Free Grammars Based on Syntactic Concept Lattices (RY), pp. 429–440.
ICALPICALP-v1-2011-AroraG #algorithm #fault
New Algorithms for Learning in Presence of Errors (SA, RG), pp. 403–415.
ICALPICALP-v1-2011-HarkinsH #algorithm #bound #game studies
Exact Learning Algorithms, Betting Games, and Circuit Lower Bounds (RCH, JMH), pp. 416–423.
LATALATA-2011-CaseJLOSS #automation #pattern matching #subclass
Automatic Learning of Subclasses of Pattern Languages (JC, SJ, TDL, YSO, PS, FS), pp. 192–203.
SFMSFM-2011-Jonsson #automaton #modelling
Learning of Automata Models Extended with Data (BJ), pp. 327–349.
SFMSFM-2011-Moschitti #automation #kernel #modelling
Kernel-Based Machines for Abstract and Easy Modeling of Automatic Learning (AM), pp. 458–503.
SFMSFM-2011-SteffenHM #automaton #perspective
Introduction to Active Automata Learning from a Practical Perspective (BS, FH, MM), pp. 256–296.
AIIDEAIIDE-2011-ChangMLR #behaviour #game studies
Learning and Evaluating Human-Like NPC Behaviors in Dynamic Games (YHC, RTM, TL, VR).
AIIDEAIIDE-2011-DereszynskiHFDHU #behaviour #game studies #modelling #probability #realtime
Learning Probabilistic Behavior Models in Real-Time Strategy Games (EWD, JH, AF, TGD, TTH, MU).
AIIDEAIIDE-2011-LinW #modelling
All the World's a Stage: Learning Character Models from Film (GIL, MAW).
AIIDEAIIDE-2011-MohanL #approach #game studies #object-oriented
An Object-Oriented Approach to Reinforcement Learning in an Action Game (SM, JEL).
AIIDEAIIDE-2011-TastanS #game studies #policy #using
Learning Policies for First Person Shooter Games Using Inverse Reinforcement Learning (BT, GRS).
CoGCIG-2011-AbdullahiL #difference
Temporal difference learning with interpolated n-tuples: Initial results from a simulated car racing environment (AAA, SML), pp. 321–328.
CoGCIG-2011-AgapitosOBT #modelling #programming #search-based #using
Learning environment models in car racing using stateful Genetic Programming (AA, MO0, AB, TT), pp. 219–226.
CoGCIG-2011-CarvalhoO
Reinforcement learning for the soccer dribbling task (AC, RO), pp. 95–101.
CoGCIG-2011-RoblesRL #game studies #monte carlo
Learning non-random moves for playing Othello: Improving Monte Carlo Tree Search (DR, PR, SML), pp. 305–312.
DiGRADiGRA-2011-Frank #design #education #game studies #question
Unexpected game calculations in educational wargaming: Design flaw or beneficial to learning? (AF).
DiGRADiGRA-2011-IacovidesASW #how #question
Making sense of game-play: How can we examine learning and involvement? (II, JA, ES, WW).
DiGRADiGRA-2011-MitgutschW #design #game studies #recursion
Subversive Game Design for Recursive Learning (KM, MW).
FDGFDG-2011-FowlerC #concept #game studies #motivation #programming
Kodu game lab: improving the motivation for learning programming concepts (AF, BC), pp. 238–240.
FDGFDG-2011-GamesK #design #game studies
Exploring adolescent's STEM learning through scaffolded game design (AIG, LK), pp. 1–8.
FDGFDG-2011-MarshXNOKH #power of
Fun and learning: the power of narrative (TM, CX, LZN, SO, EK, JH), pp. 23–29.
CoGVS-Games-2011-ChilcottS #3d #multi #online #using #web
Ageing Well and Learning through Online Immersive Participation Using a Multi-user Web 3D Environment (MC, AS), pp. 70–75.
CoGVS-Games-2011-FroschauerAGM #experience #game studies #multi #online #towards
Towards an Online Multiplayer Serious Game Providing a Joyful Experience in Learning Art History (JF, MA, DG, DM), pp. 160–163.
CoGVS-Games-2011-JaligamaL #education #online
An Online Virtual Learning Environment for Higher Education (VJ, FL), pp. 207–214.
CoGVS-Games-2011-MathieuPP #approach #multi #named
Format-Store: A Multi-agent Based Approach to Experiential Learning (PM, DP, SP), pp. 120–127.
CoGVS-Games-2011-VosinakisKZ #case study #problem
An Exploratory Study of Problem-Based Learning in Virtual Worlds (SV, PK, PZ), pp. 112–119.
CoGVS-Games-2011-VoulgariK #collaboration #game studies #interactive #multi #on the #online
On Studying Collaborative Learning Interactions in Massively Multiplayer Online Games (IV, VK), pp. 182–183.
CHICHI-2011-DavidoffZZD #coordination #product line
Learning patterns of pick-ups and drop-offs to support busy family coordination (SD, BDZ, JZ, AKD), pp. 1175–1184.
CHICHI-2011-EdgeSCZL #mobile #named
MicroMandarin: mobile language learning in context (DE, ES, KC, JZ, JAL), pp. 3169–3178.
CHICHI-2011-FiebrinkCT #evaluation #interactive
Human model evaluation in interactive supervised learning (RF, PRC, DT), pp. 147–156.
CHICHI-2011-HowisonTRA #concept #interactive
The mathematical imagery trainer: from embodied interaction to conceptual learning (MH, DT, DR, DA), pp. 1989–1998.
CHICHI-2011-JamilOPKS #collaboration #interactive
The effects of interaction techniques on talk patterns in collaborative peer learning around interactive tables (IJ, KO, MJP, AK, SS), pp. 3043–3052.
CHICHI-2011-MoravejiMMCR #development #named #social #web
ClassSearch: facilitating the development of web search skills through social learning (NM, MRM, DM, MC, NHR), pp. 1797–1806.
CHICHI-2011-ShaerSVFLW #interactive
Enhancing genomic learning through tabletop interaction (OS, MS, CV, TF, ML, HW), pp. 2817–2826.
CHICHI-2011-ToupsKHS #coordination #simulation
Zero-fidelity simulation of fire emergency response: improving team coordination learning (ZOT, AK, WAH, NS), pp. 1959–1968.
CHICHI-2011-TrustyT #web
Augmenting the web for second language vocabulary learning (AT, KNT), pp. 3179–3188.
CSCWCSCW-2011-NawahdahI #automation #education
Automatic adjustment of a virtual teacher’s model in a learning support system (MN, TI), pp. 693–696.
HCIDHM-2011-EilersM #composition #modelling #using
Learning the Relevant Percepts of Modular Hierarchical Bayesian Driver Models Using a Bayesian Information Criterion (ME, CM), pp. 463–472.
HCIDHM-2011-TangwenF #analysis #architecture #cumulative #polymorphism
Polymorphic Cumulative Learning in Integrated Cognitive Architectures for Analysis of Pilot-Aircraft Dynamic Environment (TY, SF), pp. 409–416.
HCIDUXU-v1-2011-ChenT #design #industrial #problem #student
Exploring the Learning Problems and Resources Usage of Undergraduate Industrial Design Students in Design Studio (WC, HHT), pp. 43–52.
HCIDUXU-v1-2011-GeorgeADMW #collaboration #multi
Multitouch Tables for Collaborative Object-Based Learning (JG, EdA, DD, DSM, GW), pp. 237–246.
HCIDUXU-v1-2011-LeeR #architecture #collaboration #concept #mobile
Suggested Collaborative Learning Conceptual Architecture and Applications for Mobile Devices (KL, AR), pp. 611–620.
HCIDUXU-v1-2011-Schmid #analysis #development #feedback
Development of an Augmented Feedback Application to Support Motor Learning after Stroke: Requirement Analysis (SS), pp. 305–314.
HCIDUXU-v2-2011-ArditoLRSYAC #design #game studies #pervasive
Designing Pervasive Games for Learning (CA, RL, DR, CS, NY, NMA, MFC), pp. 99–108.
HCIHCD-2011-KamihiraAN #communication #community #design #education #visual notation
Building a Shared Cross-Cultural Learning Community for Visual Communication Design Education (TK, MA, TN), pp. 397–406.
HCIHCI-MIIE-2011-MajimaNMHNHA #evaluation #mobile
Evaluation of Continuous Practice by Mobile Learning in Nursing Practical Training (YM, YN, YM, MH, YN, SH, HA), pp. 84–91.
HCIHCI-UA-2011-AdamsS
A Web-Based Learning Environment to Support Chemistry (CA, CS), pp. 3–11.
HCIHCI-UA-2011-EverardJM #question #student #what
Are MIS Students Learning What They Need to Land a Job? (AE, BMJ, SM), pp. 235–236.
HCIHCI-UA-2011-GeorgeS #collaboration #game studies
Introducing Mobility in Serious Games: Enhancing Situated and Collaborative Learning (SG, AS), pp. 12–20.
HCIHCI-UA-2011-HayakawaNOFN #framework #visualisation
Visualization Framework for Computer System Learning (EH, YN, HO, MF, YN), pp. 21–26.
HCIHCI-UA-2011-Huseyinov #adaptation #fuzzy #modelling #multi
Fuzzy Linguistic Modelling Cognitive / Learning Styles for Adaptation through Multi-level Granulation (IH), pp. 39–47.
HCIHCI-UA-2011-Klenner-Moore #process
Creating a New Context for Activity in Blended Learning Environments: Engaging the Twitchy Fingers (JKM), pp. 61–67.
HCIHCI-UA-2011-LiJN #user interface #visual notation
Haptically Enhanced User Interface to Support Science Learning of Visually Impaired (YL, SLJ, CSN), pp. 68–76.
HCIHCI-UA-2011-NagaiKI #process
A Drawing Learning Support System with Auto-evaluating Function Based on the Drawing Process Model (TN, MK, KI), pp. 97–106.
HCIHCI-UA-2011-Wang11a #interactive #network #student #tool support #using
Interactions between Human and Computer Networks: EFL College Students Using Computer Learning Tools in Remedial English Classes (ALW), pp. 107–112.
HCIHCI-UA-2011-YajimaT #collaboration
Proposal of Collaborative Learning Support Method in Risk Communications (HY, NT), pp. 113–120.
HCIHCI-UA-2011-YamaguchiMT #evaluation #online
Evaluation of Online Handwritten Characters for Penmanship Learning Support System (TY, NM, MT), pp. 121–130.
HCIHCI-UA-2011-YangCS #analysis #recognition
Facial Expression Recognition for Learning Status Analysis (MTY, YJC, YCS), pp. 131–138.
HCIHIMI-v2-2011-PohlML #hybrid #standard
Transforming a Standard Lecture into a Hybrid Learning Scenario (HMP, JTM, JL), pp. 55–61.
HCIOCSC-2011-AhmadL
Promoting Reflective Learning: The Role of Blogs in the Classroom (RA, WGL), pp. 3–11.
HCIOCSC-2011-PuseyM #collaboration #design #recommendation #wiki
Assessments in Large- and Small-Scale Wiki Collaborative Learning Environments: Recommendations for Educators and Wiki Designers (PP, GM), pp. 60–68.
ICEISICEIS-J-2011-NganBL11a #framework #monitoring #multi #query
An Event-Based Service Framework for Learning, Querying and Monitoring Multivariate Time Series (CKN, AB, JL), pp. 208–223.
ICEISICEIS-v2-2011-NganBL #framework #monitoring #multi #query
A Service Framework for Learning, Querying and Monitoring Multivariate Time Series (CKN, AB, JL), pp. 92–101.
ICEISICEIS-v4-2011-Marks #collaboration #student
Students’ Acceptance of E-Group Collaboration Learning (AM), pp. 269–274.
CIKMCIKM-2011-ArguelloDC #web
Learning to aggregate vertical results into web search results (JA, FD, JC), pp. 201–210.
CIKMCIKM-2011-CoffmanW #keyword #rank #relational
Learning to rank results in relational keyword search (JC, ACW), pp. 1689–1698.
CIKMCIKM-2011-DhillonSS #information management #modelling #multi #predict #web
Semi-supervised multi-task learning of structured prediction models for web information extraction (PSD, SS, SKS), pp. 957–966.
CIKMCIKM-2011-FeiJYLH #approach #behaviour #multi #predict #social
Content based social behavior prediction: a multi-task learning approach (HF, RJ, YY, BL, JH), pp. 995–1000.
CIKMCIKM-2011-FuLZZ #query
Do they belong to the same class: active learning by querying pairwise label homogeneity (YF, BL, XZ, CZ), pp. 2161–2164.
CIKMCIKM-2011-GiannopoulosBDS #rank
Learning to rank user intent (GG, UB, TD, TKS), pp. 195–200.
CIKMCIKM-2011-KasiviswanathanMBS #detection #taxonomy #topic #using
Emerging topic detection using dictionary learning (SPK, PM, AB, VS), pp. 745–754.
CIKMCIKM-2011-LauLBW #scalability #sentiment #web
Leveraging web 2.0 data for scalable semi-supervised learning of domain-specific sentiment lexicons (RYKL, CLL, PB, KFW), pp. 2457–2460.
CIKMCIKM-2011-LiCHLJ #collaboration #online
Collaborative online learning of user generated content (GL, KC, SCHH, WL, RJ), pp. 285–290.
CIKMCIKM-2011-LinC #data fusion #query
Query sampling for learning data fusion (TCL, PJC), pp. 141–146.
CIKMCIKM-2011-LinLWX #rank
Learning to rank with cross entropy (YL, HL, JW, KX), pp. 2057–2060.
CIKMCIKM-2011-LiuCZH #random
Learning conditional random fields with latent sparse features for acronym expansion finding (JL, JC, YZ, YH), pp. 867–872.
CIKMCIKM-2011-LiuLH #bound #fault #kernel
Learning kernels with upper bounds of leave-one-out error (YL, SL, YH), pp. 2205–2208.
CIKMCIKM-2011-NavigliFSLA #ambiguity #categorisation #modelling #semantics #word
Two birds with one stone: learning semantic models for text categorization and word sense disambiguation (RN, SF, AS, OLdL, EA), pp. 2317–2320.
CIKMCIKM-2011-OroR #approach #named
SILA: a spatial instance learning approach for deep webpages (EO, MR), pp. 2329–2332.
CIKMCIKM-2011-PandeyABHCRZ #behaviour #what
Learning to target: what works for behavioral targeting (SP, MA, AB, AOH, PC, AR, MZ), pp. 1805–1814.
CIKMCIKM-2011-RamanJS #ranking
Structured learning of two-level dynamic rankings (KR, TJ, PS), pp. 291–296.
CIKMCIKM-2011-SellamanickamGS #approach #ranking
A pairwise ranking based approach to learning with positive and unlabeled examples (SS, PG, SKS), pp. 663–672.
CIKMCIKM-2011-SzummerY #rank
Semi-supervised learning to rank with preference regularization (MS, EY), pp. 269–278.
CIKMCIKM-2011-TangLYSGGYZ #behaviour #rank
Learning to rank audience for behavioral targeting in display ads (JT, NL, JY, YS, SG, BG, SY, MZ), pp. 605–610.
CIKMCIKM-2011-UllegaddiV #category theory #query #rank #web
Learning to rank categories for web queries (PU, VV), pp. 2065–2068.
CIKMCIKM-2011-WangCWLWO #similarity
Coupled nominal similarity in unsupervised learning (CW, LC, MW, JL, WW, YO), pp. 973–978.
CIKMCIKM-2011-WangHJT #categorisation #image #metric #multi #performance
Efficient lp-norm multiple feature metric learning for image categorization (SW, QH, SJ, QT), pp. 2077–2080.
CIKMCIKM-2011-WangHLCH #recommendation
Learning to recommend questions based on public interest (JW, XH, ZL, WHC, BH), pp. 2029–2032.
CIKMCIKM-2011-WangL #framework #named #rank
CoRankBayes: bayesian learning to rank under the co-training framework and its application in keyphrase extraction (CW, SL), pp. 2241–2244.
CIKMCIKM-2011-YanGC #higher-order #query #recommendation
Context-aware query recommendation by learning high-order relation in query logs (XY, JG, XC), pp. 2073–2076.
CIKMCIKM-2011-YangZKL #how #question #why
Can irrelevant data help semi-supervised learning, why and how? (HY, SZ, IK, MRL), pp. 937–946.
CIKMCIKM-2011-YanTLSL #predict
Citation count prediction: learning to estimate future citations for literature (RY, JT, XL, DS, XL), pp. 1247–1252.
CIKMCIKM-2011-ZhaoYX #independence #information management #web
Max margin learning on domain-independent web information extraction (BZ, XY, EPX), pp. 1305–1310.
CIKMCIKM-2011-ZhuZYGX
Transfer active learning (ZZ, XZ, YY, YFG, XX), pp. 2169–2172.
ECIRECIR-2011-HofmannWR #online #rank
Balancing Exploration and Exploitation in Learning to Rank Online (KH, SW, MdR), pp. 251–263.
ECIRECIR-2011-MacdonaldO #modelling #ranking
Learning Models for Ranking Aggregates (CM, IO), pp. 517–529.
ECIRECIR-2011-ZhouH #comprehension #natural language #random
Learning Conditional Random Fields from Unaligned Data for Natural Language Understanding (DZ, YH), pp. 283–288.
ICMLICML-2011-BabenkoVDB #multi
Multiple Instance Learning with Manifold Bags (BB, NV, PD, SB), pp. 81–88.
ICMLICML-2011-BabesMLS #multi
Apprenticeship Learning About Multiple Intentions (MB, VNM, KS, MLL), pp. 897–904.
ICMLICML-2011-BazzaniFLMT #network #policy #recognition #video
Learning attentional policies for tracking and recognition in video with deep networks (LB, NdF, HL, VM, JAT), pp. 937–944.
ICMLICML-2011-BuffoniCGU #standard
Learning Scoring Functions with Order-Preserving Losses and Standardized Supervision (DB, CC, PG, NU), pp. 825–832.
ICMLICML-2011-Bylander #linear #multi #polynomial
Learning Linear Functions with Quadratic and Linear Multiplicative Updates (TB), pp. 505–512.
ICMLICML-2011-ChakrabortyS
Structure Learning in Ergodic Factored MDPs without Knowledge of the Transition Function’s In-Degree (DC, PS), pp. 737–744.
ICMLICML-2011-ChenPSDC #analysis #process
The Hierarchical Beta Process for Convolutional Factor Analysis and Deep Learning (BC, GP, GS, DBD, LC), pp. 361–368.
ICMLICML-2011-ChoRI #adaptation #strict
Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines (KC, TR, AI), pp. 105–112.
ICMLICML-2011-DauphinGB #re-engineering #scalability
Large-Scale Learning of Embeddings with Reconstruction Sampling (YD, XG, YB), pp. 945–952.
ICMLICML-2011-DinuzzoOGP #coordination #kernel
Learning Output Kernels with Block Coordinate Descent (FD, CSO, PVG, GP), pp. 49–56.
ICMLICML-2011-DudikLL #evaluation #policy #robust
Doubly Robust Policy Evaluation and Learning (MD, JL, LL), pp. 1097–1104.
ICMLICML-2011-GlorotBB #adaptation #approach #classification #scalability #sentiment
Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach (XG, AB, YB), pp. 513–520.
ICMLICML-2011-Gould #linear #markov #random
Max-margin Learning for Lower Linear Envelope Potentials in Binary Markov Random Fields (SG), pp. 193–200.
ICMLICML-2011-GuilloryB
Simultaneous Learning and Covering with Adversarial Noise (AG, JAB), pp. 369–376.
ICMLICML-2011-HarelM #multi
Learning from Multiple Outlooks (MH, SM), pp. 401–408.
ICMLICML-2011-HeL #framework #multi
A Graphbased Framework for Multi-Task Multi-View Learning (JH, RL), pp. 25–32.
ICMLICML-2011-HuWC #coordination #kernel #named #parametricity #scalability #using
BCDNPKL: Scalable Non-Parametric Kernel Learning Using Block Coordinate Descent (EH, BW, SC), pp. 209–216.
ICMLICML-2011-JawanpuriaNR #kernel #performance #using
Efficient Rule Ensemble Learning using Hierarchical Kernels (PJ, JSN, GR), pp. 161–168.
ICMLICML-2011-KangGS #multi
Learning with Whom to Share in Multi-task Feature Learning (ZK, KG, FS), pp. 521–528.
ICMLICML-2011-KuwadekarN #classification #modelling #relational
Relational Active Learning for Joint Collective Classification Models (AK, JN), pp. 385–392.
ICMLICML-2011-LeeW #identification #online #probability
Manifold Identification of Dual Averaging Methods for Regularized Stochastic Online Learning (SL, SJW), pp. 1121–1128.
ICMLICML-2011-LeNCLPN #on the #optimisation
On optimization methods for deep learning (QVL, JN, AC, AL, BP, AYN), pp. 265–272.
ICMLICML-2011-LiZSC #integration #modelling #on the #taxonomy #topic
On the Integration of Topic Modeling and Dictionary Learning (LL, MZ, GS, LC), pp. 625–632.
ICMLICML-2011-LuB #modelling
Learning Mallows Models with Pairwise Preferences (TL, CB), pp. 145–152.
ICMLICML-2011-Maaten #kernel
Learning Discriminative Fisher Kernels (LvdM), pp. 217–224.
ICMLICML-2011-MachartPARG #kernel #probability #rank
Stochastic Low-Rank Kernel Learning for Regression (PM, TP, SA, LR, HG), pp. 969–976.
ICMLICML-2011-MartensS #network #optimisation
Learning Recurrent Neural Networks with Hessian-Free Optimization (JM, IS), pp. 1033–1040.
ICMLICML-2011-NgiamCKN #energy #modelling
Learning Deep Energy Models (JN, ZC, PWK, AYN), pp. 1105–1112.
ICMLICML-2011-NgiamKKNLN #multimodal
Multimodal Deep Learning (JN, AK, MK, JN, HL, AYN), pp. 689–696.
ICMLICML-2011-NickelTK #multi
A Three-Way Model for Collective Learning on Multi-Relational Data (MN, VT, HPK), pp. 809–816.
ICMLICML-2011-OrabonaL #algorithm #kernel #multi #optimisation
Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning (FO, JL), pp. 249–256.
ICMLICML-2011-QuadriantoL #multi
Learning Multi-View Neighborhood Preserving Projections (NQ, CHL), pp. 425–432.
ICMLICML-2011-RobbianoC #plugin #ranking
Minimax Learning Rates for Bipartite Ranking and Plug-in Rules (SR, SC), pp. 441–448.
ICMLICML-2011-SaxeKCBSN #on the #random
On Random Weights and Unsupervised Feature Learning (AMS, PWK, ZC, MB, BS, AYN), pp. 1089–1096.
ICMLICML-2011-SmallWBT
The Constrained Weight Space SVM: Learning with Ranked Features (KS, BCW, CEB, TAT), pp. 865–872.
ICMLICML-2011-Sohl-DicksteinBD #probability
Minimum Probability Flow Learning (JSD, PB, MRD), pp. 905–912.
ICMLICML-2011-TamuzLBSK #adaptation #kernel
Adaptively Learning the Crowd Kernel (OT, CL, SB, OS, AK), pp. 673–680.
ICMLICML-2011-WellingT #probability
Bayesian Learning via Stochastic Gradient Langevin Dynamics (MW, YWT), pp. 681–688.
ICMLICML-2011-YangR #on the #using #visual notation
On the Use of Variational Inference for Learning Discrete Graphical Model (EY, PDR), pp. 1009–1016.
ICMLICML-2011-YanRFD
Active Learning from Crowds (YY, RR, GF, JGD), pp. 1161–1168.
KDDKDD-2011-AttenbergP #online
Online active inference and learning (JA, FJP), pp. 186–194.
KDDKDD-2011-ChakiCG
Supervised learning for provenance-similarity of binaries (SC, CC, AG), pp. 15–23.
KDDKDD-2011-ChenRT #adaptation #detection #incremental
Detecting bots via incremental LS-SVM learning with dynamic feature adaptation (FC, SR, PNT), pp. 386–394.
KDDKDD-2011-ChenZY #multi #rank #robust
Integrating low-rank and group-sparse structures for robust multi-task learning (JC, JZ, JY), pp. 42–50.
KDDKDD-2011-ChuZLTT #data type #online
Unbiased online active learning in data streams (WC, MZ, LL, AT, BLT), pp. 195–203.
KDDKDD-2011-Cormode #privacy
Personal privacy vs population privacy: learning to attack anonymization (GC), pp. 1253–1261.
KDDKDD-2011-GhaniK #detection #fault #interactive
Interactive learning for efficiently detecting errors in insurance claims (RG, MK), pp. 325–333.
KDDKDD-2011-JiangBSZL #adaptation #concept #ontology
Ontology enhancement and concept granularity learning: keeping yourself current and adaptive (SJ, LB, BS, YZ, WL), pp. 1244–1252.
KDDKDD-2011-MesterharmP #algorithm #online #using
Active learning using on-line algorithms (CM, MJP), pp. 850–858.
KDDKDD-2011-MooreYZRL #classification #network
Active learning for node classification in assortative and disassortative networks (CM, XY, YZ, JBR, TL), pp. 841–849.
KDDKDD-2011-RashidiC #induction #query
Ask me better questions: active learning queries based on rule induction (PR, DJC), pp. 904–912.
KDDKDD-2011-ValizadeganJW #multi #predict
Learning to trade off between exploration and exploitation in multiclass bandit prediction (HV, RJ, SW), pp. 204–212.
KDDKDD-2011-ZhangHLSL #approach #multi #scalability
Multi-view transfer learning with a large margin approach (DZ, JH, YL, LS, RDL), pp. 1208–1216.
KDDKDD-2011-ZhangLS
Serendipitous learning: learning beyond the predefined label space (DZ, YL, LS), pp. 1343–1351.
KDDKDD-2011-ZhouYLY #multi #predict
A multi-task learning formulation for predicting disease progression (JZ, LY, JL, JY), pp. 814–822.
KDIRKDIR-2011-ArmengolP #case study #classification #information management #lazy evaluation
Combining Two Lazy Learning Methods for Classification and Knowledge Discovery — A Case Study for Malignant Melanoma Diagnosis (EA, SP), pp. 200–207.
KDIRKDIR-2011-FilhoRM #named #rank
XHITS: Learning to Rank in a Hyperlinked Structure (FBF, RPR, RLM), pp. 385–389.
KDIRKDIR-2011-GriffithOS #collaboration #parametricity
Learning Neighbourhood-based Collaborative Filtering Parameters (JG, CO, HS), pp. 452–455.
KDIRKDIR-2011-LiVM #graph #relational #using #visual notation
Unsupervised Handwritten Graphical Symbol Learning — Using Minimum Description Length Principle on Relational Graph (JL, CVG, HM), pp. 172–178.
KDIRKDIR-2011-ReuterC #identification #similarity #using
Learning Similarity Functions for Event Identification using Support Vector Machines (TR, PC), pp. 208–215.
KEODKEOD-2011-AbbesZN #ontology #semantics
Evaluating Semantic Classes Used for Ontology Building and Learning from Texts (SBA, HZ, AN), pp. 445–448.
KEODKEOD-2011-IshakLA #approach #modelling #ontology #probability #visual notation
A Two-way Approach for Probabilistic Graphical Models Structure Learning and Ontology Enrichment (MBI, PL, NBA), pp. 189–194.
KEODKEOD-2011-KarousosPXKT #development #tool support
Development of Argumentation Skills via Learning Management Systems — Bringing together Argumentation Support Tools and Learning Management Systems (NK, SP, MNX, NIK, MT), pp. 474–477.
KMISKMIS-2011-Silva #approach #concept
Learning Organization — Concept and Proposal of a New Approach (AFdS), pp. 384–389.
MLDMMLDM-2011-CelibertoM
Investigation in Transfer Learning: Better Way to Apply Transfer Learning between Agents (LACJ, JPM), pp. 210–223.
MLDMMLDM-2011-LahbibBL #multi
Informative Variables Selection for Multi-relational Supervised Learning (DL, MB, DL), pp. 75–87.
MLDMMLDM-2011-XuGC #adaptation #kernel #multi
Adaptive Kernel Diverse Density Estimate for Multiple Instance Learning (TX, IG, DKYC), pp. 185–198.
MLDMMLDM-2011-XuM #taxonomy
Dictionary Learning Based on Laplacian Score in Sparse Coding (JX, HM), pp. 253–264.
RecSysRecSys-2011-Makrehchi #recommendation #social #topic
Social link recommendation by learning hidden topics (MM), pp. 189–196.
RecSysRecSys-2011-WuCMW #detection #named
Semi-SAD: applying semi-supervised learning to shilling attack detection (ZW, JC, BM, YW), pp. 289–292.
SEKESEKE-2011-GaoZHL #modelling
Learning action models with indeterminate effects (JG, HHZ, DjH, LL), pp. 159–162.
SEKESEKE-2011-SantosGSF #agile #empirical #implementation #towards
A view towards Organizational Learning: An empirical study on Scrum implementation (VAS, AG, ACMS, ALF), pp. 583–589.
SEKESEKE-2011-SantosWCV #case study #education #experience #re-engineering #repository
Supporting Software Engineering Education through a Learning Objects and Experience Reports Repository (RPdS, CW, HC, SV), pp. 272–275.
SEKESEKE-2011-ThiryZS #education #empirical #game studies #testing
Empirical study upon software testing learning with support from educational game (MT, AZ, ACdS), pp. 481–484.
SIGIRSIGIR-2011-AminiU #automation #detection #multi #summary
Transductive learning over automatically detected themes for multi-document summarization (MRA, NU), pp. 1193–1194.
SIGIRSIGIR-2011-AsadiMEL #pseudo #ranking #web
Pseudo test collections for learning web search ranking functions (NA, DM, TE, JJL), pp. 1073–1082.
SIGIRSIGIR-2011-DaiSD #rank
Learning to rank for freshness and relevance (ND, MS, BDD), pp. 95–104.
SIGIRSIGIR-2011-DaiSD11a #multi #optimisation #rank
Multi-objective optimization in learning to rank (ND, MS, BDD), pp. 1241–1242.
SIGIRSIGIR-2011-GaoZLLW #feedback
Learning features through feedback for blog distillation (DG, RZ, WL, RYKL, KFW), pp. 1085–1086.
SIGIRSIGIR-2011-JiYGHHZC #graph #query #web
Learning search tasks in queries and web pages via graph regularization (MJ, JY, SG, JH, XH, WVZ, ZC), pp. 55–64.
SIGIRSIGIR-2011-KumarL #rank
Learning to rank from a noisy crowd (AK, ML), pp. 1221–1222.
SIGIRSIGIR-2011-LeeHWHS #dataset #graph #image #multi #pipes and filters #scalability #using
Multi-layer graph-based semi-supervised learning for large-scale image datasets using mapreduce (WYL, LCH, GLW, WHH, YFS), pp. 1121–1122.
SIGIRSIGIR-2011-Li #graph
Learning for graphs with annotated edges (FL), pp. 1259–1260.
SIGIRSIGIR-2011-MoghaddamE #aspect-oriented #named #online
ILDA: interdependent LDA model for learning latent aspects and their ratings from online product reviews (SM, ME), pp. 665–674.
SIGIRSIGIR-2011-PolitzS #constraints #rank
Learning to rank under tight budget constraints (CP, RS), pp. 1173–1174.
SIGIRSIGIR-2011-TianL #information retrieval #interactive
Active learning to maximize accuracy vs. effort in interactive information retrieval (AT, ML), pp. 145–154.
SIGIRSIGIR-2011-WangGWL #information retrieval #parallel #rank
Parallel learning to rank for information retrieval (SW, BJG, KW, HWL), pp. 1083–1084.
SIGIRSIGIR-2011-WangRFZHB #network #online #social
Learning relevance from heterogeneous social network and its application in online targeting (CW, RR, DF, DZ, JH, GJB), pp. 655–664.
SIGIRSIGIR-2011-WangWZH #online #random
Learning online discussion structures by conditional random fields (HW, CW, CZ, JH), pp. 435–444.
SIGIRSIGIR-2011-WuYLLYX #rank #using
Learning to rank using query-level regression (JW, ZY, YL, HL, ZY, KX), pp. 1091–1092.
SIGIRSIGIR-2011-YangLSZZ #collaboration #recommendation #using
Collaborative competitive filtering: learning recommender using context of user choice (SHY, BL, AJS, HZ, ZZ), pp. 295–304.
ECMFAECMFA-2011-DolquesDFHNP #automation #model transformation
Easing Model Transformation Learning with Automatically Aligned Examples (XD, AD, JRF, MH, CN, FP), pp. 189–204.
PADLPADL-2011-Mooney
Learning Language from Its Perceptual Context (RJM), pp. 2–4.
POPLPOPL-2011-LiangTN #abstraction
Learning minimal abstractions (PL, OT, MN), pp. 31–42.
ICSEICSE-2011-BorgesGLN #adaptation #evolution #requirements #specification
Learning to adapt requirements specifications of evolving systems (RVB, ASdG, LCL, BN), pp. 856–859.
SACSAC-2011-BhaskaranNFG #behaviour #detection #online
Deceit detection via online behavioral learning (NB, IN, MGF, VG), pp. 29–30.
SACSAC-2011-FontesNPC #architecture #detection #problem
An agent-based architecture for supporting the workgroups creation and the detection of out-of-context conversation on problem-based learning in virtual learning environments (LMdOF, FMMN, AÁAP, GALdC), pp. 1175–1180.
SACSAC-2011-GomesRS #concept #data type
Learning recurring concepts from data streams with a context-aware ensemble (JBG, EMR, PACS), pp. 994–999.
SACSAC-2011-LiuLTL #framework #game studies #interactive #platform
A cognition-based interactive game platform for learning Chinese characters (CLL, CYL, JLT, CLL), pp. 1181–1186.
SACSAC-2011-NawahdahI #education #physics
Positioning a virtual teacher in an MR physical task learning support system (MN, TI), pp. 1169–1174.
SACSAC-2011-SimoesO #behaviour #game studies #modelling
Leveraging the dynamics of learning by modeling and managing psychosocial relations and behavior by means of game theory and memetics (JCS, NO), pp. 1194–1201.
SACSAC-2011-ZhangZZZX #detection #web
Harmonic functions based semi-supervised learning for web spam detection (WZ, DZ, YZ, GZ, BX), pp. 74–75.
DACDAC-2011-DingGYP #detection #named
AENEID: a generic lithography-friendly detailed router based on post-RET data learning and hotspot detection (DD, JRG, KY, DZP), pp. 795–800.
DACDAC-2011-KatzRZS #architecture #behaviour #generative #quality
Learning microarchitectural behaviors to improve stimuli generation quality (YK, MR, AZ, GS), pp. 848–853.
DACDAC-2011-WangXAP #classification #policy #power management #using
Deriving a near-optimal power management policy using model-free reinforcement learning and Bayesian classification (YW, QX, ACA, MP), pp. 41–46.
DATEDATE-2011-ArslanO #adaptation #effectiveness #optimisation #realtime
Adaptive test optimization through real time learning of test effectiveness (BA, AO), pp. 1430–1435.
FASEFASE-2011-FengKP #automation #composition #probability #reasoning
Automated Learning of Probabilistic Assumptions for Compositional Reasoning (LF, MZK, DP), pp. 2–17.
STOCSTOC-2011-BalcanH
Learning submodular functions (MFB, NJAH), pp. 793–802.
ICLPICLP-J-2011-CorapiRVPS #design #induction #using
Normative design using inductive learning (DC, AR, MDV, JAP, KS), pp. 783–799.
ICSTSAT-2011-SilverthornM #satisfiability
Learning Polarity from Structure in SAT (BS, RM), pp. 377–378.
VMCAIVMCAI-2011-HowarSM #abstraction #automation #automaton #refinement
Automata Learning with Automated Alphabet Abstraction Refinement (FH, BS, MM), pp. 263–277.
ECSAECSA-2010-MarcoGII #adaptation #lifecycle #paradigm #self
Learning from the Cell Life-Cycle: A Self-adaptive Paradigm (ADM, FG, PI, RI), pp. 485–488.
DRRDRR-2010-LiuZ #detection #documentation #image
Semi-supervised learning for detecting text-lines in noisy document images (ZL, HZ), pp. 1–10.
DRRDRR-2010-Obafemi-AjayiAF #documentation
Learning shape features for document enhancement (TOA, GA, OF), pp. 1–10.
DRRDRR-2010-ZhangZLT #recognition
A stacked sequential learning method for investigator name recognition from web-based medical articles (XZ, JZ, DXL, GRT), pp. 1–10.
TPDLECDL-2010-KozievitchTAMFH #education #image #retrieval
A Teaching Tool for Parasitology: Enhancing Learning with Annotation and Image Retrieval (NPK, RdST, FSPA, UM, EAF, EH), pp. 466–469.
HTHT-2010-PaekHS #hypermedia
Spatial contiguity and implicit learning in hypertext (SP, DH, AS), pp. 291–292.
HTHT-2010-PrataGC #personalisation
Crossmedia personalized learning contexts (AP, NG, TC), pp. 305–306.
HTHT-2010-TielletPRLC #design #evaluation
Design and evaluation of a hypervideo environment to support veterinary surgery learning (CABT, AGP, EBR, JVdL, TC), pp. 213–222.
HTHT-2010-TielletPRLC10a #named
HVet: a hypervideo environment to support veterinary surgery learning (CABT, AGP, EBR, JVdL, TC), pp. 313–314.
PODSPODS-2010-LemayMN #algorithm #top-down #xml
A learning algorithm for top-down XML transformations (AL, SM, JN), pp. 285–296.
SIGMODSIGMOD-2010-ArasuGK #on the
On active learning of record matching packages (AA, MG, RK), pp. 783–794.
SIGMODSIGMOD-2010-CortezSGM #information management #named #on-demand
ONDUX: on-demand unsupervised learning for information extraction (EC, ASdS, MAG, ESdM), pp. 807–818.
EDMEDM-2010-Bian #clustering #process #student
Clustering Student Learning Activity Data (HB), pp. 277–278.
EDMEDM-2010-BousbiaLBR #behaviour #using #web
Analyzing Learning Styles using Behavioral Indicators in Web based Learning Environments (NB, JML, AB, IR), pp. 279–280.
EDMEDM-2010-ChampaignC10a #approach
A Distillation Approach to Refining Learning Objects (JC, RC), pp. 283–284.
EDMEDM-2010-DMelloG #experience #mining
Mining Bodily Patterns of Affective Experience during Learning (SKD, ACG), pp. 31–40.
EDMEDM-2010-FengH #assessment #question #student #testing
Can We Get Better Assessment From A Tutoring System Compared to Traditional Paper Testing? Can We Have Our Cake (Better Assessment) and Eat It too (Student Learning During the Test)? (MF, NTH), pp. 41–50.
EDMEDM-2010-ForsythBGH #correlation
Higher Contributions Correlate with Higher Learning Gains (CF, HB, ACG, DFH), pp. 287–288.
EDMEDM-2010-GoldsteinBH
Pinpointing Learning Moments; A finer grain P(J) model (ABG, RSJdB, NTH), pp. 289–290.
EDMEDM-2010-JeongBJH #analysis #behaviour #effectiveness #markov #modelling #using
Analysis of Productive Learning Behaviors in a Structured Inquiry Cycle Using Hidden Markov Models (HJ, GB, JJ, LH), pp. 81–90.
EDMEDM-2010-KimC #analysis #case study #experience #sentiment #student
Sentiment Analysis in Student Experiences of Learning (SMK, RAC), pp. 111–120.
EDMEDM-2010-LehmanCO #topic
Off Topic Conversation in Expert Tutoring: Waste of Time or Learning Opportunity (BL, WLC, AO), pp. 101–110.
EDMEDM-2010-Pavlik #comprehension #reduction
Data Reduction Methods Applied to Understanding Complex Learning Hypotheses (PIPJ), pp. 311–312.
EDMEDM-2010-Rajibussalim #interactive #mining #student
Mining Students’ Interaction Data from a System that Support Learning by Reflection (R), pp. 249–256.
EDMEDM-2010-RuppSC #analysis #game studies #modelling #network #novel
Modeling Learning Trajectories with Epistemic Network Analysis: A Simulation-based Investigation of a Novel Analytic Method for Epistemic Games (AAR, SJS, YC), pp. 319–320.
EDMEDM-2010-SoundranayagamY #order #predict #question
Can Order of Access to Learning Resources Predict Success? (HS, KY), pp. 323–324.
EDMEDM-2010-XuR #analysis #network #online #social
Peer Production of Online Learning Resources: A Social Network Analysis (BX, MR), pp. 315–316.
ITiCSEITiCSE-2010-AydinolG10a #spreadsheet #video
The effect of video tutorials on learning spreadsheets (ABA, ÖG), p. 323.
ITiCSEITiCSE-2010-CoconF #education #named #online
LOMOLEHEA: learning object model for online learning based on the european higher education area (FC, EF), pp. 78–82.
ITiCSEITiCSE-2010-Cross
Promoting active learning through assignments (GWC), p. 306.
ITiCSEITiCSE-2010-Denny #collaboration #online
Motivating online collaborative learning (PD), p. 300.
ITiCSEITiCSE-2010-EganJ
Service learning in introductory computer science (MALE, MJ), pp. 8–12.
ITiCSEITiCSE-2010-HamadaS
Lego NXT as a learning tool (MH, SS), p. 321.
ITiCSEITiCSE-2010-HowardJN #behaviour #design #online #using
Reflecting on online learning designs using observed behavior (LH, JJ, CN), pp. 179–183.
ITiCSEITiCSE-2010-Larraza-MendiluzeG #game studies #process #topic #using
Changing the learning process of the input/output topic using a game in a portable console (ELM, NGV), p. 316.
ITiCSEITiCSE-2010-LeeR #algorithm #category theory #design #visualisation
Integrating categories of algorithm learning objective into algorithm visualization design: a proposal (MHL, GR), pp. 289–293.
ITiCSEITiCSE-2010-MarcosHGGMGBOGVME #delivery #mobile #online
A mobile learning tool to deliver online questionnaires (LdM, JRH, EG, AGC, JJM, JMG, RB, SO, JAG, EV, MMM, SE), p. 319.
ITiCSEITiCSE-2010-Mirolo #analysis #multi #recursion #student
Learning (through) recursion: a multidimensional analysis of the competences achieved by CS1 students (CM), pp. 160–164.
ITiCSEITiCSE-2010-QianLYL #programming
Inquiry-based active learning in introductory programming courses (KQ, CTDL, LY, JL), p. 312.
ITiCSEITiCSE-2010-TuOKKT
Developing verification-driven learning cases (ST, SJO, RK, AK, ST), pp. 58–62.
SIGITESIGITE-2010-Al-khalifa #gender
Overcoming gender segregation in service learning projects: a case from Saudi Arabia (HSAK), pp. 121–124.
SIGITESIGITE-2010-GiannakosV
Comparing a well designed webcast with traditional learning (MNG, PV), pp. 65–68.
SIGITESIGITE-2010-KayamaFKTS #exclamation
Let’s go! magical spoons: a high school learning program for information coding fundamentals (MK, TF, AK, TT, CS), pp. 95–104.
SIGITESIGITE-2010-MulwaLSSW #adaptation #education #hypermedia #overview #perspective
Adaptive educational hypermedia systems in technology enhanced learning: a literature review (CM, SL, MS, IAS, VW), pp. 73–84.
SIGITESIGITE-2010-Zhang #framework #student
Technology acceptance in learning settings from a student perspective: a theoretical framework (CZ), pp. 37–42.
ICSMEICSM-2010-BhattacharyaN #debugging #fine-grained #graph #incremental #multi
Fine-grained incremental learning and multi-feature tossing graphs to improve bug triaging (PB, IN), pp. 1–10.
PASTEPASTE-2010-FengG #fault #locality #modelling #probability
Learning universal probabilistic models for fault localization (MF, RG), pp. 81–88.
SCAMSCAM-2010-Zeller #in the large #mining #modelling
Learning from 6,000 Projects: Mining Models in the Large (AZ), pp. 3–6.
LATALATA-2010-KasprzikK #string #using
String Extension Learning Using Lattices (AK, TK), pp. 380–391.
AIIDEAIIDE-2010-SharifiZS #behaviour #game studies #using
Learning Companion Behaviors Using Reinforcement Learning in Games (AS, RZ, DS).
AIIDEAIIDE-2010-Torrey #multi #simulation
Crowd Simulation Via Multi-Agent Reinforcement Learning (LT).
CoGCIG-2010-HannaHCB #architecture #composition #game studies
Modular Reinforcement Learning architectures for artificially intelligent agents in complex game environments (CJH, RJH, DC, MMB), pp. 380–387.
CoGCIG-2010-Lucas #evolution #problem
Estimating learning rates in evolution and TDL: Results on a simple grid-world problem (SML), pp. 372–379.
CoGCIG-2010-MartinezHY #modelling
Extending neuro-evolutionary preference learning through player modeling (HPM, KH, GNY), pp. 313–320.
CoGCIG-2010-QuadfliegPKR
Learning the track and planning ahead in a car racing controller (JQ, MP, OK, GR), pp. 395–402.
FDGFDG-2010-ArenaS #exclamation #game studies #statistics #video
Stats invaders!: learning about statistics by playing a classic video game (DA, DLS), pp. 248–249.
FDGFDG-2010-BoyceB #game studies #motivation #using
BeadLoom Game: using game elements to increase motivation and learning (AKB, TB), pp. 25–31.
FDGFDG-2010-EsteyLGG #design #game studies
Investigating studio-based learning in a course on game design (AE, JL, BG, AAG), pp. 64–71.
FDGFDG-2010-NickelB #collaboration #education #game studies #multi
Games for CS education: computer-supported collaborative learning and multiplayer games (AN, TB), pp. 274–276.
FDGFDG-2010-RoweSML #difference #perspective
Individual differences in gameplay and learning: a narrative-centered learning perspective (JPR, LRS, BWM, JCL), pp. 171–178.
FDGFDG-2010-SheldonPKOCTR #approach #game studies #mobile #named #student #using
Weatherlings: a new approach to student learning using web-based mobile games (JS, JP, EK, JO, VHHC, PWT, LR), pp. 203–208.
FDGFDG-2010-TolentinoSB #design #education #game studies #social #student
Applying game design principles to social skills learning for students in special education (LMT, PS, DB), pp. 252–253.
CoGVS-Games-2010-SchmeilSJHJSH #collaboration #design #workflow
A Workflow for Designing Virtual Worlds for Collaborative Learning (AS, MS, AJ, MH, MJ, MS, BSH), pp. 151–158.
CHICHI-2010-AmershiFKT #concept #interactive #modelling #multi
Examining multiple potential models in end-user interactive concept learning (SA, JF, AK, DST), pp. 1357–1360.
CHICHI-2010-CapraMVM #collaboration #multi
Tools-at-hand and learning in multi-session, collaborative search (RGC, GM, JVM, KM), pp. 951–960.
CHICHI-2010-DornG #design #programming #web
Learning on the job: characterizing the programming knowledge and learning strategies of web designers (BD, MG), pp. 703–712.
CHICHI-2010-HuangSDWKAL #mobile #music
Mobile music touch: mobile tactile stimulation for passive learning (KH, TS, EYLD, GW, DK, CA, RL), pp. 791–800.
CHICHI-2010-IsbisterFH #design #game studies
Designing games for learning: insights from conversations with designers (KI, MF, CH), pp. 2041–2044.
CHICHI-2010-KumarTSCKC #case study #mobile
An exploratory study of unsupervised mobile learning in rural India (AK, AT, GS, DC, MK, JC), pp. 743–752.
CHICHI-2010-TianLWWLKSDC #game studies #mobile
Let’s play chinese characters: mobile learning approaches via culturally inspired group games (FT, FL, JW, HW, WL, MK, VS, GD, JC), pp. 1603–1612.
CHICHI-2010-Weilenmann #how #interactive #mobile
Learning to text: an interaction analytic study of how an interaction analytic study of how seniors learn to enter text on mobile phones (AW), pp. 1135–1144.
ICEISICEIS-AIDSS-2010-AhdabG #network #performance
Efficient Learning of Dynamic Bayesian Networks from Timed Data (AA, MLG), pp. 226–231.
ICEISICEIS-AIDSS-2010-MasvoulaKM #overview
A Review of Learning Methods Enhanced in Strategies of Negotiating Agents (MM, PK, DM), pp. 212–219.
ICEISICEIS-AIDSS-2010-MoriyasuYN #self #using
Supervised Learning for Agent Positioning by using Self-organizing Map (KM, TY, HN), pp. 368–372.
ICEISICEIS-HCI-2010-DiosERR #collaboration
Virtual and Collaborative Environment for Learning Maths (AQD, AHE, IVR, ÁMdR), pp. 86–90.
CIKMCIKM-2010-BethardJ #behaviour #modelling
Who should I cite: learning literature search models from citation behavior (SB, DJ), pp. 609–618.
CIKMCIKM-2010-BilottiECN #constraints #rank #semantics
Rank learning for factoid question answering with linguistic and semantic constraints (MWB, JLE, JGC, EN), pp. 459–468.
CIKMCIKM-2010-BingSJZL #documentation #mining #ontology #representation
Learning ontology resolution for document representation and its applications in text mining (LB, BS, SJ, YZ, WL), pp. 1713–1716.
CIKMCIKM-2010-CebronB #parallel
Active learning in parallel universes (NC, MRB), pp. 1621–1624.
CIKMCIKM-2010-ComarTJ #multi #network
Multi task learning on multiple related networks (PMC, PNT, AKJ), pp. 1737–1740.
CIKMCIKM-2010-DuNL #adaptation
Adapting cost-sensitive learning for reject option (JD, EAN, CXL), pp. 1865–1868.
CIKMCIKM-2010-EatondJ #clustering #constraints #multi
Multi-view clustering with constraint propagation for learning with an incomplete mapping between views (EE, Md, SJ), pp. 389–398.
CIKMCIKM-2010-FangSS #clustering #multi
Multilevel manifold learning with application to spectral clustering (HrF, SS, YS), pp. 419–428.
CIKMCIKM-2010-FujinoUN #classification #robust
A robust semi-supervised classification method for transfer learning (AF, NU, MN), pp. 379–388.
CIKMCIKM-2010-He #classification #sentiment
Learning sentiment classification model from labeled features (YH), pp. 1685–1688.
CIKMCIKM-2010-HeMW #algorithm #evaluation #metric #optimisation #rank
Directly optimizing evaluation measures in learning to rank based on the clonal selection algorithm (QH, JM, SW), pp. 1449–1452.
CIKMCIKM-2010-KouCZZ #ranking
Learning to blend rankings: a monotonic transformation to blend rankings from heterogeneous domains (ZK, YC, ZZ, HZ), pp. 1921–1924.
CIKMCIKM-2010-LadY #documentation #feedback #novel #rank
Learning to rank relevant and novel documents through user feedback (AL, YY), pp. 469–478.
CIKMCIKM-2010-LinLYJS #rank
Learning to rank with groups (YL, HL, ZY, SJ, XS), pp. 1589–1592.
CIKMCIKM-2010-MoonLCLZC #feedback #online #ranking #realtime #using
Online learning for recency search ranking using real-time user feedback (TM, LL, WC, CL, ZZ, YC), pp. 1501–1504.
CIKMCIKM-2010-NguyenYLF #case study #experience #multi #ranking #using
Experiences with using SVM-based learning for multi-objective ranking (LTN, WGY, RL, OF), pp. 1917–1920.
CIKMCIKM-2010-ShiZT
Combining link and content for collective active learning (LS, YZ, JT), pp. 1829–1832.
CIKMCIKM-2010-SonPS #classification #estimation #naive bayes
Learning naïve bayes transfer classifier throughclass-wise test distribution estimation (JWS, SBP, HJS), pp. 1729–1732.
CIKMCIKM-2010-TakamuraO #summary
Learning to generate summary as structured output (HT, MO), pp. 1437–1440.
CIKMCIKM-2010-YangKL #feature model #multi #online
Online learning for multi-task feature selection (HY, IK, MRL), pp. 1693–1696.
CIKMCIKM-2010-ZhangWWCZHZ #modelling
Learning click models via probit bayesian inference (YZ, DW, GW, WC, ZZ, BH, LZ), pp. 439–448.
CIKMCIKM-2010-ZhaoBCGWZ #concurrent #online #recommendation #thread
Learning a user-thread alignment manifold for thread recommendation in online forum (JZ, JB, CC, ZG, CW, CZ), pp. 559–568.
CIKMCIKM-2010-ZhuZGX #classification #incremental
Transfer incremental learning for pattern classification (ZZ, XZ, YFG, XX), pp. 1709–1712.
ECIRECIR-2010-MendozaMFP #query #web
Learning to Distribute Queries into Web Search Nodes (MM, MM, FF, BP), pp. 281–292.
ECIRECIR-2010-PengMO #ranking
Learning to Select a Ranking Function (JP, CM, IO), pp. 114–126.
ICMLICML-2010-BilgicMG
Active Learning for Networked Data (MB, LM, LG), pp. 79–86.
ICMLICML-2010-BordesUW #ambiguity #ranking #semantics
Label Ranking under Ambiguous Supervision for Learning Semantic Correspondences (AB, NU, JW), pp. 103–110.
ICMLICML-2010-BouzyM #game studies #matrix #multi
Multi-agent Learning Experiments on Repeated Matrix Games (BB, MM), pp. 119–126.
ICMLICML-2010-BradleyG #random
Learning Tree Conditional Random Fields (JKB, CG), pp. 127–134.
ICMLICML-2010-CaniniSG #categorisation #modelling #process
Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process (KRC, MMS, TLG), pp. 151–158.
ICMLICML-2010-CaoLY #multi #predict
Transfer Learning for Collective Link Prediction in Multiple Heterogenous Domains (BC, NNL, QY), pp. 159–166.
ICMLICML-2010-Cesa-BianchiSS #performance
Efficient Learning with Partially Observed Attributes (NCB, SSS, OS), pp. 183–190.
ICMLICML-2010-ChakrabortyS #convergence #multi #safety
Convergence, Targeted Optimality, and Safety in Multiagent Learning (DC, PS), pp. 191–198.
ICMLICML-2010-ChangSGR
Structured Output Learning with Indirect Supervision (MWC, VS, DG, DR), pp. 199–206.
ICMLICML-2010-CortesMR #algorithm #kernel
Two-Stage Learning Kernel Algorithms (CC, MM, AR), pp. 239–246.
ICMLICML-2010-CortesMR10a #bound #kernel
Generalization Bounds for Learning Kernels (CC, MM, AR), pp. 247–254.
ICMLICML-2010-DavisD #bottom-up #markov #network
Bottom-Up Learning of Markov Network Structure (JD, PMD), pp. 271–278.
ICMLICML-2010-DeselaersF #multi #random
A Conditional Random Field for Multiple-Instance Learning (TD, VF), pp. 287–294.
ICMLICML-2010-DillonBL #analysis #generative
Asymptotic Analysis of Generative Semi-Supervised Learning (JVD, KB, GL), pp. 295–302.
ICMLICML-2010-DruckM #generative #modelling #using
High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models (GD, AM), pp. 319–326.
ICMLICML-2010-GavishNC #graph #multi #theory and practice
Multiscale Wavelets on Trees, Graphs and High Dimensional Data: Theory and Applications to Semi Supervised Learning (MG, BN, RRC), pp. 367–374.
ICMLICML-2010-GomesK #data type #parametricity
Budgeted Nonparametric Learning from Data Streams (RG, AK), pp. 391–398.
ICMLICML-2010-GregorL #approximate #performance
Learning Fast Approximations of Sparse Coding (KG, YL), pp. 399–406.
ICMLICML-2010-GrubbB #composition #network
Boosted Backpropagation Learning for Training Deep Modular Networks (AG, JAB), pp. 407–414.
ICMLICML-2010-HarpaleY #adaptation #multi
Active Learning for Multi-Task Adaptive Filtering (AH, YY), pp. 431–438.
ICMLICML-2010-HonorioS #modelling #multi #visual notation
Multi-Task Learning of Gaussian Graphical Models (JH, DS), pp. 447–454.
ICMLICML-2010-HuangG #independence #ranking
Learning Hierarchical Riffle Independent Groupings from Rankings (JH, CG), pp. 455–462.
ICMLICML-2010-HueV #kernel #on the
On learning with kernels for unordered pairs (MH, JPV), pp. 463–470.
ICMLICML-2010-JenattonMOB #taxonomy
Proximal Methods for Sparse Hierarchical Dictionary Learning (RJ, JM, GO, FRB), pp. 487–494.
ICMLICML-2010-KimT10a #multi #process
Gaussian Processes Multiple Instance Learning (MK, FDlT), pp. 535–542.
ICMLICML-2010-KokD #logic #markov #network #using
Learning Markov Logic Networks Using Structural Motifs (SK, PMD), pp. 551–558.
ICMLICML-2010-KulisB #online
Implicit Online Learning (BK, PLB), pp. 575–582.
ICMLICML-2010-LazaricG #multi
Bayesian Multi-Task Reinforcement Learning (AL, MG), pp. 599–606.
ICMLICML-2010-LiangJK #approach #source code
Learning Programs: A Hierarchical Bayesian Approach (PL, MIJ, DK), pp. 639–646.
ICMLICML-2010-LiangS #interactive #multi #on the
On the Interaction between Norm and Dimensionality: Multiple Regimes in Learning (PL, NS), pp. 647–654.
ICMLICML-2010-LiPSG #parametricity
Budgeted Distribution Learning of Belief Net Parameters (LL, BP, CS, RG), pp. 879–886.
ICMLICML-2010-LiuHC #graph #scalability
Large Graph Construction for Scalable Semi-Supervised Learning (WL, JH, SFC), pp. 679–686.
ICMLICML-2010-LiuNLL #analysis #graph #relational
Learning Temporal Causal Graphs for Relational Time-Series Analysis (YL, ANM, ACL, YL), pp. 687–694.
ICMLICML-2010-LizotteBM #analysis #multi #performance #random
Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis (DJL, MHB, SAM), pp. 695–702.
ICMLICML-2010-MaeiSBS #approximate #towards
Toward Off-Policy Learning Control with Function Approximation (HRM, CS, SB, RSS), pp. 719–726.
ICMLICML-2010-Mahmud
Constructing States for Reinforcement Learning (MMHM), pp. 727–734.
ICMLICML-2010-Martens #optimisation
Deep learning via Hessian-free optimization (JM), pp. 735–742.
ICMLICML-2010-Martens10a #linear
Learning the Linear Dynamical System with ASOS (JM), pp. 743–750.
ICMLICML-2010-McFeeL #metric #rank
Metric Learning to Rank (BM, GRGL), pp. 775–782.
ICMLICML-2010-MeshiSJG #approximate
Learning Efficiently with Approximate Inference via Dual Losses (OM, DS, TSJ, AG), pp. 783–790.
ICMLICML-2010-MorimuraSKHT #approximate #parametricity
Nonparametric Return Distribution Approximation for Reinforcement Learning (TM, MS, HK, HH, TT), pp. 799–806.
ICMLICML-2010-OntanonP #approach #induction #multi
Multiagent Inductive Learning: an Argumentation-based Approach (SO, EP), pp. 839–846.
ICMLICML-2010-Salakhutdinov #adaptation #using
Learning Deep Boltzmann Machines using Adaptive MCMC (RS), pp. 943–950.
ICMLICML-2010-SlivkinsRG #documentation #ranking #scalability
Learning optimally diverse rankings over large document collections (AS, FR, SG), pp. 983–990.
ICMLICML-2010-SnyderB #multi
Climbing the Tower of Babel: Unsupervised Multilingual Learning (BS, RB), pp. 29–36.
ICMLICML-2010-SzitaS #bound #complexity #modelling
Model-based reinforcement learning with nearly tight exploration complexity bounds (IS, CS), pp. 1031–1038.
ICMLICML-2010-TanWT #dataset #feature model
Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets (MT, LW, IWT), pp. 1047–1054.
ICMLICML-2010-TomiokaSSK #algorithm #matrix #performance #rank
A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices (RT, TS, MS, HK), pp. 1087–1094.
ICMLICML-2010-WalshSLD
Generalizing Apprenticeship Learning across Hypothesis Classes (TJW, KS, MLL, CD), pp. 1119–1126.
ICMLICML-2010-WangKC
Sequential Projection Learning for Hashing with Compact Codes (JW, SK, SFC), pp. 1127–1134.
ICMLICML-2010-XuJYKL #kernel #multi #performance
Simple and Efficient Multiple Kernel Learning by Group Lasso (ZX, RJ, HY, IK, MRL), pp. 1175–1182.
ICMLICML-2010-YangJJ
Learning from Noisy Side Information by Generalized Maximum Entropy Model (TY, RJ, AKJ), pp. 1199–1206.
ICMLICML-2010-YangXKL #online
Online Learning for Group Lasso (HY, ZX, IK, MRL), pp. 1191–1198.
ICMLICML-2010-ZhaoH #framework #named #online
OTL: A Framework of Online Transfer Learning (PZ, SCHH), pp. 1231–1238.
ICMLICML-2010-ZhuGJRHK #modelling
Cognitive Models of Test-Item Effects in Human Category Learning (XZ, BRG, KSJ, TTR, JH, CK), pp. 1247–1254.
ICPRICPR-2010-AlmaksourAQC #classification #evolution #fuzzy #gesture #incremental #recognition
Evolving Fuzzy Classifiers: Application to Incremental Learning of Handwritten Gesture Recognition Systems (AA, ÉA, SQ, MC), pp. 4056–4059.
ICPRICPR-2010-AmateR #modelling #probability
Learning Probabilistic Models of Contours (LA, MJR), pp. 645–648.
ICPRICPR-2010-AroraS #algorithm #performance
An Efficient and Stable Algorithm for Learning Rotations (RA, WAS), pp. 2993–2996.
ICPRICPR-2010-AtmosukartoSH #3d #programming #search-based #using
The Use of Genetic Programming for Learning 3D Craniofacial Shape Quantifications (IA, LGS, CH), pp. 2444–2447.
ICPRICPR-2010-BaghshahS #constraints #kernel #performance
Efficient Kernel Learning from Constraints and Unlabeled Data (MSB, SBS), pp. 3364–3367.
ICPRICPR-2010-BalujaC #performance #retrieval
Beyond “Near Duplicates”: Learning Hash Codes for Efficient Similar-Image Retrieval (SB, MC), pp. 543–547.
ICPRICPR-2010-BanderaMM #incremental #mobile #visual notation
Incremental Learning of Visual Landmarks for Mobile Robotics (AB, RM, RVM), pp. 4255–4258.
ICPRICPR-2010-BlondelSU #online #recognition
Unsupervised Learning of Stroke Tagger for Online Kanji Handwriting Recognition (MB, KS, KU), pp. 1973–1976.
ICPRICPR-2010-BoltonG #framework #multi #optimisation #random #set
Cross Entropy Optimization of the Random Set Framework for Multiple Instance Learning (JB, PDG), pp. 3907–3910.
ICPRICPR-2010-BuyssensR #verification
Learning Sparse Face Features: Application to Face Verification (PB, MR), pp. 670–673.
ICPRICPR-2010-CarneiroN #architecture
The Fusion of Deep Learning Architectures and Particle Filtering Applied to Lip Tracking (GC, JCN), pp. 2065–2068.
ICPRICPR-2010-Cevikalp #distance #metric #polynomial #programming
Semi-supervised Distance Metric Learning by Quadratic Programming (HC), pp. 3352–3355.
ICPRICPR-2010-ChenF #graph
Semi-supervised Graph Learning: Near Strangers or Distant Relatives (WC, GF), pp. 3368–3371.
ICPRICPR-2010-CohenP #performance #robust
Reinforcement Learning for Robust and Efficient Real-World Tracking (AC, VP), pp. 2989–2992.
ICPRICPR-2010-DagAKS #categorisation
Learning Affordances for Categorizing Objects and Their Properties (ND, IA, SK, ES), pp. 3089–3092.
ICPRICPR-2010-DitzlerPC #algorithm #incremental
An Incremental Learning Algorithm for Non-stationary Environments and Class Imbalance (GD, RP, NVC), pp. 2997–3000.
ICPRICPR-2010-DundarBRJSG #approach #classification #multi #towards
A Multiple Instance Learning Approach toward Optimal Classification of Pathology Slides (MD, SB, VCR, RKJ, OS, MNG), pp. 2732–2735.
ICPRICPR-2010-ErdoganS #classification #framework #linear
A Unifying Framework for Learning the Linear Combiners for Classifier Ensembles (HE, MUS), pp. 2985–2988.
ICPRICPR-2010-FanHM #classification #metric
Learning Metrics for Shape Classification and Discrimination (YF, DH, WM), pp. 2652–2655.
ICPRICPR-2010-FausserS #approximate
Learning a Strategy with Neural Approximated Temporal-Difference Methods in English Draughts (SF, FS), pp. 2925–2928.
ICPRICPR-2010-FengZH #detection #online #self
Online Learning with Self-Organizing Maps for Anomaly Detection in Crowd Scenes (JF, CZ, PH), pp. 3599–3602.
ICPRICPR-2010-FuLTZ #classification #music #naive bayes #retrieval
Learning Naive Bayes Classifiers for Music Classification and Retrieval (ZF, GL, KMT, DZ), pp. 4589–4592.
ICPRICPR-2010-GuoBC #approach #using
Support Vectors Selection for Supervised Learning Using an Ensemble Approach (LG, SB, NC), pp. 37–40.
ICPRICPR-2010-GuoZCZG #documentation
Unsupervised Learning from Linked Documents (ZG, SZ, YC, ZZ, YG), pp. 730–733.
ICPRICPR-2010-HanCR10a #concept #interactive #recognition #semantics
Semi-supervised and Interactive Semantic Concept Learning for Scene Recognition (XHH, YWC, XR), pp. 3045–3048.
ICPRICPR-2010-HanFD #prototype #recognition #set
Discriminative Prototype Learning in Open Set Face Recognition (ZH, CF, XD), pp. 2696–2699.
ICPRICPR-2010-HuangY #recognition
Learning Virtual HD Model for Bi-model Emotional Speaker Recognition (TH, YY), pp. 1614–1617.
ICPRICPR-2010-HurWL #estimation #invariant
View Invariant Body Pose Estimation Based on Biased Manifold Learning (DH, CW, SWL), pp. 3866–3869.
ICPRICPR-2010-JhuoL #kernel #multi #recognition
Boosted Multiple Kernel Learning for Scene Category Recognition (IHJ, DTL), pp. 3504–3507.
ICPRICPR-2010-JiaCLW #image #performance
Efficient Learning to Label Images (KJ, LC, NL, LW), pp. 942–945.
ICPRICPR-2010-JokoKY #linear #modelling
Learning Non-linear Dynamical Systems by Alignment of Local Linear Models (MJ, YK, TY), pp. 1084–1087.
ICPRICPR-2010-JoshiP #adaptation #detection #incremental
Scene-Adaptive Human Detection with Incremental Active Learning (AJJ, FP), pp. 2760–2763.
ICPRICPR-2010-KamarainenI #canonical #detection
Learning and Detection of Object Landmarks in Canonical Object Space (JKK, JI), pp. 1409–1412.
ICPRICPR-2010-KappSM #adaptation #incremental
Adaptive Incremental Learning with an Ensemble of Support Vector Machines (MNK, RS, PM), pp. 4048–4051.
ICPRICPR-2010-KimuraKSNMSI #canonical #correlation #named #performance
SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations (AK, HK, MS, TN, EM, HS, KI), pp. 2933–2936.
ICPRICPR-2010-LiLD #using
Learning GMM Using Elliptically Contoured Distributions (BL, WL, LD), pp. 511–514.
ICPRICPR-2010-LiuA #semantics #using
Learning Scene Semantics Using Fiedler Embedding (JL, SA), pp. 3627–3630.
ICPRICPR-2010-LiuLH #multi #representation #using
Semi-supervised Trajectory Learning Using a Multi-Scale Key Point Based Trajectory Representation (YL, XL, WH), pp. 3525–3528.
ICPRICPR-2010-LuoN #classification #fault #multi #problem
Employing Decoding of Specific Error Correcting Codes as a New Classification Criterion in Multiclass Learning Problems (YL, KN), pp. 4238–4241.
ICPRICPR-2010-NiSRM #multi #online
Particle Filter Tracking with Online Multiple Instance Learning (ZN, SS, AR, BSM), pp. 2616–2619.
ICPRICPR-2010-OhH #process #using #video
Unsupervised Learning of Activities in Video Using Scene Context (SO, AH), pp. 3579–3582.
ICPRICPR-2010-PapadopoulosMKS #analysis #approach #image #semantics #statistics
A Statistical Learning Approach to Spatial Context Exploitation for Semantic Image Analysis (GTP, VM, IK, MGS), pp. 3138–3142.
ICPRICPR-2010-PhilippotBB #algorithm #classification #network #online
Bayesian Networks Learning Algorithms for Online Form Classification (EP, YB, AB), pp. 1981–1984.
ICPRICPR-2010-PuS #probability #verification
Probabilistic Measure for Signature Verification Based on Bayesian Learning (DP, SNS), pp. 1188–1191.
ICPRICPR-2010-RevaudLAB #graph #performance #recognition #robust
Learning an Efficient and Robust Graph Matching Procedure for Specific Object Recognition (JR, GL, YA, AB), pp. 754–757.
ICPRICPR-2010-RicciTZ #kernel
Learning Pedestrian Trajectories with Kernels (ER, FT, GZ), pp. 149–152.
ICPRICPR-2010-SangWW #modelling #top-down #visual notation
A Biologically-Inspired Top-Down Learning Model Based on Visual Attention (NS, LW, YW), pp. 3736–3739.
ICPRICPR-2010-Sarkar #classification #documentation #image
Learning Image Anchor Templates for Document Classification and Data Extraction (PS), pp. 3428–3431.
ICPRICPR-2010-Sato #classification #design #kernel
A New Learning Formulation for Kernel Classifier Design (AS), pp. 2897–2900.
ICPRICPR-2010-ShenYS
Learning Discriminative Features Based on Distribution (JS, WY, CS), pp. 1401–1404.
ICPRICPR-2010-SodaI #composition #dataset #integration
Decomposition Methods and Learning Approaches for Imbalanced Dataset: An Experimental Integration (PS, GI), pp. 3117–3120.
ICPRICPR-2010-SternigRB #classification #multi
Inverse Multiple Instance Learning for Classifier Grids (SS, PMR, HB), pp. 770–773.
ICPRICPR-2010-SuLT10a #documentation #framework #self
A Self-Training Learning Document Binarization Framework (BS, SL, CLT), pp. 3187–3190.
ICPRICPR-2010-SunSHE #locality #metric
Localized Supervised Metric Learning on Temporal Physiological Data (JS, DMS, JH, SE), pp. 4149–4152.
ICPRICPR-2010-TaxHVP #clustering #concept #detection #multi #using
The Detection of Concept Frames Using Clustering Multi-instance Learning (DMJT, EH, MFV, MP), pp. 2917–2920.
ICPRICPR-2010-TorkiEL #multi #representation #set
Learning a Joint Manifold Representation from Multiple Data Sets (MT, AME, CSL), pp. 1068–1071.
ICPRICPR-2010-WangAYL #bottom-up #estimation #top-down #using
Combined Top-Down/Bottom-Up Human Articulated Pose Estimation Using AdaBoost Learning (SW, HA, TY, SL), pp. 3670–3673.
ICPRICPR-2010-WangJHT #higher-order #kernel #multi
Multiple Kernel Learning with High Order Kernels (SW, SJ, QH, QT), pp. 2138–2141.
ICPRICPR-2010-WangM #order #process #using
Gaussian Process Learning from Order Relationships Using Expectation Propagation (RW, SJM), pp. 605–608.
ICPRICPR-2010-WidhalmB
Learning Major Pedestrian Flows in Crowded Scenes (PW, NB), pp. 4064–4067.
ICPRICPR-2010-WuLW #image #retrieval #using
Enhancing SVM Active Learning for Image Retrieval Using Semi-supervised Bias-Ensemble (JW, ML, CLW), pp. 3175–3178.
ICPRICPR-2010-XingAL #detection #multi
Multiple Human Tracking Based on Multi-view Upper-Body Detection and Discriminative Learning (JX, HA, SL), pp. 1698–1701.
ICPRICPR-2010-YaegashiY #kernel #multi #recognition #using
Geotagged Photo Recognition Using Corresponding Aerial Photos with Multiple Kernel Learning (KY, KY), pp. 3272–3275.
ICPRICPR-2010-ZhangLD #approach #kernel #multi #named #novel
AdaMKL: A Novel Biconvex Multiple Kernel Learning Approach (ZZ, ZNL, MSD), pp. 2126–2129.
ICPRICPR-2010-ZhangWL #categorisation #kernel
Learning the Kernel Combination for Object Categorization (DZ, XW, BL), pp. 2929–2932.
ICPRICPR-2010-ZhangZYK #classification #detection #representation #taxonomy
Microaneurysm (MA) Detection via Sparse Representation Classifier with MA and Non-MA Dictionary Learning (BZ, LZ, JY, FK), pp. 277–280.
ICPRICPR-2010-ZhouLLT #canonical #image #visual notation
Canonical Image Selection by Visual Context Learning (WZ, YL, HL, QT), pp. 834–837.
ICPRICPR-2010-ZhuHYL #behaviour #metric #prototype #recognition #using
Prototype Learning Using Metric Learning Based Behavior Recognition (PZ, WH, CY, LL), pp. 2604–2607.
ICPRICPR-2010-ZouY #image #kernel
Learning the Relationship Between High and Low Resolution Images in Kernel Space for Face Super Resolution (WWWZ, PCY), pp. 1152–1155.
KDDKDD-2010-AbeMPRJTBACKDG #optimisation #using
Optimizing debt collections using constrained reinforcement learning (NA, PM, CP, CKR, DLJ, VPT, JJB, GFA, BRC, MK, MD, TG), pp. 75–84.
KDDKDD-2010-AgarwalCE #online #performance #recommendation
Fast online learning through offline initialization for time-sensitive recommendation (DA, BCC, PE), pp. 703–712.
KDDKDD-2010-AttenbergP #classification #modelling #why
Why label when you can search?: alternatives to active learning for applying human resources to build classification models under extreme class imbalance (JA, FJP), pp. 423–432.
KDDKDD-2010-BozorgiSSV #heuristic #predict
Beyond heuristics: learning to classify vulnerabilities and predict exploits (MB, LKS, SS, GMV), pp. 105–114.
KDDKDD-2010-ChapelleSVWZT #multi #ranking #web
Multi-task learning for boosting with application to web search ranking (OC, PKS, SV, KQW, YZ, BLT), pp. 1189–1198.
KDDKDD-2010-ChenLY #multi #rank
Learning incoherent sparse and low-rank patterns from multiple tasks (JC, JL, JY), pp. 1179–1188.
KDDKDD-2010-DasMSO #algorithm #case study #detection #kernel #multi #safety
Multiple kernel learning for heterogeneous anomaly detection: algorithm and aviation safety case study (SD, BLM, ANS, NCO), pp. 47–56.
KDDKDD-2010-GuptaPATV #retrieval #social #social media
Nonnegative shared subspace learning and its application to social media retrieval (SKG, DQP, BA, TT, SV), pp. 1169–1178.
KDDKDD-2010-HoTL #metric #reduction #sequence #similarity
Tropical cyclone event sequence similarity search via dimensionality reduction and metric learning (SSH, WT, WTL), pp. 135–144.
KDDKDD-2010-HuhF #modelling #topic
Discriminative topic modeling based on manifold learning (SH, SEF), pp. 653–662.
KDDKDD-2010-Lee #classification
Learning to combine discriminative classifiers: confidence based (CHL), pp. 743–752.
KDDKDD-2010-LiuMTLL #metric #optimisation #using
Semi-supervised sparse metric learning using alternating linearization optimization (WL, SM, DT, JL, PL), pp. 1139–1148.
KDDKDD-2010-LiuZ
Learning with cost intervals (XYL, ZHZ), pp. 403–412.
KDDKDD-2010-SomaiyaJR #modelling
Mixture models for learning low-dimensional roles in high-dimensional data (MS, CMJ, SR), pp. 909–918.
KDDKDD-2010-WallaceSBT
Active learning for biomedical citation screening (BCW, KS, CEB, TAT), pp. 173–182.
KDDKDD-2010-ZhangY #metric
Transfer metric learning by learning task relationships (YZ, DYY), pp. 1199–1208.
KDDKDD-2010-ZhangZ #dependence #multi
Multi-label learning by exploiting label dependency (MLZ, KZ), pp. 999–1008.
KDDKDD-2010-ZhuLX #feature model #incremental #markov #named #performance #random
Grafting-light: fast, incremental feature selection and structure learning of Markov random fields (JZ, NL, EPX), pp. 303–312.
KDIRKDIR-2010-Cebron #representation #towards
Towards Learning with Objects in a Hierarchical Representation (NC), pp. 326–329.
KDIRKDIR-2010-LourencoF #clustering #multi
Selectively Learning Clusters in Multi-EAC (AL, ALNF), pp. 491–499.
KDIRKDIR-2010-ParviainenRML #approximate #infinity #network
Interpreting Extreme Learning Machine as an Approximation to an Infinite Neural Network (EP, JR, YM, AL), pp. 65–73.
KEODKEOD-2010-ArdilaAL #kernel #multi #ontology
Multiple Kernel Learning for Ontology Instance Matching (DA, JA, FL), pp. 311–318.
KEODKEOD-2010-Braham #assessment #metric
A Knowledge Metric with Applications to Learning Assessment (RB), pp. 5–9.
KEODKEOD-2010-GilCM #case study #evaluation #ontology
A Systemic Methodology for Ontology Learning — An Academic Case Study and Evaluation (RG, LC, MJMB), pp. 206–212.
KEODKEOD-2010-Girardi #ontology
Guiding Ontology Learning and Population by Knowledge System Goals (RG), pp. 480–484.
KMISKMIS-2010-JuvonenO
Studying IT Team Entrepreneurship as a Learning Organization (PJ, PO), pp. 332–337.
RecSysRecSys-2010-LipczakM #performance #recommendation
Learning in efficient tag recommendation (ML, EEM), pp. 167–174.
RecSysRecSys-2010-MelloAZ #impact analysis #rating
Active learning driven by rating impact analysis (CERdM, MAA, GZ), pp. 341–344.
RecSysRecSys-2010-ShiLH #collaboration #matrix #rank
List-wise learning to rank with matrix factorization for collaborative filtering (YS, ML, AH), pp. 269–272.
SEKESEKE-2010-JuniorLAMW #impact analysis #multi #using
Impact Analysis Model for Brasília Area Control Center using Multi-agent System with Reinforcement Learning (ACdAJ, AFL, CRFdA, ACMAdM, LW), pp. 499–502.
SEKESEKE-2010-Yeh #animation #human-computer #interactive
The effects of human-computer interaction modes for weak learners in an animation learning environment (YFY), pp. 18–23.
SIGIRSIGIR-2010-BalasubramanianA
Learning to select rankers (NB, JA), pp. 855–856.
SIGIRSIGIR-2010-DangBC #query #rank
Learning to rank query reformulations (VD, MB, WBC), pp. 807–808.
SIGIRSIGIR-2010-DaveV
Learning the click-through rate for rare/new ads from similar ads (KSD, VV), pp. 897–898.
SIGIRSIGIR-2010-GaoCWZ #rank #using
Learning to rank only using training data from related domain (WG, PC, KFW, AZ), pp. 162–169.
SIGIRSIGIR-2010-HajishirziYK #adaptation #detection #similarity
Adaptive near-duplicate detection via similarity learning (HH, WtY, AK), pp. 419–426.
SIGIRSIGIR-2010-Liu #information retrieval #rank
Learning to rank for information retrieval (TYL), p. 904.
SIGIRSIGIR-2010-LiuW #email #multi
Multi-field learning for email spam filtering (WL, TW), pp. 745–746.
SIGIRSIGIR-2010-LiuYSCCL #behaviour #rank
Learning to rank audience for behavioral targeting (NL, JY, DS, DC, ZC, YL), pp. 719–720.
SIGIRSIGIR-2010-LongCZCZT #optimisation #ranking
Active learning for ranking through expected loss optimization (BL, OC, YZ, YC, ZZ, BLT), pp. 267–274.
SIGIRSIGIR-2010-MojdehC #consistency #using
Semi-supervised spam filtering using aggressive consistency learning (MM, GVC), pp. 751–752.
SIGIRSIGIR-2010-Wang #modelling #retrieval
Learning hidden variable models for blog retrieval (MW), p. 922.
SIGIRSIGIR-2010-WangLM #rank
Learning to efficiently rank (LW, JJL, DM), pp. 138–145.
SIGIRSIGIR-2010-WangWVL #clustering #documentation #metric
Text document clustering with metric learning (JW, SW, HQV, GL), pp. 783–784.
SIGIRSIGIR-2010-YanZJLYC #framework
A co-learning framework for learning user search intents from rule-generated training data (JY, ZZ, LJ, YL, SY, ZC), pp. 895–896.
SIGIRSIGIR-2010-YueGCZJ #evaluation #retrieval #statistics
Learning more powerful test statistics for click-based retrieval evaluation (YY, YG, OC, YZ, TJ), pp. 507–514.
SACSAC-2010-AppiceCM
Transductive learning for spatial regression with co-training (AA, MC, DM), pp. 1065–1070.
SACSAC-2010-AyyappanWN #algorithm #constraints #named #network #scalability
MICHO: a scalable constraint-based algorithm for learning Bayesian networks (MA, YKW, WKN), pp. 985–989.
SACSAC-2010-CostaFGMO #mining #modelling
Mining models of exceptional objects through rule learning (GC, FF, MG, GM, RO), pp. 1078–1082.
CASECASE-2010-DoroodgarN #architecture
A hierarchical reinforcement learning based control architecture for semi-autonomous rescue robots in cluttered environments (BD, GN), pp. 948–953.
CASECASE-2010-LiYG
Learning compliance control of robot manipulators in contact with the unknown environment (YL, CY, SSG), pp. 644–649.
DACDAC-2010-CallegariDWA #classification #using
Classification rule learning using subgroup discovery of cross-domain attributes responsible for design-silicon mismatch (NC, DGD, LCW, MSA), pp. 374–379.
DACDAC-2010-LaiJW #abstraction #named
BooM: a decision procedure for boolean matching with abstraction and dynamic learning (CFL, JHRJ, KHW), pp. 499–504.
STOCSTOC-2010-KalaiMV
Efficiently learning mixtures of two Gaussians (ATK, AM, GV), pp. 553–562.
CAVCAV-2010-BolligKKLNP #automaton #framework #named
libalf: The Automata Learning Framework (BB, JPK, CK, ML, DN, DRP), pp. 360–364.
CAVCAV-2010-ChenCFTTW #automation #reasoning
Automated Assume-Guarantee Reasoning through Implicit Learning (YFC, EMC, AF, MHT, YKT, BYW), pp. 511–526.
CAVCAV-2010-SinghGP #abstraction #component #interface
Learning Component Interfaces with May and Must Abstractions (RS, DG, CSP), pp. 527–542.
ICLPICLP-2010-Balduccini10 #heuristic #set
Learning Domain-Specific Heuristics for Answer Set Solvers (MB), pp. 14–23.
ICLPICLP-2010-Pahlavi10 #higher-order #logic
Higher-order Logic Learning and λ-Progol (NP), pp. 281–285.
ICLPICLP-J-2010-SneyersMVKS #logic #probability
CHR(PRISM)-based probabilistic logic learning (JS, WM, JV, YK, TS), pp. 433–447.
ISSTAISSTA-2010-GruskaWZ #detection #lightweight
Learning from 6, 000 projects: lightweight cross-project anomaly detection (NG, AW, AZ), pp. 119–130.
ICSTSAT-2010-Ben-SassonJ #bound #strict
Lower Bounds for Width-Restricted Clause Learning on Small Width Formulas (EBS, JJ), pp. 16–29.
ICSTSAT-2010-KlieberSGC
A Non-prenex, Non-clausal QBF Solver with Game-State Learning (WK, SS, SG, EMC), pp. 128–142.
VMCAIVMCAI-2010-JungKWY #abstraction #algorithm #invariant
Deriving Invariants by Algorithmic Learning, Decision Procedures, and Predicate Abstraction (YJ, SK, BYW, KY), pp. 180–196.
DRRDRR-2009-ZhangZLT
A semi-supervised learning method to classify grant-support zone in web-based medical articles (XZ, JZ, DXL, GRT), pp. 1–10.
TPDLECDL-2009-Orde #library
Digital Libraries — New Landscapes for Lifelong Learning? The “InfoLitGlobal”-Project (HvO), pp. 477–478.
TPDLECDL-2009-SuttonG #concept #education
Conceptual Discovery of Educational Resources through Learning Objectives (SAS, DG), pp. 380–383.
TPDLECDL-2009-TakhirovSA #personalisation
Organizing Learning Objects for Personalized eLearning Services (NT, IS, TA), pp. 384–387.
HTHT-2009-AlAghaB #approach #hypermedia #towards
Towards a constructivist approach to learning from hypertext (IA, LB), pp. 51–56.
ICDARICDAR-2009-AbdulkaderC #fault #low cost #multi #using
Low Cost Correction of OCR Errors Using Learning in a Multi-Engine Environment (AA, MRC), pp. 576–580.
ICDARICDAR-2009-AlmaksourA #incremental #online #performance #recognition
Fast Incremental Learning Strategy Driven by Confusion Reject for Online Handwriting Recognition (AA, ÉA), pp. 81–85.
ICDARICDAR-2009-BallS #recognition
Semi-supervised Learning for Handwriting Recognition (GRB, SNS), pp. 26–30.
ICDARICDAR-2009-FrinkenB #network #recognition #word
Evaluating Retraining Rules for Semi-Supervised Learning in Neural Network Based Cursive Word Recognition (VF, HB), pp. 31–35.
ICDARICDAR-2009-KaeL #on the fly #problem
Learning on the Fly: Font-Free Approaches to Difficult OCR Problems (AK, EGLM), pp. 571–575.
ICDARICDAR-2009-MansjurWJ #automation #categorisation #classification #kernel #topic #using
Using Kernel Density Classifier with Topic Model and Cost Sensitive Learning for Automatic Text Categorization (DSM, TSW, BHJ), pp. 1086–1090.
ICDARICDAR-2009-Silva #analysis #documentation #markov #modelling
Learning Rich Hidden Markov Models in Document Analysis: Table Location (ACeS), pp. 843–847.
ICDARICDAR-2009-StefanoFFM #classification #evolution #network
Learning Bayesian Networks by Evolution for Classifier Combination (CDS, FF, ASdF, AM), pp. 966–970.
ICDARICDAR-2009-TewariN #adaptation
Learning and Adaptation for Improving Handwritten Character Recognizers (NCT, AMN), pp. 86–90.
ICDARICDAR-2009-WangLJ #modelling #segmentation #statistics #string
Statistical Modeling and Learning for Recognition-Based Handwritten Numeral String Segmentation (YW, XL, YJ), pp. 421–425.
ICDARICDAR-2009-ZhuGGZ #framework #online #probability #recognition
A Probabilistic Framework for Soft Target Learning in Online Cursive Handwriting Recognition (XZ, YG, FJG, LXZ), pp. 1246–1250.
JCDLJCDL-2009-MartinsGLP #case study #quality
Learning to assess the quality of scientific conferences: a case study in computer science (WSM, MAG, AHFL, GLP), pp. 193–202.
SIGMODSIGMOD-2009-BabuGM #nondeterminism #scalability
Large-scale uncertainty management systems: learning and exploiting your data (SB, SG, KM), pp. 995–998.
VLDBVLDB-2009-ArasuCK #string
Learning String Transformations From Examples (AA, SC, RK), pp. 514–525.
VLDBVLDB-2009-PandaHBB #named #parallel #pipes and filters
PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce (BP, JH, SB, RJB), pp. 1426–1437.
CSEETCSEET-2009-Armarego #student
Displacing the Sage on the Stage: Student Control of Learning (JA), pp. 198–201.
CSEETCSEET-2009-ChaoR #agile #student
Agile Software Factory for Student Service Learning (JC, MR), pp. 34–40.
CSEETCSEET-2009-Goel #education #re-engineering
Enriching the Culture of Software Engineering Education through Theories of Knowledge and Learning (SG), p. 279.
CSEETCSEET-2009-RichardsonD #problem #re-engineering
Problem Based Learning in the Software Engineering Classroom (IR, YD), pp. 174–181.
CSEETCSEET-2009-Rosso-Llopart #education #re-engineering
An Examination of Learning Technologies That Support Software Engineering and Education (MRL), pp. 294–295.
EDMEDM-2009-AbbasS #using
an Argument Learning Environment Using Agent-Based ITS (ALES) (SA, HS), pp. 200–209.
EDMEDM-2009-FengBH #composition #education #using
Using Learning Decomposition and Bootstrapping with Randomization to Compare the Impact of Different Educational Interventions on Learning (MF, JB, NTH), pp. 51–60.
EDMEDM-2009-GongRBH #question #self #student
Does Self-Discipline impact students’ knowledge and learning? (YG, DR, JB, NTH), pp. 61–70.
EDMEDM-2009-HershkovitzN #consistency #online #student
Consistency of Students’ Pace in Online Learning (AH, RN), pp. 71–80.
EDMEDM-2009-PavlikCK #analysis #automation #domain model #modelling #using
Learning Factors Transfer Analysis: Using Learning Curve Analysis to Automatically Generate Domain Models (PIPJ, HC, KRK), pp. 121–130.
EDMEDM-2009-PrataBCRC #collaboration #comprehension #detection
Detecting and Understanding the Impact of Cognitive and Interpersonal Conflict in Computer Supported Collaborative Learning Environments (DP, RSJdB, EC, CPR, YC), pp. 131–140.
EDMEDM-2009-QuevedoM #assessment #education #process
Obtaining weights of a rubric through a pairwise learning model when the assessment process involves more than one lecturer (JRQ, EM), pp. 289–298.
EDMEDM-2009-ZafraV #multi #predict #programming #search-based #student
Predicting Student Grades in Learning Management Systems with Multiple Instance Learning Genetic Programming (AZ, SV), pp. 309–318.
ITiCSEITiCSE-2009-AltinBEKOSSSMPR09a #experience #student #tool support #user interface #using
Use of intuitive tools to enhance student learning and user experience (RA, MB, NE, CK, ÖCÖ, MS, HS, DS, CCM, CP, CRR), p. 365.
ITiCSEITiCSE-2009-AndersonL #collaboration #community #student
Exploring technologies for building collaborative learning communities among diverse student populations (NA, CCL), pp. 243–247.
ITiCSEITiCSE-2009-BuendiaCB #approach
An instructional approach to drive computer science courses through virtual learning environments (FB, JCC, JVB), pp. 6–10.
ITiCSEITiCSE-2009-CukiermanT #student
The academic enhancement program: encouraging students to learn about learning as part of their computing science courses (DC, DMT), pp. 171–175.
ITiCSEITiCSE-2009-Draganova #mobile #using
Use of mobile phone technologies in learning (CD), p. 399.
ITiCSEITiCSE-2009-Ginat #composition
Interleaved pattern composition and scaffolded learning (DG), pp. 109–113.
ITiCSEITiCSE-2009-Hwang09a #education #operating system
Blended learning for teaching operating systems with Windows (SwH), p. 380.
ITiCSEITiCSE-2009-Lasserre #adaptation #programming
Adaptation of team-based learning on a first term programming class (PL), pp. 186–190.
ITiCSEITiCSE-2009-Martin
Cooperative learning to support the lacks of PBL (JGM), p. 343.
ITiCSEITiCSE-2009-MhiriR #development #named
AARTIC: development of an intelligent environment for human learning (FM, SR), p. 359.
ITiCSEITiCSE-2009-MoraPJC #assessment #collaboration #student
Learning method based on collaborative assessment performed by the students: an application to computer science (HMM, MTSP, RCJ, JMGC), p. 372.
ITiCSEITiCSE-2009-Palmer-BrownDL #feedback
Guided learning via diagnostic feedback to question responses (DPB, CD, SWL), p. 362.
ITiCSEITiCSE-2009-Pantaleev #named #visual notation
Dzver: a visual computer science learning environment (AP), p. 387.
ITiCSEITiCSE-2009-Radenski
Freedom of choice as motivational factor for active learning (AR), pp. 21–25.
ITiCSEITiCSE-2009-Sondergaard #student
Learning from and with peers: the different roles of student peer reviewing (HS), pp. 31–35.
ITiCSEITiCSE-2009-TsengHH #collaboration #education #framework #platform #ubiquitous
A collaborative ubiquitous learning platform for computer science education (JCRT, SYYH, GJH), p. 368.
ITiCSEITiCSE-2009-Velazquez-IturbideP #algorithm #interactive
Active learning of greedy algorithms by means of interactive experimentation (JÁVI, APC), pp. 119–123.
ITiCSEITiCSE-2009-VillalobosCJ #interactive #programming #using
Developing programming skills by using interactive learning objects (JV, NAC, CJ), pp. 151–155.
ITiCSEITiCSE-2009-WangHCT #behaviour #collaboration
The role of collective efficacy and collaborative learning behavior in learning computer science through CSCL (SLW, GHH, JCC, PST), p. 352.
ITiCSEITiCSE-2009-WhiteI #case study #education #experience #research
Relating research and teaching: learning from experiences and beliefs (SW, AI), pp. 75–79.
ITiCSEITiCSE-2009-WiesnerB #concept #how #question
How do robots foster the learning of basic concepts in informatics? (BW, TB), p. 403.
ITiCSEITiCSE-2009-ZanderTSMMHF
Learning styles: novices decide (CZ, LT, BS, LM, RM, BH, SF), pp. 223–227.
SIGITESIGITE-2009-Krichen #evolution #online #question
Evolving online learning: can attention to learning styles make it more personal? (JPK), pp. 8–12.
SIGITESIGITE-2009-StanleyC #simulation
Rhythm learning with electronic simulation (TDS, DC), pp. 24–28.
SIGITESIGITE-2009-StanleyC09a
Six years of sustainable IT service learning (TDS, DC), pp. 87–90.
MSRMSR-2009-AyewahP #fault
Learning from defect removals (NA, WP), pp. 179–182.
ICALPICALP-v1-2009-KlivansLS
Learning Halfspaces with Malicious Noise (ARK, PML, RAS), pp. 609–621.
LATALATA-2009-Akama #commutative
Commutative Regular Shuffle Closed Languages, Noetherian Property, and Learning Theory (YA), pp. 93–104.
LATALATA-2009-Gierasimczuk #logic
Learning by Erasing in Dynamic Epistemic Logic (NG), pp. 362–373.
LATALATA-2009-Jain
Hypothesis Spaces for Learning (SJ), pp. 43–58.
AIIDEAIIDE-2009-TanC #adaptation #game studies #named
IMPLANT: An Integrated MDP and POMDP Learning AgeNT for Adaptive Games (CTT, HLC).
AIIDEAIIDE-2009-ZhaoS #behaviour #game studies #modelling #using
Learning Character Behaviors Using Agent Modeling in Games (RZ, DS).
CoGCIG-2009-BurrowL #difference #evolution #game studies
Evolution versus Temporal Difference Learning for learning to play Ms. Pac-Man (PB, SML), pp. 53–60.
CoGCIG-2009-CardamoneLL #using
Learning drivers for TORCS through imitation using supervised methods (LC, DL, PLL), pp. 148–155.
CoGCIG-2009-GalliLL #policy
Learning a context-aware weapon selection policy for Unreal Tournament III (LG, DL, PLL), pp. 310–316.
CoGCIG-2009-GalwayCB #difference #game studies #using
Improving Temporal Difference game agent control using a dynamic exploration during control learning (LG, DC, MMB), pp. 38–45.
CoGCIG-2009-HoornTS
Hierarchical controller learning in a First-Person Shooter (NvH, JT, JS), pp. 294–301.
CoGCIG-2009-Lucas09b #difference
Temporal difference learning with interpolated table value functions (SML), pp. 32–37.
CoGCIG-2009-SzubertJK #difference
Coevolutionary Temporal Difference Learning for Othello (MGS, WJ, KK), pp. 104–111.
DiGRADiGRA-2009-Bonanno #collaboration #game studies
A Process-oriented pedagogy for collaborative game-based learning [Abstract] (PB).
DiGRADiGRA-2009-Duncan #design #game studies
Bridging Gaming and Designing: Two Sites of Informal Design Learning [Abstract] (SCD).
DiGRADiGRA-2009-Hung #education #game studies #order #video
The Order of Play: Seeing, Teaching, and Learning Meaning in Video Games (ACYH).
DiGRADiGRA-2009-MerkelSH #adaptation #game studies
Complexities of Gaming Cultures: Adolescent gamers adapting and transforming learning [Abstracts] (LM, KS, TH).
DiGRADiGRA-2009-Pearce #case study #collaboration #community #game studies
Collaboration, Creativity and Learning in a Play Community: A Study of The University of There (CP).
DiGRADiGRA-2009-PereiraR #design #game studies #guidelines
Design Guidelines for Learning Games: the Living Forest Game Design Case (LLP, LGR).
DiGRADiGRA-2009-RyanS #comprehension #game studies #interactive #using #video
Evaluating Interactive Entertainment using Breakdown: Understanding Embodied Learning in Video Games (WR, MAS).
FDGFDG-2009-GibsonG #game studies #online #student
Online recruitment and engagement of students in game and simulation-based STEM learning (DCG, SG), pp. 285–290.
FDGFDG-2009-HoldsworthL #case study
GPS-enabled mobiles for learning shortest paths: a pilot study (JJH, SML), pp. 86–90.
FDGFDG-2009-McGill09a #education #effectiveness #game studies
Evaluating the effectiveness of hypothesis-based digital learning games in high school science curriculum (MM), pp. 344–345.
FDGFDG-2009-ThomasY #framework #game studies #independence #towards
Toward a domain-independent framework to automate scaffolding of task-based learning in digital games (JMT, RMY), pp. 331–332.
CoGVS-Games-2009-BloomfieldL #assessment #multi
Multi-Modal Learning and Assessment in Second Life with quizHUD (PRB, DL), pp. 217–218.
CoGVS-Games-2009-FreitasRLMP #case study #evaluation #experience
Developing an Evaluation Methodology for Immersive Learning Experiences in a Virtual World (SdF, GRM, FL, GDM, AP), pp. 43–50.
CoGVS-Games-2009-JarvisF #evaluation
Evaluation of an Immersive Learning Programme to Support Triage Training (SJ, SdF), pp. 117–122.
CHICHI-2009-BrandtGLDK #programming #web
Two studies of opportunistic programming: interleaving web foraging, learning, and writing code (JB, PJG, JL, MD, SRK), pp. 1589–1598.
CHICHI-2009-HaradaWMBL #people
Longitudinal study of people learning to use continuous voice-based cursor control (SH, JOW, JM, JAB, JAL), pp. 347–356.
CHICHI-2009-KammererNPC #social
Signpost from the masses: learning effects in an exploratory social tag search browser (YK, RN, PP, EHhC), pp. 625–634.
CHICHI-2009-LoveJTH #assessment #predict
Learning to predict information needs: context-aware display as a cognitive aid and an assessment tool (BCL, MJ, MTT, MH), pp. 1351–1360.
CHICHI-2009-RosnerB
Learning from IKEA hacking: I’m not one to decoupage a tabletop and call it a day (DR, JB), pp. 419–422.
CHICHI-2009-Thom-SantelliM
Learning by seeing: photo viewing in the workplace (JTS, DRM), pp. 2081–2090.
CHICHI-2009-TorreyCM #how #internet
Learning how: the search for craft knowledge on the internet (CT, EFC, DWM), pp. 1371–1380.
HCIDHM-2009-FallonCP #assessment #risk management
Learning from Risk Assessment in Radiotherapy (EFF, LC, WJvdP), pp. 502–511.
HCIDHM-2009-HashagenZSZ #adaptation #implementation #interactive #pattern matching #pattern recognition #recognition
Adaptive Motion Pattern Recognition: Implementing Playful Learning through Embodied Interaction (AH, CZ, HS, SZ), pp. 105–114.
HCIHCD-2009-FerranGMM #design #repository
User Centered Design of a Learning Object Repository (NF, AEGR, EM, JM), pp. 679–688.
HCIHCI-AUII-2009-McMullenW #assessment #design
Relationship Learning Software: Design and Assessment (KAM, GHW), pp. 631–640.
HCIHCI-AUII-2009-ZarraonandiaVDA #protocol
A Virtual Environment for Learning Aiport Emergency Management Protocols (TZ, MRRV, PD, IA), pp. 228–235.
HCIHCI-NIMT-2009-AlexanderAA #framework #gesture #incremental #open source #realtime #recognition
An Open Source Framework for Real-Time, Incremental, Static and Dynamic Hand Gesture Learning and Recognition (TCA, HSA, GCA), pp. 123–130.
HCIHCI-VAD-2009-ChalfounF #3d
Optimal Affective Conditions for Subconscious Learning in a 3D Intelligent Tutoring System (PC, CF), pp. 39–48.
HCIHCI-VAD-2009-ChenGSEJ #detection
Computer-Based Learning to Improve Breast Cancer Detection Skills (YC, AGG, HJS, AE, JJ), pp. 49–57.
HCIHCI-VAD-2009-DogusoyC #comprehension #eye tracking #process
An Innovative Way of Understanding Learning Processes: Eye Tracking (BD, ), pp. 94–100.
HCIHCI-VAD-2009-FicarraCV #evaluation
Communicability for Virtual Learning: Evaluation (FVCF, MCF, PMV), pp. 68–77.
HCIHCI-VAD-2009-KashiwagiXSKO #physics #process
A Language Learning System Utilizing RFID Technology for Total Physical Response Activities (HK, YX, YS, MK, KO), pp. 119–128.
HCIHCI-VAD-2009-Lane
Promoting Metacognition in Immersive Cultural Learning Environments (HCL), pp. 129–139.
HCIHCI-VAD-2009-MampadiCG #adaptation #hypermedia #information management #using
The Effects of Prior Knowledge on the Use of Adaptive Hypermedia Learning Systems (FM, SYC, GG), pp. 156–165.
HCIHCI-VAD-2009-MazzolaM #adaptation #student
Supporting Learners in Adaptive Learning Environments through the Enhancement of the Student Model (LM, RM), pp. 166–175.
HCIHCI-VAD-2009-SaC #development #mobile #personalisation #tool support
Supporting End-User Development of Personalized Mobile Learning Tools (MdS, LC), pp. 217–225.
HCIHCI-VAD-2009-SuLHC #mobile
Developing a Usable Mobile Flight Case Learning System in Air Traffic Control Miscommunications (KWS, KYL, PHH, ITC), pp. 770–777.
HCIHCI-VAD-2009-TesorieroFGLP #interactive
Interactive Learning Panels (RT, HF, JAG, MDL, VMRP), pp. 236–245.
HCIHCI-VAD-2009-ZhangLBAMY #development #simulation #visualisation
Development of a Visualised Sound Simulation Environment: An e-Approach to a Constructivist Way of Learning (JZ, BL, IB, LA, YM, SY), pp. 266–275.
HCIHIMI-II-2009-JacobsonMM #collaboration #interactive #lifecycle #named
HILAS: Human Interaction in the Lifecycle of Aviation Systems — Collaboration, Innovation and Learning (DJ, NM, BM), pp. 786–796.
HCIHIMI-II-2009-LiuZL #collaboration #design #effectiveness #empirical #perspective
An Empirical Investigation on the Effectiveness of Virtual Learning Environment in Supporting Collaborative Learning: A System Design Perspective (NL, YZ, JL), pp. 650–659.
HCIHIMI-II-2009-MarusterFH #design #personalisation
Personalization for Specific Users: Designing Decision Support Systems to Support Stimulating Learning Environments (LM, NRF, RJFvH), pp. 660–668.
HCIHIMI-II-2009-NakamuraS
Construction of Systematic Learning Support System of Business Theory and Method (YN, KS), pp. 669–678.
HCIHIMI-II-2009-NishinoH #embedded #named #visualisation
Minato: Integrated Visualization Environment for Embedded Systems Learning (YN, EH), pp. 325–333.
HCIHIMI-II-2009-PrecelEA #design #online #student #towards
Learning by Design in a Digital World: Students’ Attitudes towards a New Pedagogical Model for Online Academic Learning (KP, YEA, YA), pp. 679–688.
HCIHIMI-II-2009-ReichlH #education
Promoting a Central Learning Management System by Encouraging Its Use for Other Purposes Than Teaching (FR, AH), pp. 689–698.
HCIHIMI-II-2009-Terawaki #framework
Framework for Supporting Decision Making in Learning Management System Selection (YT), pp. 699–707.
HCIHIMI-II-2009-Wang09c #adaptation #design #development
The Design and Development of an Adaptive Web-Based Learning System (CW), pp. 716–725.
HCIIDGD-2009-ZhongLL #similarity
Exploring the Influences of Individualism-Collectivism on Individual’s Perceived Participation Equality in Virtual Learning Teams (YZ, NL, JL), pp. 207–216.
HCIOCSC-2009-BramanVDJ
Learning Computer Science Fundamentals through Virtual Environments (JB, GV, AMAD, AJ), pp. 423–431.
HCIOCSC-2009-ConlonP #distance #video
A Discussion of Video Capturing to Assist in Distance Learning (MC, VP), pp. 432–441.
HCIOCSC-2009-OganAKJ #education #game studies #question #social
Antecedents of Attributions in an Educational Game for Social Learning: Who’s to Blame? (AO, VA, JK, CJ), pp. 593–602.
HCIOCSC-2009-Pozzi #community #online #social
Evaluating the Social Dimension in Online Learning Communities (FP), pp. 498–506.
HCIOCSC-2009-PuseyM #education #heuristic #implementation #wiki
Heuristics for Implementation of Wiki Technology in Higher Education Learning (PP, GM), pp. 507–514.
ICEISICEIS-AIDSS-2009-BombiniMBFE #framework #logic programming
A Logic Programming Framework for Learning by Imitation (GB, NDM, TMAB, SF, FE), pp. 218–223.
ICEISICEIS-AIDSS-2009-YangLSKCGP #graph
Graph Structure Learning for Task Ordering (YY, AL, HS, BK, CMC, RG, KP), pp. 164–169.
ICEISICEIS-HCI-2009-Casalino #aspect-oriented
An Innovative Model of Trans-national Learning Environment for European Senior Civil Servants — Organizational Aspects and Governance (NC), pp. 148–153.
ICEISICEIS-J-2009-LealQ #named #repository
CrimsonHex: A Service Oriented Repository of Specialised Learning Objects (JPL, RQ), pp. 102–113.
ICEISICEIS-SAIC-2009-CastroFSC #programming
Fleshing Out Clues on Group Programming Learning (TC, HF, LS, ANdCJ), pp. 68–73.
CIKMCIKM-2009-BaiZXZSTZC #multi #rank #web
Multi-task learning for learning to rank in web search (JB, KZ, GRX, HZ, GS, BLT, ZZ, YC), pp. 1549–1552.
CIKMCIKM-2009-CetintasSY #query
Learning from past queries for resource selection (SC, LS, HY), pp. 1867–1870.
CIKMCIKM-2009-ChenLAA #image #modelling #online #probability #topic
Probabilistic models for topic learning from images and captions in online biomedical literatures (XC, CL, YA, PA), pp. 495–504.
CIKMCIKM-2009-ChenWL #kernel #novel #rank
Learning to rank with a novel kernel perceptron method (XwC, HW, XL), pp. 505–512.
CIKMCIKM-2009-GargS #classification
Active learning in partially supervised classification (PG, SS), pp. 1783–1786.
CIKMCIKM-2009-HeLL #graph
Graph-based transfer learning (JH, YL, RDL), pp. 937–946.
CIKMCIKM-2009-KuoCW #rank
Learning to rank from Bayesian decision inference (JWK, PJC, HMW), pp. 827–836.
CIKMCIKM-2009-MeloW #towards
Towards a universal wordnet by learning from combined evidence (GdM, GW), pp. 513–522.
CIKMCIKM-2009-Paranjpe #documentation #feedback
Learning document aboutness from implicit user feedback and document structure (DP), pp. 365–374.
CIKMCIKM-2009-PasternackR
Learning better transliterations (JP, DR), pp. 177–186.
CIKMCIKM-2009-QiCKKW
Combining labeled and unlabeled data with word-class distribution learning (YQ, RC, PPK, KK, JW), pp. 1737–1740.
CIKMCIKM-2009-QuanzH #scalability
Large margin transductive transfer learning (BQ, JH), pp. 1327–1336.
CIKMCIKM-2009-SunCSSWL #recommendation
Learning to recommend questions based on user ratings (KS, YC, XS, YIS, XW, CYL), pp. 751–758.
CIKMCIKM-2009-SunMG09a #graph #online #rank
Learning to rank graphs for online similar graph search (BS, PM, CLG), pp. 1871–1874.
CIKMCIKM-2009-TangL #behaviour #scalability #social
Scalable learning of collective behavior based on sparse social dimensions (LT, HL), pp. 1107–1116.
CIKMCIKM-2009-WangHLS #comprehension #query #semantics #web
Semi-supervised learning of semantic classes for query understanding: from the web and for the web (YYW, RH, XL, JS), pp. 37–46.
CIKMCIKM-2009-WangML #programming #question #rank #search-based #using
Learning to rank using evolutionary computation: immune programming or genetic programming? (SW, JM, JL), pp. 1879–1882.
CIKMCIKM-2009-WuCZZ #approach #definite clause grammar #novel #rank #using
Smoothing DCG for learning to rank: a novel approach using smoothed hinge functions (MW, YC, ZZ, HZ), pp. 1923–1926.
CIKMCIKM-2009-YapB
Experiments on pattern-based relation learning (WY, TB), pp. 1657–1660.
CIKMCIKM-2009-ZhangMCM #fuzzy #ontology #semantics #uml #web
Fuzzy semantic web ontology learning from fuzzy UML model (FZ, ZMM, JC, XM), pp. 1007–1016.
CIKMCIKM-2009-ZhangXSYD #evaluation #named
ROSE: retail outlet site evaluation by learning with both sample and feature preference (BZ, MX, JYS, WJY, JD), pp. 1397–1404.
CIKMCIKM-2009-ZhuCWZWC #divide and conquer #query #ranking
To divide and conquer search ranking by learning query difficulty (ZAZ, WC, TW, CZ, GW, ZC), pp. 1883–1886.
CIKMCIKM-2009-ZhuWZ
Label correspondence learning for part-of-speech annotation transformation (MZ, HW, JZ), pp. 1461–1464.
ECIRECIR-2009-DonmezC #optimisation #rank
Active Sampling for Rank Learning via Optimizing the Area under the ROC Curve (PD, JGC), pp. 78–89.
ECIRECIR-2009-EsuliS #classification #multi
Active Learning Strategies for Multi-Label Text Classification (AE, FS), pp. 102–113.
ECIRECIR-2009-GeraniCC #retrieval
Investigating Learning Approaches for Blog Post Opinion Retrieval (SG, MJC, FC), pp. 313–324.
ECIRECIR-2009-LeaseAC #query #rank
Regression Rank: Learning to Meet the Opportunity of Descriptive Queries (ML, JA, WBC), pp. 90–101.
ICMLICML-2009-AdamsG #named #parametricity
Archipelago: nonparametric Bayesian semi-supervised learning (RPA, ZG), pp. 1–8.
ICMLICML-2009-BengioLCW #education
Curriculum learning (YB, JL, RC, JW), pp. 41–48.
ICMLICML-2009-BeygelzimerDL
Importance weighted active learning (AB, SD, JL), pp. 49–56.
ICMLICML-2009-BurlW
Active learning for directed exploration of complex systems (MCB, EW), pp. 89–96.
ICMLICML-2009-CamposZJ #constraints #network #using
Structure learning of Bayesian networks using constraints (CPdC, ZZ, QJ), pp. 113–120.
ICMLICML-2009-ChengHH #ranking
Decision tree and instance-based learning for label ranking (WC, JCH, EH), pp. 161–168.
ICMLICML-2009-ChenGR #kernel
Learning kernels from indefinite similarities (YC, MRG, BR), pp. 145–152.
ICMLICML-2009-ChenTLY #multi
A convex formulation for learning shared structures from multiple tasks (JC, LT, JL, JY), pp. 137–144.
ICMLICML-2009-ChoS #analysis #modelling
Learning dictionaries of stable autoregressive models for audio scene analysis (YC, LKS), pp. 169–176.
ICMLICML-2009-Cortes #kernel #performance #question
Invited talk: Can learning kernels help performance? (CC), p. 1.
ICMLICML-2009-DaiJXYY #framework #named
EigenTransfer: a unified framework for transfer learning (WD, OJ, GRX, QY, YY), pp. 193–200.
ICMLICML-2009-DasguptaL #summary #tutorial
Tutorial summary: Active learning (SD, JL), p. 18.
ICMLICML-2009-DiukLL #adaptation #feature model #problem
The adaptive k-meteorologists problem and its application to structure learning and feature selection in reinforcement learning (CD, LL, BRL), pp. 249–256.
ICMLICML-2009-DoLF #online
Proximal regularization for online and batch learning (CBD, QVL, CSF), pp. 257–264.
ICMLICML-2009-FarhangfarGS #image
Learning to segment from a few well-selected training images (AF, RG, CS), pp. 305–312.
ICMLICML-2009-FooDN #algorithm #multi
A majorization-minimization algorithm for (multiple) hyperparameter learning (CSF, CBD, AYN), pp. 321–328.
ICMLICML-2009-Freund #game studies #online
Invited talk: Drifting games, boosting and online learning (YF), p. 2.
ICMLICML-2009-GermainLLM #classification #linear
PAC-Bayesian learning of linear classifiers (PG, AL, FL, MM), pp. 353–360.
ICMLICML-2009-HazanS #algorithm #performance
Efficient learning algorithms for changing environments (EH, CS), pp. 393–400.
ICMLICML-2009-HuangS #linear #sequence
Learning linear dynamical systems without sequence information (TKH, JGS), pp. 425–432.
ICMLICML-2009-HuangZM
Learning with structured sparsity (JH, TZ, DNM), pp. 417–424.
ICMLICML-2009-JebaraWC #graph
Graph construction and b-matching for semi-supervised learning (TJ, JW, SFC), pp. 441–448.
ICMLICML-2009-JetchevT #predict
Trajectory prediction: learning to map situations to robot trajectories (NJ, MT), pp. 449–456.
ICMLICML-2009-KarampatziakisK #predict
Learning prediction suffix trees with Winnow (NK, DK), pp. 489–496.
ICMLICML-2009-KokD #logic #markov #network
Learning Markov logic network structure via hypergraph lifting (SK, PMD), pp. 505–512.
ICMLICML-2009-KolterN09a #difference #feature model
Regularization and feature selection in least-squares temporal difference learning (JZK, AYN), pp. 521–528.
ICMLICML-2009-KotlowskiS #constraints
Rule learning with monotonicity constraints (WK, RS), pp. 537–544.
ICMLICML-2009-KowalskiSR #kernel #multi
Multiple indefinite kernel learning with mixed norm regularization (MK, MS, LR), pp. 545–552.
ICMLICML-2009-KunegisL #graph transformation #predict
Learning spectral graph transformations for link prediction (JK, AL), pp. 561–568.
ICMLICML-2009-LangfordSZ #modelling
Learning nonlinear dynamic models (JL, RS, TZ), pp. 593–600.
ICMLICML-2009-LeeGRN #network #scalability
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations (HL, RBG, RR, AYN), pp. 609–616.
ICMLICML-2009-LiangJK #exponential #metric #product line
Learning from measurements in exponential families (PL, MIJ, DK), pp. 641–648.
ICMLICML-2009-LiKZ #using
Semi-supervised learning using label mean (YFL, JTK, ZHZ), pp. 633–640.
ICMLICML-2009-LiYX #collaboration #generative
Transfer learning for collaborative filtering via a rating-matrix generative model (BL, QY, XX), pp. 617–624.
ICMLICML-2009-LuJD #geometry #metric
Geometry-aware metric learning (ZL, PJ, ISD), pp. 673–680.
ICMLICML-2009-MairalBPS #online #taxonomy
Online dictionary learning for sparse coding (JM, FRB, JP, GS), pp. 689–696.
ICMLICML-2009-MaSSV #identification #online #scalability
Identifying suspicious URLs: an application of large-scale online learning (JM, LKS, SS, GMV), pp. 681–688.
ICMLICML-2009-MobahiCW #video
Deep learning from temporal coherence in video (HM, RC, JW), pp. 737–744.
ICMLICML-2009-NeumannMP
Learning complex motions by sequencing simpler motion templates (GN, WM, JP), pp. 753–760.
ICMLICML-2009-Niv #summary #tutorial
Tutorial summary: The neuroscience of reinforcement learning (YN), p. 16.
ICMLICML-2009-NowozinJ #clustering #graph #linear #programming
Solution stability in linear programming relaxations: graph partitioning and unsupervised learning (SN, SJ), pp. 769–776.
ICMLICML-2009-PazisL #policy
Binary action search for learning continuous-action control policies (JP, MGL), pp. 793–800.
ICMLICML-2009-PoczosASGS #exclamation
Learning when to stop thinking and do something! (BP, YAY, CS, RG, NRS), pp. 825–832.
ICMLICML-2009-QiTZCZ #metric #performance
An efficient sparse metric learning in high-dimensional space via l1-penalized log-determinant regularization (GJQ, JT, ZJZ, TSC, HJZ), pp. 841–848.
ICMLICML-2009-RainaMN #scalability #using
Large-scale deep unsupervised learning using graphics processors (RR, AM, AYN), pp. 873–880.
ICMLICML-2009-RaykarYZJFVBM #multi #trust
Supervised learning from multiple experts: whom to trust when everyone lies a bit (VCR, SY, LHZ, AKJ, CF, GHV, LB, LM), pp. 889–896.
ICMLICML-2009-RoyLW #consistency #modelling #probability #visual notation
Learning structurally consistent undirected probabilistic graphical models (SR, TL, MWW), pp. 905–912.
ICMLICML-2009-SuttonMPBSSW #approximate #linear #performance
Fast gradient-descent methods for temporal-difference learning with linear function approximation (RSS, HRM, DP, SB, DS, CS, EW), pp. 993–1000.
ICMLICML-2009-SzitaL #polynomial
Optimistic initialization and greediness lead to polynomial time learning in factored MDPs (IS, AL), pp. 1001–1008.
ICMLICML-2009-TaylorP #approximate #kernel
Kernelized value function approximation for reinforcement learning (GT, RP), pp. 1017–1024.
ICMLICML-2009-Tillman #distributed #independence
Structure learning with independent non-identically distributed data (RET), pp. 1041–1048.
ICMLICML-2009-TrespY #dependence #summary #tutorial
Tutorial summary: Learning with dependencies between several response variables (VT, KY), p. 14.
ICMLICML-2009-VarmaB #kernel #multi #performance
More generality in efficient multiple kernel learning (MV, BRB), pp. 1065–1072.
ICMLICML-2009-VlassisT
Model-free reinforcement learning as mixture learning (NV, MT), pp. 1081–1088.
ICMLICML-2009-VolkovsZ #named #ranking
BoltzRank: learning to maximize expected ranking gain (MV, RSZ), pp. 1089–1096.
ICMLICML-2009-WeinbergerDLSA #multi #scalability
Feature hashing for large scale multitask learning (KQW, AD, JL, AJS, JA), pp. 1113–1120.
ICMLICML-2009-XuWS #predict
Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning (LX, MW, DS), pp. 1137–1144.
ICMLICML-2009-YangJY #online
Online learning by ellipsoid method (LY, RJ, JY), pp. 1153–1160.
ICMLICML-2009-YuanH #feature model #robust
Robust feature extraction via information theoretic learning (XY, BGH), pp. 1193–1200.
ICMLICML-2009-YuilleZ #composition
Compositional noisy-logical learning (ALY, SZ), pp. 1209–1216.
ICMLICML-2009-YuJ
Learning structural SVMs with latent variables (CNJY, TJ), pp. 1169–1176.
ICMLICML-2009-ZhangKP #prototype #scalability
Prototype vector machine for large scale semi-supervised learning (KZ, JTK, BP), pp. 1233–1240.
ICMLICML-2009-ZhangSFD
Learning non-redundant codebooks for classifying complex objects (WZ, AS, XF, TGD), pp. 1241–1248.
ICMLICML-2009-ZhanLLZ #metric #using
Learning instance specific distances using metric propagation (DCZ, ML, YFL, ZHZ), pp. 1225–1232.
ICMLICML-2009-ZhouSL #multi
Multi-instance learning by treating instances as non-I.I.D. samples (ZHZ, YYS, YFL), pp. 1249–1256.
ICMLICML-2009-ZhuangTH #kernel #named #parametricity
SimpleNPKL: simple non-parametric kernel learning (JZ, IWT, SCHH), pp. 1273–1280.
KDDKDD-2009-BeygelzimerL
The offset tree for learning with partial labels (AB, JL), pp. 129–138.
KDDKDD-2009-ChenCBT #optimisation #random
Constrained optimization for validation-guided conditional random field learning (MC, YC, MRB, AET), pp. 189–198.
KDDKDD-2009-DonmezCS
Efficiently learning the accuracy of labeling sources for selective sampling (PD, JGC, JGS), pp. 259–268.
KDDKDD-2009-DundarHBRR #case study #dataset #detection #using
Learning with a non-exhaustive training dataset: a case study: detection of bacteria cultures using optical-scattering technology (MD, EDH, AKB, JPR, BR), pp. 279–288.
KDDKDD-2009-GamaSR #algorithm #evaluation
Issues in evaluation of stream learning algorithms (JG, RS, PPR), pp. 329–338.
KDDKDD-2009-GaoFSH
Heterogeneous source consensus learning via decision propagation and negotiation (JG, WF, YS, JH), pp. 339–348.
KDDKDD-2009-GeXZSGW #multi
Multi-focal learning and its application to customer service support (YG, HX, WZ, RKS, XG, WW), pp. 349–358.
KDDKDD-2009-GuptaBR
Catching the drift: learning broad matches from clickthrough data (SG, MB, MR), pp. 1165–1174.
KDDKDD-2009-LiuKJ #graph #monitoring
Learning dynamic temporal graphs for oil-production equipment monitoring system (YL, JRK, OJ), pp. 1225–1234.
KDDKDD-2009-Macskassy #empirical #graph #metric #using
Using graph-based metrics with empirical risk minimization to speed up active learning on networked data (SAM), pp. 597–606.
KDDKDD-2009-MaSSV #detection #web
Beyond blacklists: learning to detect malicious web sites from suspicious URLs (JM, LKS, SS, GMV), pp. 1245–1254.
KDDKDD-2009-RendleMNS #ranking #recommendation
Learning optimal ranking with tensor factorization for tag recommendation (SR, LBM, AN, LST), pp. 727–736.
KDDKDD-2009-TangL #relational #social
Relational learning via latent social dimensions (LT, HL), pp. 817–826.
KDDKDD-2009-WangSAL #fault #network
Learning, indexing, and diagnosing network faults (TW, MS, DA, LL), pp. 857–866.
KDDKDD-2009-YangSWC #classification #effectiveness #multi
Effective multi-label active learning for text classification (BY, JTS, TW, ZC), pp. 917–926.
KDDKDD-2009-YouHC #biology #network
Learning patterns in the dynamics of biological networks (CHY, LBH, DJC), pp. 977–986.
KDIRKDIR-2009-CallejaFGA #set
A Learning Method for Imbalanced Data Sets (JdlC, OF, JG, RMAP), pp. 307–310.
KDIRKDIR-2009-ZhouZK #collaboration
The Collaborative Learning Agent (CLA) in Trident Warrior 08 Exercise (CZ, YZ, CK), pp. 323–328.
KEODKEOD-2009-Aussenac-GillesK #documentation #ontology #xml
Ontology Learning by Analyzing XML Document Structure and Content (NAG, MK), pp. 159–165.
KEODKEOD-2009-FreddoT #evolution #folksonomy #ontology #semantics #social #web
Integrating Social Web with Semantic Web — Ontology Learning and Ontology Evolution from Folksonomies (ARF, CAT), pp. 247–253.
KMISKMIS-2009-DevedzicJPN #collaboration #research
Learning Scenarios and Services for an SME — Collaboration between an SME and a Research Team (VD, JJ, VP, KN), pp. 218–223.
KMISKMIS-2009-DochevA #semantics #towards #web
Towards Semantic Web Enhanced Learning (DD, GA), pp. 212–217.
MLDMMLDM-2009-BouthinonSV #ambiguity #concept
Concept Learning from (Very) Ambiguous Examples (DB, HS, VV), pp. 465–478.
MLDMMLDM-2009-ChanguelLB #automation #html
A General Learning Method for Automatic Title Extraction from HTML Pages (SC, NL, BBM), pp. 704–718.
MLDMMLDM-2009-LeeCWL
Learning with a Quadruped Chopstick Robot (WCL, JCC, SzW, KML), pp. 603–616.
MLDMMLDM-2009-Mendes-MoreiraJSS #approach #case study
Ensemble Learning: A Study on Different Variants of the Dynamic Selection Approach (JMM, AMJ, CS, JFdS), pp. 191–205.
MLDMMLDM-2009-StrumbeljRK
Learning Betting Tips from Users’ Bet Selections (ES, MRS, IK), pp. 678–688.
RecSysRecSys-2009-MaLK #recommendation #trust
Learning to recommend with trust and distrust relationships (HM, MRL, IK), pp. 189–196.
RecSysRecSys-2009-OMahonyS #recommendation
Learning to recommend helpful hotel reviews (MPO, BS), pp. 305–308.
SEKESEKE-2009-FarZYA #concept #documentation #semantics #using
Realization of Semantic Search Using Concept Learning and Document Annotation Agents (BHF, CZ, Z(Y, MA), pp. 164–169.
SEKESEKE-2009-TianCYL #approach #modelling #music #ontology
An Ontology-based Model Driven Approach for a Music Learning System (YT, FC, HY, LL), pp. 739–744.
SEKESEKE-2009-Ye #collaboration #education #re-engineering
An Academia-Industry Collaborative Teaching and Learning Model for Software Engineering Education (HY), pp. 301–305.
SIGIRSIGIR-2009-BanerjeeCR #query #rank
Learning to rank for quantity consensus queries (SB, SC, GR), pp. 243–250.
SIGIRSIGIR-2009-CormackCB #rank
Reciprocal rank fusion outperforms condorcet and individual rank learning methods (GVC, CLAC, SB), pp. 758–759.
SIGIRSIGIR-2009-CumminsO #framework #information retrieval #proximity
Learning in a pairwise term-term proximity framework for information retrieval (RC, CO), pp. 251–258.
SIGIRSIGIR-2009-HuangH #approach #information retrieval #ranking
A bayesian learning approach to promoting diversity in ranking for biomedical information retrieval (XH, QH), pp. 307–314.
SIGIRSIGIR-2009-MaKL #recommendation #social #trust
Learning to recommend with social trust ensemble (HM, IK, MRL), pp. 203–210.
SIGIRSIGIR-2009-SunQTW #metric #rank #ranking #robust
Robust sparse rank learning for non-smooth ranking measures (ZS, TQ, QT, JW), pp. 259–266.
SIGIRSIGIR-2009-YangWGH #query #ranking #web
Query sampling for ranking learning in web search (LY, LW, BG, XSH), pp. 754–755.
SIGIRSIGIR-2009-YilmazR #rank
Deep versus shallow judgments in learning to rank (EY, SR), pp. 662–663.
RERE-2009-KnaussSS #heuristic #requirements
Learning to Write Better Requirements through Heuristic Critiques (EK, KS, KS), pp. 387–388.
ESEC-FSEESEC-FSE-2009-BruchMM #code completion
Learning from examples to improve code completion systems (MB, MM, MM), pp. 213–222.
ICSEICSE-2009-AlrajehKRU #modelling #requirements
Learning operational requirements from goal models (DA, JK, AR, SU), pp. 265–275.
SACSAC-2009-LiuTS #classification #complexity #using
Assessing complexity of service-oriented computing using learning classifier systems (LL, ST, HS), pp. 2170–2171.
SACSAC-2009-Manine #information management #multi #ontology
Learning the ontological theory of an information extraction system in the multi-predicate ILP setting (APM), pp. 1578–1582.
SACSAC-2009-MaoLPCH #approach #detection #multi
Semi-supervised co-training and active learning based approach for multi-view intrusion detection (CHM, HML, DP, TC, SYH), pp. 2042–2048.
SACSAC-2009-RoeslerHC #case study #distance #multi
A new multimedia synchronous distance learning system: the IVA study case (VR, RH, CHC), pp. 1765–1770.
SACSAC-2009-SchmitzbergerRNRP #architecture
Thin client architecture in support of remote radiology learning (FFS, JER, SN, GDR, DSP), pp. 842–846.
SACSAC-2009-WangCH #multi #music #retrieval
Music retrieval based on a multi-samples selection strategy for support vector machine active learning (TW, GC, PH), pp. 1750–1751.
CASECASE-2009-BountourelisR #algorithm
Customized learning algorithms for episodic tasks with acyclic state spaces (TB, SR), pp. 627–634.
CASECASE-2009-SolisT #comprehension #towards
Towards enhancing the understanding of human motor learning (JS, AT), pp. 591–596.
CGOCGO-2009-MaoS #evolution #predict #virtual machine
Cross-Input Learning and Discriminative Prediction in Evolvable Virtual Machines (FM, XS), pp. 92–101.
DACDAC-2009-MarrBBH
A learning digital computer (BM, AB, SB, PEH), pp. 617–618.
DATEDATE-2009-RichterJE #framework #verification
Learning early-stage platform dimensioning from late-stage timing verification (KR, MJ, RE), pp. 851–857.
HPDCHPDC-2009-Reeuwijk #data flow #framework #named #peer-to-peer #self #using
Maestro: a self-organizing peer-to-peer dataflow framework using reinforcement learning (CvR), pp. 187–196.
STOCSTOC-2009-KleinbergPT #game studies #multi
Multiplicative updates outperform generic no-regret learning in congestion games: extended abstract (RK, GP, ÉT), pp. 533–542.
STOCSTOC-2009-Sellie #random
Exact learning of random DNF over the uniform distribution (LS), pp. 45–54.
TACASTACAS-2009-ChenFCTW #automaton #composition #verification
Learning Minimal Separating DFA’s for Compositional Verification (YFC, AF, EMC, YKT, BYW), pp. 31–45.
ICLPICLP-2009-Raedt #logic #probability #tutorial
Probabilistic Logic Learning — A Tutorial Abstract (LDR), p. 39.
ICSTSAT-2009-DilkinaGS
Backdoors in the Context of Learning (BND, CPG, AS), pp. 73–79.
ICSTSAT-2009-Johannsen #bound #exponential #strict
An Exponential Lower Bound for Width-Restricted Clause Learning (JJ), pp. 128–140.
TPDLECDL-2008-GuCAKSMB #library #personalisation
Personalizing the Selection of Digital Library Resources to Support Intentional Learning (QG, SdlC, FA, HJK, TS, JHM, KRB), pp. 244–255.
HTHT-2008-HeoY #empirical #information management
An empirical study of the learning effect of an ontology-driven information system (MH, MY), pp. 225–226.
HTHT-2008-KetterlEB #social #web
Social selected learning content out of web lectures (MK, JE, JB), pp. 231–232.
HTHT-2008-LawlessHW #corpus #education
Enhancing access to open corpus educational content: learning in the wild (SL, LH, VW), pp. 167–174.
JCDLJCDL-2008-McArthurZ #education #towards
From nsdl 1.0 to nsdl 2.0: towards a comprehensive cyberinfrastructure for teaching and learning (DJM, LLZ), pp. 66–69.
VLDBVLDB-2008-NguyenNF
Learning to extract form labels (HN, THN, JF), pp. 684–694.
VLDBVLDB-2008-TalukdarJMCIPG #query
Learning to create data-integrating queries (PPT, MJ, MSM, KC, ZGI, FCNP, SG), pp. 785–796.
CSEETCSEET-2008-BarbosaSM #education #experience #testing
An Experience on Applying Learning Mechanisms for Teaching Inspection and Software Testing (EFB, SdRSdS, JCM), pp. 189–196.
CSEETCSEET-2008-RasR #information management #using
Improving Knowledge Acquisition in Capstone Projects Using Learning Spaces for Experiential Learning (ER, JR), pp. 77–84.
CSEETCSEET-2008-RyooFJ #design #education #game studies #object-oriented #problem #re-engineering
Teaching Object-Oriented Software Engineering through Problem-Based Learning in the Context of Game Design (JR, FF, DSJ), pp. 137–144.
ITiCSEITiCSE-2008-Abad #case study #distributed #experience
Learning through creating learning objects: experiences with a class project in a distributed systems course (CLA), pp. 255–259.
ITiCSEITiCSE-2008-Bower #online
The “instructed-teacher”: a computer science online learning pedagogical pattern (MB), pp. 189–193.
ITiCSEITiCSE-2008-Burrell #object-oriented #process #programming #source code #visualisation
Learning object oriented programming: unique visualizations of individuals learning styles, activities and the programs produced (CJB), p. 339.
ITiCSEITiCSE-2008-CerboDS #collaboration
Extending moodle for collaborative learning (FDC, GD, GS), p. 324.
ITiCSEITiCSE-2008-CharltonMD #performance #social
Evaluating the extent to which sociability and social presence affects learning performance (TC, LM, MD), p. 342.
ITiCSEITiCSE-2008-ChidanandanS #question
Adopting pen-based technology to facilitate active learning in the classroom: is it right for you? (AC, SMS), p. 343.
ITiCSEITiCSE-2008-Goelman #collaboration #database
Databases, non-majors and collaborative learning: a ternary relationships (DG), pp. 27–31.
ITiCSEITiCSE-2008-Jackova #programming
Learning for mastery in an introductory programming course (JJ), p. 352.
ITiCSEITiCSE-2008-Kolikant #education #framework
Computer-science education as a cultural encounter: a socio-cultural framework for articulating learning difficulties (YBDK), pp. 291–295.
ITiCSEITiCSE-2008-Kolling #ide #named #object-oriented #programming #visual notation
Greenfoot: a highly graphical ide for learning object-oriented programming (MK), p. 327.
ITiCSEITiCSE-2008-MorenoICM #database #design #distance #education #towards #using
Using accessible digital resources for teaching database design: towards an inclusive distance learning proposal (LM, AI, EC, PM), pp. 32–36.
ITiCSEITiCSE-2008-MurphyPK #approach #distance #education #programming
A distance learning approach to teaching eXtreme programming (CM, DBP, GEK), pp. 199–203.
ITiCSEITiCSE-2008-PerezMF #operating system
Cooperative learning in operating systems laboratory (JEP, JGM, IMF), p. 323.
ITiCSEITiCSE-2008-Shaban-NejadH #education #towards
Web-based dynamic learning through lexical chaining: a step forward towards knowledge-driven education (ASN, VH), p. 375.
ITiCSEITiCSE-2008-SierraCF
An environment for supporting active learning in courses on language processing (JLS, AMFPC, AFV), pp. 128–132.
SIGITESIGITE-2008-MillerD
Employers’ perspectives on it learning outcomes (CSM, LD), pp. 213–218.
SIGITESIGITE-2008-Sabin #collaboration
A collaborative and experiential learning model powered by real-world projects (MS), pp. 157–164.
ICSMEICSM-2008-Hou #design #framework
Investigating the effects of framework design knowledge in example-based framework learning (DH), pp. 37–46.
CIAACIAA-2008-GarciaPAR #automaton #finite #nondeterminism #regular expression #using
Learning Regular Languages Using Nondeterministic Finite Automata (PG, MVdP, GIA, JR), pp. 92–101.
ICALPICALP-A-2008-Dachman-SoledLMSWW #encryption
Optimal Cryptographic Hardness of Learning Monotone Functions (DDS, HKL, TM, RAS, AW, HW), pp. 36–47.
AIIDEAIIDE-2008-CutumisuS #game studies
A Demonstration of Agent Learning with Action-Dependent Learning Rates in Computer Role-Playing Games (MC, DS).
AIIDEAIIDE-2008-CutumisuSBS #game studies #using
Agent Learning using Action-Dependent Learning Rates in Computer Role-Playing Games (MC, DS, MHB, RSS).
AIIDEAIIDE-2008-HefnyHSA #game studies #named
Cerberus: Applying Supervised and Reinforcement Learning Techniques to Capture the Flag Games (ASH, AAH, MMS, AFA).
AIIDEAIIDE-2008-KerrCC #game studies
Learning and Playing in Wubble World (WK, PRC, YHC).
AIIDEAIIDE-2008-McPartlandG #game studies
Learning to be a Bot: Reinforcement Learning in Shooter Games (MM, MG).
AIIDEAIIDE-2008-UlamJG #adaptation #game studies #modelling
Combining Model-Based Meta-Reasoning and Reinforcement Learning for Adapting Game-Playing Agents (PU, JJ, AKG0).
CoGCIG-2008-Blair #evaluation #network #symmetry
Learning position evaluation for Go with Internal Symmetry Networks (AB), pp. 199–204.
CoGCIG-2008-InoueS #classification #game studies #hybrid #video
Applying GA for reward allotment in an event-driven hybrid learning classifier system for soccer video games (YI, YS), pp. 296–303.
CoGCIG-2008-Lucas #difference #evolution
Investigating learning rates for evolution and temporal difference learning (SML), pp. 1–7.
CoGCIG-2008-MarivateM #game studies #social
Social Learning methods in board game agents (VNM, TM), pp. 323–328.
CoGCIG-2008-McPartlandG #multi
Creating a multi-purpose first person shooter bot with reinforcement learning (MM, MG), pp. 143–150.
CoGCIG-2008-MujtabaB #multi
Survival by continuous learning in a dynamic multiple task environment (HM, ARB), pp. 304–309.
CoGCIG-2008-OsakiSTK #difference #evaluation #probability #using
An Othello evaluation function based on Temporal Difference Learning using probability of winning (YO, KS, YT, YK), pp. 205–211.
CoGCIG-2008-Sahraei-ArdakaniRA #game studies
Hierarchical Nash-Q learning in continuous games (MSA, ARK, MNA), pp. 290–295.
CoGCIG-2008-SharmaKG #game studies #generative
Learning and knowledge generation in General Games (SS, ZK, SDG), pp. 329–335.
CoGCIG-2008-WenderW #game studies #using
Using reinforcement learning for city site selection in the turn-based strategy game Civilization IV (SW, IDW), pp. 372–377.
CHICHI-2008-CostabileALABP #challenge #exclamation #mobile
Explore! possibilities and challenges of mobile learning (MFC, ADA, RL, CA, PB, TP), pp. 145–154.
CHICHI-2008-FogartyTKW #concept #image #interactive #named
CueFlik: interactive concept learning in image search (JF, DST, AK, SAJW), pp. 29–38.
CHICHI-2008-Grammenos #game studies
Game over: learning by dying (DG), pp. 1443–1452.
CHICHI-2008-McQuigganRL
The effects of empathetic virtual characters on presence in narrative-centered learning environments (SWM, JPR, JCL), pp. 1511–1520.
CHICHI-2008-OganAJ #predict #using
Pause, predict, and ponder: use of narrative videos to improve cultural discussion and learning (AO, VA, CJ), pp. 155–162.
CHICHI-2008-WangM #interactive
Human-Currency Interaction: learning from virtual currency use in China (YW, SDM), pp. 25–28.
ICEISICEIS-AIDSS-2008-MorgadoPR #evaluation #quality
An Evaluation Instrument for Learning Object Quality and Management (EMM, FJGP, ÁBR), pp. 327–332.
ICEISICEIS-AIDSS-2008-StateCRP #algorithm #classification
A New Learning Algorithm for Classification in the Reduced Space (LS, CC, IR, PV), pp. 155–160.
ICEISICEIS-HCI-2008-CarvalhoS #lessons learnt #usability
The Importance of Usability Criteria on Learning Management Systems: Lessons Learned (AFPdC, JCAS), pp. 154–159.
ICEISICEIS-HCI-2008-DamaseviciusT #design #re-engineering #user interface
Learning Object Reengineering Based on Principles for Usable User Interface Design (RD, LT), pp. 124–129.
ICEISICEIS-HCI-2008-GarciaMDS #interface #visualisation
An Interface Environment for Learning Object Search and Pre-Visualisation (LSG, ROdOM, AID, MSS), pp. 240–247.
ICEISICEIS-HCI-2008-MileyRM
Traditional Learning Vs. e-LEARNING — Some Results from Training Call Centre Personnel (MM, JAR, CM), pp. 299–307.
ICEISICEIS-J-2008-GullaBK08a #ontology
Association Rules and Cosine Similarities in Ontology Relationship Learning (JAG, TB, GSK), pp. 201–212.
ICEISICEIS-SAIC-2008-CanalesP #architecture #semantics #web
Learning Technology System Architecture Based on Agents and Semantic Web (ACC, RPV), pp. 127–132.
CIKMCIKM-2008-BroderCFGJMMP
To swing or not to swing: learning when (not) to advertise (AZB, MC, MF, EG, VJ, DM, VM, VP), pp. 1003–1012.
CIKMCIKM-2008-DonmezC #multi
Proactive learning: cost-sensitive active learning with multiple imperfect oracles (PD, JGC), pp. 619–628.
CIKMCIKM-2008-DouSYW #question #ranking #web
Are click-through data adequate for learning web search rankings? (ZD, RS, XY, JRW), pp. 73–82.
CIKMCIKM-2008-HoefelE #classification #sequence
Learning a two-stage SVM/CRF sequence classifier (GH, CE), pp. 271–278.
CIKMCIKM-2008-LuoZHXH #multi
Transfer learning from multiple source domains via consensus regularization (PL, FZ, HX, YX, QH), pp. 103–112.
CIKMCIKM-2008-MaYKL #query #semantics
Learning latent semantic relations from clickthrough data for query suggestion (HM, HY, IK, MRL), pp. 709–718.
CIKMCIKM-2008-MilneW #wiki
Learning to link with wikipedia (DNM, IHW), pp. 509–518.
CIKMCIKM-2008-NiXLH #approach
Group-based learning: a boosting approach (WN, JX, HL, YH), pp. 1443–1444.
CIKMCIKM-2008-WangCZL #constraints #metric
Semi-supervised metric learning by maximizing constraint margin (FW, SC, CZ, TL), pp. 1457–1458.
ECIRECIR-2008-AyacheQ #corpus #using #video
Video Corpus Annotation Using Active Learning (SA, GQ), pp. 187–198.
ICMLICML-2008-BarrettN #multi #policy
Learning all optimal policies with multiple criteria (LB, SN), pp. 41–47.
ICMLICML-2008-BickelBLS #multi
Multi-task learning for HIV therapy screening (SB, JB, TL, TS), pp. 56–63.
ICMLICML-2008-BryanS
Actively learning level-sets of composite functions (BB, JGS), pp. 80–87.
ICMLICML-2008-CaruanaKY #empirical #evaluation
An empirical evaluation of supervised learning in high dimensions (RC, NK, AY), pp. 96–103.
ICMLICML-2008-ChenM
Learning to sportscast: a test of grounded language acquisition (DLC, RJM), pp. 128–135.
ICMLICML-2008-CoatesAN #multi
Learning for control from multiple demonstrations (AC, PA, AYN), pp. 144–151.
ICMLICML-2008-CollobertW #architecture #multi #natural language #network
A unified architecture for natural language processing: deep neural networks with multitask learning (RC, JW), pp. 160–167.
ICMLICML-2008-DasguptaH
Hierarchical sampling for active learning (SD, DH), pp. 208–215.
ICMLICML-2008-DekelS
Learning to classify with missing and corrupted features (OD, OS), pp. 216–223.
ICMLICML-2008-DickHS #infinity #semistructured data
Learning from incomplete data with infinite imputations (UD, PH, TS), pp. 232–239.
ICMLICML-2008-DiukCL #object-oriented #performance #representation
An object-oriented representation for efficient reinforcement learning (CD, AC, MLL), pp. 240–247.
ICMLICML-2008-DonmezC #optimisation #rank #reduction
Optimizing estimated loss reduction for active sampling in rank learning (PD, JGC), pp. 248–255.
ICMLICML-2008-DoshiPR #using
Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs (FD, JP, NR), pp. 256–263.
ICMLICML-2008-DuchiSSC #performance
Efficient projections onto the l1-ball for learning in high dimensions (JCD, SSS, YS, TC), pp. 272–279.
ICMLICML-2008-EpshteynVD
Active reinforcement learning (AE, AV, GD), pp. 296–303.
ICMLICML-2008-FrankMP
Reinforcement learning in the presence of rare events (JF, SM, DP), pp. 336–343.
ICMLICML-2008-GonenA #kernel #locality #multi
Localized multiple kernel learning (MG, EA), pp. 352–359.
ICMLICML-2008-GordonGM #game studies
No-regret learning in convex games (GJG, AG, CM), pp. 360–367.
ICMLICML-2008-HamL #analysis
Grassmann discriminant analysis: a unifying view on subspace-based learning (JH, DDL), pp. 376–383.
ICMLICML-2008-HoiJ #kernel
Active kernel learning (SCHH, RJ), pp. 400–407.
ICMLICML-2008-HuynhM #logic #markov #network #parametricity
Discriminative structure and parameter learning for Markov logic networks (TNH, RJM), pp. 416–423.
ICMLICML-2008-KolterCNGD #programming
Space-indexed dynamic programming: learning to follow trajectories (JZK, AC, AYN, YG, CD), pp. 488–495.
ICMLICML-2008-LanLQML #rank
Query-level stability and generalization in learning to rank (YL, TYL, TQ, ZM, HL), pp. 512–519.
ICMLICML-2008-LazaricRB
Transfer of samples in batch reinforcement learning (AL, MR, AB), pp. 544–551.
ICMLICML-2008-LiLW #framework #self #what
Knows what it knows: a framework for self-aware learning (LL, MLL, TJW), pp. 568–575.
ICMLICML-2008-LoeffFR #approximate #named
ManifoldBoost: stagewise function approximation for fully-, semi- and un-supervised learning (NL, DAF, DR), pp. 600–607.
ICMLICML-2008-MekaJCD #online #rank
Rank minimization via online learning (RM, PJ, CC, ISD), pp. 656–663.
ICMLICML-2008-MeloMR #analysis #approximate
An analysis of reinforcement learning with function approximation (FSM, SPM, MIR), pp. 664–671.
ICMLICML-2008-NowozinB #approach
A decoupled approach to exemplar-based unsupervised learning (SN, GHB), pp. 704–711.
ICMLICML-2008-OuyangG #ranking
Learning dissimilarities by ranking: from SDP to QP (HO, AGG), pp. 728–735.
ICMLICML-2008-ParrLTPL #analysis #approximate #feature model #linear #modelling
An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning (RP, LL, GT, CPW, MLL), pp. 752–759.
ICMLICML-2008-PuolamakiAK #query
Learning to learn implicit queries from gaze patterns (KP, AA, SK), pp. 760–767.
ICMLICML-2008-RadlinskiKJ #multi #ranking
Learning diverse rankings with multi-armed bandits (FR, RK, TJ), pp. 784–791.
ICMLICML-2008-RanzatoS #documentation #network
Semi-supervised learning of compact document representations with deep networks (MR, MS), pp. 792–799.
ICMLICML-2008-RaykarKBDR #automation #feature model #induction #multi
Bayesian multiple instance learning: automatic feature selection and inductive transfer (VCR, BK, JB, MD, RBR), pp. 808–815.
ICMLICML-2008-ReisingerSM #kernel #online
Online kernel selection for Bayesian reinforcement learning (JR, PS, RM), pp. 816–823.
ICMLICML-2008-SakumaKW #privacy
Privacy-preserving reinforcement learning (JS, SK, RNW), pp. 864–871.
ICMLICML-2008-ShiBY #modelling #using
Data spectroscopy: learning mixture models using eigenspaces of convolution operators (TS, MB, BY), pp. 936–943.
ICMLICML-2008-SilverSM
Sample-based learning and search with permanent and transient memories (DS, RSS, MM), pp. 968–975.
ICMLICML-2008-SindhwaniR #multi
An RKHS for multi-view learning and manifold co-regularization (VS, DSR), pp. 976–983.
ICMLICML-2008-SokolovskaCY #modelling #probability
The asymptotics of semi-supervised learning in discriminative probabilistic models (NS, OC, FY), pp. 984–991.
ICMLICML-2008-SuZLM #network #parametricity
Discriminative parameter learning for Bayesian networks (JS, HZ, CXL, SM), pp. 1016–1023.
ICMLICML-2008-SyedBS #linear #programming #using
Apprenticeship learning using linear programming (US, MHB, RES), pp. 1032–1039.
ICMLICML-2008-SzafranskiGR #kernel
Composite kernel learning (MS, YG, AR), pp. 1040–1047.
ICMLICML-2008-WangYZ #adaptation #kernel #multi
Adaptive p-posterior mixture-model kernels for multiple instance learning (HYW, QY, HZ), pp. 1136–1143.
ICMLICML-2008-WangZ #multi #on the
On multi-view active learning and the combination with semi-supervised learning (WW, ZHZ), pp. 1152–1159.
ICMLICML-2008-WeinbergerS #distance #implementation #metric #performance
Fast solvers and efficient implementations for distance metric learning (KQW, LKS), pp. 1160–1167.
ICMLICML-2008-WestonRC
Deep learning via semi-supervised embedding (JW, FR, RC), pp. 1168–1175.
ICMLICML-2008-WingateS #exponential #predict #product line
Efficiently learning linear-linear exponential family predictive representations of state (DW, SPS), pp. 1176–1183.
ICMLICML-2008-XiaLWZL #algorithm #approach #rank
Listwise approach to learning to rank: theory and algorithm (FX, TYL, JW, WZ, HL), pp. 1192–1199.
ICMLICML-2008-YaoL #difference
Preconditioned temporal difference learning (HY, ZQL), pp. 1208–1215.
ICPRICPR-2008-AlpcanB #algorithm #distributed #parallel
A discrete-time parallel update algorithm for distributed learning (TA, CB), pp. 1–4.
ICPRICPR-2008-Arevalillo-HerraezFD #image #metric #retrieval #similarity
Learning combined similarity measures from user data for image retrieval (MAH, FJF, JD), pp. 1–4.
ICPRICPR-2008-BasakLC #summary #video
Video summarization with supervised learning (JB, VL, SC), pp. 1–4.
ICPRICPR-2008-CamposJ #constraints #network #parametricity #using
Improving Bayesian Network parameter learning using constraints (CPdC, QJ), pp. 1–4.
ICPRICPR-2008-ChangLAH08a #collaboration #image #using
Using collaborative learning for image contrast enhancement (YC, DJL, JKA, YH), pp. 1–4.
ICPRICPR-2008-DehzangiMCL #classification #fuzzy #speech #using
Fuzzy rule selection using Iterative Rule Learning for speech data classification (OD, BM, CES, HL), pp. 1–4.
ICPRICPR-2008-DuinP #difference #matrix #on the
On refining dissimilarity matrices for an improved NN learning (RPWD, EP), pp. 1–4.
ICPRICPR-2008-FabletLSMCB #using
Weakly supervised learning using proportion-based information: An application to fisheries acoustics (RF, RL, CS, JM, PC, JMB), pp. 1–4.
ICPRICPR-2008-FuR #multi #performance
Fast multiple instance learning via L1, 2 logistic regression (ZF, ARK), pp. 1–4.
ICPRICPR-2008-FuSHLT #image #kernel #multi #set
Multiple kernel learning from sets of partially matching image features (SYF, GS, ZGH, ZzL, MT), pp. 1–4.
ICPRICPR-2008-GhanemVW #relational
Learning in imbalanced relational data (ASG, SV, GAWW), pp. 1–4.
ICPRICPR-2008-GongC #graph #online #optimisation #realtime #segmentation #using
Real-time foreground segmentation on GPUs using local online learning and global graph cut optimization (MG, LC), pp. 1–4.
ICPRICPR-2008-GuiHY #consistency
An improvement on learning with local and global consistency (JG, DSH, ZY), pp. 1–4.
ICPRICPR-2008-HuAS08a #using
Learning motion patterns in crowded scenes using motion flow field (MH, SA, MS), pp. 1–5.
ICPRICPR-2008-HuWJHG #detection #online
Human reappearance detection based on on-line learning (LH, YW, SJ, QH, WG), pp. 1–4.
ICPRICPR-2008-JinLH #prototype
Prototype learning with margin-based conditional log-likelihood loss (XJ, CLL, XH), pp. 1–4.
ICPRICPR-2008-JradGB #constraints #multi #performance
Supervised learning rule selection for multiclass decision with performance constraints (NJ, EGM, PB), pp. 1–4.
ICPRICPR-2008-KarnickMP #approach #classification #concept #incremental #multi #using
Incremental learning in non-stationary environments with concept drift using a multiple classifier based approach (MTK, MM, RP), pp. 1–4.
ICPRICPR-2008-LiaoJ #network #parametricity #semistructured data
Exploiting qualitative domain knowledge for learning Bayesian network parameters with incomplete data (WL, QJ), pp. 1–4.
ICPRICPR-2008-LiaoL #kernel #novel #robust
A novel robust kernel for appearance-based learning (CTL, SHL), pp. 1–4.
ICPRICPR-2008-LiDM #feature model #locality #using
Localized feature selection for Gaussian mixtures using variational learning (YL, MD, YM), pp. 1–4.
ICPRICPR-2008-LiuWBM #kernel #linear
Semi-supervised learning by locally linear embedding in kernel space (RL, YW, TB, DM), pp. 1–4.
ICPRICPR-2008-LiuZDY #detection #sequence #video
Video attention: Learning to detect a salient object sequence (TL, NZ, WD, ZY), pp. 1–4.
ICPRICPR-2008-LuFJW #classification #framework #metric #reduction #visualisation
Metric Learning: A general dimension reduction framework for classification and visualization (CL, GF, JJ, PSPW), pp. 1–4.
ICPRICPR-2008-NaYKC
Relevant pattern selection for subspace learning (JHN, SMY, MK, JYC), pp. 1–4.
ICPRICPR-2008-NguyenBP #approach #set
A supervised learning approach for imbalanced data sets (GHN, AB, SLP), pp. 1–4.
ICPRICPR-2008-NingXZGH #detection #difference
Temporal difference learning to detect unsafe system states (HN, WX, YZ, YG, TSH), pp. 1–4.
ICPRICPR-2008-PerezO #invariant #programming #search-based
Learning invariant region descriptor operators with genetic programming and the F-measure (CBP, GO), pp. 1–4.
ICPRICPR-2008-QuQY
Learning a discriminative sparse tri-value transform (ZQ, GQ, PCY), pp. 1–4.
ICPRICPR-2008-SudoOTKA #detection #incremental #online
Online anomal movement detection based on unsupervised incremental learning (KS, TO, HT, HK, KA), pp. 1–4.
ICPRICPR-2008-TorselloD #generative #graph
Supervised learning of a generative model for edge-weighted graphs (AT, DLD), pp. 1–4.
ICPRICPR-2008-WangWCW #algorithm #clustering
A clustering algorithm combine the FCM algorithm with supervised learning normal mixture model (WW, CW, XC, AW), pp. 1–4.
ICPRICPR-2008-WangZ #collaboration #distributed
Collaborative learning by boosting in distributed environments (SW, CZ), pp. 1–4.
ICPRICPR-2008-WuF #3d #classification #multi #using
Multiple view based 3D object classification using ensemble learning of local subspaces (JW, KF), pp. 1–4.
ICPRICPR-2008-ZhaoGLJ #modelling
Spatio-temporal patches for night background modeling by subspace learning (YZ, HG, LL, YJ), pp. 1–4.
ICPRICPR-2008-Zhu #documentation #image
Augment document image binarization by learning (YZ), pp. 1–4.
ICPRICPR-2008-ZhuBQ #lazy evaluation
Bagging very weak learners with lazy local learning (XZ, CB, WQ), pp. 1–4.
KDDKDD-2008-ChakrabartiKSB #ranking
Structured learning for non-smooth ranking losses (SC, RK, US, CB), pp. 88–96.
KDDKDD-2008-ChengT
Semi-supervised learning with data calibration for long-term time series forecasting (HC, PNT), pp. 133–141.
KDDKDD-2008-ChenJCLWY #classification #kernel
Learning subspace kernels for classification (JC, SJ, BC, QL, MW, JY), pp. 106–114.
KDDKDD-2008-CuiDSAJ
Learning methods for lung tumor markerless gating in image-guided radiotherapy (YC, JGD, GCS, BMA, SBJ), pp. 902–910.
KDDKDD-2008-DavisD #metric #problem
Structured metric learning for high dimensional problems (JVD, ISD), pp. 195–203.
KDDKDD-2008-ElkanN #classification
Learning classifiers from only positive and unlabeled data (CE, KN), pp. 213–220.
KDDKDD-2008-LiFGMF #linear #named #parallel #performance
Cut-and-stitch: efficient parallel learning of linear dynamical systems on smps (LL, WF, FG, TCM, CF), pp. 471–479.
KDDKDD-2008-LingD #query
Active learning with direct query construction (CXL, JD), pp. 480–487.
KDDKDD-2008-LingDXYY
Spectral domain-transfer learning (XL, WD, GRX, QY, YY), pp. 488–496.
KDDKDD-2008-MadaniH #on the
On updates that constrain the features’ connections during learning (OM, JH), pp. 515–523.
KDDKDD-2008-SinghG #matrix #relational
Relational learning via collective matrix factorization (APS, GJG), pp. 650–658.
KDDKDD-2008-SunJY #classification #multi
Hypergraph spectral learning for multi-label classification (LS, SJ, JY), pp. 668–676.
KDDKDD-2008-WuLCC #symmetry
Asymmetric support vector machines: low false-positive learning under the user tolerance (SHW, KPL, CMC, MSC), pp. 749–757.
KDDKDD-2008-WuXC #clustering #incremental #named
SAIL: summation-based incremental learning for information-theoretic clustering (JW, HX, JC), pp. 740–748.
KDDKDD-2008-ZhangSPN #documentation #multi #topic #web
Learning from multi-topic web documents for contextual advertisement (YZ, ACS, JCP, MN), pp. 1051–1059.
RecSysRecSys-2008-DrachslerHK #navigation
Navigation support for learners in informal learning environments (HD, HGKH, RK), pp. 303–306.
SIGIRSIGIR-2008-AminiTG #algorithm #ranking
A boosting algorithm for learning bipartite ranking functions with partially labeled data (MRA, TVT, CG), pp. 99–106.
SIGIRSIGIR-2008-ChenJYW #clustering #debugging #information retrieval
Information retrieval on bug locations by learning co-located bug report clusters (IXC, HJ, CZY, PJW), pp. 801–802.
SIGIRSIGIR-2008-DruckMM #using
Learning from labeled features using generalized expectation criteria (GD, GSM, AM), pp. 595–602.
SIGIRSIGIR-2008-DuhK #rank
Learning to rank with partially-labeled data (KD, KK), pp. 251–258.
SIGIRSIGIR-2008-GuiverS #process #rank
Learning to rank with SoftRank and Gaussian processes (JG, ES), pp. 259–266.
SIGIRSIGIR-2008-HarpaleY #collaboration #personalisation
Personalized active learning for collaborative filtering (AH, YY), pp. 91–98.
SIGIRSIGIR-2008-LeeKJ #algorithm #constraints
Fixed-threshold SMO for Joint Constraint Learning Algorithm of Structural SVM (CL, HK, MGJ), pp. 829–830.
SIGIRSIGIR-2008-LiWA #graph #query
Learning query intent from regularized click graphs (XL, YYW, AA), pp. 339–346.
SIGIRSIGIR-2008-TsaiWC #case study #information retrieval #multi
A study of learning a merge model for multilingual information retrieval (MFT, YTW, HHC), pp. 195–202.
SIGIRSIGIR-2008-VelosoAGM #rank #using
Learning to rank at query-time using association rules (AV, HMdA, MAG, WMJ), pp. 267–274.
SIGIRSIGIR-2008-WangZZ #image #retrieval #semantic gap #web
Learning to reduce the semantic gap in web image retrieval and annotation (CW, LZ, HJZ), pp. 355–362.
SIGIRSIGIR-2008-XuLLLM #evaluation #metric #optimisation #rank
Directly optimizing evaluation measures in learning to rank (JX, TYL, ML, HL, WYM), pp. 107–114.
SIGIRSIGIR-2008-YuZXG #categorisation #design #using
trNon-greedy active learning for text categorization using convex ansductive experimental design (KY, SZ, WX, YG), pp. 635–642.
SIGIRSIGIR-2008-ZhangL #multi
Learning with support vector machines for query-by-multiple-examples (DZ, WSL), pp. 835–836.
SIGIRSIGIR-2008-ZhouXZY #rank
Learning to rank with ties (KZ, GRX, HZ, YY), pp. 275–282.
OOPSLAOOPSLA-2008-SimpkinsBIM #adaptation #programming language #towards
Towards adaptive programming: integrating reinforcement learning into a programming language (CS, SB, CLIJ, MM), pp. 603–614.
RERE-2008-JonesLML #requirements
Use and Influence of Creative Ideas and Requirements for a Work-Integrated Learning System (SJ, PL, NAMM, SNL), pp. 289–294.
RERE-2008-RegevGW #approach #education #requirements
Requirements Engineering Education in the 21st Century, An Experiential Learning Approach (GR, DCG, AW), pp. 85–94.
SACSAC-2008-CarvalhoAZ #health #process
Learning activities on health care supported by common sense knowledge (AFPdC, JCAS, SZM), pp. 1385–1389.
SACSAC-2008-CorreaLSM #composition #network
Neural network based systems for computer-aided musical composition: supervised x unsupervised learning (DCC, ALML, JHS, JFM), pp. 1738–1742.
SACSAC-2008-MartinsSBPS #information retrieval #ubiquitous
Context-aware information retrieval on a ubiquitous medical learning environment (DSM, LHZS, MB, AFdP, WLdS), pp. 2348–2349.
SACSAC-2008-StrapparavaM #identification
Learning to identify emotions in text (CS, RM), pp. 1556–1560.
SACSAC-2008-SungCM #clustering #concept #lifecycle #ontology #performance #using #web
Efficient concept clustering for ontology learning using an event life cycle on the web (SS, SC, DM), pp. 2310–2314.
ATEMATEM-J-2006-DubeyJA #context-free grammar #set
Learning context-free grammar rules from a set of program (AD, PJ, SKA), pp. 223–240.
ASPLOSASPLOS-2008-LuPSZ #concurrent #debugging
Learning from mistakes: a comprehensive study on real world concurrency bug characteristics (SL, SP, ES, YZ), pp. 329–339.
CASECASE-2008-StabelliniZ #approach #network #self
Interference aware self-organization for wireless sensor networks: A reinforcement learning approach (LS, JZ), pp. 560–565.
CASECASE-2008-WeiP #implementation #industrial
An implementation of iterative learning control in industrial production machines (DW, RP), pp. 472–477.
DACDAC-2008-BastaniKWC #predict #set
Speedpath prediction based on learning from a small set of examples (PB, KK, LCW, EC), pp. 217–222.
DACDAC-2008-CoskunRG #multi #online #using
Temperature management in multiprocessor SoCs using online learning (AKC, TSR, KCG), pp. 890–893.
PDPPDP-2008-GelgonN #distributed
Decentralized Learning of a Gaussian Mixture with Variational Bayes-based Aggregation (MG, AN), pp. 422–428.
STOCSTOC-2008-BlumLR #approach #database #privacy
A learning theory approach to non-interactive database privacy (AB, KL, AR), pp. 609–618.
STOCSTOC-2008-Feldman #algorithm
Evolvability from learning algorithms (VF), pp. 619–628.
STOCSTOC-2008-GopalanKK
Agnostically learning decision trees (PG, ATK, ARK), pp. 527–536.
STOCSTOC-2008-KalaiMV #on the
On agnostic boosting and parity learning (ATK, YM, EV), pp. 629–638.
STOCSTOC-2008-KhotS #on the
On hardness of learning intersection of two halfspaces (SK, RS), pp. 345–354.
ISSTAISSTA-2008-SankaranarayananCIG
Dynamic inference of likely data preconditions over predicates by tree learning (SS, SC, FI, AG), pp. 295–306.
ICSTSAT-2008-StachniakB #satisfiability
Speeding-Up Non-clausal Local Search for Propositional Satisfiability with Clause Learning (ZS, AB), pp. 257–270.
TPDLECDL-2007-Fitzgerald #development #education #library
Applications for Digital Libraries in Language Learning and the Professional Development of Teachers (AF), pp. 579–582.
HTHT-2007-BrownFB
Real users, real results: examining the limitations of learning styles within AEH (EJB, TF, TJB), pp. 57–66.
HTHT-2007-FigueiraL #interactive #using #visualisation
Interaction visualization in web-based learning using igraphs (ÁRF, JBL), pp. 45–46.
HTHT-2007-GodboleJMR #concept #interactive #towards
Toward interactive learning by concept ordering (SG, SJ, SM, GR), pp. 149–150.
HTHT-2007-LeblancA #using
Using forum in an organizational learning context (AL, MHA), pp. 41–42.
ICDARICDAR-2007-ChenLJ #pseudo #recognition
Learning Handwritten Digit Recognition by the Max-Min Posterior Pseudo-Probabilities Method (XC, XL, YJ), pp. 342–346.
ICDARICDAR-2007-Dengel #classification #documentation
Learning of Pattern-Based Rules for Document Classification (AD), pp. 123–127.
ICDARICDAR-2007-EspositoFMB #automation #documentation #first-order #incremental #logic #web
Incremental Learning of First Order Logic Theories for the Automatic Annotations of Web Documents (FE, SF, NDM, TMAB), pp. 1093–1097.
ICDARICDAR-2007-YeVRSL
Learning to Group Text Lines and Regions in Freeform Handwritten Notes (MY, PAV, SR, HS, CL), pp. 28–32.
JCDLJCDL-2007-KeMF #classification #collaboration #distributed #documentation
Collaborative classifier agents: studying the impact of learning in distributed document classification (WK, JM, YF), pp. 428–437.
JCDLJCDL-2007-MimnoM07a #library
Organizing the OCA: learning faceted subjects from a library of digital books (DMM, AM), pp. 376–385.
JCDLJCDL-2007-ReckerGWHMP #case study #how #online
A study of how online learning resource are used (MR, SG, AEW, SH, XM, BP), pp. 179–180.
JCDLJCDL-2007-ThengTLZGCCSYDLV #collaboration #empirical #mobile
Mobile G-Portal supporting collaborative sharing and learning in geography fieldwork: an empirical study (YLT, KLT, EPL, JZ, DHLG, KC, CHC, AS, HY, NHD, YL, MCV), pp. 462–471.
CSEETCSEET-2007-Armarego
Learning from Reflection: Practitioners as Adult Learners (JA), pp. 55–63.
CSEETCSEET-2007-KanerP #education #testing
Practice and Transfer of Learning in the Teaching of Software Testing (CK, SP), pp. 157–166.
CSEETCSEET-2007-KrogstieB #collaboration #re-engineering #student
Cross-Community Collaboration and Learning in Customer-Driven Software Engineering Student Projects (BRK, BB), pp. 336–343.
CSEETCSEET-2007-PortK #case study #experience #re-engineering
Laptop Enabled Active Learning in the Software Engineering Classroom: An Experience Report (DP, RK), pp. 262–274.
CSEETCSEET-2007-Staron #analysis #student #using
Using Students as Subjects in Experiments--A Quantitative Analysis of the Influence of Experimentation on Students’ Learning Proces (MS), pp. 221–228.
ITiCSEITiCSE-2007-AlstesL #named #network #online #programming
VERKKOKE: learning routing and network programming online (AA, JL), pp. 91–95.
ITiCSEITiCSE-2007-Arnold #interactive #logic
Introducing propositional logic and queueing theory with the infotraffic interactive learning environments (RA), p. 356.
ITiCSEITiCSE-2007-BagleyC #collaboration #java #programming
Collaboration and the importance for novices in learning java computer programming (CAB, CCC), pp. 211–215.
ITiCSEITiCSE-2007-BarnesRPCG #game studies #named
Game2Learn: building CS1 learning games for retention (TB, HR, EP, AC, AG), pp. 121–125.
ITiCSEITiCSE-2007-CassenSALN #generative #interactive #visual notation
A visual learning engine for interactive generation ofinstructional materials (TC, KRS, JA, DL, AN), p. 319.
ITiCSEITiCSE-2007-CukiermanT
Learning strategies sessions within the classroom in computing science university courses (DC, DMT), p. 341.
ITiCSEITiCSE-2007-GalpinSC #student
Learning styles and personality types of computer science students at a South African university (VCG, IDS, PyC), pp. 201–205.
ITiCSEITiCSE-2007-HonigP #experience #outsourcing #re-engineering
A classroom outsourcing experience for software engineering learning (WLH, TP), pp. 181–185.
ITiCSEITiCSE-2007-KorteAPG #approach #education #novel
Learning by game-building: a novel approach to theoretical computer science education (LK, SA, HP, JG), pp. 53–57.
ITiCSEITiCSE-2007-LeidlR #how #question
How will future learning work in the third dimension? (ML, GR), p. 329.
ITiCSEITiCSE-2007-OliverGMA #using
Using disruptive technology for explorative learning (IO, KG, AM, CA), pp. 96–100.
ITiCSEITiCSE-2007-Sanchez-TorrubiaTC #algorithm #graph #interactive #tool support
New interactive tools for graph algorithms active learning (MGST, CTB, JC), p. 337.
SIGITESIGITE-2007-ChanFL #collaboration
Facilitating cross-cultural learning through collaborative skypecasting (AC, MF, MJWL), pp. 59–66.
SIGITESIGITE-2007-Frye #education #network
Wireless sensor networks: learning and teaching (LMF), pp. 269–270.
SIGITESIGITE-2007-Krichen #education #online
Investigating learning styles in the online educational environment (JPK), pp. 127–134.
SIGITESIGITE-2007-LeungC #information management
Knowledge management system for electronic learning of IT skills (CHL, YYC), pp. 53–58.
SIGITESIGITE-2007-MiertschinW #concept #using
Using concept maps to navigate complex learning environments (SLM, CLW), pp. 175–184.
SIGITESIGITE-2007-RutherfoordR #design #how
Universal instructional design for learning how to apply in a virtual world (RHR, JKR), pp. 141–146.
SIGITESIGITE-2007-SabinH #education #online
Teaching and learning in live online classrooms (MS, BH), pp. 41–48.
SIGITESIGITE-2007-WebsterM #experience #student
Student reflections on an academic service learning experience in a computer science classroom (LDW, EJM), pp. 207–212.
ICSMEICSM-2007-CorboGP #source code
Smart Formatter: Learning Coding Style from Existing Source Code (FC, CDG, MDP), pp. 525–526.
IFMIFM-2007-OostdijkRTVW #encryption #protocol #testing #verification
Integrating Verification, Testing, and Learning for Cryptographic Protocols (MO, VR, JT, RGdV, TACW), pp. 538–557.
AIIDEAIIDE-2007-ZhangN #sequence
Learning a Table Soccer Robot a New Action Sequence by Observing and Imitating (DZ0, BN), pp. 61–67.
CoGCIG-2007-AkatsukaS #classification #game studies #video
Reward Allotment Considered Roles for Learning Classifier System For Soccer Video Games (YA, YS), pp. 288–295.
CoGCIG-2007-KimCC #evolution #hybrid
Hybrid of Evolution and Reinforcement Learning for Othello Players (KJK, HC, SBC), pp. 203–209.
CoGCIG-2007-KnittelBS #concept
Concept Accessibility as Basis for Evolutionary Reinforcement Learning of Dots and Boxes (AK, TB, AS), pp. 140–145.
CoGCIG-2007-LucasT #difference #evolution
Point-to-Point Car Racing: an Initial Study of Evolution Versus Temporal Difference Learning (SML, JT), pp. 260–267.
CoGCIG-2007-Manning #difference #evaluation #network
Temporal Difference Learning of an Othello Evaluation Function for a Small Neural Network with Shared Weights (EPM), pp. 216–223.
CoGCIG-2007-Mayer #difference
Board Representations for Neural Go Players Learning by Temporal Difference (HAM), pp. 183–188.
CoGCIG-2007-NaveedCH #game studies #hybrid
Hybrid Evolutionary Learning Approaches for The Virus Game (MHN, PIC, MAH), pp. 196–202.
CoGCIG-2007-QuekG #adaptation #evolution
Adaptation of Iterated Prisoner's Dilemma Strategies by Evolution and Learning (HQ, CKG), pp. 40–47.
CoGCIG-2007-RiedmillerG #case study #experience #game studies #on the
On Experiences in a Complex and Competitive Gaming Domain: Reinforcement Learning Meets RoboCup (MAR, TG), pp. 17–23.
CoGCIG-2007-RunarssonJ #evaluation #using
Effect of look-ahead search depth in learning position evaluation functions for Othello using -greedy exploration (TPR, EOJ), pp. 210–215.
CoGCIG-2007-WittkampBW #comparison #game studies #programming #search-based
A Comparison of Genetic Programming and Look-up Table Learning for the Game of Spoof (MW, LB, RLW), pp. 63–71.
DiGRADiGRA-2007-KirjavainenNK #development #game studies
Team Structure in the Development of Game-based Learning Environments (AK, TN, MK).
DiGRADiGRA-2007-Magnussen #education #game studies
Teacher roles in learning games - When games become situated in schools (RM).
DiGRADiGRA-2007-PepplerK #education #game studies #what
What Videogame Making Can Teach Us About Literacy and Learning: Alternative Pathways into Participatory Culture (KAP, YBK).
DiGRADiGRA-2007-SorensenM #education #game studies #perspective
Serious Games in language learning and teaching - a theoretical perspective (BHS, BM).
CHICHI-2007-CockburnKAZ #interface
Hard lessons: effort-inducing interfaces benefit spatial learning (AC, POK, JA, SZ), pp. 1571–1580.
CHICHI-2007-GrossmanDB #online
Strategies for accelerating on-line learning of hotkeys (TG, PD, RB), pp. 1591–1600.
CHICHI-2007-KamRDTC #design #framework #locality
Localized iterative design for language learning in underdeveloped regions: the PACE framework (MK, DR, VD, AT, JFC), pp. 1097–1106.
CHICHI-2007-ZimmermanTSHMCM #approach #automation #named
Vio: a mixed-initiative approach to learning and automating procedural update tasks (JZ, AT, IS, IH, KM, JC, RMM), pp. 1445–1454.
HCIHCI-AS-2007-CarusiM #education #interactive #process
An Essay About the Relevance of Educational Interactive Systems in the Learning Process (AC, CRM), pp. 183–189.
HCIHCI-AS-2007-ChoK #collaboration #contest
Suppressing Competition in a Computer-Supported Collaborative Learning System (KC, BK), pp. 208–214.
HCIHCI-AS-2007-KimJCHH
The Effect of Tangible Pedagogical Agents on Children’s Interest and Learning (JhK, DhJ, HSC, JYH, KHH), pp. 270–277.
HCIHCI-AS-2007-LiuKL #approach
Breaking the Traditional E-Learning Mould: Support for the Learning Preference Approach (FL, JK, LL), pp. 294–301.
HCIHCI-AS-2007-LuYTHY #difference #named
KaLeSy-CJ: Kanji Learning System Focusing on Differences Between Chinese and Japanese (SL, NY, HT, TH, TY), pp. 302–311.
HCIHCI-AS-2007-SaC07a #detection #ubiquitous
Detecting Learning Difficulties on Ubiquitous Scenarios (MdS, LC), pp. 235–244.
HCIHCI-AS-2007-SanchezSS #game studies #mobile
Mobile Game-Based Methodology for Science Learning (JS, AS, MS), pp. 322–331.
HCIHCI-AS-2007-ShenHB #collaboration #comparison #online
Group Collaboration and Learning Through Online Assessments: Comparison of Collaborative and Participatory Online Exams (JS, SRH, MB), pp. 332–340.
HCIHCI-AS-2007-ThengW #usability
Perceived Usefulness and Usability of Weblogs for Collaborating Learning (YLT, ELYW), pp. 361–370.
HCIHCI-AS-2007-XiaoCR #authentication #collaboration #process
Support Case-Based Authentic Learning Activities: A Collaborative Case Commenting Tool and a Collaborative Case Builder (LX, JMC, MBR), pp. 371–380.
HCIHCI-AS-2007-YuC #collaboration #process
Creating Computer Supported Collaborative Learning Activities with IMS LD (DY, XC), pp. 391–400.
HCIHCI-MIE-2007-FabriEM
Emotionally Expressive Avatars for Chatting, Learning and Therapeutic Intervention (MF, SYAE, DJM), pp. 275–285.
HCIHCI-MIE-2007-SerbanTM #behaviour #interface #predict
A Learning Interface Agent for User Behavior Prediction (GS, AT, GSM), pp. 508–517.
HCIHIMI-IIE-2007-AlsharaI #integration #using
Business Integration Using the Interdisciplinary Project Based Learning Model (IPBL) (OKA, MI), pp. 823–833.
HCIHIMI-IIE-2007-BaeckerBCLRMWW #distributed #interactive #realtime
Webcasting Made Interactive: Integrating Real-Time Videoconferencing in Distributed Learning Spaces (RB, JPB, RC, SL, KR, CM, AW, PW), pp. 269–278.
HCIHIMI-IIE-2007-BaeckerFBCC #chat #interactive #persistent
Webcasting Made Interactive: Persistent Chat for Text Dialogue During and About Learning Events (RB, DF, LB, CC, DC), pp. 260–268.
HCIHIMI-IIE-2007-IbrahimA #interactive
Impact of Interactive Learning on Knowledge Retention (MI, OAS), pp. 347–355.
HCIHIMI-IIE-2007-JeongL #interactive #ubiquitous
Context Aware Human Computer Interaction for Ubiquitous Learning (CJ, EL), pp. 364–373.
HCIHIMI-IIE-2007-TsengLH #mobile
A Mobile Environment for Chinese Language Learning (CCT, CHL, WLH), pp. 485–489.
HCIOCSC-2007-ChenY07a #collaboration #design #difference #industrial
The Differences Between the Influences of Synchronous and Asynchronous Modes on Collaborative Learning Project of Industrial Design (WC, MY), pp. 275–283.
HCIOCSC-2007-ChoC #collaboration #self
Self-Awareness in a Computer Supported Collaborative Learning Environment (KC, MHC), pp. 284–291.
ICEISICEIS-AIDSS-2007-PessiotTUAG #collaboration #rank
Learning to Rank for Collaborative Filtering (JFP, TVT, NU, MRA, PG), pp. 145–151.
ICEISICEIS-AIDSS-2007-RamabadranG #approach #flexibility
Intelligent E-Learning Systems — An Intelligent Approach to Flexible Learning Methodologies (SR, VG), pp. 107–112.
ICEISICEIS-AIDSS-2007-YingboJJ #predict #process #using #workflow
Using Decision Tree Learning to Predict Workflow Activity Time Consumption (YL, JW, JS), pp. 69–75.
ICEISICEIS-EIS-2007-Rodriguez #collaboration #coordination #education #modelling #process
A Modeling Language for Collaborative Learning Educational Units — Supporting the Coordination of Collaborative Activities (MCR), pp. 334–339.
ICEISICEIS-J-2007-LuciaFPT07a #collaboration #distributed
A Service Oriented Collaborative Distributed Learning Object Management System (ADL, RF, IP, GT), pp. 341–354.
ICEISICEIS-SAIC-2007-LuciaFPT #collaboration #distributed #named
CD-LOMAS: A Collaborative Distributed Learning Object Management System (ADL, RF, IP, GT), pp. 34–44.
ICEISICEIS-SAIC-2007-MorgadoRP #evaluation
Key Issues for Learning Objects Evaluation (EMM, ÁBR, FJGP), pp. 149–154.
CIKMCIKM-2007-ErtekinHBG #classification
Learning on the border: active learning in imbalanced data classification (SE, JH, LB, CLG), pp. 127–136.
CIKMCIKM-2007-LiuTZ #network
Ensembling Bayesian network structure learning on limited data (FL, FT, QZ), pp. 927–930.
CIKMCIKM-2007-OuyangLL #summary #topic
Developing learning strategies for topic-based summarization (OY, SL, WL), pp. 79–86.
CIKMCIKM-2007-Pereira
Learning to join everything (FP0), pp. 9–10.
CIKMCIKM-2007-SongZYZD #distance #estimation #metric #ranking
Ranking with semi-supervised distance metric learning and its application to housing potential estimation (YS, BZ, WJY, CZ, JD), pp. 975–978.
CIKMCIKM-2007-WangJZZ #summary #web
Learning query-biased web page summarization (CW, FJ, LZ, HJZ), pp. 555–562.
ECIRECIR-2007-DavyL #categorisation #query
Active Learning with History-Based Query Selection for Text Categorisation (MD, SL), pp. 695–698.
ECIRECIR-2007-Gori
Learning in Hyperlinked Environments (MG), p. 3.
ECIRECIR-2007-Monz #query
Model Tree Learning for Query Term Weighting in Question Answering (CM), pp. 589–596.
ECIRECIR-2007-XuAZ #feedback
Incorporating Diversity and Density in Active Learning for Relevance Feedback (ZX, RA, YZ), pp. 246–257.
ECIRECIR-2007-YeungBCK #approach #documentation
A Bayesian Approach for Learning Document Type Relevance (PCKY, SB, CLAC, MK), pp. 753–756.
ICMLICML-2007-AgarwalC #graph #random #rank
Learning random walks to rank nodes in graphs (AA, SC), pp. 9–16.
ICMLICML-2007-AndoZ #generative
Two-view feature generation model for semi-supervised learning (RKA, TZ), pp. 25–32.
ICMLICML-2007-Azran #algorithm #markov #multi #random
The rendezvous algorithm: multiclass semi-supervised learning with Markov random walks (AA), pp. 49–56.
ICMLICML-2007-Bar-HillelW #distance #similarity
Learning distance function by coding similarity (ABH, DW), pp. 65–72.
ICMLICML-2007-BickelBS
Discriminative learning for differing training and test distributions (SB, MB, TS), pp. 81–88.
ICMLICML-2007-BunescuM #multi
Multiple instance learning for sparse positive bags (RCB, RJM), pp. 105–112.
ICMLICML-2007-CaoQLTL #approach #rank
Learning to rank: from pairwise approach to listwise approach (ZC, TQ, TYL, MFT, HL), pp. 129–136.
ICMLICML-2007-ChengV #image
Learning to compress images and videos (LC, SVNV), pp. 161–168.
ICMLICML-2007-DaiYXY
Boosting for transfer learning (WD, QY, GRX, YY), pp. 193–200.
ICMLICML-2007-DavisKJSD #metric
Information-theoretic metric learning (JVD, BK, PJ, SS, ISD), pp. 209–216.
ICMLICML-2007-DollarRB #algorithm #analysis
Non-isometric manifold learning: analysis and an algorithm (PD, VR, SJB), pp. 241–248.
ICMLICML-2007-Hanneke #bound #complexity
A bound on the label complexity of agnostic active learning (SH), pp. 353–360.
ICMLICML-2007-HoiJL #constraints #kernel #matrix #parametricity
Learning nonparametric kernel matrices from pairwise constraints (SCHH, RJ, MRL), pp. 361–368.
ICMLICML-2007-HulseKN
Experimental perspectives on learning from imbalanced data (JVH, TMK, AN), pp. 935–942.
ICMLICML-2007-Jaeger #network #parametricity #relational
Parameter learning for relational Bayesian networks (MJ), pp. 369–376.
ICMLICML-2007-KimP #recursion
A recursive method for discriminative mixture learning (MK, VP), pp. 409–416.
ICMLICML-2007-KrauseG #approach #process
Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach (AK, CG), pp. 449–456.
ICMLICML-2007-KropotovV #on the
On one method of non-diagonal regularization in sparse Bayesian learning (DK, DV), pp. 457–464.
ICMLICML-2007-LeeCVK #multi
Learning a meta-level prior for feature relevance from multiple related tasks (SIL, VC, DV, DK), pp. 489–496.
ICMLICML-2007-LiLL #scalability
Large-scale RLSC learning without agony (WL, KHL, KSL), pp. 529–536.
ICMLICML-2007-LiYW #distance #framework #metric #reduction
A transductive framework of distance metric learning by spectral dimensionality reduction (FL, JY, JW), pp. 513–520.
ICMLICML-2007-Mahadevan #3d #adaptation #multi #using
Adaptive mesh compression in 3D computer graphics using multiscale manifold learning (SM), pp. 585–592.
ICMLICML-2007-MannM #robust #scalability
Simple, robust, scalable semi-supervised learning via expectation regularization (GSM, AM), pp. 593–600.
ICMLICML-2007-MihalkovaM #bottom-up #logic #markov #network
Bottom-up learning of Markov logic network structure (LM, RJM), pp. 625–632.
ICMLICML-2007-MoschittiZ #effectiveness #kernel #performance #relational
Fast and effective kernels for relational learning from texts (AM, FMZ), pp. 649–656.
ICMLICML-2007-NiCD #multi #process
Multi-task learning for sequential data via iHMMs and the nested Dirichlet process (KN, LC, DBD), pp. 689–696.
ICMLICML-2007-OsentoskiM
Learning state-action basis functions for hierarchical MDPs (SO, SM), pp. 705–712.
ICMLICML-2007-ParkerFT #performance #query #retrieval
Learning for efficient retrieval of structured data with noisy queries (CP, AF, PT), pp. 729–736.
ICMLICML-2007-PetersS
Reinforcement learning by reward-weighted regression for operational space control (JP, SS), pp. 745–750.
ICMLICML-2007-PhuaF #approximate #linear
Tracking value function dynamics to improve reinforcement learning with piecewise linear function approximation (CWP, RF), pp. 751–758.
ICMLICML-2007-RainaBLPN #self
Self-taught learning: transfer learning from unlabeled data (RR, AB, HL, BP, AYN), pp. 759–766.
ICMLICML-2007-RakotomamonjyBCG #kernel #multi #performance
More efficiency in multiple kernel learning (AR, FRB, SC, YG), pp. 775–782.
ICMLICML-2007-SternHG #game studies
Learning to solve game trees (DHS, RH, TG), pp. 839–846.
ICMLICML-2007-SunJSF #algorithm #kernel
A kernel-based causal learning algorithm (XS, DJ, BS, KF), pp. 855–862.
ICMLICML-2007-TaylorS
Cross-domain transfer for reinforcement learning (MET, PS), pp. 879–886.
ICMLICML-2007-WachmanK #kernel #order
Learning from interpretations: a rooted kernel for ordered hypergraphs (GW, RK), pp. 943–950.
ICMLICML-2007-WangYF #difference #on the
On learning with dissimilarity functions (LW, CY, JF), pp. 991–998.
ICMLICML-2007-WangZQ #metric #towards
Dirichlet aggregation: unsupervised learning towards an optimal metric for proportional data (HYW, HZ, HQ), pp. 959–966.
ICMLICML-2007-WilsonFRT #approach #multi
Multi-task reinforcement learning: a hierarchical Bayesian approach (AW, AF, SR, PT), pp. 1015–1022.
ICMLICML-2007-WoznicaKH
Learning to combine distances for complex representations (AW, AK, MH), pp. 1031–1038.
ICMLICML-2007-WuYYS
Local learning projections (MW, KY, SY, BS), pp. 1039–1046.
ICMLICML-2007-XueDC #flexibility #matrix #multi #process
The matrix stick-breaking process for flexible multi-task learning (YX, DBD, LC), pp. 1063–1070.
ICMLICML-2007-XuF #linear #on the #ranking
On learning linear ranking functions for beam search (YX, AF), pp. 1047–1054.
ICMLICML-2007-YeCJ #kernel #parametricity #programming
Discriminant kernel and regularization parameter learning via semidefinite programming (JY, JC, SJ), pp. 1095–1102.
ICMLICML-2007-YuTY #multi #robust
Robust multi-task learning with t-processes (SY, VT, KY), pp. 1103–1110.
ICMLICML-2007-ZhangAV #multi #random
Conditional random fields for multi-agent reinforcement learning (XZ, DA, SVNV), pp. 1143–1150.
ICMLICML-2007-ZhaoL #feature model
Spectral feature selection for supervised and unsupervised learning (ZZ, HL), pp. 1151–1157.
ICMLICML-2007-ZhouB #clustering #multi
Spectral clustering and transductive learning with multiple views (DZ, CJCB), pp. 1159–1166.
ICMLICML-2007-ZhouX #multi #on the
On the relation between multi-instance learning and semi-supervised learning (ZHZ, JMX), pp. 1167–1174.
ICMLICML-2007-ZienO #kernel #multi
Multiclass multiple kernel learning (AZ, CSO), pp. 1191–1198.
KDDKDD-2007-ChenZYL #adaptation #clustering #distance #metric
Nonlinear adaptive distance metric learning for clustering (JC, ZZ, JY, HL), pp. 123–132.
KDDKDD-2007-DeodharG #clustering #framework
A framework for simultaneous co-clustering and learning from complex data (MD, JG), pp. 250–259.
KDDKDD-2007-DingSJL #framework #kernel #recommendation #using
A learning framework using Green’s function and kernel regularization with application to recommender system (CHQD, RJ, TL, HDS), pp. 260–269.
KDDKDD-2007-GuoZXF #data mining #database #mining #multimodal
Enhanced max margin learning on multimodal data mining in a multimedia database (ZG, ZZ, EPX, CF), pp. 340–349.
KDDKDD-2007-Parthasarathy #data mining #mining
Data mining at the crossroads: successes, failures and learning from them (SP), pp. 1053–1055.
KDDKDD-2007-RadlinskiJ #ranking
Active exploration for learning rankings from clickthrough data (FR, TJ), pp. 570–579.
KDDKDD-2007-Schickel-ZuberF #clustering #recommendation #using
Using hierarchical clustering for learning theontologies used in recommendation systems (VSZ, BF), pp. 599–608.
KDDKDD-2007-Sculley #feedback
Practical learning from one-sided feedback (DS), pp. 609–618.
KDDKDD-2007-ShengL
Partial example acquisition in cost-sensitive learning (VSS, CXL), pp. 638–646.
KDDKDD-2007-YeJC #analysis #kernel #matrix #polynomial #programming
Learning the kernel matrix in discriminant analysis via quadratically constrained quadratic programming (JY, SJ, JC), pp. 854–863.
MLDMMLDM-2007-CeciABM #relational
Transductive Learning from Relational Data (MC, AA, NB, DM), pp. 324–338.
MLDMMLDM-2007-EkdahlK #classification #on the
On Concentration of Discrete Distributions with Applications to Supervised Learning of Classifiers (ME, TK), pp. 2–16.
MLDMMLDM-2007-GomezF #2d #algorithm #evolution #hybrid #image
A Hybrid Algorithm Based on Evolution Strategies and Instance-Based Learning, Used in Two-Dimensional Fitting of Brightness Profiles in Galaxy Images (JCG, OF), pp. 716–726.
MLDMMLDM-2007-JiangI
Dynamic Distance-Based Active Learning with SVM (JJ, HHSI), pp. 296–309.
MLDMMLDM-2007-Kertesz-FarkasKP #classification #equivalence
Equivalence Learning in Protein Classification (AKF, AK, SP), pp. 824–837.
MLDMMLDM-2007-Lehmann #hybrid #ontology
Hybrid Learning of Ontology Classes (JL), pp. 883–898.
MLDMMLDM-2007-VanderlooyMS #empirical #evaluation
Off-Line Learning with Transductive Confidence Machines: An Empirical Evaluation (SV, LvdM, IGSK), pp. 310–323.
RecSysRecSys-2007-RubensS #collaboration
Influence-based collaborative active learning (NR, MS), pp. 145–148.
RecSysRecSys-2007-TaghipourKG #approach #recommendation #web
Usage-based web recommendations: a reinforcement learning approach (NT, AAK, SSG), pp. 113–120.
RecSysRecSys-2007-TiemannP #hybrid #music #recommendation #towards
Towards ensemble learning for hybrid music recommendation (MT, SP), pp. 177–178.
SEKESEKE-2007-FarEHA #approach #concept #named #ontology #statistics
Adjudicator: A Statistical Approach for Learning Ontology Concepts from Peer Agents (BHF, AHE, NH, MA), p. 654–?.
SEKESEKE-2007-FollecoKHS #quality
Learning from Software Quality Data with Class Imbalance and Noise (AF, TMK, JVH, CS), p. 487–?.
SIGIRSIGIR-2007-EfthimiadisF #education #information retrieval #named
IR-Toolbox: an experiential learning tool for teaching IR (ENE, NGF), p. 914.
SIGIRSIGIR-2007-ErtekinHG #problem
Active learning for class imbalance problem (SE, JH, CLG), pp. 823–824.
SIGIRSIGIR-2007-JansenSB #online #paradigm
Viewing online searching within a learning paradigm (BJJ, BKS, DLB), pp. 859–860.
SIGIRSIGIR-2007-VelipasaogluSP #constraints
Improving active learning recall via disjunctive boolean constraints (EV, HS, JOP), pp. 893–894.
SIGIRSIGIR-2007-XuL07a #rank
Learning to rank collections (JX, XL), pp. 765–766.
SIGIRSIGIR-2007-ZhangHRJ #query #using
Query rewriting using active learning for sponsored search (WVZ, XH, BR, RJ), pp. 853–854.
SIGIRSIGIR-2007-ZhengCSZ #framework #ranking #using
A regression framework for learning ranking functions using relative relevance judgments (ZZ, KC, GS, HZ), pp. 287–294.
ICSEICSE-2007-Staron #education #evaluation #process #re-engineering #student #using
Using Experiments in Software Engineering as an Auxiliary Tool for Teaching — A Qualitative Evaluation from the Perspective of Students’ Learning Process (MS), pp. 673–676.
ICSEICSE-2007-Zualkernan #programming #using
Using Soloman-Felder Learning Style Index to Evaluate Pedagogical Resources for Introductory Programming Classes (IAZ), pp. 723–726.
SACSAC-2007-BarratT #recognition
A progressive learning method for symbols recognition (SB, ST), pp. 627–631.
SACSAC-2007-RulloCP #categorisation
Learning rules with negation for text categorization (PR, CC, VLP), pp. 409–416.
DATEDATE-2007-Huang
Dynamic learning based scan chain diagnosis (YH0), pp. 510–515.
PPoPPPPoPP-2007-LeeBSSSM #modelling #parallel #performance
Methods of inference and learning for performance modeling of parallel applications (BCL, DMB, BRdS, MS, KS, SAM), pp. 249–258.
STOCSTOC-2007-GuhaM #algorithm #approximate #problem
Approximation algorithms for budgeted learning problems (SG, KM), pp. 104–113.
TACASTACAS-2007-BolligKKL #design #game studies #modelling #synthesis
Replaying Play In and Play Out: Synthesis of Design Models from Scenarios by Learning (BB, JPK, CK, ML), pp. 435–450.
CAVCAV-2007-SinhaC #composition #lazy evaluation #satisfiability #using #verification
SAT-Based Compositional Verification Using Lazy Learning (NS, EMC), pp. 39–54.
ICSTSAT-2007-ArgelichM #satisfiability
Partial Max-SAT Solvers with Clause Learning (JA, FM), pp. 28–40.
FATESTestCom-FATES-2007-ShahbazLG #component #integration #testing
Learning and Integration of Parameterized Components Through Testing (MS, KL, RG), pp. 319–334.
VMCAIVMCAI-2007-Madhusudan #algorithm #verification
Learning Algorithms and Formal Verification (PM), p. 214.
DocEngDocEng-2006-ChidlovskiiFL #documentation #interface #named
ALDAI: active learning documents annotation interface (BC, JF, LL), pp. 184–185.
DocEngDocEng-2006-LecerfC #documentation
Document annotation by active learning techniques (LL, BC), pp. 125–127.
TPDLECDL-2006-LeeTGF #approach #design
An Exploratory Factor Analytic Approach to Understand Design Features for Academic Learning Environments (SSL, YLT, DHLG, SSBF), pp. 315–328.
TPDLECDL-2006-WuW #library #towards
Towards a Digital Library for Language Learning (SW, IHW), pp. 341–352.
JCDLJCDL-2006-CarvalhoGLS
Learning to deduplicate (MGdC, MAG, AHFL, ASdS), pp. 41–50.
JCDLJCDL-2006-CouncillLZDBLSG #metadata #online
Learning metadata from the evidence in an on-line citation matching scheme (IGC, HL, ZZ, SD, LB, WCL, AS, CLG), pp. 276–285.
JCDLJCDL-2006-MoenMEPS #analysis #metadata
Learning from artifacts: metadata utilization analysis (WEM, SDM, AE, SP, GS), pp. 270–271.
JCDLJCDL-2006-NicholsBDT #library
Learning by building digital libraries (DMN, DB, JSD, MBT), pp. 185–186.
VLDBVLDB-2006-ShivamBC #cost analysis #modelling #optimisation
Active and Accelerated Learning of Cost Models for Optimizing Scientific Applications (PS, SB, JSC), pp. 535–546.
CSEETCSEET-2006-WangS #re-engineering
Writing as a Tool for Learning Software Engineering (AIW, CFS), pp. 35–42.
ITiCSEITiCSE-2006-AmzadO #modelling
Model based project centered team learning (IA, AJO), p. 328.
ITiCSEITiCSE-2006-BirdC #problem
Building a search engine to drive problem-based learning (SB, JRC), pp. 153–157.
ITiCSEITiCSE-2006-Ellis06a #approach #named #self
Self-grading: an approach to supporting self-directed learning (HJCE), p. 349.
ITiCSEITiCSE-2006-GriswoldS #performance #scalability #ubiquitous
Ubiquitous presenter: fast, scalable active learning for the whole classroom (WGG, BS), p. 358.
ITiCSEITiCSE-2006-HielscherW #automaton #education #formal method #named
AtoCC: learning environment for teaching theory of automata and formal languages (MH, CW), p. 306.
ITiCSEITiCSE-2006-HughesP #object-oriented #programming #student
ASSISTing CS1 students to learn: learning approaches and object-oriented programming (JH, DRP), pp. 275–279.
ITiCSEITiCSE-2006-KeenanPCM #agile
Learning project planning the agile way (FK, SP, GC, KM), p. 324.
ITiCSEITiCSE-2006-OKellyG #approach #education #problem #programming
RoboCode & problem-based learning: a non-prescriptive approach to teaching programming (JO, JPG), pp. 217–221.
ITiCSEITiCSE-2006-PlimmerA #education #human-computer
Peer teaching extends HCI learning (BP, RA), pp. 53–57.
ITiCSEITiCSE-2006-Quade #hybrid #re-engineering
Developing a hybrid software engineering curse that promotes project-based active learning (AMQ), p. 308.
ITiCSEITiCSE-2006-Rodger #automaton #formal method
Learning automata and formal languages interactively with JFLAP (SHR), p. 360.
SIGITESIGITE-2006-Gutierrez #approach #named #security
Stingray: a hands-on approach to learning information security (FG), pp. 53–58.
AIIDEAIIDE-2006-WhiteB #game studies #multi
The Self Organization of Context for Learning in MultiAgent Games (CDW, DB), pp. 92–97.
CoGCIG-2006-BouzyC #monte carlo
Monte-Carlo Go Reinforcement Learning Experiments (BB, GC), pp. 187–194.
CoGCIG-2006-KarpovDVSM #evaluation #game studies #integration
Integration and Evaluation of Exploration-Based Learning in Games (IK, TD, CV, KOS, RM), pp. 39–44.
CoGCIG-2006-LucasR #co-evolution #difference #evaluation
Temporal Difference Learning Versus Co-Evolution for Acquiring Othello Position Evaluation (SML, TPR), pp. 52–59.
CoGCIG-2006-PietroBW #adaptation #comparison #game studies #modelling
A Comparison of Different Adaptive Learning Techniques for Opponent Modelling in the Game of Guess It (ADP, LB, RLW), pp. 173–180.
CHICHI-2006-GweonRCZ #adaptation #collaboration #online
Providing support for adaptive scripting in an on-line collaborative learning environment (GG, CPR, RC, ZZ), pp. 251–260.
CHICHI-2006-Moher #distributed #embedded #simulation
Embedded phenomena: supporting science learning with classroom-sized distributed simulations (TM), pp. 691–700.
CHICHI-2006-SiekCR #how #people
Pride and prejudice: learning how chronically ill people think about food (KAS, KHC, YR), pp. 947–950.
CSCWCSCW-2006-Danis #collaboration #performance
Forms of collaboration in high performance computing: exploring implications for learning (CD), pp. 501–504.
CSCWCSCW-2006-RazaviI #behaviour #information management
A grounded theory of information sharing behavior in a personal learning space (MNR, LI), pp. 459–468.
ICEISICEIS-HCI-2006-Patokorpi
Constructivist Instructional Principles, Learner Psychology and Technological Enablers of Learning (EP), pp. 103–109.
ICEISICEIS-SAIC-2006-LuciaFGPT #legacy #migration #multi #video
Migrating Legacy Video Lectures to Multimedia Learning Objects (ADL, RF, MG, IP, GT), pp. 51–58.
ICEISICEIS-SAIC-2006-MarjanovicSMRG #approach #collaboration #process
Supporting Complex Collaborative Learning Activities — The Libresource Approach (OM, HSM, PM, FAR, CG), pp. 59–65.
ICEISICEIS-SAIC-2006-OliveiraGSBC #adaptation #automation #framework #multi
A Multi-Agent Based Framework for Supporting Learning in Adaptive Automated Negotiation (RSdO, HG, AS, IIB, EdBC), pp. 153–158.
CIKMCIKM-2006-ZhaZFS #difference #query #retrieval #web
Incorporating query difference for learning retrieval functions in world wide web search (HZ, ZZ, HF, GS), pp. 307–316.
ECIRECIR-2006-VildjiounaiteK
Learning Links Between a User’s Calendar and Information Needs (EV, VK), pp. 557–560.
ICMLICML-2006-AbbeelQN #modelling #using
Using inaccurate models in reinforcement learning (PA, MQ, AYN), pp. 1–8.
ICMLICML-2006-AgarwalBB #graph #higher-order
Higher order learning with graphs (SA, KB, SB), pp. 17–24.
ICMLICML-2006-AsgharbeygiSL #difference #relational
Relational temporal difference learning (NA, DJS, PL), pp. 49–56.
ICMLICML-2006-BalcanB #formal method #on the #similarity
On a theory of learning with similarity functions (MFB, AB), pp. 73–80.
ICMLICML-2006-BalcanBL
Agnostic active learning (MFB, AB, JL), pp. 65–72.
ICMLICML-2006-BowlingMJNW #policy #predict #using
Learning predictive state representations using non-blind policies (MHB, PM, MJ, JN, DFW), pp. 129–136.
ICMLICML-2006-BrefeldS
Semi-supervised learning for structured output variables (UB, TS), pp. 145–152.
ICMLICML-2006-CaruanaN #algorithm #comparison #empirical
An empirical comparison of supervised learning algorithms (RC, ANM), pp. 161–168.
ICMLICML-2006-CheungK #framework #multi
A regularization framework for multiple-instance learning (PMC, JTK), pp. 193–200.
ICMLICML-2006-ConitzerG #algorithm #online #problem
Learning algorithms for online principal-agent problems (and selling goods online) (VC, NG), pp. 209–216.
ICMLICML-2006-DegrisSW #markov #problem #process
Learning the structure of Factored Markov Decision Processes in reinforcement learning problems (TD, OS, PHW), pp. 257–264.
ICMLICML-2006-DenisMR #classification #naive bayes #performance
Efficient learning of Naive Bayes classifiers under class-conditional classification noise (FD, CNM, LR), pp. 265–272.
ICMLICML-2006-desJardinsEW #set
Learning user preferences for sets of objects (Md, EE, KW), pp. 273–280.
ICMLICML-2006-EpshteynD
Qualitative reinforcement learning (AE, GD), pp. 305–312.
ICMLICML-2006-FinkSSU #multi #online
Online multiclass learning by interclass hypothesis sharing (MF, SSS, YS, SU), pp. 313–320.
ICMLICML-2006-GlobersonR #robust
Nightmare at test time: robust learning by feature deletion (AG, STR), pp. 353–360.
ICMLICML-2006-Haffner #kernel #performance
Fast transpose methods for kernel learning on sparse data (PH), pp. 385–392.
ICMLICML-2006-Hanneke #analysis #graph
An analysis of graph cut size for transductive learning (SH), pp. 393–399.
ICMLICML-2006-HertzBW #classification #kernel
Learning a kernel function for classification with small training samples (TH, ABH, DW), pp. 401–408.
ICMLICML-2006-HoiJZL #classification #image
Batch mode active learning and its application to medical image classification (SCHH, RJ, JZ, MRL), pp. 417–424.
ICMLICML-2006-KellerMP #approximate #automation #programming
Automatic basis function construction for approximate dynamic programming and reinforcement learning (PWK, SM, DP), pp. 449–456.
ICMLICML-2006-KonidarisB #information management
Autonomous shaping: knowledge transfer in reinforcement learning (GK, AGB), pp. 489–496.
ICMLICML-2006-KulisSD #kernel #matrix #rank
Learning low-rank kernel matrices (BK, MAS, ISD), pp. 505–512.
ICMLICML-2006-McAuleyCSF #higher-order #image
Learning high-order MRF priors of color images (JJM, TSC, AJS, MOF), pp. 617–624.
ICMLICML-2006-NaorR
Learning to impersonate (MN, GNR), pp. 649–656.
ICMLICML-2006-NejatiLK #network
Learning hierarchical task networks by observation (NN, PL, TK), pp. 665–672.
ICMLICML-2006-NevmyvakaFK #execution
Reinforcement learning for optimized trade execution (YN, YF, MK), pp. 673–680.
ICMLICML-2006-PoupartVHR
An analytic solution to discrete Bayesian reinforcement learning (PP, NAV, JH, KR), pp. 697–704.
ICMLICML-2006-RahmaniG #multi #named
MISSL: multiple-instance semi-supervised learning (RR, SAG), pp. 705–712.
ICMLICML-2006-RainaNK #using
Constructing informative priors using transfer learning (RR, AYN, DK), pp. 713–720.
ICMLICML-2006-RuckertK #approach #statistics
A statistical approach to rule learning (UR, SK), pp. 785–792.
ICMLICML-2006-SenG #markov #network
Cost-sensitive learning with conditional Markov networks (PS, LG), pp. 801–808.
ICMLICML-2006-SilvaS #metric #modelling
Bayesian learning of measurement and structural models (RBdAeS, RS), pp. 825–832.
ICMLICML-2006-SinghiL #bias #classification #set
Feature subset selection bias for classification learning (SKS, HL), pp. 849–856.
ICMLICML-2006-SongE #human-computer #interface
Classifying EEG for brain-computer interfaces: learning optimal filters for dynamical system features (LS, JE), pp. 857–864.
ICMLICML-2006-StrehlLWLL
PAC model-free reinforcement learning (ALS, LL, EW, JL, MLL), pp. 881–888.
ICMLICML-2006-StrehlMLH #problem
Experience-efficient learning in associative bandit problems (ALS, CM, MLL, HH), pp. 889–896.
ICMLICML-2006-XuWSS #predict
Discriminative unsupervised learning of structured predictors (LX, DFW, FS, DS), pp. 1057–1064.
ICMLICML-2006-YuBT #design
Active learning via transductive experimental design (KY, JB, VT), pp. 1081–1088.
ICPRICPR-v1-2006-Al-ZubiS #adaptation
Learning to Imitate Human Movement to Adapt to Environmental Changes (SAZ, GS), pp. 191–194.
ICPRICPR-v1-2006-FredJ #clustering #similarity
Learning Pairwise Similarity for Data Clustering (ALNF, AKJ), pp. 925–928.
ICPRICPR-v1-2006-IshidaTIMM #generative #identification
Identification of degraded traffic sign symbols by a generative learning method (HI, TT, II, YM, HM), pp. 531–534.
ICPRICPR-v1-2006-JiangXT
Shape Alignment by Learning a Landmark-PDM Coupled Model (YJ, JX, HTT), pp. 959–962.
ICPRICPR-v1-2006-KoTSH #image #segmentation
A New Image Segmentation Method for Removing Background of Object Movies by Learning Shape Priors (CHK, YPT, ZCS, YPH), pp. 323–326.
ICPRICPR-v1-2006-OngB #clustering
Learning Wormholes for Sparsely Labelled Clustering (EJO, RB), pp. 916–919.
ICPRICPR-v1-2006-TavakkoliNB #detection #recursion #robust
Robust Recursive Learning for Foreground Region Detection in Videos with Quasi-Stationary Backgrounds (AT, MN, GB), pp. 315–318.
ICPRICPR-v1-2006-YousfiACC #database #image
Supervised Learning for Guiding Hierarchy Construction: Application to Osteo-Articular Medical Images Database (KY, CA, JPC, JC), pp. 484–487.
ICPRICPR-v2-2006-AutioL #online #sequence
Online Learning of Discriminative Patterns from Unlimited Sequences of Candidates (IA, JTL), pp. 437–440.
ICPRICPR-v2-2006-ChenJY #reduction #robust
Robust Nonlinear Dimensionality Reduction for Manifold Learning (HC, GJ, KY), pp. 447–450.
ICPRICPR-v2-2006-GaoLL #approach #classification #optimisation
An ensemble classifier learning approach to ROC optimization (SG, CHL, JHL), pp. 679–682.
ICPRICPR-v2-2006-GuoQ #3d
Learning and Inference of 3D Human Poses from Gaussian Mixture Modeled Silhouettes (FG, GQ), pp. 43–47.
ICPRICPR-v2-2006-HarpazH #geometry
Exploiting the Geometry of Gene Expression Patterns for Unsupervised Learning (RH, RMH), pp. 670–674.
ICPRICPR-v2-2006-JinM #parametricity #recognition
A Non-Parametric HMM Learning Method for Shape Dynamics with Application to Human Motion Recognition (NJ, FM), pp. 29–32.
ICPRICPR-v2-2006-JonssonF
Correspondence-free Associative Learning (EJ, MF), pp. 441–446.
ICPRICPR-v2-2006-KelmPM #classification #generative #multi
Combining Generative and Discriminative Methods for Pixel Classification with Multi-Conditional Learning (BMK, CP, AM), pp. 828–832.
ICPRICPR-v2-2006-LernerM #classification #image #network
Learning Bayesian Networks for Cytogenetic Image Classification (BL, RM), pp. 772–775.
ICPRICPR-v2-2006-PungprasertyingCK #analysis #approach #migration #performance
Migration Analysis: An Alternative Approach for Analyzing Learning Performance (PP, RC, BK), pp. 837–840.
ICPRICPR-v2-2006-ScalzoP
Unsupervised Learning of Dense Hierarchical Appearance Represe (FS, JHP), pp. 395–398.
ICPRICPR-v2-2006-StefanoDMF
Improving Dynamic Learning Vector Quantization (CDS, CD, AM, ASdF), pp. 804–807.
ICPRICPR-v2-2006-SungZL #scalability #set
Accelerating the SVM Learning for Very Large Data Sets (ES, YZ, XL), pp. 484–489.
ICPRICPR-v2-2006-WuLZH
A Semi-supervised SVM for Manifold Learning (ZW, ChL, JZ, JH), pp. 490–493.
ICPRICPR-v2-2006-XuWH #algorithm
A maximum margin discriminative learning algorithm for temporal signals (WX, JW, ZH), pp. 460–463.
ICPRICPR-v2-2006-ZhangPB #classification #representation
Learning Optimal Filter Representation for Texture Classification (PZ, JP, BPB), pp. 1138–1141.
ICPRICPR-v2-2006-ZhangR #incremental
A New Data Selection Principle for Semi-Supervised Incremental Learning (RZ, AIR), pp. 780–783.
ICPRICPR-v2-2006-ZhengL #analysis #component #kernel #locality #problem
Regularized Locality Preserving Learning of Pre-Image Problem in Kernel Principal Component Analysis (WSZ, JHL), pp. 456–459.
ICPRICPR-v2-2006-ZhengLY #kernel #problem
Weakly Supervised Learning on Pre-image Problem in Kernel Methods (WSZ, JHL, PCY), pp. 711–715.
ICPRICPR-v2-2006-ZouL #performance #sequence
The Generalization Performance of Learning Machine Based on Phi-mixing Sequence (BZ, LL), pp. 548–551.
ICPRICPR-v3-2006-AlahariPJ #online #recognition
Learning Mixtures of Offline and Online features for Handwritten Stroke Recognition (KA, SLP, CVJ), pp. 379–382.
ICPRICPR-v3-2006-GunselK
Perceptual Audio Watermarking by Learning in Wavelet Domain (BG, SK), pp. 383–386.
ICPRICPR-v3-2006-IsukapalliE #identification #policy
Learning Policies for Efficiently Identifying Objects of Many Classes (RI, AME, RG), pp. 356–361.
ICPRICPR-v3-2006-Martinez-ArroyoS #classification #naive bayes
Learning an Optimal Naive Bayes Classifier (MMA, LES), pp. 1236–1239.
ICPRICPR-v3-2006-TangelderS #image #multi #online #recognition #representation
Learning a Sparse Representation from Multiple Still Images for On-Line Face Recognition in an Unconstrained Environment (JWHT, BAMS), pp. 1087–1090.
ICPRICPR-v3-2006-YangL06a #3d #image #sequence #using
Reconstructing 3D Human Body Pose from Stereo Image Sequences Using Hierarchical Human Body Model Learning (HDY, SWL), pp. 1004–1007.
ICPRICPR-v4-2006-Martinez-ArroyoS06a #classification #naive bayes
Learning an Optimal Naive Bayes Classifier (MMA, LES), p. 958.
ICPRICPR-v4-2006-YangLPZZ #detection
Active Learning Based Pedestrian Detection in Real Scenes (TY, JL, QP, CZ, YZ), pp. 904–907.
ICPRICPR-v4-2006-ZhengLL #network
Control Double Inverted Pendulum by Reinforcement Learning with Double CMAC Network (YZ, SL, ZL), pp. 639–642.
KDDKDD-2006-AbeZL #detection
Outlier detection by active learning (NA, BZ, JL), pp. 504–509.
KDDKDD-2006-AgarwalCA #rank
Learning to rank networked entities (AA, SC, SA), pp. 14–23.
KDDKDD-2006-CarvalhoC #feature model #online #performance
Single-pass online learning: performance, voting schemes and online feature selection (VRC, WWC), pp. 548–553.
KDDKDD-2006-HoiLC #classification #kernel
Learning the unified kernel machines for classification (SCHH, MRL, EYC), pp. 187–196.
KDDKDD-2006-LongWZY #graph
Unsupervised learning on k-partite graphs (BL, XW, Z(Z, PSY), pp. 317–326.
KDDKDD-2006-RosalesF #linear #metric #programming
Learning sparse metrics via linear programming (RR, GF), pp. 367–373.
SIGIRSIGIR-2006-AgichteinBDR #interactive #modelling #predict #web
Learning user interaction models for predicting web search result preferences (EA, EB, STD, RR), pp. 3–10.
SIGIRSIGIR-2006-CarteretteP #ranking
Learning a ranking from pairwise preferences (BC, DP), pp. 629–630.
SIGIRSIGIR-2006-HuangZL #taxonomy
Refining hierarchical taxonomy structure via semi-supervised learning (RH, ZZ, WL), pp. 653–654.
SIGIRSIGIR-2006-LacerdaCGFZR
Learning to advertise (AL, MC, MAG, WF, NZ, BARN), pp. 549–556.
SIGIRSIGIR-2006-WuJ #framework #graph #multi
A graph-based framework for relation propagation and its application to multi-label learning (MW, RJ), pp. 717–718.
SIGIRSIGIR-2006-ZhaZFS #difference #information retrieval #query
Incorporating query difference for learning retrieval functions in information retrieval (HZ, ZZ, HF, GS), pp. 721–722.
SACSAC-2006-CraigL #classification #using
Protein classification using transductive learning on phylogenetic profiles (RAC, LL), pp. 161–166.
SACSAC-2006-Ferrer-TroyanoAS #classification #data type #incremental
Data streams classification by incremental rule learning with parameterized generalization (FJFT, JSAR, JCRS), pp. 657–661.
SACSAC-2006-PechenizkiyPT #feature model #reduction
The impact of sample reduction on PCA-based feature extraction for supervised learning (MP, SP, AT), pp. 553–558.
CASECASE-2006-ReveliotisB #algorithm #performance
Efficient learning algorithms for episodic tasks with acyclic state spaces (SR, TB), pp. 411–418.
CASECASE-2006-ZhouD #game studies
An evolutionary game model on supply chains learning through imitation (MZ, FD), pp. 645–648.
DACDAC-2006-WangGG #deduction #difference #logic
Predicate learning and selective theory deduction for a difference logic solver (CW, AG, MKG), pp. 235–240.
PDPPDP-2006-ClematisFQ #distributed
Interacting with Learning Objects in a Distributed Environment (AC, PF, AQ), pp. 322–329.
PDPPDP-2006-NiksereshtG #distributed #multi #performance #retrieval #scalability
Fast Decentralized Learning of a Gaussian Mixture Model for Large-Scale Multimedia Retrieval (AN, MG), pp. 373–379.
FASEFASE-2006-RaffeltS #automaton #library #named
LearnLib: A Library for Automata Learning and Experimentation (HR, BS), pp. 377–380.
STOCSTOC-2006-AngluinACW #injection
Learning a circuit by injecting values (DA, JA, JC, YW), pp. 584–593.
STOCSTOC-2006-Feldman #approximate #logic #query
Hardness of approximate two-level logic minimization and PAC learning with membership queries (VF), pp. 363–372.
CAVCAV-2006-VardhanV #named #verification
LEVER: A Tool for Learning Based Verification (AV, MV), pp. 471–474.
FATESFATES-RV-2006-VeanesRC #online #testing
Online Testing with Reinforcement Learning (MV, PR, CC), pp. 240–253.
ICLPICLP-2006-Aguilar-Solis #approach #constraints #parsing #semantics
Learning Semantic Parsers: A Constraint Handling Rule Approach (DAS), pp. 447–448.
ICSTSAT-2006-YuM #constraints #linear #smt
Lemma Learning in SMT on Linear Constraints (YY, SM), pp. 142–155.
TPDLECDL-2005-GohSZWLTHC #personalisation #using
Managing Geography Learning Objects Using Personalized Project Spaces in G-Portal (DHLG, AS, WZ, DW, EPL, YLT, JGH, CHC), pp. 336–343.
TPDLECDL-2005-NajjarKVD #empirical #evaluation
Finding Appropriate Learning Objects: An Empirical Evaluation (JN, JK, RV, ED), pp. 323–335.
HTHT-2005-BerlangaG #adaptation #design #modelling #navigation #specification #using
Modelling adaptive navigation support techniques using the IMS learning design specification (AJB, FJG), pp. 148–150.
ICDARICDAR-2005-BargeronVS #detection
Boosting-based Transductive Learning for Text Detection (DB, PAV, PYS), pp. 1166–1171.
ICDARICDAR-2005-CeciBM #comprehension #documentation #image #logic #relational #statistics
Relational Learning techniques for Document Image Understanding: Comparing Statistical and Logical approaches (MC, MB, DM), pp. 473–477.
ICDARICDAR-2005-FengHG #approach #semantics #web
A Learning Approach to Discovering Web Page Semantic Structures (JF, PH, MG), pp. 1055–1059.
ICDARICDAR-2005-LavenLR #analysis #approach #documentation #image #statistics
A Statistical Learning Approach To Document Image Analysis (KL, SL, STR), pp. 357–361.
ICDARICDAR-2005-RaghavendraNSRS #online #prototype #recognition
Prototype Learning Methods for Online Handwriting Recognition (BSR, CKN, GS, AGR, MS), pp. 287–291.
ICDARICDAR-2005-Szummer #diagrams #random
Learning Diagram Parts with Hidden Random Fields (MS), pp. 1188–1193.
JCDLJCDL-2005-ChangHTLTG #education
Evaluating G-portal for geography learning and teaching (CHC, JGH, YLT, EPL, TST, DHLG), pp. 21–22.
JCDLJCDL-2005-FoxG #education #library
Introduction to (teaching/learning about) digital libraries (EAF, MAG), p. 419.
SIGMODSIGMOD-2005-BragaCCR #named #query #visual notation #xml
XQBE: a visual environment for learning XML query languages (DB, AC, SC, AR), pp. 903–905.
VLDBVLDB-2005-ZhangHJLZ #cost analysis #query #statistics #xml
Statistical Learning Techniques for Costing XML Queries (NZ, PJH, VJ, GML, CZ), pp. 289–300.
CSEETCSEET-2005-BunseGOPS #education #re-engineering
xd Software Engineering Education Applying a Blended Learning Strategy for (CB, IG, MO, CP, SSN), pp. 95–102.
CSEETCSEET-2005-Ellis #online #re-engineering
Autonomous Learning in Online and Traditional Versions of a Software Engineering Course (HJCE), pp. 69–76.
CSEETCSEET-2005-Liu #communication #issue tracking #re-engineering #student #tool support #using
Using Issue Tracking Tools to Facilitate Student Learning of Communication Skills in Software Engineering Courses (CL), pp. 61–68.
ITiCSEITiCSE-2005-AmershiACCMMP #design #usability
Designing CIspace: pedagogy and usability in a learning environment for AI (SA, NA, GC, CC, AKM, HM, DP), pp. 178–182.
ITiCSEITiCSE-2005-ChamillardS #education
Learning styles across the curriculum (ATC, RES), pp. 241–245.
ITiCSEITiCSE-2005-DavisW #convergence #education #multi
A research-led curriculum in multimedia: learning about convergence (HCD, SW), pp. 29–33.
ITiCSEITiCSE-2005-Dick #analysis #assessment #design #student
Student interviews as a tool for assessment and learning in a systems analysis and design course (MD), pp. 24–28.
ITiCSEITiCSE-2005-Granger #collaboration #communication #concept
Learning technical concepts with collaboration and communication skills (MJG), p. 391.
ITiCSEITiCSE-2005-HurtadoV
Learning UNIX in first year of computer engineering (MASH, CVP), p. 392.
ITiCSEITiCSE-2005-LoftusR #programming #question
Extreme programming promotes extreme learning? (CWL, MR), pp. 311–315.
ITiCSEITiCSE-2005-Marcelino #programming
Learning repetition structures in programming (MJM), p. 351.
ITiCSEITiCSE-2005-NugentSSPL #design #development #validation
Design, development, and validation of a learning object for CS1 (GN, LKS, AS, SP, JL), p. 370.
ITiCSEITiCSE-2005-Truong
The environment for learning to program (NT), p. 383.
ITiCSEITiCSE-2005-TruongBR #web
Learning to program through the web (NT, PB, PR), pp. 9–13.
ITiCSEITiCSE-2005-Vinha #reuse #theory and practice
Reusable learning objects: theory to practice (AV), p. 413.
SIGITESIGITE-2005-AbernethyTPR #repository
A learning object repository in support of introductory IT courses (KA, KT, GP, HR), pp. 223–227.
SIGITESIGITE-2005-Backhouse #analysis #design
Learning individual group skills for software analysis and design in Africa (JB), pp. 107–112.
SIGITESIGITE-2005-BaileyMB
Creative learning with practical applications for 802.11 wireless communications (MGB, JHM, NHB), pp. 369–370.
SIGITESIGITE-2005-FulbrightR #student
IPC incorporated: a student-run IT services company for experiential learning (RF, RLR), pp. 211–216.
SIGITESIGITE-2005-IqbalE #assessment #education
Scenario based method for teaching, learning and assessment (RI, PE), pp. 261–266.
SIGITESIGITE-2005-MarchantT #student #using
Using pre-release software to SPUR student learning (AM, BT), pp. 143–148.
SIGITESIGITE-2005-OliverP
Mixed-project-based learning methodology in computer electronic engineering (JO, MP), pp. 291–294.
SIGITESIGITE-2005-PatchaS #development #distance #internet
Development of an internet based distance learning program at Virginia Tech (AP, GS), pp. 379–380.
SIGITESIGITE-2005-Prayaga05a #game studies #student
Game technology as a tool to actively engage K-12 students in the act of learning (LP), pp. 307–310.
SIGITESIGITE-2005-WillisM #tool support
Mind tools for enhancing thinking and learning skills (CLW, SLM), pp. 249–254.
MSRMSR-2005-HuangL #mining #process #verification #version control
Mining version histories to verify the learning process of Legitimate Peripheral Participants (SKH, KmL), pp. 21–25.
CIAACIAA-2005-GarciaRCA #question
Is Learning RFSAs Better Than Learning DFAs? (PG, JR, AC, GIA), pp. 343–344.
CIAACIAA-2005-HigueraPT #automaton #finite #probability #recognition
Learning Stochastic Finite Automata for Musical Style Recognition (CdlH, FP, FT), pp. 345–346.
AIIDEAIIDE-2005-GorniakB #sequence
Sequence Learning by Backward Chaining in Synthetic Characters (PG, BB), pp. 51–56.
AIIDEAIIDE-2005-StanleyCM #game studies #realtime #video
Real-time Learning in the NERO Video Game (KOS, RC, RM), pp. 159–160.
CoGCIG-2005-BradleyH #adaptation #game studies #using
Adapting Reinforcement Learning for Computer Games: Using Group Utility Functions (JB, GH).
CoGCIG-2005-DenzingerW #behaviour
Combining Coaching and Learning to Create Cooperative Character Behavior (JD, CW).
CoGCIG-2005-KokHBV #coordination
Utile Coordination: Learning Interdependencies Among Cooperative Agents (JRK, PJH, BB, NAV).
CoGCIG-2005-YangG #multi #overview #towards
A Survey on Multiagent Reinforcement Learning Towards Multi-Robot Systems (EY, DG).
DiGRADiGRA-2005-Becker #education #game studies #how
How Are Games Educational? Learning Theories Embodied in Games (KB).
DiGRADiGRA-2005-BeckerJ #game studies #question #what
Games for Learning: Are Schools Ready for What's to Come? (KB, MJ).
DiGRADiGRA-2005-DobsonHCM #experience #game studies
From the real-world data to game world experience: A method for developing plausible & engaging learning games (MWD, DH, CC, DEM).
DiGRADiGRA-2005-Eaton #comprehension #game studies
Narrative comprehension in computer games: Implications for literacy and learning (IE).
DiGRADiGRA-2005-Engeli #design #editing #game studies
Playful Play with Games: Linking Level Editing to Learning in Art and Design (ME).
DiGRADiGRA-2005-Folmann #game studies #music
Game Music - learning from the Movies (TBF).
DiGRADiGRA-2005-Galarneau #authentication #case study #experience #game studies #simulation
Authentic Learning Experiences Through Play: Games, Simulations and the Construction of Knowledge (LG).
DiGRADiGRA-2005-Galarneau05a #analysis #community #ecosystem #game studies #multi #online #social
Spontaneous Communities of Learning: A Social Analysis of Learning Ecosystems in Massively Multiplayer Online Gaming (MMOG) Environments (LG).
DiGRADiGRA-2005-Hayes #social
Learning and Literacies in the Social World of Tony Hawk Underground 2 (ERH).
DiGRADiGRA-2005-KaoGK #game studies #multi #quote
“A Totally Different World”: Playing and Learning in Multi-User Virtual Environments (LK, CG, YBK).
DiGRADiGRA-2005-Magnussen #framework #game studies #platform
Learning Games as a Platform for Simulated Science Practice (RM).
DiGRADiGRA-2005-NeulightK #case study #experience #multi #what
What happens if you catch Whypox? Children's learning experiences of infectious disease in a multi-user virtual environment (NN, YBK).
DiGRADiGRA-2005-ParasB #design #education #effectiveness #game studies #motivation
Game, Motivation, and Effective Learning: An Integrated Model for Educational Game Design (BSP, JB).
DiGRADiGRA-2005-Pelletier05a #design #education #game studies
Studying Games in School: learning and teaching about game design, play and culture (CP).
DiGRADiGRA-2005-SauveLPBAK #game studies #online #realtime
Playing And Learning Without Borders: A Real-time Online Play Environment (LS, VL, WP, GMB, VGSA, DK).
DiGRADiGRA-2005-SweedykL #game studies
Games, Metaphor, and Learning (ES, MdL).
DiGRADiGRA-2005-UlicsakSFWF #design #game studies
Time out? Exploring the role of reflection in the design of games for learning (MHU, SS, KF, BW).
DiGRADiGRA-2005-WilliamsonSS #game studies #peer-to-peer #social
Racing Academy: peer-to-peer learning in a social racing game (BW, SS, RS).
CHICHI-2005-BondarenkoJ
Dcuments at Hand: Learning from Paper to Improve Digital Technologies (OB, RJ), pp. 121–130.
CHICHI-2005-XieLGM #image
Learning user interest for image browsing on small-form-factor devices (XX, HL, SG, WYM), pp. 671–680.
CHICHI-2005-YeeP #named #online #using
StudioBRIDGE: using group, location, and event information to bridge online and offline encounters for co-located learning groups (SY, KSP), pp. 551–560.
EDOCEDOC-2005-FerreiraF #lifecycle #workflow
Learning, planning, and the life cycle of workflow management (DRF, HMF), pp. 39–46.
ICEISICEIS-v2-2005-ColaceSVF #algorithm #approach #multi #network
A Bayesian Networks Structural Learning Algorithm Based on a Multiexpert Approach (FC, MDS, MV, PF), pp. 194–200.
ICEISICEIS-v2-2005-LokugeA #hybrid #multi
Handling Multiple Events in Hybrid BDI Agents with Reinforcement Learning: A Container Application (PL, DA), pp. 83–90.
ICEISICEIS-v5-2005-Fernandez-CaballeroGBL #adaptation #architecture #distance
Distance Learning by Intelligent Tutoring System. Part I: Agent-Based Architecture for User-Centred Adaptivity (AFC, JMG, FB, EL), pp. 75–82.
ICEISICEIS-v5-2005-Fernandez-CaballeroGLB #adaptation #distance #education #student
Distance Learning by Intelligent Tutoring System. Part II: Student/Teacher Adaptivity in an Engineering Course (AFC, JMG, EL, FB), pp. 148–153.
ICEISICEIS-v5-2005-Goren-Bar #evaluation #interactive #student
Student’s Evaluation of Web-Based Learning Technologies in a Humancomputer Interaction Course (DGB), pp. 206–212.
ICEISICEIS-v5-2005-IslamARR #distance #mobile
Mobile Telephone Technology as a Distance Learning Tool (YMI, MA, ZR, MR), pp. 226–232.
ICEISICEIS-v5-2005-LeR #named
LINC: A Web-Based Learning Tool for Mixed-Mode Learning (THL, JR), pp. 154–160.
CIKMCIKM-2005-AminiTULG #documentation #using #xml
Learning to summarise XML documents using content and structure (MRA, AT, NU, ML, PG), pp. 297–298.
CIKMCIKM-2005-RoussinovFN05a #approach #information retrieval
Discretization based learning approach to information retrieval (DR, WF, FADN), pp. 321–322.
CIKMCIKM-2005-XiongSK #database #multi #privacy
Privacy leakage in multi-relational databases via pattern based semi-supervised learning (HX, MS, VK), pp. 355–356.
ICMLICML-2005-AbbeelN
Exploration and apprenticeship learning in reinforcement learning (PA, AYN), pp. 1–8.
ICMLICML-2005-AndersonM #algorithm #markov #modelling
Active learning for Hidden Markov Models: objective functions and algorithms (BA, AM), pp. 9–16.
ICMLICML-2005-BlockeelPS #multi
Multi-instance tree learning (HB, DP, AS), pp. 57–64.
ICMLICML-2005-BurgeL #network
Learning class-discriminative dynamic Bayesian networks (JB, TL), pp. 97–104.
ICMLICML-2005-BurgesSRLDHH #rank #using
Learning to rank using gradient descent (CJCB, TS, ER, AL, MD, NH, GNH), pp. 89–96.
ICMLICML-2005-ChangK
Hedged learning: regret-minimization with learning experts (YHC, LPK), pp. 121–128.
ICMLICML-2005-ChuG #process
Preference learning with Gaussian processes (WC, ZG), pp. 137–144.
ICMLICML-2005-CortesMW
A general regression technique for learning transductions (CC, MM, JW), pp. 153–160.
ICMLICML-2005-CrandallG #game studies
Learning to compete, compromise, and cooperate in repeated general-sum games (JWC, MAG), pp. 161–168.
ICMLICML-2005-DaumeM #approximate #optimisation #predict #scalability
Learning as search optimization: approximate large margin methods for structured prediction (HDI, DM), pp. 169–176.
ICMLICML-2005-DrakeV
A practical generalization of Fourier-based learning (AD, DV), pp. 185–192.
ICMLICML-2005-DriessensD #first-order #modelling
Combining model-based and instance-based learning for first order regression (KD, SD), pp. 193–200.
ICMLICML-2005-EngelMM #process
Reinforcement learning with Gaussian processes (YE, SM, RM), pp. 201–208.
ICMLICML-2005-GirolamiR #kernel #modelling
Hierarchic Bayesian models for kernel learning (MG, SR), pp. 241–248.
ICMLICML-2005-GroisW #approach #comprehension
Learning strategies for story comprehension: a reinforcement learning approach (EG, DCW), pp. 257–264.
ICMLICML-2005-HerbsterPW #graph #online
Online learning over graphs (MH, MP, LW), pp. 305–312.
ICMLICML-2005-IlghamiMNA #approximate
Learning approximate preconditions for methods in hierarchical plans (OI, HMA, DSN, DWA), pp. 337–344.
ICMLICML-2005-JingPR #classification #naive bayes #network #performance
Efficient discriminative learning of Bayesian network classifier via boosted augmented naive Bayes (YJ, VP, JMR), pp. 369–376.
ICMLICML-2005-JodogneP #interactive #visual notation
Interactive learning of mappings from visual percepts to actions (SJ, JHP), pp. 393–400.
ICMLICML-2005-KokD #logic #markov #network
Learning the structure of Markov logic networks (SK, PMD), pp. 441–448.
ICMLICML-2005-LangfordZ #classification #performance
Relating reinforcement learning performance to classification performance (JL, BZ), pp. 473–480.
ICMLICML-2005-Mahadevan
Proto-value functions: developmental reinforcement learning (SM), pp. 553–560.
ICMLICML-2005-MichelsSN #using
High speed obstacle avoidance using monocular vision and reinforcement learning (JM, AS, AYN), pp. 593–600.
ICMLICML-2005-NatarajanT #multi
Dynamic preferences in multi-criteria reinforcement learning (SN, PT), pp. 601–608.
ICMLICML-2005-NatarajanTADFR #first-order #modelling #probability
Learning first-order probabilistic models with combining rules (SN, PT, EA, TGD, AF, ACR), pp. 609–616.
ICMLICML-2005-Niculescu-MizilC #predict
Predicting good probabilities with supervised learning (ANM, RC), pp. 625–632.
ICMLICML-2005-OntanonP #multi
Recycling data for multi-agent learning (SO, EP), pp. 633–640.
ICMLICML-2005-PernkopfB #classification #generative #network #parametricity
Discriminative versus generative parameter and structure learning of Bayesian network classifiers (FP, JAB), pp. 657–664.
ICMLICML-2005-RayC #comparison #empirical #multi
Supervised versus multiple instance learning: an empirical comparison (SR, MC), pp. 697–704.
ICMLICML-2005-RosellHRP #why
Why skewing works: learning difficult Boolean functions with greedy tree learners (BR, LH, SR, DP), pp. 728–735.
ICMLICML-2005-RousuSSS #classification #modelling #multi
Learning hierarchical multi-category text classification models (JR, CS, SS, JST), pp. 744–751.
ICMLICML-2005-SiddiqiM #performance
Fast inference and learning in large-state-space HMMs (SMS, AWM), pp. 800–807.
ICMLICML-2005-SilvaS #identification #modelling
New d-separation identification results for learning continuous latent variable models (RBdAeS, RS), pp. 808–815.
ICMLICML-2005-SimsekWB #clustering #graph #identification
Identifying useful subgoals in reinforcement learning by local graph partitioning (ÖS, APW, AGB), pp. 816–823.
ICMLICML-2005-SindhwaniNB
Beyond the point cloud: from transductive to semi-supervised learning (VS, PN, MB), pp. 824–831.
ICMLICML-2005-SinghPGBB #analysis
Active learning for sampling in time-series experiments with application to gene expression analysis (RS, NP, DKG, BB, ZBJ), pp. 832–839.
ICMLICML-2005-SunD #approach
Explanation-Augmented SVM: an approach to incorporating domain knowledge into SVM learning (QS, GD), pp. 864–871.
ICMLICML-2005-TaskarCKG #approach #modelling #predict #scalability
Learning structured prediction models: a large margin approach (BT, VC, DK, CG), pp. 896–903.
ICMLICML-2005-ToussaintV #modelling
Learning discontinuities with products-of-sigmoids for switching between local models (MT, SV), pp. 904–911.
ICMLICML-2005-Wiewiora #predict
Learning predictive representations from a history (EW), pp. 964–971.
ICMLICML-2005-WolfeJS #predict
Learning predictive state representations in dynamical systems without reset (BW, MRJ, SPS), pp. 980–987.
ICMLICML-2005-XuTYYK #relational
Dirichlet enhanced relational learning (ZX, VT, KY, SY, HPK), pp. 1004–1011.
ICMLICML-2005-YuTS #multi #process
Learning Gaussian processes from multiple tasks (KY, VT, AS), pp. 1012–1019.
ICMLICML-2005-ZhouHS #graph
Learning from labeled and unlabeled data on a directed graph (DZ, JH, BS), pp. 1036–1043.
ICMLICML-2005-ZhuL #graph #induction #modelling #scalability
Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning (XZ, JDL), pp. 1052–1059.
KDDKDD-2005-FanLH #image #mining #semantics #statistics
Mining images on semantics via statistical learning (JF, HL, MSH), pp. 22–31.
KDDKDD-2005-LowdM
Adversarial learning (DL, CM), pp. 641–647.
KDDKDD-2005-MeruguG #data flow #distributed #framework #semistructured data
A distributed learning framework for heterogeneous data sources (SM, JG), pp. 208–217.
KDDKDD-2005-PhanNHH
Improving discriminative sequential learning with rare--but--important associations (XHP, MLN, TBH, SH), pp. 304–313.
KDDKDD-2005-RadlinskiJ #feedback #query #rank
Query chains: learning to rank from implicit feedback (FR, TJ), pp. 239–248.
KDDKDD-2005-YangL #predict
Learning to predict train wheel failures (CY, SL), pp. 516–525.
LSOLSO-2005-Fajtak
Kick-off Workshops and Project Retrospectives: A Good Learning Software Organization Practice (FFF), pp. 112–114.
LSOLSO-2005-Salo #agile #development #validation
Systematical Validation of Learning in Agile Software Development Environment (OS), pp. 92–96.
MLDMMLDM-2005-BunkeDIK #analysis #graph #predict
Analysis of Time Series of Graphs: Prediction of Node Presence by Means of Decision Tree Learning (HB, PJD, CI, MK), pp. 366–375.
MLDMMLDM-2005-GhoshGYB05a #parametricity
Determining Regularization Parameters for Derivative Free Neural Learning (RG, MG, JY, AMB), pp. 71–79.
MLDMMLDM-2005-KuhnertK #feedback
Autonomous Vehicle Steering Based on Evaluative Feedback by Reinforcement Learning (KDK, MK), pp. 405–414.
MLDMMLDM-2005-ScalzoP #visual notation
Unsupervised Learning of Visual Feature Hierarchies (FS, JHP), pp. 243–252.
MLDMMLDM-2005-SilvaJNP #geometry #metric #using
Diagnosis of Lung Nodule Using Reinforcement Learning and Geometric Measures (ACS, VRdSJ, AdAN, ACdP), pp. 295–304.
SEKESEKE-2005-GaoCMYB #modelling #object-oriented
An Object-Oriented Modeling Learning Support System With Inspection Comments (TG, KMLC, HM, ILY, FBB), pp. 211–216.
SEKESEKE-2005-HongCC #fuzzy #performance
Learning Efficiency Improvement of Fuzzy CMAC by Aitken Acceleration Method (CMH, CMC, HYC), pp. 556–595.
SEKESEKE-2005-KinjoH #modelling #object-oriented
An Object-Oriented Modeling Learning Support System With Inspection Comments (TK, AH), pp. 223–228.
SEKESEKE-2005-SiciliaCR #ontology #process
Ontologies of Software Artifacts and Activities: Resource Annotation and Application to Learning Technologies (MÁS, JJC, DR), pp. 145–150.
SIGIRSIGIR-2005-JensenBGFC #predict #query #visual notation #web
Predicting query difficulty on the web by learning visual clues (ECJ, SMB, DAG, OF, AC), pp. 615–616.
SIGIRSIGIR-2005-ViolaN #context-free grammar #using
Learning to extract information from semi-structured text using a discriminative context free grammar (PAV, MN), pp. 330–337.
SIGIRSIGIR-2005-Yom-TovFCD #detection #distributed #information retrieval #query
Learning to estimate query difficulty: including applications to missing content detection and distributed information retrieval (EYT, SF, DC, AD), pp. 512–519.
ASEASE-2005-VardhanV #branch #verification
Learning to verify branching time properties (AV, MV), pp. 325–328.
ESEC-FSEESEC-FSE-2005-ChatleyT #eclipse #named
KenyaEclipse: learning to program in eclipse (RC, TT), pp. 245–248.
SACSAC-2005-BoninoCP #automation #concept #network
Automatic learning of text-to-concept mappings exploiting WordNet-like lexical networks (DB, FC, FP), pp. 1639–1644.
SACSAC-2005-Ferrer-TroyanoAS #data type #incremental
Incremental rule learning based on example nearness from numerical data streams (FJFT, JSAR, JCRS), pp. 568–572.
SACSAC-2005-FradkinK #classification
Methods for learning classifier combinations: no clear winner (DF, PBK), pp. 1038–1043.
SACSAC-2005-GamaMR #data type
Learning decision trees from dynamic data streams (JG, PM, PPR), pp. 573–577.
SACSAC-2005-KatayamaKN #process
Reinforcement learning agents with primary knowledge designed by analytic hierarchy process (KK, TK, HN), pp. 14–21.
SACSAC-2005-LunaLSHHB
Learning system to introduce GIS to civil engineers (RL, WTL, JMS, RHH, MGH, MB), pp. 1737–1738.
SACSAC-2005-PandeyGM #algorithm #probability #scheduling
Stochastic scheduling of active support vector learning algorithms (GP, HG, PM), pp. 38–42.
SACSAC-2005-TebriBC #incremental
Incremental profile learning based on a reinforcement method (HT, MB, CC), pp. 1096–1101.
SACSAC-2005-ZhangM #privacy
Privacy preserving learning in negotiation (SZ, FM), pp. 821–825.
DACDAC-2005-ParthasarathyICB
Structural search for RTL with predicate learning (GP, MKI, KTC, FB), pp. 451–456.
DATEDATE-2005-ChandrasekarH #fault #generative #incremental #integration #performance #satisfiability #testing
Integration of Learning Techniques into Incremental Satisfiability for Efficient Path-Delay Fault Test Generation (KC, MSH), pp. 1002–1007.
DATEDATE-2005-IyerPC #constraints #performance #theorem proving
Efficient Conflict-Based Learning in an RTL Circuit Constraint Solver (MKI, GP, KTC), pp. 666–671.
STOCSTOC-2005-KaplanKM
Learning with attribute costs (HK, EK, YM), pp. 356–365.
STOCSTOC-2005-MosselR #markov #modelling
Learning nonsingular phylogenies and hidden Markov models (EM, SR), pp. 366–375.
STOCSTOC-2005-Regev #encryption #fault #linear #on the #random
On lattices, learning with errors, random linear codes, and cryptography (OR), pp. 84–93.
CAVCAV-2005-AlurMN #composition #verification
Symbolic Compositional Verification by Learning Assumptions (RA, PM, WN), pp. 548–562.
CAVCAV-2005-LoginovRS #abstraction #induction #refinement
Abstraction Refinement via Inductive Learning (AL, TWR, SS), pp. 519–533.
ICSTSAT-2005-GentR
Local and Global Complete Solution Learning Methods for QBF (IPG, AGDR), pp. 91–106.
WICSAWICSA-2004-BardramCH #approach #architecture #design #prototype
Architectural Prototyping: An Approach for Grounding Architectural Design and Learning (JB, HBC, KMH), pp. 15–24.
DocEngDocEng-2004-ChidlovskiiF #documentation #legacy
Supervised learning for the legacy document conversion (BC, JF), pp. 220–228.
HTHT-2004-DavisB #case study #experience #migration
Experiences migrating microcosm learning materials (HCD, RAB), pp. 141–142.
JCDLJCDL-2004-ChampenyBLGMDFSMMJ #design #evaluation #implementation #process
Developing a digital learning environment: an evaluation of design and implementation processes (LC, CLB, GHL, AJGS, KAM, LD, JRF, LJS, PDM, REM, RAJ), pp. 37–46.
JCDLJCDL-2004-HanGZLT #ambiguity
Two supervised learning approaches for name disambiguation in author citations (HH, CLG, HZ, CL, KT), pp. 296–305.
JCDLJCDL-2004-PanGSHH #evaluation #experience #usability
Usability, learning, and subjective experience: user evaluation of K-MODDL in an undergraduate class (BP, GG, JS, HH, DH), pp. 188–189.
CSEETCSEET-2004-HazzanT #aspect-oriented #education #process #re-engineering
Reflection Processes in the Teaching and Learning of Human Aspects of Software Engineering (OH, JET), pp. 32–38.
CSEETCSEET-2004-Milewski #human-computer
Software Engineers and HCI Practitioners Learning to Work Together: A Preliminary Look at Expectations (AEM), pp. 45–49.
ITiCSEITiCSE-2004-ArgolloHMBFBLMR #collaboration #research #student
Graduate students learning strategies through research collaboration (EA, MH, DM, GB, PCF, FB, EL, JCM, DR), p. 262.
ITiCSEITiCSE-2004-ChesnevarGM #automaton #formal method
Didactic strategies for promoting significant learning in formal languages and automata theory (CIC, MPG, AGM), pp. 7–11.
ITiCSEITiCSE-2004-Dixon #automation #education
A single CASE environment for teaching and learning (MD), p. 271.
ITiCSEITiCSE-2004-Ford04a #generative #programming
A learning object generator for programming (LF), p. 268.
ITiCSEITiCSE-2004-Garner #programming #using
The use of a code restructuring tool in the learning of programming (SG), p. 277.
ITiCSEITiCSE-2004-Kerren #education #generative
Generation as method for explorative learning in computer science education (AK), pp. 77–81.
ITiCSEITiCSE-2004-Kumar #java #programming
Web-based tutors for learning programming in C++/Java (AK), p. 266.
ITiCSEITiCSE-2004-LeskaR #concept #game studies #using
Learning O-O concepts in CS I using game projects (CL, JRR), p. 237.
ITiCSEITiCSE-2004-McKennaL #concept
Constructivist or instructivist: pedagogical concepts practically applied to a computer learning environment (PM, BL), pp. 166–170.
ITiCSEITiCSE-2004-MelinC #student
Project oriented student work: learning & examination (UM, SC), pp. 87–91.
ITiCSEITiCSE-2004-PaciniFF #database #problem #spreadsheet #tool support
Learning problem solving with spreadsheet and database tools (GP, GF, AF), p. 267.
ITiCSEITiCSE-2004-PahlBK #database #interactive #multi
Supporting active database learning and training through interactive multimedia (CP, RB, CK), pp. 27–31.
ITiCSEITiCSE-2004-PowellMGFR #programming
Dyslexia and learning computer programming (NP, DJM, JG, JF, JR), p. 242.
ITiCSEITiCSE-2004-RamalingamLW #modelling #self
Self-efficacy and mental models in learning to program (VR, DL, SW), pp. 171–175.
ITiCSEITiCSE-2004-RatcliffeHE #collaboration #student
Enhancing student learning through collaboration (MR, JH, WE), p. 272.
ITiCSEITiCSE-2004-SadiqOSL #named #online #sql
SQLator: an online SQL learning workbench (SWS, MEO, WS, JYCL), pp. 223–227.
ITiCSEITiCSE-2004-Sheard #community
Electronic learning communities: strategies for establishment and management (JS), pp. 37–41.
ITiCSEITiCSE-2004-SimonAHS #case study #experience #tablet
Preliminary experiences with a tablet PC based system to support active learning in computer science courses (BS, REA, CH, JS), pp. 213–217.
ITiCSEITiCSE-2004-SitthiworachartJ #assessment #effectiveness #programming
Effective peer assessment for learning computer programming (JS, MJ), pp. 122–126.
ITiCSEITiCSE-2004-WangC #assessment #online #performance
Extending e-books with annotation, online support and assessment mechanisms to increase efficiency of learning (CYW, GDC), pp. 132–136.
SIGITESIGITE-2004-AlotaibyCWWS #named
Teacher-driven: web-based learning system (FTA, JXC, EJW, HW, DS), p. 284.
SIGITESIGITE-2004-Crowley #design #security
Experiential learning and security lab design (EC), pp. 169–176.
SIGITESIGITE-2004-Dark #assessment #performance #risk management #security #student
Assessing student performance outcomes in an information security risk assessment, service learning course (MJD), pp. 73–78.
SIGITESIGITE-2004-DoubledayK
Shared extensible learning spaces (ND, SK), pp. 144–148.
SIGITESIGITE-2004-FriedmanS #community #development #education
Application development for informal learning environments: where IT education, community outreach, baseball and history intersect (RSF, MS), pp. 111–117.
SIGITESIGITE-2004-McMahon #c# #case study #dot-net #education #framework #how #what
How can you teach what you don’t know?: a case study of learning and teaching microsoft .NET framework and C# (REM), p. 269.
IWPCIWPC-2004-HammoudaGKS #diagrams #modelling #uml
Tool-Supported Customization of UML Class Diagrams for Learning Complex System Models (IH, OG, KK, TS), pp. 24–33.
ICALPICALP-2004-AlonA
Learning a Hidden Subgraph (NA, VA), pp. 110–121.
CSCWCSCW-2004-CubranicMSB #case study #development
Learning from project history: a case study for software development (DC, GCM, JS, KSB), pp. 82–91.
ICEISICEIS-v2-2004-BendouM #graph #network
Learning Bayesian Networks with Largest Chain Graphs (MB, PM), pp. 184–190.
ICEISICEIS-v2-2004-ColaceSVF #algorithm #automation #ontology
A Semi-Automatic Bayesian Algorithm for Ontology Learning (FC, MDS, MV, PF), pp. 191–196.
ICEISICEIS-v2-2004-ColaceSVF04a #algorithm #comparison #network
Bayesian Network Structural Learning from Data: An Algorithms Comparison (FC, MDS, MV, PF), pp. 527–530.
ICEISICEIS-v2-2004-Kabiri #approximate #comparison #network
A Comparison Between the Proportional Keen Approximator and the Neural Networks Learning Methods (PK), pp. 159–164.
ICEISICEIS-v3-2004-Nobre #complexity #design
Organisational Learning — Foundational Roots for Design for Complexity (ÂLN), pp. 85–93.
ICEISICEIS-v4-2004-Carneiro #challenge #network #process
Learning Processes and the Role of Technological Networks as an Innovative Challenge (AC), pp. 497–501.
ICEISICEIS-v4-2004-FloresGVS
Amplia Learning Environment: A Proposal for Pedagogical Negotiation (CDF, JCG, RMV, LJS), pp. 279–286.
ICEISICEIS-v5-2004-JantkeLGGTT #data mining #mining
Learning by Doing and Learning when Doing: Dovetailing E-Learning and Decision Support with a Data Mining Tutor (KPJ, SL, GG, PAG, BT, BT), pp. 238–241.
ICEISICEIS-v5-2004-SalcedoY #library #metadata
Supporting Course Sequencing in a Digital Library: Usage of Dynamic Metadata for Learning Objects (RMS, YY), pp. 319–324.
ICEISICEIS-v5-2004-SantanaS #hypermedia
Accessing Hypermedia Systems Efectiveness in Learning Contexts (SS, AS), pp. 250–253.
CIKMCIKM-2004-LiO #identification #music
Semi-supervised learning for music artists style identification (TL, MO), pp. 152–153.
CIKMCIKM-2004-LiuZYYYCBM #metric #similarity
Learning similarity measures in non-orthogonal space (NL, BZ, JY, QY, SY, ZC, FB, WYM), pp. 334–341.
CIKMCIKM-2004-MaZMS #framework #query #similarity #using
A framework for refining similarity queries using learning techniques (YM, QZ, SM, DYS), pp. 158–159.
ICMLICML-2004-AgarwalT #3d
Learning to track 3D human motion from silhouettes (AA, BT).
ICMLICML-2004-BachLJ #algorithm #kernel #multi
Multiple kernel learning, conic duality, and the SMO algorithm (FRB, GRGL, MIJ).
ICMLICML-2004-BahamondeBDQLCAG #case study #set
Feature subset selection for learning preferences: a case study (AB, GFB, JD, JRQ, OL, JJdC, JA, FG).
ICMLICML-2004-BilenkoBM #clustering #constraints #metric
Integrating constraints and metric learning in semi-supervised clustering (MB, SB, RJM).
ICMLICML-2004-BlumLRR #random #using
Semi-supervised learning using randomized mincuts (AB, JDL, MRR, RR).
ICMLICML-2004-Bouckaert #classification
Estimating replicability of classifier learning experiments (RRB).
ICMLICML-2004-BrefeldS
Co-EM support vector learning (UB, TS).
ICMLICML-2004-Brinker #ranking
Active learning of label ranking functions (KB).
ICMLICML-2004-CastilloW #case study #comparative #multi
A comparative study on methods for reducing myopia of hill-climbing search in multirelational learning (LPC, SW).
ICMLICML-2004-ConitzerS #bound #communication #complexity #game studies
Communication complexity as a lower bound for learning in games (VC, TS).
ICMLICML-2004-EliazarP #mobile #modelling #probability
Learning probabilistic motion models for mobile robots (AIE, RP).
ICMLICML-2004-GaoWLC #approach #categorisation #multi #robust
A MFoM learning approach to robust multiclass multi-label text categorization (SG, WW, CHL, TSC).
ICMLICML-2004-GoldenbergM #scalability
Tractable learning of large Bayes net structures from sparse data (AG, AWM).
ICMLICML-2004-GrossmanD #classification #network
Learning Bayesian network classifiers by maximizing conditional likelihood (DG, PMD).
ICMLICML-2004-HuangYKL #classification #scalability
Learning large margin classifiers locally and globally (KH, HY, IK, MRL).
ICMLICML-2004-JamesS #predict
Learning and discovery of predictive state representations in dynamical systems with reset (MRJ, SPS).
ICMLICML-2004-KashimaT #algorithm #graph #kernel #sequence
Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs (HK, YT).
ICMLICML-2004-LawrenceP
Learning to learn with the informative vector machine (NDL, JCP).
ICMLICML-2004-MannorMHK #abstraction #clustering
Dynamic abstraction in reinforcement learning via clustering (SM, IM, AH, UK).
ICMLICML-2004-MelvilleM
Diverse ensembles for active learning (PM, RJM).
ICMLICML-2004-MerkeS #approximate #convergence #linear
Convergence of synchronous reinforcement learning with linear function approximation (AM, RS).
ICMLICML-2004-MoralesS #behaviour
Learning to fly by combining reinforcement learning with behavioural cloning (EFM, CS).
ICMLICML-2004-NatteeSNO #first-order #mining #multi
Learning first-order rules from data with multiple parts: applications on mining chemical compound data (CN, SS, MN, TO).
ICMLICML-2004-NguyenS #clustering #using
Active learning using pre-clustering (HTN, AWMS).
ICMLICML-2004-OngMCS #kernel
Learning with non-positive kernels (CSO, XM, SC, AJS).
ICMLICML-2004-PieterN
Apprenticeship learning via inverse reinforcement learning (PA, AYN).
ICMLICML-2004-Potts #incremental #linear
Incremental learning of linear model trees (DP).
ICMLICML-2004-RosalesAF #clustering #using
Learning to cluster using local neighborhood structure (RR, KA, BJF).
ICMLICML-2004-RosencrantzGT #predict
Learning low dimensional predictive representations (MR, GJG, ST).
ICMLICML-2004-RuckertK #bound #towards
Towards tight bounds for rule learning (UR, SK).
ICMLICML-2004-RudarySP #adaptation #constraints #reasoning
Adaptive cognitive orthotics: combining reinforcement learning and constraint-based temporal reasoning (MRR, SPS, MEP).
ICMLICML-2004-Ryabko #online
Online learning of conditionally I.I.D. data (DR).
ICMLICML-2004-Shalev-ShwartzSN #online #pseudo
Online and batch learning of pseudo-metrics (SSS, YS, AYN).
ICMLICML-2004-SimsekB #abstraction #identification #using
Using relative novelty to identify useful temporal abstractions in reinforcement learning (ÖS, AGB).
ICMLICML-2004-TaoSVO #approximate #multi
SVM-based generalized multiple-instance learning via approximate box counting (QT, SDS, NVV, TTO).
ICMLICML-2004-TaskarCK #markov #network
Learning associative Markov networks (BT, VC, DK).
ICMLICML-2004-ToutanovaMN #dependence #modelling #random #word
Learning random walk models for inducing word dependency distributions (KT, CDM, AYN).
ICMLICML-2004-WeinbergerSS #kernel #matrix #reduction
Learning a kernel matrix for nonlinear dimensionality reduction (KQW, FS, LKS).
ICMLICML-2004-Zadrozny #bias #classification
Learning and evaluating classifiers under sample selection bias (BZ).
ICMLICML-2004-ZhangYK #algorithm #kernel #matrix #using
Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm (ZZ, DYY, JTK).
ICPRICPR-v1-2004-BouguilaZ #finite #modelling
A Powreful Finite Mixture Model Based on the Generalized Dirichlet Distribution: Unsupervised Learning and Applications (NB, DZ), pp. 280–283.
ICPRICPR-v1-2004-GocciaSD #classification #fuzzy #recognition
Learning Optimal Classifier Through Fuzzy Recognition Rate Maximization (MG, CS, SGD), pp. 204–207.
ICPRICPR-v1-2004-GokcenJD #bound
Comparing Optimal Bounding Ellipsoid and Support Vector Machine Active Learning (IG, DJ, JRD), pp. 172–175.
ICPRICPR-v1-2004-LeangB
Learning Integrated Perception-Based Speed Control (PL, BB), pp. 813–816.
ICPRICPR-v1-2004-YiKZ #classification
Classifier Combination based on Active Learning (XY, ZK, CZ), pp. 184–187.
ICPRICPR-v2-2004-FangQ #detection
Learning Sample Subspace with Application to Face Detection (JF, GQ), pp. 423–426.
ICPRICPR-v2-2004-JingZLZZ #image #retrieval
Learning in Hidden Annotation-Based Image Retrieval (FJ, BZ, ML, HZ, JZ), pp. 1001–1004.
ICPRICPR-v2-2004-KaneS #classification #image #network
Bayesian Network Structure Learning and Inference in Indoor vs. Outdoor Image Classification (MJK, AES), pp. 479–482.
ICPRICPR-v2-2004-LindgrenH #component #image #independence #representation
Learning High-level Independent Components of Images through a Spectral Representation (JTL, AH), pp. 72–75.
ICPRICPR-v2-2004-LiuS
Reinforcement Learning-Based Feature Learning for Object Tracking (FL, JS), pp. 748–751.
ICPRICPR-v2-2004-SageB
Joint Spatial and Temporal Structure Learning for Task based Control (KS, HB), pp. 48–51.
ICPRICPR-v2-2004-ZiouB #analysis #finite #image #using
Unsupervised Learning of a Finite Gamma Mixture Using MML: Application to SAR Image Analysis (DZ, NB), pp. 68–71.
ICPRICPR-v3-2004-FanG
Hierarchical Object Indexing and Sequential Learning (XF, DG), pp. 65–68.
ICPRICPR-v3-2004-KoKB04a #multi #problem
Improved N-Division Output Coding for Multiclass Learning Problems (JK, EK, HB), pp. 470–473.
ICPRICPR-v3-2004-LuoKGHSRH #multi
Active Learning to Recognize Multiple Types of Plankton (TL, KK, DBG, LOH, SS, AR, TH), pp. 478–481.
ICPRICPR-v3-2004-MakiharaSS #interactive #online #recognition
Online Learning of Color Transformation for Interactive Object Recognition under Various Lighting Conditions (YM, YS, NS), pp. 161–164.
ICPRICPR-v3-2004-NeuhausB #approach #distance #edit distance #graph #probability
A Probabilistic Approach to Learning Costs for Graph Edit Distance (MN, HB), pp. 389–393.
ICPRICPR-v3-2004-ParedesV #fault #nearest neighbour #prototype #reduction
Learning Prototypes and Distances (LPD). A Prototype Reduction Technique based on Nearest Neighbor Error Minimization (RP, EV), pp. 442–445.
ICPRICPR-v3-2004-ShiNGY #classification
Critical Vector Learning to Construct RBF Classifiers (DS, GSN, JG, DSY), pp. 359–362.
ICPRICPR-v4-2004-Cardenas #classification #multi #prototype #string
A Learning Model for Multiple-Prototype Classification of Strings (RAM), pp. 420–423.
ICPRICPR-v4-2004-ChenC04a #bidirectional #dependence #network
Improvement of Bidirectional Recurrent Neural Network for Learning Long-Term Dependencies (JC, NSC), pp. 593–596.
ICPRICPR-v4-2004-FabletJB #automation #estimation #image #statistics #using
Automatic Fish Age Estimation from Otolith Images using Statistical Learning (RF, NLJ, AB), pp. 503–506.
ICPRICPR-v4-2004-McKennaN #using
Learning Spatial Context from Tracking using Penalised Likelihoods (SJM, HNC), pp. 138–141.
ICPRICPR-v4-2004-PeternelL #probability #recognition #visual notation
Visual Learning and Recognition of a Probabilistic Spatio-Temporal Model of Cyclic Human Locomotion (MP, AL), pp. 146–149.
ICPRICPR-v4-2004-QinandS04a #algorithm #kernel #novel #prototype
A Novel Kernel Prototype-Based Learning Algorithm (AKQ, PNS), pp. 621–624.
ICPRICPR-v4-2004-RaytchevYS #estimation
Head Pose Estimation by Nonlinear Manifold Learning (BR, IY, KS), pp. 462–466.
ICPRICPR-v4-2004-SamsonB #clustering #parallel #robust #video
Learning Classes for Video Interpretation with a Robust Parallel Clustering Method (VS, PB), pp. 569–572.
ICPRICPR-v4-2004-StefanoDM #approach
A Dynamic Approach to Learning Vector Quantization (CDS, CD, AM), pp. 601–604.
ICPRICPR-v4-2004-WuCW04a #recognition
Face Recognition Based on Discriminative Manifold Learning (YW, KLC, LW), pp. 171–174.
KDDKDD-2004-AbeVAS
Cross channel optimized marketing by reinforcement learning (NA, NKV, CA, RS), pp. 767–772.
KDDKDD-2004-AbeZL #multi
An iterative method for multi-class cost-sensitive learning (NA, BZ, JL), pp. 3–11.
KDDKDD-2004-CaruanaN #analysis #data mining #empirical #metric #mining #performance
Data mining in metric space: an empirical analysis of supervised learning performance criteria (RC, ANM), pp. 69–78.
KDDKDD-2004-EvgeniouP #multi
Regularized multi--task learning (TE, MP), pp. 109–117.
KDDKDD-2004-KolterM #bytecode #detection
Learning to detect malicious executables in the wild (JZK, MAM), pp. 470–478.
KDDKDD-2004-KummamuruKA #difference #metric
Learning spatially variant dissimilarity (SVaD) measures (KK, RK, RA), pp. 611–616.
KDDKDD-2004-PopesculU #clustering #concept #relational #statistics
Cluster-based concept invention for statistical relational learning (AP, LHU), pp. 665–670.
KDDKDD-2004-TruongLB #dataset #random #using
Learning a complex metabolomic dataset using random forests and support vector machines (YT, XL, CB), pp. 835–840.
KRKR-2004-PasulaZK #probability #relational
Learning Probabilistic Relational Planning Rules (HP, LSZ, LPK), pp. 683–691.
LSOLSO-2004-ChauM #agile #tool support
Tool Support for Inter-team Learning in Agile Software Organizations (TC, FM), pp. 98–109.
LSOLSO-2004-FalboRBT #how #risk management #using
Learning How to Manage Risks Using Organizational Knowledge (RdAF, FBR, GB, DFT), pp. 7–18.
LSOLSO-2004-HolzM #past present future #research
Research on Learning Software Organizations — Past, Present, and Future (HH, GM), pp. 1–6.
LSOLSO-2004-MelnikR
Impreciseness and Its Value from the Perspective of Software Organizations and Learning (GM, MMR), pp. 122–130.
LSOLSO-2004-Roth-Berghofer
Learning from HOMER, a Case-Based Help Desk Support System (TRB), pp. 88–97.
LSOLSO-2004-SousaAO #maintenance
Learning Software Maintenance Organizations (KDdS, NA, KMdO), pp. 67–77.
SEKESEKE-2004-DantasBW #game studies #project management
A Simulation-Based Game for Project Management Experiential Learning (ARD, MdOB, CMLW), pp. 19–24.
SEKESEKE-2004-MaxvilleLA #component
Learning to Select Software Components (VM, CPL, JA), pp. 421–426.
SIGIRSIGIR-2004-LamHC #mining #similarity
Learning phonetic similarity for matching named entity translations and mining new translations (WL, RH, PSC), pp. 289–296.
SIGIRSIGIR-2004-RoussinovR #web
Learning patterns to answer open domain questions on the web (DR, JARF), pp. 500–501.
SIGIRSIGIR-2004-XiLB #effectiveness #ranking
Learning effective ranking functions for newsgroup search (WX, JL, EB), pp. 394–401.
SIGIRSIGIR-2004-ZengHCMM #clustering #web
Learning to cluster web search results (HJZ, QCH, ZC, WYM, JM), pp. 210–217.
RERE-2004-HaleyNST #categorisation #requirements
The Conundrum of Categorising Requirements: Managing Requirements for Learning on the Move (DTH, BN, HCS, JT), pp. 309–314.
SACSAC-2004-BergholzC #interface #query #web
Learning query languages of Web interfaces (AB, BC), pp. 1114–1121.
SACSAC-2004-DerntlM #case study #concept #evaluation #experience
Patterns for blended, Person-Centered learning: strategy, concepts, experiences, and evaluation (MD, RMP), pp. 916–923.
SACSAC-2004-HatalaREW #communication #implementation #network #repository
The eduSource Communication Language: implementing open network for learning repositories and services (MH, GR, TE, JW), pp. 957–962.
SACSAC-2004-NeelyLEBNG #architecture #distributed
An architecture for supporting vicarious learning in a distributed environment (SN, HL, DME, JB, JN, XG), pp. 963–970.
SACSAC-2004-ZaneroS #detection
Unsupervised learning techniques for an intrusion detection system (SZ, SMS), pp. 412–419.
DACDAC-2004-WangMCA #on the
On path-based learning and its applications in delay test and diagnosis (LCW, TMM, KTC, MSA), pp. 492–497.
DATEDATE-v1-2004-Wang #simulation #validation
Regression Simulation: Applying Path-Based Learning In Delay Test and Post-Silicon Validation (LCW), pp. 692–695.
PDPPDP-2004-AsensioDHMABO #collaboration #component #development
Collaborative Learning Patterns: Assisting the Development of Component-Based CSCL Applications (JIAP, YAD, MH, AMM, FJÁ, MTB, CAO), pp. 218–224.
PDPPDP-2004-RieraLSAVB #collaboration #communication #multi
Study of Communication in a Multi-Agent System for Collaborative Learning Scenarios (AR, ML, ESV, RRA, XAVS, SB), pp. 233–240.
STOCSTOC-2004-AwerbuchK #adaptation #distributed #feedback #geometry
Adaptive routing with end-to-end feedback: distributed learning and geometric approaches (BA, RDK), pp. 45–53.
SATSAT-2004-SangBBKP #component #effectiveness
Combining Component Caching and Clause Learning for Effective Model Counting (TS, FB, PB, HAK, TP), pp. 20–28.
TPDLECDL-2003-QinG #education #metadata
Incorporating Educational Vocabulary in Learning Object Metadata Schemes (JQ, CJG), pp. 52–57.
TPDLECDL-2003-SmithABFHNTU #concept
The ADEPT Concept-Based Digital Learning Environment (TRS, DA, OAB, MF, WH, RN, TT, AU), pp. 300–312.
ICDARICDAR-2003-Legal-AyalaF #approach #image #segmentation
Image Segmentation By Learning Approach (HALA, JF), pp. 819–823.
ICDARICDAR-2003-RyuK #recognition #word
Learning the lexicon from raw texts for open-vocabulary Korean word recognition (SR, JHK), pp. 202–206.
ICDARICDAR-2003-ShimizuOWK #image #network
Mirror Image Learning for Autoassociative Neural Networks (SS, WO, TW, FK), pp. 804–808.
ICDARICDAR-2003-TakahashiN #recognition
A class-modular GLVQ ensemble with outlier learning for handwritten digit recognition (KT, DN), pp. 268–272.
JCDLJCDL-2003-OldenettelMR #approach #library
Integrating Digital Libraries into Learning Environments: The LEBONED Approach (FO, MM, DR), pp. 280–290.
JCDLJCDL-2003-SooLLCC #automation #ontology #retrieval #semantics
Automated Semantic Annotation and Retrieval Based on Sharable Ontology and Case-Based Learning Techniques (VWS, CYL, CCL, SLC, CcC), p. 61–?.
JCDLJCDL-2003-SouthwickS #library
Learning Digital Library Technology Across Borders (SBS, RS), pp. 179–181.
CSEETCSEET-2003-AlfonsoM #re-engineering
Learning Software Engineering with Group Work (MIA, FM), p. 309–?.
ITiCSEITiCSE-2003-ChalkBP #design #education #programming
Designing and evaluating learning objects for introductory programming education (PC, CB, PP), p. 240.
ITiCSEITiCSE-2003-DemetriadisTP #multi #student #towards #using
A phenomenographic study of students’ attitudes toward the use of multiple media for learning (SND, ET, ASP), pp. 183–187.
ITiCSEITiCSE-2003-EkateriniSP #education #problem
Teaching IT in secondary education through problem-based learning could be really beneficial (GE, BS, GP), p. 243.
ITiCSEITiCSE-2003-Garvin-DoxasB #interactive
Creating learning environments that support interaction (KGD, LJB), p. 276.
ITiCSEITiCSE-2003-GunawardenaA #approach #education #programming
A customized learning objects approach to teaching programming (AG, VA), p. 264.
ITiCSEITiCSE-2003-KurhilaMNFT #peer-to-peer #web
Peer-to-peer learning with open-ended writable Web (JK, MM, PN, PF, HT), pp. 173–177.
ITiCSEITiCSE-2003-Leska #java #user interface #using
Learning to develop GUIs in Java using closed labs (CL), p. 228.
ITiCSEITiCSE-2003-LynchM #student
The winds of change: students’ comfort level in different learning environments (KL, SM), pp. 70–73.
ITiCSEITiCSE-2003-MirmotahariHK #architecture
Difficulties learning computer architecture (OM, CH, JK), p. 247.
ITiCSEITiCSE-2003-Nodelman #programming #theory and practice
Learning computer graphics by programming: linking theory and practice (VN), p. 261.
ITiCSEITiCSE-2003-PearsPE #online
Enriching online learning resources with “explanograms” (ANP, LP, CE), p. 237.
ICSMEICSM-2003-LinosB #maintenance #re-engineering
Service Learning in Software Engineering and Maintenance (PKL, CBK), p. 336–?.
WCREWCRE-2003-Murphy
Learning from the Past (GCM), pp. 2–3.
DLTDLT-2003-DrewesH #education
Learning a Regular Tree Language from a Teacher (FD, JH), pp. 279–291.
HaskellHaskell-2003-HeerenLI #haskell
Helium, for learning Haskell (BH, DL, AvI), pp. 62–71.
ICEISICEIS-v2-2003-BendouM #network #semistructured data
Learning Bayesian Networks From Noisy Data (MB, PM), pp. 26–33.
ICEISICEIS-v2-2003-ColaceSFV #network #ontology
Ontology Learning Through Bayesian Networks (FC, MDS, PF, MV), pp. 430–433.
ICEISICEIS-v2-2003-KeeniGS #network #on the #performance #using
On Fast Learning of Neural Networks Using Back Propagation (KK, KG, HS), pp. 266–271.
ICEISICEIS-v2-2003-Koehler #automation #database #health #network
Tool for Automatic Learning of Bayesian Networks From Database: An Application in the Health Area (CK), pp. 474–481.
ICEISICEIS-v4-2003-SemeraroLDL
Learning User Profiles for Intelligent Search (GS, PL, MD, OL), pp. 426–429.
ICEISICEIS-v4-2003-TyrvainenJS #case study #on the
On Estimating the Amount of Learning Materials a Case Study (PT, MJ, AS), pp. 127–135.
CIKMCIKM-2003-ZhangOR #using
Learning cross-document structural relationships using boosting (ZZ, JO, DRR), pp. 124–130.
ECIRECIR-2003-TianC #collaboration #rating #recommendation #similarity
Learning User Similarity and Rating Style for Collaborative Recommendation (LFT, KWC), pp. 135–145.
ICMLICML-2003-Bar-HillelHSW #distance #equivalence #using
Learning Distance Functions using Equivalence Relations (ABH, TH, NS, DW), pp. 11–18.
ICMLICML-2003-BaramEL #algorithm #online
Online Choice of Active Learning Algorithms (YB, REY, KL), pp. 19–26.
ICMLICML-2003-BerardiCEM #analysis #layout #logic programming #source code
Learning Logic Programs for Layout Analysis Correction (MB, MC, FE, DM), pp. 27–34.
ICMLICML-2003-Bouckaert #algorithm #testing
Choosing Between Two Learning Algorithms Based on Calibrated Tests (RRB), pp. 51–58.
ICMLICML-2003-Brinker
Incorporating Diversity in Active Learning with Support Vector Machines (KB), pp. 59–66.
ICMLICML-2003-BrownW #ambiguity #composition #network #using
The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods (GB, JLW), pp. 67–74.
ICMLICML-2003-CerquidesM #modelling #naive bayes
Tractable Bayesian Learning of Tree Augmented Naive Bayes Models (JC, RLdM), pp. 75–82.
ICMLICML-2003-ConitzerS #algorithm #multi #named #self
AWESOME: A General Multiagent Learning Algorithm that Converges in Self-Play and Learns a Best Response Against Stationary Opponents (VC, TS), pp. 83–90.
ICMLICML-2003-CozmanCC #modelling
Semi-Supervised Learning of Mixture Models (FGC, IC, MCC), pp. 99–106.
ICMLICML-2003-CumbyR #kernel #on the #relational
On Kernel Methods for Relational Learning (CMC, DR), pp. 107–114.
ICMLICML-2003-DriessensR #relational
Relational Instance Based Regression for Relational Reinforcement Learning (KD, JR), pp. 123–130.
ICMLICML-2003-EngelMM #approach #difference #process
Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning (YE, SM, RM), pp. 154–161.
ICMLICML-2003-Even-DarMM
Action Elimination and Stopping Conditions for Reinforcement Learning (EED, SM, YM), pp. 162–169.
ICMLICML-2003-GargR
Margin Distribution and Learning (AG, DR), pp. 210–217.
ICMLICML-2003-GeibelW
Perceptron Based Learning with Example Dependent and Noisy Costs (PG, FW), pp. 218–225.
ICMLICML-2003-IsaacS
Goal-directed Learning to Fly (AI, CS), pp. 258–265.
ICMLICML-2003-Joachims #clustering #graph
Transductive Learning via Spectral Graph Partitioning (TJ), pp. 290–297.
ICMLICML-2003-KennedyJ #problem
Characteristics of Long-term Learning in Soar and its Application to the Utility Problem (WGK, KADJ), pp. 337–344.
ICMLICML-2003-KirshnerPS #permutation
Unsupervised Learning with Permuted Data (SK, SP, PS), pp. 345–352.
ICMLICML-2003-KotnikK #self
The Significance of Temporal-Difference Learning in Self-Play Training TD-Rummy versus EVO-rummy (CK, JKK), pp. 369–375.
ICMLICML-2003-KrawiecB #synthesis #visual notation
Visual Learning by Evolutionary Feature Synthesis (KK, BB), pp. 376–383.
ICMLICML-2003-KwokT #kernel
Learning with Idealized Kernels (JTK, IWT), pp. 400–407.
ICMLICML-2003-LagoudakisP #classification
Reinforcement Learning as Classification: Leveraging Modern Classifiers (MGL, RP), pp. 424–431.
ICMLICML-2003-LaudD #analysis
The Influence of Reward on the Speed of Reinforcement Learning: An Analysis of Shaping (AL, GD), pp. 440–447.
ICMLICML-2003-LeeL #using
Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression (WSL, BL), pp. 448–455.
ICMLICML-2003-McGovernJ #identification #multi #predict #relational #using
Identifying Predictive Structures in Relational Data Using Multiple Instance Learning (AM, DJ), pp. 528–535.
ICMLICML-2003-MooreW #network
Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure Learning (AWM, WKW), pp. 552–559.
ICMLICML-2003-OntanonP #multi
Justification-based Multiagent Learning (SO, EP), pp. 576–583.
ICMLICML-2003-RichardsonD #multi
Learning with Knowledge from Multiple Experts (MR, PMD), pp. 624–631.
ICMLICML-2003-RuckertK #probability
Stochastic Local Search in k-Term DNF Learning (UR, SK), pp. 648–655.
ICMLICML-2003-RussellZ
Q-Decomposition for Reinforcement Learning Agents (SJR, AZ), pp. 656–663.
ICMLICML-2003-SinghLJPS #predict
Learning Predictive State Representations (SPS, MLL, NKJ, DP, PS), pp. 712–719.
ICMLICML-2003-StimpsonG #approach #social
Learning To Cooperate in a Social Dilemma: A Satisficing Approach to Bargaining (JLS, MAG), pp. 728–735.
ICMLICML-2003-TaskarWK #testing
Learning on the Test Data: Leveraging Unseen Features (BT, MFW, DK), pp. 744–751.
ICMLICML-2003-WangD #modelling #policy
Model-based Policy Gradient Reinforcement Learning (XW, TGD), pp. 776–783.
ICMLICML-2003-WangSPZ #modelling #principle
Learning Mixture Models with the Latent Maximum Entropy Principle (SW, DS, FP, YZ), pp. 784–791.
ICMLICML-2003-WiewioraCE
Principled Methods for Advising Reinforcement Learning Agents (EW, GWC, CE), pp. 792–799.
ICMLICML-2003-WinnerV #named
DISTILL: Learning Domain-Specific Planners by Example (EW, MMV), pp. 800–807.
ICMLICML-2003-WuC #adaptation
Adaptive Feature-Space Conformal Transformation for Imbalanced-Data Learning (GW, EYC), pp. 816–823.
ICMLICML-2003-Zhang #kernel #metric #multi #representation #scalability #towards
Learning Metrics via Discriminant Kernels and Multidimensional Scaling: Toward Expected Euclidean Representation (ZZ), pp. 872–879.
ICMLICML-2003-ZhangH #taxonomy
Learning from Attribute Value Taxonomies and Partially Specified Instances (JZ, VH), pp. 880–887.
ICMLICML-2003-ZhangXC #adaptation
Exploration and Exploitation in Adaptive Filtering Based on Bayesian Active Learning (YZ, WX, JPC), pp. 896–903.
ICMLICML-2003-ZhuGL #using
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions (XZ, ZG, JDL), pp. 912–919.
KDDKDD-2003-Koller #relational #statistics
Statistical learning from relational data (DK), p. 4.
KDDKDD-2003-NevilleJFH #probability #relational
Learning relational probability trees (JN, DJ, LF, MH), pp. 625–630.
KDDKDD-2003-SarawagiCG #named #probability #topic
Cross-training: learning probabilistic mappings between topics (SS, SC, SG), pp. 177–186.
MLDMMLDM-2003-ComiteGT #multi
Learning Multi-label Alternating Decision Trees from Texts and Data (FDC, RG, MT), pp. 35–49.
MLDMMLDM-2003-Craw #reasoning
Introspective Learning to Build Case-Based Reasoning (CBR) Knowledge Containers (SC), pp. 1–6.
MLDMMLDM-2003-KrawiecB #recognition
Coevolutionary Feature Learning for Object Recognition (KK, BB), pp. 224–238.
MLDMMLDM-2003-KuhnertK #classification #image
A Learning Autonomous Driver System on the Basis of Image Classification and Evolutional Learning (KDK, MK), pp. 400–412.
SEKESEKE-2003-ChenJ #fuzzy #induction #information management #multi #named
MFILM: a multi-dimensional fuzzy inductive learning method for knowledge acquisition (YTC, BJ), pp. 445–449.
SIGIRSIGIR-2003-GaoWLC #approach #categorisation
A maximal figure-of-merit learning approach to text categorization (SG, WW, CHL, TSC), pp. 174–181.
SACSAC-2003-LiZLO #classification #functional #semistructured data
Gene Functional Classification by Semisupervised Learning from Heterogeneous Data (TL, SZ, QL, MO), pp. 78–82.
SACSAC-2003-RumetshoferW #adaptation #approach #aspect-oriented
An Approach for Adaptable Learning Systems with Respect to Psychological Aspects (HR, WW), pp. 558–563.
DACDAC-2003-GuptaGWYA #bound #model checking #satisfiability
Learning from BDDs in SAT-based bounded model checking (AG, MKG, CW, ZY, PA), pp. 824–829.
DATEDATE-2003-LuWCH #correlation #satisfiability
A Circuit SAT Solver With Signal Correlation Guided Learning (FL, LCW, KTC, RCYH), pp. 10892–10897.
PDPPDP-2003-SanchezLARVB #architecture #multi
A multi-tiered agent-based architecture for a cooperative learning environment (ESV, ML, RRA, AR, XAVS, SB), pp. 500–506.
STOCSTOC-2003-MosselOS
Learning juntas (EM, RO, RAS), pp. 206–212.
TACASTACAS-2003-CobleighGP #composition #verification
Learning Assumptions for Compositional Verification (JMC, DG, CSP), pp. 331–346.
CAVCAV-2003-HungarNS #automaton #optimisation
Domain-Specific Optimization in Automata Learning (HH, ON, BS), pp. 315–327.
ICSTSAT-2003-SabharwalBK #performance #problem #using
Using Problem Structure for Efficient Clause Learning (AS, PB, HAK), pp. 242–256.
JCDLJCDL-2002-McMartinT #library
Digital library services for authors of learning materials (FPM, YT), pp. 117–118.
SIGMODSIGMOD-2002-MarklL
Learning table access cardinalities with LEO (VM, GML), p. 613.
VLDBVLDB-2002-SarawagiBKM #alias #interactive #named
ALIAS: An Active Learning led Interactive Deduplication System (SS, AB, AK, CM), pp. 1103–1106.
CSEETCSEET-2002-Armarego #design #problem
Advanced Software Design: A Case in Problem-Based Learning (JA), pp. 44–54.
CSEETCSEET-2002-UmphressH #education #process
Software Process as a Foundation for Teaching, Learning and Accrediting (DAU, JAHJ), pp. 160–169.
ITiCSEITiCSE-2002-CarboneS #education #question #student #what
A studio-based teaching and learning model in IT: what do first year students think? (AC, JS), pp. 213–217.
ITiCSEITiCSE-2002-Cassel #network
Very active learning of network routing (LNC), p. 195.
ITiCSEITiCSE-2002-Chalk #aspect-oriented #education #human-computer #using
Evaluating the use of a virtual learning environment for teaching aspects of HCI (PC), pp. 125–129.
ITiCSEITiCSE-2002-FabregaMJM #network
A virtual network laboratory for learning IP networking (LF, JM, TJ, DM), pp. 161–164.
ITiCSEITiCSE-2002-HansenR #collaboration #education #modelling #object-oriented #tool support
Tool support for collaborative teaching and learning of object-oriented modeling (KMH, AVR), pp. 146–150.
ITiCSEITiCSE-2002-Hazzan #abstraction #concept
Reducing abstraction level when learning computability theory concepts (OH), pp. 156–160.
ITiCSEITiCSE-2002-Lapidot #experience #self
Self-assessment as a powerful learning experience (TL), p. 198.
ITiCSEITiCSE-2002-LastDHW #collaboration #student
Learning from students: continuous improvement in international collaboration (MZL, MD, MLH, MW), pp. 136–140.
ITiCSEITiCSE-2002-Nygaard #object-oriented
COOL (comprehensive object-oriented learning) (KN), p. 218.
ITiCSEITiCSE-2002-ParkinsonR #performance #question
Do cognitive styles affect learning performance in different computer media? (AP, JAR), pp. 39–43.
ITiCSEITiCSE-2002-VanDeGriftA #assessment #framework #tool support
Learning to support the instructor: classroom assessment tools as discussion frameworks in CS 1 (TV, RJA), pp. 19–23.
ITiCSEITiCSE-2002-WaltersASBK
Increasing learning and decreasing costs in a computer fluency course (DW, CA, BS, DTB, HK), pp. 208–212.
CHICHI-2002-Ehret #user interface #visual notation
Learning where to look: location learning in graphical user interfaces (BDE), pp. 211–218.
CHICHI-2002-SnowdonG #experience
Diffusing information in organizational settings: learning from experience (DS, AG), pp. 331–338.
CHICHI-2002-ZhaiSA
Movement model, hits distribution and learning in virtual keyboarding (SZ, AES, JA), pp. 17–24.
ICEISICEIS-2002-FloresG #algorithm #case study #estimation #fuzzy #problem
Applicability of Estimation of Distribution Algorithms to the Fuzzy Rule Learning Problem: A Preliminary Study (MJF, JAG), pp. 350–357.
ICEISICEIS-2002-IglesiasMCCF #database #design #education #fault
Learning to Teach Database Design by Trial and Error (AI, PM, DC, EC, FF), pp. 500–505.
ICEISICEIS-2002-SantosNASR #classification #data mining #database #mining #using
Augmented Data Mining over Clinical Databases Using Learning Classifier Systems (MFS, JN, AA, ÁMS, FR), pp. 512–516.
CIKMCIKM-2002-HuangCA #comparison #web
Comparison of interestingness functions for learning web usage patterns (XH, NC, AA), pp. 617–620.
ICMLICML-2002-BianchettiRS #concept #constraints #relational
Constraint-based Learning of Long Relational Concepts (JAB, CR, MS), pp. 35–42.
ICMLICML-2002-ChisholmT #random
Learning Decision Rules by Randomized Iterative Local Search (MC, PT), pp. 75–82.
ICMLICML-2002-DietterichBMS #probability #refinement
Action Refinement in Reinforcement Learning by Probability Smoothing (TGD, DB, RLdM, CS), pp. 107–114.
ICMLICML-2002-DriessensD #relational
Integrating Experimentation and Guidance in Relational Reinforcement Learning (KD, SD), pp. 115–122.
ICMLICML-2002-FerriFH #using
Learning Decision Trees Using the Area Under the ROC Curve (CF, PAF, JHO), pp. 139–146.
ICMLICML-2002-GhavamzadehM
Hierarchically Optimal Average Reward Reinforcement Learning (MG, SM), pp. 195–202.
ICMLICML-2002-GonzalezHC #concept #graph #relational
Graph-Based Relational Concept Learning (JAG, LBH, DJC), pp. 219–226.
ICMLICML-2002-GuestrinLP #coordination
Coordinated Reinforcement Learning (CG, MGL, RP), pp. 227–234.
ICMLICML-2002-GuestrinPS #modelling
Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs (CG, RP, DS), pp. 235–242.
ICMLICML-2002-Hengst
Discovering Hierarchy in Reinforcement Learning with HEXQ (BH), pp. 243–250.
ICMLICML-2002-JensenN #bias #feature model #relational
Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning (DJ, JN), pp. 259–266.
ICMLICML-2002-KakadeL #approximate
Approximately Optimal Approximate Reinforcement Learning (SK, JL), pp. 267–274.
ICMLICML-2002-LanckrietCBGJ #kernel #matrix #programming
Learning the Kernel Matrix with Semi-Definite Programming (GRGL, NC, PLB, LEG, MIJ), pp. 323–330.
ICMLICML-2002-LaudD #behaviour
Reinforcement Learning and Shaping: Encouraging Intended Behaviors (AL, GD), pp. 355–362.
ICMLICML-2002-LeckieR #distributed #probability
Learning to Share Distributed Probabilistic Beliefs (CL, KR), pp. 371–378.
ICMLICML-2002-MerkeS #approximate #convergence
A Necessary Condition of Convergence for Reinforcement Learning with Function Approximation (AM, RS), pp. 411–418.
ICMLICML-2002-Mladenic #normalisation #using #word
Learning word normalization using word suffix and context from unlabeled data (DM), pp. 427–434.
ICMLICML-2002-MusleaMK #multi #robust
Active + Semi-supervised Learning = Robust Multi-View Learning (IM, SM, CAK), pp. 435–442.
ICMLICML-2002-OatesDB #context-free grammar
Learning k-Reversible Context-Free Grammars from Positive Structural Examples (TO, DD, VB), pp. 459–465.
ICMLICML-2002-OLZ #using
Stock Trading System Using Reinforcement Learning with Cooperative Agents (JO, JWL, BTZ), pp. 451–458.
ICMLICML-2002-PanangadanD #2d #correlation #navigation
Learning Spatial and Temporal Correlation for Navigation in a 2-Dimensional Continuous World (AP, MGD), pp. 474–481.
ICMLICML-2002-ParkZ
A Boosted Maximum Entropy Model for Learning Text Chunking (SBP, BTZ), pp. 482–489.
ICMLICML-2002-PeshkinS #experience
Learning from Scarce Experience (LP, CRS), pp. 498–505.
ICMLICML-2002-PickettB #algorithm #named
PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning (MP, AGB), pp. 506–513.
ICMLICML-2002-Ryan #automation #behaviour #modelling #using
Using Abstract Models of Behaviours to Automatically Generate Reinforcement Learning Hierarchies (MRKR), pp. 522–529.
ICMLICML-2002-SeriT #modelling
Model-based Hierarchical Average-reward Reinforcement Learning (SS, PT), pp. 562–569.
ICMLICML-2002-ShapiroL #using
Separating Skills from Preference: Using Learning to Program by Reward (DGS, PL), pp. 570–577.
ICMLICML-2002-Stirling
Learning to Fly by Controlling Dynamic Instabilities (DS), pp. 586–593.
ICMLICML-2002-ThamDR #classification #markov #monte carlo #using
Sparse Bayesian Learning for Regression and Classification using Markov Chain Monte Carlo (SST, AD, KR), pp. 634–641.
ICMLICML-2002-ZhangGYF #image #multi #retrieval #using
Content-Based Image Retrieval Using Multiple-Instance Learning (QZ, SAG, WY, JEF), pp. 682–689.
ICMLICML-2002-ZubekD #heuristic
Pruning Improves Heuristic Search for Cost-Sensitive Learning (VBZ, TGD), pp. 19–26.
ICPRICPR-v1-2002-HadidKP #analysis #linear #using
Unsupervised Learning Using Locally Linear Embedding: Experiments with Face Pose Analysis (AH, OK, MP), pp. 111–114.
ICPRICPR-v1-2002-HaroE #video
Learning Video Processing by Example (AH, IAE), pp. 487–491.
ICPRICPR-v1-2002-RobertsMR #3d #online
Online Appearance Learning or 3D Articulated Human Tracking (TJR, SJM, IWR), pp. 425–428.
ICPRICPR-v2-2002-Al-ShaherH #modelling #online #performance
Fast On-Line learning of Point Distribution Models (AAAS, ERH), pp. 208–211.
ICPRICPR-v2-2002-Amin #prototype #using
Prototyping Structural Description Using Decision Tree Learning Techniques (AA), pp. 76–79.
ICPRICPR-v2-2002-ChiuLY #personalisation
Learning User Preference in a Personalized CBIR Systeml (CYC, HCL, SNY), p. 532–?.
ICPRICPR-v2-2002-ChoCWS #adaptation #classification #data type #image #representation #robust
Robust Learning in Adaptive Processing of Data Structures for Tree Representation Based Image Classification (SYC, ZC, ZW, WCS), pp. 108–111.
ICPRICPR-v2-2002-KherfiZB #feedback #image #retrieval
Learning from Negative Example in Relevance Feedback for Content-Based Image Retrieval (MLK, DZ, AB), pp. 933–936.
ICPRICPR-v2-2002-Lashkia
Learning with Relevant Features and Examples (GVL), pp. 68–71.
ICPRICPR-v2-2002-LiuB #concept #semantics #video #visual notation
Learning Semantic Visual Concepts from Video (JL, BB), pp. 1061–1064.
ICPRICPR-v2-2002-RiviereMMTPF #graph #markov #random #relational #using
Relational Graph Labelling Using Learning Techniques and Markov Random Fields (DR, JFM, JMM, FT, DPO, VF), pp. 172–175.
ICPRICPR-v2-2002-SeokL #algorithm #analysis #approach #difference #probability
The Analysis of a Stochastic Differential Approach for Langevine Comepetitive Learning Algorithm (JS, JWL), pp. 80–83.
ICPRICPR-v2-2002-ShiWOK #case study #comparative #image
Comparative Study on Mirror Image Learning (MIL) and GLVQ (MS, TW, WO, FK), p. 248–?.
ICPRICPR-v2-2002-TohM #approach #network
A Global Transformation Approach to RBF Neural Network Learning (KAT, KZM), pp. 96–99.
ICPRICPR-v2-2002-Torkkola02a #feature model #problem
Learning Feature Transforms Is an Easier Problem Than Feature Selection (KT), pp. 104–107.
ICPRICPR-v2-2002-WechslerDL #process #using
Hierarchical Interpretation of Human Activities Using Competitive Learning (HW, ZD, FL), pp. 338–341.
ICPRICPR-v3-2002-ArtacJL #incremental #online #recognition #visual notation
Incremental PCA or On-Line Visual Learning and Recognition (MA, MJ, AL), pp. 781–784.
ICPRICPR-v3-2002-BaesensECV #classification #markov #monte carlo #network #using
Learning Bayesian Network Classifiers for Credit Scoring Using Markov Chain Monte Carlo Search (BB, MEP, RC, JV), pp. 49–52.
ICPRICPR-v3-2002-ChartierL #image #network
Learning and Extracting Edges from Images by a Modified Hopfield Neural Network (SC, RL), pp. 431–434.
ICPRICPR-v3-2002-ChoudhuryRPP #detection #network
Boosting and Structure Learning in Dynamic Bayesian Networks for Audio-Visual Speaker Detection (TC, JMR, VP, AP), p. 789–?.
ICPRICPR-v3-2002-HoqueFG #classification #performance
The Effect of the Inhibition-Compensation Learning Scheme on n-tuple Based Classifier Performance (SH, MCF, RMG), pp. 452–455.
ICPRICPR-v3-2002-LuoWH02a #approach #graph
Graph Spectral Approach for Learning View Structure (BL, RCW, ERH), pp. 785–788.
ICPRICPR-v3-2002-Sakano #how #query #search-based
Genetic Translator: How to Apply Query Learning to Practical OCR (HS), pp. 184–187.
ICPRICPR-v3-2002-SinghR #recognition #robust
Background Learning for Robust Face Recognition (RKS, ANR), pp. 525–528.
ICPRICPR-v3-2002-SuW #identification #process
A Learning Process to the Identification of Feature Points on Chinese Characters (YMS, JFW), pp. 93–97.
ICPRICPR-v4-2002-KubotaMK #fault #optimisation
A Discriminative Learning Criterion for the Overall Optimization of Error and Reject (SK, HM, YK), pp. 98–102.
ICPRICPR-v4-2002-LiuSF #classification #polynomial
Learning Quadratic Discriminant Function for Handwritten Character Classification (CLL, HS, HF), pp. 44–47.
KDDKDD-2002-AntalGF #clustering #network #on the
On the potential of domain literature for clustering and Bayesian network learning (PA, PG, GF), pp. 405–414.
KDDKDD-2002-Ben-DavidGS #data flow #framework
A theoretical framework for learning from a pool of disparate data sources (SBD, JG, RS), pp. 443–449.
KDDKDD-2002-CohenR #clustering #integration #scalability #set
Learning to match and cluster large high-dimensional data sets for data integration (WWC, JR), pp. 475–480.
KDDKDD-2002-KruengkraiJ #algorithm #classification #parallel
A parallel learning algorithm for text classification (CK, CJ), pp. 201–206.
KDDKDD-2002-MahoneyC #detection #modelling #network #novel
Learning nonstationary models of normal network traffic for detecting novel attacks (MVM, PKC), pp. 376–385.
KDDKDD-2002-PednaultAZ
Sequential cost-sensitive decision making with reinforcement learning (EPDP, NA, BZ), pp. 259–268.
KDDKDD-2002-SarawagiB #interactive #using
Interactive deduplication using active learning (SS, AB), pp. 269–278.
KDDKDD-2002-TejadaKM #identification #independence #string
Learning domain-independent string transformation weights for high accuracy object identification (ST, CAK, SM), pp. 350–359.
KDDKDD-2002-YuHC #classification #named #using #web
PEBL: positive example based learning for Web page classification using SVM (HY, JH, KCCC), pp. 239–248.
KRKR-2002-BeygelzimerR #complexity #network
Inference Complexity as a Model-Selection Criterion for Learning Bayesian Networks (AB, IR), pp. 558–567.
LSOLSO-2002-AngkasaputraPRT #collaboration #implementation
The Collaborative Learning Methodology CORONET-Train: Implementation and Guidance (NA, DP, ER, ST), pp. 13–24.
LSOLSO-2002-HenningerM #agile #concept #development #question
Learning Software Organizations and Agile Software Development: Complementary or Contradictory Concepts? (SH, FM), pp. 1–3.
LSOLSO-2002-HofmannW #approach #community
Building Communities among Software Engineers: The ViSEK Approach to Intra- and Inter-Organizational Learning (BH, VW), pp. 25–33.
LSOLSO-2002-NeuB #comprehension #process #simulation
Learning and Understanding a Software Process through Simulation of Its Underlying Model (HN, UBK), pp. 81–93.
LSOLSO-2002-Ruhe #paradigm #re-engineering
Software Engineering Decision Support ? A New Paradigm for Learning Software Organizations (GR), pp. 104–113.
SEKESEKE-2002-ArndtCGM #distance #multi #re-engineering #xml
An XML-based approch to multimedia software engineering for distance learning (TA, SKC, AG, PM), pp. 525–532.
SEKESEKE-2002-GrutznerAP #approach #information management
A systematic approach to produce small courseware modules for combined learning and knowledge management environements (IG, NA, DP), pp. 533–539.
SEKESEKE-2002-MaidantchikMS #requirements
Learning organizational knowledge: an evolutionary proposal for requirements engineering (CM, MM, GS), pp. 151–157.
SEKESEKE-2002-TortoraSVD #multi
A multilevel learning management system (GT, MS, GV, PD), pp. 541–547.
SIGIRSIGIR-2002-AminiG #summary #using
The use of unlabeled data to improve supervised learning for text summarization (MRA, PG), pp. 105–112.
SACSAC-2002-BoughanemT #adaptation #incremental
Incremental adaptive filtering: profile learning and threshold calibration (MB, MT), pp. 640–644.
SACSAC-2002-ElishRF #collaboration #network
Evaluating collaborative software in supporting organizational learning with Bayesian Networks (MOE, DCR, JEF), pp. 992–996.
SACSAC-2002-NevesBR #classification #game studies
Learning the risk board game with classifier systems (AN, OB, ACR), pp. 585–589.
SACSAC-2002-SeleznyovM #detection
Learning temporal patterns for anomaly intrusion detection (AS, OM), pp. 209–213.
HPCAHPCA-2002-CintraT #parallel #thread
Speculative Multithreading Eliminating Squashes through Learning Cross-Thread Violations in Speculative Parallelization for Multiprocessors (MHC, JT), pp. 43–54.
STOCSTOC-2002-HellersteinR #using
Exact learning of DNF formulas using DNF hypotheses (LH, VR), pp. 465–473.
ICLPICLP-2002-MartinNSS #logic #prolog
Learning in Logic with RichProlog (EM, PMN, AS, FS), pp. 239–254.
TPDLECDL-2001-ColemanSBM #library
Learning Spaces in Digital Libraries (AC, TRS, OAB, REM), pp. 251–262.
HTHT-2001-ConlanHLWA #adaptation #metadata
Extending eductional metadata schemas to describe adaptive learning resources (OC, CH, PL, VPW, DA), pp. 161–162.
ICDARICDAR-2001-DongKS #framework #multi #pattern matching #pattern recognition #recognition
A Multi-Net Local Learning Framework for Pattern Recognition (JxD, AK, CYS), pp. 328–332.
ICDARICDAR-2001-HoqueF #classification
An Improved Learning Scheme for the Moving Window Classifier (SH, MCF), pp. 607–611.
ICDARICDAR-2001-KobayashiNMSA #flexibility #recognition #statistics #using
Handwritten Numeral Recognition Using Flexible Matching Based on Learning of Stroke Statistics (TK, KN, HM, TS, KA), pp. 612–616.
ICDARICDAR-2001-ValvenyM #using
Learning of Structural Descriptions of Graphic Symbols Using Deformable Template Matching (EV, EM), pp. 455–459.
ICDARICDAR-2001-WakabayashiSOK #image #recognition
Accuracy Improvement of Handwritten Numeral Recognition by Mirror Image Learning (TW, MS, WO, FK), pp. 338–343.
JCDLJCDL-2001-LaleufS #component #repository
A component repository for learning objects: a progress report (JRL, AMS), pp. 33–40.
JCDLJCDL-2001-MacColl
Project ANGEL: an open virtual learning envoronment with sophisticated access management (JM), pp. 122–123.
VLDBVLDB-2001-StillgerLMK #named
LEO — DB2’s LEarning Optimizer (MS, GML, VM, MK), pp. 19–28.
CSEETCSEET-2001-ArmaregoFR #development #online #re-engineering
Constructing Software Engineering Knowledge: Development of an Online Learning Environment (JA, LF, GGR), pp. 258–267.
CSEETCSEET-2001-RatcliffeTW
A Learning Environment for First Year Software Engineers (MR, LT, JW), pp. 268–275.
ITiCSEITiCSE-2001-CarboneHMG #programming
Characteristics of programming exercises that lead to poor learning tendencies: Part II (AC, JH, IM, DG), pp. 93–96.
ITiCSEITiCSE-2001-Chalk
Scaffolding learning in virtual environments (PC), pp. 85–88.
ITiCSEITiCSE-2001-ChoiC #design #education #interactive #multi #object-oriented #using
Using interactive multimedia for teaching and learning object oriented software design (SHC, SC), p. 176.
ITiCSEITiCSE-2001-CiesielskiM #algorithm #animation #student #using
Using animation of state space algorithms to overcome student learning difficulties (VC, PM), pp. 97–100.
ITiCSEITiCSE-2001-Ginat #algorithm #problem
Metacognitive awareness utilized for learning control elements in algorithmic problem solving (DG), pp. 81–84.
ITiCSEITiCSE-2001-Kumar #c++ #interactive #pointer
Learning the interaction between pointers and scope in C++ (ANK), pp. 45–48.
ITiCSEITiCSE-2001-McCaugheyA #community #education #network
The learning and teaching support network promoting best practice in the information and computer science academic community (AM, SA), p. 175.
ITiCSEITiCSE-2001-Putnik #integration #on the
On integration of learning and technology (ZP), p. 185.
ITiCSEITiCSE-2001-Rosbottom #distance #education #hybrid
Hybrid learning — a safe route into web-based open and distance learning for the computer science teacher (JR), pp. 89–92.
ITiCSEITiCSE-2001-ThomasL #distance #fault #student #using
Observational studies of student errors in a distance learning environment using a remote recording and replay tool (PT, KL), pp. 117–120.
ICALPICALP-2001-Servedio #quantum
Separating Quantum and Classical Learning (RAS), pp. 1065–1080.
FLOPSFLOPS-2001-Ferri-RamirezHR #functional #incremental #logic programming #source code
Incremental Learning of Functional Logic Programs (CF, JHO, MJRQ), pp. 233–247.
FLOPSFLOPS-2001-Sato #logic programming #source code
Parameterized Logic Programs where Computing Meets Learning (TS), pp. 40–60.
CHICHI-2001-CorbettA #feedback
Locus of feedback control in computer-based tutoring: impact on learning rate, achievement and attitudes (ATC, JRA), pp. 245–252.
CHICHI-2001-RossonS #education #reuse #simulation
Teachers as simulation programmers: minimalist learning and reuse (MBR, CDS), pp. 237–244.
VISSOFTSVIS-2001-Faltin #algorithm #constraints #interactive
Structure and Constraints in Interactive Exploratory Algorithm Learning (NF), pp. 213–226.
VISSOFTSVIS-2001-RossG #education #named #web
Hypertextbooks: Animated, Active Learning, Comprehensive Teaching and Learning Resources for the Web (RJR, MTG), pp. 269–284.
ICEISICEIS-v2-2001-AudyBF #information management
Information Systems Planning: Contributions from Organizational Learning (JLNA, JLB, HF), pp. 873–879.
ICEISICEIS-v2-2001-BressanAAG #3d #multi #web
Multiuser 3D Learning Environments in the Web (CMB, SdA, RBdA, CG), pp. 1170–1173.
CIKMCIKM-2001-NottelmannF #classification #datalog #probability
Learning Probabilistic Datalog Rules for Information Classification and Transformation (HN, NF), pp. 387–394.
ICMLICML-2001-AmarDGZ #multi
Multiple-Instance Learning of Real-Valued Data (RAA, DRD, SAG, QZ), pp. 3–10.
ICMLICML-2001-BlumC #graph #using
Learning from Labeled and Unlabeled Data using Graph Mincuts (AB, SC), pp. 19–26.
ICMLICML-2001-BowlingV #convergence
Convergence of Gradient Dynamics with a Variable Learning Rate (MHB, MMV), pp. 27–34.
ICMLICML-2001-ChajewskaKO #behaviour
Learning an Agent’s Utility Function by Observing Behavior (UC, DK, DO), pp. 35–42.
ICMLICML-2001-ChoiR #approximate #difference #fixpoint #performance
A Generalized Kalman Filter for Fixed Point Approximation and Efficient Temporal Difference Learning (DC, BVR), pp. 43–50.
ICMLICML-2001-EngelM #embedded #markov #process
Learning Embedded Maps of Markov Processes (YE, SM), pp. 138–145.
ICMLICML-2001-Furnkranz
Round Robin Rule Learning (JF), pp. 146–153.
ICMLICML-2001-Geibel #bound
Reinforcement Learning with Bounded Risk (PG), pp. 162–169.
ICMLICML-2001-GetoorFKT #modelling #probability #relational
Learning Probabilistic Models of Relational Structure (LG, NF, DK, BT), pp. 170–177.
ICMLICML-2001-GhavamzadehM
Continuous-Time Hierarchical Reinforcement Learning (MG, SM), pp. 186–193.
ICMLICML-2001-GlickmanS #memory management #policy #probability #search-based
Evolutionary Search, Stochastic Policies with Memory, and Reinforcement Learning with Hidden State (MRG, KPS), pp. 194–201.
ICMLICML-2001-JafariGGE #equilibrium #game studies #nash #on the
On No-Regret Learning, Fictitious Play, and Nash Equilibrium (AJ, AG, DG, GE), pp. 226–233.
ICMLICML-2001-JinH #approach #information retrieval #word
Learning to Select Good Title Words: An New Approach based on Reverse Information Retrieval (RJ, AGH), pp. 242–249.
ICMLICML-2001-Krawiec #comparison
Pairwise Comparison of Hypotheses in Evolutionary Learning (KK), pp. 266–273.
ICMLICML-2001-Lee #collaboration #recommendation
Collaborative Learning and Recommender Systems (WSL), pp. 314–321.
ICMLICML-2001-MarchandS #set
Learning with the Set Covering Machine (MM, JST), pp. 345–352.
ICMLICML-2001-McGovernB #automation #using
Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density (AM, AGB), pp. 361–368.
ICMLICML-2001-PerkinsB #set
Lyapunov-Constrained Action Sets for Reinforcement Learning (TJP, AGB), pp. 409–416.
ICMLICML-2001-PrecupSD #approximate #difference
Off-Policy Temporal Difference Learning with Function Approximation (DP, RSS, SD), pp. 417–424.
ICMLICML-2001-RoyM #estimation #fault #reduction #towards
Toward Optimal Active Learning through Sampling Estimation of Error Reduction (NR, AM), pp. 441–448.
ICMLICML-2001-SatoK #markov #problem
Average-Reward Reinforcement Learning for Variance Penalized Markov Decision Problems (MS, SK), pp. 473–480.
ICMLICML-2001-SingerV #implementation #performance
Learning to Generate Fast Signal Processing Implementations (BS, MMV), pp. 529–536.
ICMLICML-2001-StoneS #scalability #towards
Scaling Reinforcement Learning toward RoboCup Soccer (PS, RSS), pp. 537–544.
ICMLICML-2001-Venkataraman
A procedure for unsupervised lexicon learning (AV), pp. 569–576.
ICMLICML-2001-Wiering #using
Reinforcement Learning in Dynamic Environments using Instantiated Information (MW), pp. 585–592.
ICMLICML-2001-Wyatt #using
Exploration Control in Reinforcement Learning using Optimistic Model Selection (JLW), pp. 593–600.
ICMLICML-2001-ZinkevichB #markov #multi #process #symmetry
Symmetry in Markov Decision Processes and its Implications for Single Agent and Multiagent Learning (MZ, TRB), p. 632–?.
KDDKDD-2001-KaltonLWY #clustering
Generalized clustering, supervised learning, and data assignment (AK, PL, KW, JPY), pp. 299–304.
KDDKDD-2001-ZadroznyE
Learning and making decisions when costs and probabilities are both unknown (BZ, CE), pp. 204–213.
LSOLSO-2001-FeldmannA #on the
On the Status of Learning Software Organizations in the Year 2001 (RLF, KDA), pp. 2–7.
LSOLSO-2001-Henninger
Organizational Learning in Dynamic Domains (SH), pp. 8–16.
LSOLSO-2001-PfahlADR #collaboration #named
CORONET-Train: A Methodology for Web-Based Collaborative Learning in Software Organisations (DP, NA, CD, GR), pp. 37–51.
LSOLSO-2001-Segal #case study #process
Organisational Learning and Software Process Improvement: A Case Study (JS), pp. 68–82.
LSOLSO-2001-StarkloffP #approach #development
Process-Integrated Learning: The ADVISOR Approach for Corporate Development (PS, KP), pp. 152–162.
MLDMMLDM-2001-BhanuD #clustering #concept #feedback #fuzzy
Concepts Learning with Fuzzy Clustering and Relevance Feedback (BB, AD), pp. 102–116.
MLDMMLDM-2001-DongKS #framework #recognition
Local Learning Framework for Recognition of Lowercase Handwritten Characters (JxD, AK, CYS), pp. 226–238.
MLDMMLDM-2001-Fernau #xml
Learning XML Grammars (HF), pp. 73–87.
MLDMMLDM-2001-KollmarH #feature model
Feature Selection for a Real-World Learning Task (DK, DHH), pp. 157–172.
MLDMMLDM-2001-Krawiec #comparison #on the #using #visual notation
On the Use of Pairwise Comparison of Hypotheses in Evolutionary Learning Applied to Learning from Visual Examples (KK), pp. 307–321.
MLDMMLDM-2001-Krzyzak #classification #network #using
Nonlinear Function Learning and Classification Using Optimal Radial Basis Function Networks (AK), pp. 217–225.
MLDMMLDM-2001-LinderP #how
How to Automate Neural Net Based Learning (RL, SJP), pp. 206–216.
MLDMMLDM-2001-ShiWOK #image #recognition
Mirror Image Learning for Handwritten Numeral Recognition (MS, TW, WO, FK), pp. 239–248.
SEKESEKE-2001-NavarroH #adaptation #game studies
Adapting Game Technology to Support Individual and Organizational Learning (EON, AvdH), pp. 347–354.
SEKESEKE-2001-PfahlR
System Dynamics as an Enabling Technology for Learning in Software Organizations (DP, GR), pp. 355–362.
SIGIRSIGIR-2001-Joachims #classification #statistics
A Statistical Learning Model of Text Classification for Support Vector Machines (TJ), pp. 128–136.
SIGIRSIGIR-2001-LeeS #clustering #image #retrieval #using
Intelligent Object-based Image Retrieval Using Cluster-driven Personal Preference Learning (KML, WNS), pp. 436–437.
RERE-2001-Kovitz #backtracking #development
Is Backtracking so Bad? The Role of Learning in Software Development (BK), p. 272.
SACSAC-2001-KallesK #design #game studies #on the #using #verification
On verifying game designs and playing strategies using reinforcement learning (DK, PK), pp. 6–11.
SACSAC-2001-LeeGA #multi
A multi-neural-network learning for lot sizing and sequencing on a flow-shop (IL, JNDG, ADA), pp. 36–40.
SACSAC-2001-OkabeY #documentation #interactive #relational #retrieval
Interactive document retrieval with relational learning (MO, SY), pp. 27–31.
DACDAC-2001-GizdarskiF #complexity #framework
A Framework for Low Complexity Static Learning (EG, HF), pp. 546–549.
DATEDATE-2001-NovikovG #multi #performance
An efficient learning procedure for multiple implication checks (YN, EIG), pp. 127–135.
STOCSTOC-2001-KlivansS01a
Learning DNF in time 2Õ(n1/3) (AK, RAS), pp. 258–265.
STOCSTOC-2001-SanjeevK
Learning mixtures of arbitrary gaussians (SA, RK), pp. 247–257.
ICSTSAT-2001-LagoudakisL #branch #satisfiability
Learning to Select Branching Rules in the DPLL Procedure for Satisfiability (MGL, MLL), pp. 344–359.
DL-2000-MooneyR #categorisation #recommendation #using
Content-based book recommending using learning for text categorization (RJM, LR), pp. 195–204.
DL-2000-VaughanD
Learning the shape of information: a longitudinal study of Web-news reading (MWV, AD), pp. 236–237.
TPDLECDL-2000-SemeraroEFF #interactive #library #profiling #tool support
Interaction Profiling in Digital Libraries through Learning Tools (GS, FE, NF, SF), pp. 229–238.
HTHT-2000-FischerS #adaptation #automation #hypermedia
Automatic creation of exercises in adaptive hypermedia learning systems (SF, RS), pp. 49–55.
HTHT-2000-SpalterS #distance #hypermedia #jit #reuse
Reusable hypertext structures for distance and JIT learning (AMS, RMS), pp. 29–38.
SIGMODSIGMOD-2000-ChenDLT #named #query #web
Fact: A Learning Based Web Query Processing System (SC, YD, HL, ZT), p. 587.
SIGMODSIGMOD-2000-WattezCBFF #benchmark #metric #query
Benchmarking Queries over Trees: Learning the Hard Truth the Hard Way (FW, SC, VB, GF, CF), pp. 510–511.
VLDBVLDB-2000-DiaoLCT #query #towards #web
Toward Learning Based Web Query Processing (YD, HL, SC, ZT), pp. 317–328.
CSEETCSEET-2000-KorneckiZE #concept #programming #realtime
Learning Real-Time Programming Concepts through VxWorks Lab Experiments (AJK, JZ, DE), p. 294–?.
ITiCSEITiCSE-2000-Chalk #re-engineering #using
Apprenticeship learning of software engineering using Webworlds (PC), pp. 112–115.
ITiCSEITiCSE-2000-Hobbs #assessment #email
Email groups for learning and assessment (MH), p. 183.
ITiCSEITiCSE-2000-KhuriH #algorithm #image #interactive
Interactive packages for learning image compression algorithms (SK, HCH), pp. 73–76.
ITiCSEITiCSE-2000-OuCLL #web
Instructional instruments for Web group learning systems: the grouping, intervention, and strategy (KLO, GDC, CCL, BJL), pp. 69–72.
ITiCSEITiCSE-2000-RosbottomCF #online
A generic model for on-line learning (JR, JC, DF), pp. 108–111.
ITiCSEITiCSE-2000-SpalterS #case study #education #experience #interactive
Integrating interactive computer-based learning experiences into established curricula: a case study (AMS, RMS), pp. 116–119.
CHICHI-2000-CorbettT #difference
Instructional interventions in computer-based tutoring: differential impact on learning time and accuracy (ATC, HJT), pp. 97–104.
CSCWCSCW-2000-CadizBSGGJ #collaboration #distance #distributed #video
Distance learning through distributed collaborative video viewing (JJC, AB, ES, AG, JG, GJ), pp. 135–144.
CSCWCSCW-2000-SingleySFFS #algebra #collaboration
Algebra jam: supporting teamwork and managing roles in a collaborative learning environment (MKS, MS, PGF, RGF, SS), pp. 145–154.
ICEISICEIS-2000-KleinerSB #estimation
Self Organizing Maps for Value Estimation to Solve Reinforcement Learning Tasks (AK, BS, OB), pp. 149–156.
ICEISICEIS-2000-NobreC #information management
Information Systems and Learning Organisations (ALN, MPeC), pp. 327–332.
ICEISICEIS-2000-PetersHW #database #design #distributed
Action Learning in a Decentralized Organization-The Case of Designing a Distributed Database (SCAP, MSHH, CEW), pp. 519–520.
CIKMCIKM-2000-GhaniJ #database #multi
Learning a Monolingual Language Model from a Multilingual Text Database (RG, RJ), pp. 187–193.
CIKMCIKM-2000-LamL #documentation
Learning to Extract Hierarchical Information from Semi-structured Documents (WL, WYL), pp. 250–257.
ICMLICML-2000-AlerBI #information management #representation
Knowledge Representation Issues in Control Knowledge Learning (RA, DB, PI), pp. 1–8.
ICMLICML-2000-AllenG #comparison #empirical
Model Selection Criteria for Learning Belief Nets: An Empirical Comparison (TVA, RG), pp. 1047–1054.
ICMLICML-2000-BaxterB
Reinforcement Learning in POMDP’s via Direct Gradient Ascent (JB, PLB), pp. 41–48.
ICMLICML-2000-BoschZ #in memory #multi
Unpacking Multi-valued Symbolic Features and Classes in Memory-Based Language Learning (AvdB, JZ), pp. 1055–1062.
ICMLICML-2000-Bowling #convergence #multi #problem
Convergence Problems of General-Sum Multiagent Reinforcement Learning (MHB), pp. 89–94.
ICMLICML-2000-CampbellCS #classification #query #scalability
Query Learning with Large Margin Classifiers (CC, NC, AJS), pp. 111–118.
ICMLICML-2000-ChangCM
Learning to Create Customized Authority Lists (HC, DC, AM), pp. 127–134.
ICMLICML-2000-ChoiY #database
Learning to Select Text Databases with Neural Nets (YSC, SIY), pp. 135–142.
ICMLICML-2000-ChownD #approach #divide and conquer #information management
A Divide and Conquer Approach to Learning from Prior Knowledge (EC, TGD), pp. 143–150.
ICMLICML-2000-CoelhoG #approach
Learning in Non-stationary Conditions: A Control Theoretic Approach (JACJ, RAG), pp. 151–158.
ICMLICML-2000-Cohen #automation #concept #web
Automatically Extracting Features for Concept Learning from the Web (WWC), pp. 159–166.
ICMLICML-2000-CohnC #documentation #identification
Learning to Probabilistically Identify Authoritative Documents (DC, HC), pp. 167–174.
ICMLICML-2000-ConradtTVS #online
On-line Learning for Humanoid Robot Systems (JC, GT, SV, SS), pp. 191–198.
ICMLICML-2000-CravenPSBG #coordination #multi #using
Using Multiple Levels of Learning and Diverse Evidence to Uncover Coordinately Controlled Genes (MC, DP, JWS, JB, JDG), pp. 199–206.
ICMLICML-2000-DeJong #empirical
Hidden Strengths and Limitations: An Empirical Investigation of Reinforcement Learning (GD), pp. 215–222.
ICMLICML-2000-DyB #identification #order #set
Feature Subset Selection and Order Identification for Unsupervised Learning (JGD, CEB), pp. 247–254.
ICMLICML-2000-FariasR #approximate #fixpoint
Fixed Points of Approximate Value Iteration and Temporal-Difference Learning (DPdF, BVR), pp. 207–214.
ICMLICML-2000-FernG #empirical #online
Online Ensemble Learning: An Empirical Study (AF, RG), pp. 279–286.
ICMLICML-2000-FiechterR #scalability
Learning Subjective Functions with Large Margins (CNF, SR), pp. 287–294.
ICMLICML-2000-ForsterW #bound
Relative Loss Bounds for Temporal-Difference Learning (JF, MKW), pp. 295–302.
ICMLICML-2000-GiordanaSSB #framework #relational
Analyzing Relational Learning in the Phase Transition Framework (AG, LS, MS, MB), pp. 311–318.
ICMLICML-2000-GoldbergM #modelling #multi
Learning Multiple Models for Reward Maximization (DG, MJM), pp. 319–326.
ICMLICML-2000-GoldmanZ
Enhancing Supervised Learning with Unlabeled Data (SAG, YZ), pp. 327–334.
ICMLICML-2000-GordonM
Learning Filaments (GJG, AM), pp. 335–342.
ICMLICML-2000-HallH #information retrieval #multi #natural language
Learning Curved Multinomial Subfamilies for Natural Language Processing and Information Retrieval (KBH, TH), pp. 351–358.
ICMLICML-2000-Heskes #empirical
Empirical Bayes for Learning to Learn (TH), pp. 367–374.
ICMLICML-2000-HougenGS #approach
An Integrated Connectionist Approach to Reinforcement Learning for Robotic Control (DFH, MLG, JRS), pp. 383–390.
ICMLICML-2000-HuangSK #constraints #declarative
Learning Declarative Control Rules for Constraint-BAsed Planning (YCH, BS, HAK), pp. 415–422.
ICMLICML-2000-KatayamaKK #using
A Universal Generalization for Temporal-Difference Learning Using Haar Basis Functions (SK, HK, SK), pp. 447–454.
ICMLICML-2000-Khardon
Learning Horn Expressions with LogAn-H (RK), pp. 471–478.
ICMLICML-2000-KimN #network #set
Learning Bayesian Networks for Diverse and Varying numbers of Evidence Sets (ZWK, RN), pp. 479–486.
ICMLICML-2000-LagoudakisL #algorithm #using
Algorithm Selection using Reinforcement Learning (MGL, MLL), pp. 511–518.
ICMLICML-2000-LaneB #interface #reduction
Data Reduction Techniques for Instance-Based Learning from Human/Computer Interface Data (TL, CEB), pp. 519–526.
ICMLICML-2000-LauerR #algorithm #distributed #multi
An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems (ML, MAR), pp. 535–542.
ICMLICML-2000-Li #online
Selective Voting for Perception-like Online Learning (YL), pp. 559–566.
ICMLICML-2000-MamitsukaA #database #mining #performance #query #scalability
Efficient Mining from Large Databases by Query Learning (HM, NA), pp. 575–582.
ICMLICML-2000-MorimotoD #behaviour #using
Acquisition of Stand-up Behavior by a Real Robot using Hierarchical Reinforcement Learning (JM, KD), pp. 623–630.
ICMLICML-2000-MuggletonBS #biology #product line #sequence
Learning Chomsky-like Grammars for Biological Sequence Families (SM, CHB, AS), pp. 631–638.
ICMLICML-2000-NgR #algorithm
Algorithms for Inverse Reinforcement Learning (AYN, SJR), pp. 663–670.
ICMLICML-2000-NikovskiN #mobile #modelling #navigation #probability
Learning Probabilistic Models for Decision-Theoretic Navigation of Mobile Robots (DN, IRN), pp. 671–678.
ICMLICML-2000-PaccanaroH #concept #distributed #linear
Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space (AP, GEH), pp. 711–718.
ICMLICML-2000-PennockMGH #algorithm
A Normative Examination of Ensemble Learning Algorithms (DMP, PMRI, CLG, EH), pp. 735–742.
ICMLICML-2000-PfahringerBG #algorithm
Meta-Learning by Landmarking Various Learning Algorithms (BP, HB, CGGC), pp. 743–750.
ICMLICML-2000-PiaterG #development #visual notation
Constructive Feature Learning and the Development of Visual Expertise (JHP, RAG), pp. 751–758.
ICMLICML-2000-Randlov #physics #problem
Shaping in Reinforcement Learning by Changing the Physics of the Problem (JR), pp. 767–774.
ICMLICML-2000-RandlovBR #algorithm
Combining Reinforcement Learning with a Local Control Algorithm (JR, AGB, MTR), pp. 775–782.
ICMLICML-2000-Reynolds #adaptation #bound #clustering
Adaptive Resolution Model-Free Reinforcement Learning: Decision Boundary Partitioning (SIR), pp. 783–790.
ICMLICML-2000-RichterS #modelling
Knowledge Propagation in Model-based Reinforcement Learning Tasks (CR, JS), pp. 791–798.
ICMLICML-2000-RyanR
Learning to Fly: An Application of Hierarchical Reinforcement Learning (MRKR, MDR), pp. 807–814.
ICMLICML-2000-SannerALL #performance
Achieving Efficient and Cognitively Plausible Learning in Backgammon (SS, JRA, CL, MCL), pp. 823–830.
ICMLICML-2000-SchohnC #less is more
Less is More: Active Learning with Support Vector Machines (GS, DC), pp. 839–846.
ICMLICML-2000-SchuurmansS #adaptation
An Adaptive Regularization Criterion for Supervised Learning (DS, FS), pp. 847–854.
ICMLICML-2000-SegalK #incremental
Incremental Learning in SwiftFile (RS, JOK), pp. 863–870.
ICMLICML-2000-ShultzR #comparison #knowledge-based #multi #using
Using Knowledge to Speed Learning: A Comparison of Knowledge-based Cascade-correlation and Multi-task Learning (TRS, FR), pp. 871–878.
ICMLICML-2000-SilvaL #hybrid
Obtaining Simplified Rule Bases by Hybrid Learning (RBdAeS, TBL), pp. 879–886.
ICMLICML-2000-SingerV #modelling #performance #predict
Learning to Predict Performance from Formula Modeling and Training Data (BS, MMV), pp. 887–894.
ICMLICML-2000-SmartK
Practical Reinforcement Learning in Continuous Spaces (WDS, LPK), pp. 903–910.
ICMLICML-2000-SohT #image #using
Using Learning by Discovery to Segment Remotely Sensed Images (LKS, CT), pp. 919–926.
ICMLICML-2000-Strens #framework
A Bayesian Framework for Reinforcement Learning (MJAS), pp. 943–950.
ICMLICML-2000-Talavera #concept #feature model #incremental #probability
Feature Selection and Incremental Learning of Probabilistic Concept Hierarchies (LT), pp. 951–958.
ICMLICML-2000-TellerV #evolution #performance #programming
Efficient Learning Through Evolution: Neural Programming and Internal Reinforcement (AT, MMV), pp. 959–966.
ICMLICML-2000-TongK #classification
Support Vector Machine Active Learning with Application sto Text Classification (ST, DK), pp. 999–1006.
ICMLICML-2000-TorkkolaC
Mutual Information in Learning Feature Transformations (KT, WMC), pp. 1015–1022.
ICMLICML-2000-TowellPM
Learning Priorities From Noisy Examples (GGT, TP, MRM), pp. 1031–1038.
ICMLICML-2000-VaithyanathanD
Hierarchical Unsupervised Learning (SV, BD), pp. 1039–1046.
ICMLICML-2000-Veeser #approach #automaton #finite
An Evolutionary Approach to Evidence-Based Learning of Deterministic Finite Automata (SV), pp. 1071–1078.
ICMLICML-2000-VijayakumarS #incremental #realtime
Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space (SV, SS), pp. 1079–1086.
ICMLICML-2000-WnagZ #approach #lazy evaluation #multi #problem
Solving the Multiple-Instance Problem: A Lazy Learning Approach (JW, JDZ), pp. 1119–1126.
ICMLICML-2000-YangAP #effectiveness #multi #validation
Combining Multiple Learning Strategies for Effective Cross Validation (YY, TA, TP), pp. 1167–1174.
ICMLICML-2000-Zaanen #recursion #syntax #using
Bootstrapping Syntax and Recursion using Alginment-Based Learning (MvZ), pp. 1063–1070.
ICPRICPR-v1-2000-BhanuF #image #interactive #segmentation
Learning Based Interactive Image Segmentation (BB, SF), pp. 1299–1302.
ICPRICPR-v1-2000-LiuW #recognition #representation
Learning the Face Space — Representation and Recognition (CL, HW), pp. 1249–1256.
ICPRICPR-v1-2000-NelsonS #3d #empirical #modelling #recognition
Learning 3D Recognition Models for General Objects from Unlabeled Imagery: An Experiment in Intelligent Brute Force (RCN, AS), pp. 1001–1008.
ICPRICPR-v1-2000-PalettaPP #analysis #recognition #using
Learning Temporal Context in Active Object Recognition Using Bayesian Analysis (LP, MP, AP), pp. 1695–1699.
ICPRICPR-v1-2000-PiaterG #network #recognition
Feature Learning for Recognition with Bayesian Networks (JHP, RAG), pp. 1017–1020.
ICPRICPR-v2-2000-BuhmannZ #clustering
Active Learning for Hierarchical Pairwise Data Clustering (JMB, TZ), pp. 2186–2189.
ICPRICPR-v2-2000-BurrellP #algorithm #detection #online #parametricity #probability #process
Sequential Algorithms for Detecting Changes in Acting Stochastic Processes and On-Line Learning of their Operational Parameters (AB, TPK), pp. 2656–2659.
ICPRICPR-v2-2000-Caelli #feature model #image #modelling #performance #predict
Learning Image Feature Extraction: Modeling, Tracking and Predicting Human Performance (TC), pp. 2215–2218.
ICPRICPR-v2-2000-ChouS #algorithm #classification #multi
A Hierarchical Multiple Classifier Learning Algorithm (YYC, LGS), pp. 2152–2155.
ICPRICPR-v2-2000-Figueiredo #approximate #on the
On Gaussian Radial Basis Function Approximations: Interpretation, Extensions, and Learning Strategies (MATF), pp. 2618–2621.
ICPRICPR-v2-2000-HiraokaHHMMY #algorithm #analysis #linear
Successive Learning of Linear Discriminant Analysis: Sanger-Type Algorithm (KH, KiH, MH, HM, TM, SY), pp. 2664–2667.
ICPRICPR-v2-2000-HongH #sequence
Learning to Extract Temporal Signal Patterns from Temporal Signal Sequence (PH, TSH), pp. 2648–2651.
ICPRICPR-v2-2000-KavallieratouSFK #segmentation #using
Handwritten Character Segmentation Using Transformation-Based Learning (EK, ES, NF, GKK), pp. 2634–2637.
ICPRICPR-v2-2000-KeglKN #classification #complexity #network
Radial Basis Function Networks and Complexity Regularization in Function Learning and Classification (BK, AK, HN), pp. 2081–2086.
ICPRICPR-v2-2000-LawK #clustering #modelling #sequence
Rival Penalized Competitive Learning for Model-Based Sequence Clustering (MHCL, JTK), pp. 2195–2198.
ICPRICPR-v2-2000-LohRW #incremental #named #network
IFOSART: A Noise Resistant Neural Network Capable of Incremental Learning (AWKL, MCR, GAWW), pp. 2985–2988.
ICPRICPR-v2-2000-MitraMP #database #incremental #scalability
Data Condensation in Large Databases by Incremental Learning with Support Vector Machines (PM, CAM, SKP), pp. 2708–2711.
ICPRICPR-v2-2000-MugurelVW #incremental #multi #on the #recognition
On the Incremental Learning and Recognition of the Pattern of Movement of Multiple Labeled Objects in Dynamic Scenes (ML, SV, GAWW), pp. 2652–2655.
ICPRICPR-v2-2000-NaphadeCHF #modelling #multi
Learning Sparse Multiple Cause Models (MRN, LSC, TSH, BJF), pp. 2642–2647.
ICPRICPR-v2-2000-Sato #classification #fault
A Learning Method for Definite Canonicalization Based on Minimum Classification Error (AS), pp. 2199–2202.
ICPRICPR-v4-2000-HeisterkampPD #image #query #retrieval
Feature Relevance Learning with Query Shifting for Content-Based Image Retrieval (DRH, JP, HKD), pp. 4250–4253.
ICPRICPR-v4-2000-IskeRMS #behaviour #navigation
A Bootstrapping Method for Autonomous and in Site Learning of Generic Navigation Behavior (BI, UR, KM, JS), pp. 4656–4659.
KDDKDD-2000-IyengarAZ #adaptation #using
Active learning using adaptive resampling (VSI, CA, TZ), pp. 91–98.
KDDKDD-2000-KimSM #feature model #search-based
Feature selection in unsupervised learning via evolutionary search (YK, WNS, FM), pp. 365–369.
KDDKDD-2000-YamanishiTWM #algorithm #detection #finite #online #using
On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms (KY, JiT, GJW, PM), pp. 320–324.
KRKR-2000-BisoRS #constraints
Experimental Results on Learning Soft Constraints (AB, FR, AS), pp. 435–444.
KRKR-2000-CumbyR #relational
Relational Representations that Facilitate Learning (CMC, DR), pp. 425–434.
KRKR-2000-MartinG #concept #policy #using
Learning Generalized Policies in Planning Using Concept Languages (MM, HG), pp. 667–677.
SIGIRSIGIR-2000-AsadovS #documentation #navigation #semantics
Semantic Explorer — navigation in documents collections, Proxima Daily — learning personal newspaper (VA, SS), p. 388.
SIGIRSIGIR-2000-Hofmann #modelling #probability #web
Learning probabilistic models of the Web (TH), pp. 369–371.
SIGIRSIGIR-2000-ZhaiJE #adaptation #approach #heuristic
Exploration of a heuristic approach to threshold learning in adaptive filtering (CZ, PJ, DAE), pp. 360–362.
TOOLSTOOLS-EUROPE-2000-NobleW #game studies
GOF Pursuit — Learning Patterns by Playing (JN, CW), p. 462.
ICSEICSE-2000-Ramakrishnan #interactive #internet #named #object-oriented #testing #visual notation
LIGHTVIEWS — visual interactive Internet environment for learning OO software testing (SR), pp. 692–695.
SACSAC-2000-BarraCPGRS #distance #education
Teach++: A Cooperative Distance Learning and Teaching Environment (MB, GC, UFP, VG, CR, VS), pp. 124–130.
SACSAC-2000-PereiraC #adaptation #behaviour #information retrieval
The Influence of Learning in the Behaviour of Information Retrieval Adaptive Agents (FBP, EC), pp. 452–457.
SACSAC-2000-RoselliCLPS
WWW-Based Cooperative Learning (TR, CC, SL, MVP, GS), pp. 1014–1020.
FASEFASE-2000-Hernandez-OralloR #lifecycle #quality
Software as Learning: Quality Factors and Life-Cycle Revised (JHO, MJRQ), pp. 147–162.
STOCSTOC-2000-BlumKW #problem #query #statistics
Noise-tolerant learning, the parity problem, and the statistical query model (AB, AK, HW), pp. 435–440.
ICLPCL-2000-KameyaS #logic programming #performance #source code
Efficient EM Learning with Tabulation for Parameterized Logic Programs (YK, TS), pp. 269–284.
HTHT-1999-SeebergSRFS
Individual Tables of Contents in Web-Based Learning Systems (CS, AS, KR, SF, RS), pp. 167–168.
ICDARICDAR-1999-HebertPG #detection #incremental #using
Cursive Character Detection using Incremental Learning (JFH, MP, NG), pp. 808–811.
ICDARICDAR-1999-Ho #identification #keyword #performance #word
Fast Identification of Stop Words for Font Learning and Keyword Spotting (TKH), pp. 333–336.
ICDARICDAR-1999-LebourgeoisBE #using
Structure Relation between Classes for Supervised Learning using Pretopology (FL, MB, HE), pp. 33–36.
ICDARICDAR-1999-LiN #classification #documentation
A Document Classification and Extraction System with Learning Ability (XL, PAN), pp. 197–200.
ICDARICDAR-1999-LiuN99a #algorithm #classification #nearest neighbour #prototype #recognition
Prototype Learning Algorithms for Nearest Neighbor Classifier with Application to Handwritten Character Recognition (CLL, MN), pp. 378–381.
ICDARICDAR-1999-MiletzkiBS
Continuous Learning Systems: Postal Address Readers with Built-In Learning Capability (UM, TB, HS), pp. 329–332.
ICDARICDAR-1999-Walischewski #automation
Learning Regions of Interest in Postal Automation (HW), pp. 317–320.
ITiCSEITiCSE-1999-Ben-AriK #concurrent #parallel #process
Thinking parallel: the process of learning concurrency (MBA, YBDK), pp. 13–16.
ITiCSEITiCSE-1999-Clear #collaboration #concept #education #interactive
A collaborative learning trial between New Zealand and Sweden-using Lotus Notes Domino in teaching the concepts of Human Computer Interaction (TC), pp. 111–114.
ITiCSEITiCSE-1999-DavyJ #education #programming
Research-led innovation in teaching and learning programming (JD, TJ), pp. 5–8.
ITiCSEITiCSE-1999-DeeR #approach #education
ACOM (“computing for all”): an integrated approach to the teaching and learning of information technology (HD, PR), p. 195.
ITiCSEITiCSE-1999-Faltin #algorithm #design #game studies
Designing courseware on algorithms for active learning with virtual board games (NF), pp. 135–138.
ITiCSEITiCSE-1999-HabermanG #distance #education
Distance learning model with local workshop sessions applied to in-service teacher training (BH, DG), pp. 64–67.
ITiCSEITiCSE-1999-LowderH #feedback #student
Web-based student feedback to improve learning (JL, DH), pp. 151–154.
ITiCSEITiCSE-1999-MiaoPW #collaboration
Combining the metaphors of an institute and of networked computers for building collaborative learning environments (YM, HRP, MW), p. 188.
ITiCSEITiCSE-1999-ScherzP
An organizer for project-based learning and instruction in computer science (ZS, SP), pp. 88–90.
ITiCSEITiCSE-1999-SheardH #student
A special learning environment for repeat students (JS, DH), pp. 56–59.
ITiCSEITiCSE-1999-Taylor99a #education
Math link: linking curriculum, instructional strategies, and technology to enhance teaching and learning (HGT), p. 201.
ITiCSEITiCSE-1999-Utting #education
Gathering and disseminating good practice at teaching and learning conferences (IU), p. 202.
ICALPICALP-1999-Watanabe
From Computational Learning Theory to Discovery Science (OW0), pp. 134–148.
CIAAWIA-1999-BrauneDKW #animation #automaton #finite #generative
Animation of the Generation and Computation of Finite Automata for Learning Software (BB, SD, AK, RW), pp. 39–47.
AGTIVEAGTIVE-1999-FischerKB #fuzzy #graph
Learning and Rewriting in Fuzzy Rule Graphs (IF, MK, MRB), pp. 263–270.
CHICHI-1999-MoherJOG
Bridging Strategies for VR-Based Learning (TGM, AEJ, SO, MG), pp. 536–543.
CHICHI-1999-PlowmanKLST #design #multi
Designing Multimedia for Learning: Narrative Guidance and Narrative Construction (LP, RL, DL, MS, JT), pp. 310–317.
CHICHI-1999-Soto #analysis #quality #semantics
Learning and Performing by Exploration: Label Quality Measured by Latent Semantic Analysis (RS), pp. 418–425.
HCIHCI-CCAD-1999-BrownS #development #education #people
An illustrated methodology for the development of virtual learning environments for use by people in special needs education (DJB, DSS), pp. 1105–1110.
HCIHCI-CCAD-1999-CarroMR #adaptation #education
Teaching tasks in an adaptive learning environment (RMC, RM, EP, PR), pp. 740–744.
HCIHCI-CCAD-1999-Chiu #algorithm #approach #search-based #using
Learning path planning using genetic algorithm approach (CC), pp. 71–75.
HCIHCI-CCAD-1999-Danielsson #network
Learning in networks (UD), pp. 407–411.
HCIHCI-CCAD-1999-FachB #adaptation #design
Training wheels: an “old” method for designing modern and adaptable learning environments (PWF, MB), pp. 725–729.
HCIHCI-CCAD-1999-HartmannSMGS #tool support
Tools for computer-supported learning in organisations (EAH, DS, KM, MG, HS), pp. 377–381.
HCIHCI-CCAD-1999-JohnsonO #multi #problem #using
Innovative mathematical learning environments — Using multimedia to solve real world problems (LFJ, POJ), pp. 677–681.
HCIHCI-CCAD-1999-KashiharaUT #visualisation
Visualizing knowledge structure for exploratory learning in hyperspace (AK, HU, JT), pp. 667–671.
HCIHCI-CCAD-1999-KasviKVPR
Supporting a learning operative organization (JJJK, IK, MV, AP, LR), pp. 197–201.
HCIHCI-CCAD-1999-KutayHW #human-computer
Achieving learning outcomes in HCI for computing — an experiential testbed (CK, PH, GW), pp. 626–631.
HCIHCI-CCAD-1999-MatsumotoNMK #human-computer #interactive #process
Learning human-computer interactive process of learning with intelligence tutoring systems (TM, HN, EM, KK), pp. 1216–1220.
HCIHCI-CCAD-1999-McNeese #analysis #metric #performance #process #protocol #using
Making sense of teamwork: the use of protocol analysis / performance measures to reveal cooperative work processes in a situated learning environment (MDM), pp. 502–506.
HCIHCI-CCAD-1999-NealI #case study #distance #education #experience
Asynchronous distance learning for corporate education: experiences with Lotus LearningSpace (LN, DI), pp. 750–754.
HCIHCI-CCAD-1999-OppermannS #adaptation #mobile
Adaptive mobile museum guide for information and learning on demand (RO, MS), pp. 642–646.
HCIHCI-CCAD-1999-PatelKR
Cognitive apprenticeship based learning environment in numeric domains (AP, K, DR), pp. 637–641.
HCIHCI-CCAD-1999-Seufert #named #network
PLATO — “electronic cookbook” for Internet-based learning networks (SS), pp. 707–711.
HCIHCI-CCAD-1999-Siemer-Matravers #collaboration
Collaborative learning — a cure for intelligent tutoring systems (JSM), pp. 652–656.
HCIHCI-CCAD-1999-SinitsaM #interactive #taxonomy
Interactive dictionary in a context of learning (KMS, AM), pp. 662–666.
HCIHCI-CCAD-1999-YenWNL #case study #design #education #information management
Design of a computer-mediated environment to capture and evaluate knowledge transfer and learning: a case study in a larger higher education class (SY, BW, JN, LJL), pp. 735–739.
HCIHCI-EI-1999-AzarovM #aspect-oriented #distance
Psychological Aspects of the Organization of the Distance Learning (SSA, OVM), pp. 124–128.
HCIHCI-EI-1999-ChengYH #design #distributed #human-computer #interface
Cognition and Learning in Distributed Design Environments: Experimental Studies and Human-Computer Interfaces (FC, YHY, HH), pp. 631–635.
HCIHCI-EI-1999-HuangWC #programming
A Flow-chart Based Learning System for Computer Programming (KHH, KW, SYC), pp. 1298–1302.
HCIHCI-EI-1999-Nyssen #towards
Training Simulators in Anesthesia: Towards a Hierarchy of Learning Situations (ASN), pp. 890–894.
HCIHCI-EI-1999-PentlandRW #adaptation #gesture #interface #word
Perceptual Intelligence: learning gestures and words for individualized, adaptive interfaces (AP, DR, CRW), pp. 286–290.
HCIHCI-EI-1999-ScharKK #concept #multi #named
Multimedia: the Effect of Picture, Voice & Text for the Learning of Concepts and Principles (SGS, JK, HK), pp. 456–460.
HCIHCI-EI-1999-TanoT #adaptation #user interface
User Adaptation of the Pen-based User Interface by Reinforcement Learning (ST, MT), pp. 233–237.
HCIHCI-EI-1999-ThissenS #concept #design #internet #student
A New Concept for Designing Internet Learning Applications for Students of Electrical Engineering (DT, BS), pp. 590–594.
ICEISICEIS-1999-Habrant #database #network #predict #search-based
Structure Learning of Bayesian Networks from Databases by Genetic Algorithms-Application to Time Series Prediction in Finance (JH), pp. 225–231.
CIKMCIKM-1999-AponWD #approach #parallel
A Learning Approach to Processor Allocation in Parallel Systems (AWA, TDW, LWD), pp. 531–537.
CIKMCIKM-1999-WidyantoroIY #adaptation #algorithm
An Adaptive Algorithm for Learning Changes in User Interests (DHW, TRI, JY), pp. 405–412.
ICMLICML-1999-AbeL #concept #linear #probability #using
Associative Reinforcement Learning using Linear Probabilistic Concepts (NA, PML), pp. 3–11.
ICMLICML-1999-AbeN #internet
Learning to Optimally Schedule Internet Banner Advertisements (NA, AN), pp. 12–21.
ICMLICML-1999-BontempiBB #predict
Local Learning for Iterated Time-Series Prediction (GB, MB, HB), pp. 32–38.
ICMLICML-1999-Bosch #abstraction #in memory
Instance-Family Abstraction in Memory-Based Language Learning (AvdB), pp. 39–48.
ICMLICML-1999-Boyan #difference
Least-Squares Temporal Difference Learning (JAB), pp. 49–56.
ICMLICML-1999-BrodieD #induction #using
Learning to Ride a Bicycle using Iterated Phantom Induction (MB, GD), pp. 57–66.
ICMLICML-1999-FreundM #algorithm
The Alternating Decision Tree Learning Algorithm (YF, LM), pp. 124–133.
ICMLICML-1999-GervasioIL #adaptation #evaluation #scheduling
Learning User Evaluation Functions for Adaptive Scheduling Assistance (MTG, WI, PL), pp. 152–161.
ICMLICML-1999-IijimaYYK #adaptation #behaviour #distributed
Distributed Robotic Learning: Adaptive Behavior Acquisition for Distributed Autonomous Swimming Robot in Real World (DI, WY, HY, YK), pp. 191–199.
ICMLICML-1999-Kadous #multi
Learning Comprehensible Descriptions of Multivariate Time Series (MWK), pp. 454–463.
ICMLICML-1999-LentL #performance
Learning Hierarchical Performance Knowledge by Observation (MvL, JEL), pp. 229–238.
ICMLICML-1999-MorikBJ #approach #case study #knowledge-based #monitoring #statistics
Combining Statistical Learning with a Knowledge-Based Approach — A Case Study in Intensive Care Monitoring (KM, PB, TJ), pp. 268–277.
ICMLICML-1999-PalhangS #induction #logic programming
Learning Discriminatory and Descriptive Rules by an Inductive Logic Programming System (MP, AS), pp. 288–297.
ICMLICML-1999-PeshkinMK #memory management #policy
Learning Policies with External Memory (LP, NM, LPK), pp. 307–314.
ICMLICML-1999-PriceB #multi
Implicit Imitation in Multiagent Reinforcement Learning (BP, CB), pp. 325–334.
ICMLICML-1999-RennieM #using #web
Using Reinforcement Learning to Spider the Web Efficiently (JR, AM), pp. 335–343.
ICMLICML-1999-SakakibaraK #context-free grammar #using
GA-based Learning of Context-Free Grammars using Tabular Representations (YS, MK), pp. 354–360.
ICMLICML-1999-ThompsonCM #information management #natural language #parsing
Active Learning for Natural Language Parsing and Information Extraction (CAT, MEC, RJM), pp. 406–414.
ICMLICML-1999-ThrunLF #markov #modelling #monte carlo #parametricity #probability #process
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes (ST, JL, DF), pp. 415–424.
ICMLICML-1999-VaithyanathanD #clustering #documentation
Model Selection in Unsupervised Learning with Applications To Document Clustering (SV, BD), pp. 433–443.
ICMLICML-1999-Zhang #approach
An Region-Based Learning Approach to Discovering Temporal Structures in Data (WZ), pp. 484–492.
ICMLICML-1999-ZhengWT #lazy evaluation #naive bayes
Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees (ZZ, GIW, KMT), pp. 493–502.
ICMLICML-1999-ZhouB #algorithm #approach #hybrid #memory management #parametricity #requirements
A Hybrid Lazy-Eager Approach to Reducing the Computation and Memory Requirements of Local Parametric Learning Algorithms (YZ, CEB), p. 503–?.
KDDKDD-1999-FanSZ #distributed #online #scalability
The Application of AdaBoost for Distributed, Scalable and On-Line Learning (WF, SJS, JZ), pp. 362–366.
KDDKDD-1999-SyedLS99a #concept #incremental
Handling Concept Drifts in Incremental Learning with Support Vector Machines (NAS, HL, KKS), pp. 317–321.
MLDMMLDM-1999-AizenbergAK #algorithm #image #multi #recognition
Multi-valued and Universal Binary Neurons: Learning Algorithms, Application to Image Processing and Recognition (INA, NNA, GAK), pp. 21–35.
MLDMMLDM-1999-AltamuraELM #documentation
Symbolic Learning Techniques in Paper Document Processing (OA, FE, FAL, DM), pp. 159–173.
MLDMMLDM-1999-GiacintoR #automation #classification #design #multi
Automatic Design of Multiple Classifier Systems by Unsupervised Learning (GG, FR), pp. 131–143.
MLDMMLDM-1999-Jahn #image #preprocessor
Unsupervised Learning of Local Mean Gray Values for Image Pre-processing (HJ), pp. 64–74.
MLDMMLDM-1999-KingL #clustering #information retrieval
Non-hierarchical Clustering with Rival Penalized Competitive Learning for Information Retrieval (IK, TKL), pp. 116–130.
MLDMMLDM-1999-Petrou #pattern matching #pattern recognition #recognition
Learning in Pattern Recognition (MP), pp. 1–12.
OOPSLAOOPSLA-1999-KerstenM #aspect-oriented #case study #named #programming #using
Atlas: A Case Study in Building a Web-Based Learning Environment using Aspect-oriented Programming (MK, GCM), pp. 340–352.
TOOLSTOOLS-USA-1999-Ramakrishnan #community #distributed #education #testing #visualisation
Visualizing O-O Testing in Virtual Communities — Distributed Teaching and Learning (SR), p. 300–?.
SACSAC-1999-VenkataramanaR #automaton #framework
A Learning Automata Based Framework for Task Assignment in Heterogeneous Computing Systems (RDV, NR), pp. 541–547.
DATEDATE-1999-Marques-SilvaG #equivalence #recursion #satisfiability #using
Combinational Equivalence Checking Using Satisfiability and Recursive Learning (JPMS, TG), pp. 145–149.
STOCSTOC-1999-Servedio #complexity
Computational Sample Complexity and Attribute-Efficient Learning (RAS), pp. 701–710.
CSLCSL-1999-Balcazar #consistency #query
The Consistency Dimension, Compactness, and Query Learning (JLB), pp. 2–13.
ICLPICLP-1999-SatoF #logic programming
Reactive Logic Programming by Reinforcement Learning (TS, SF), p. 617.
TPDLECDL-1998-PaliourasPKSM #community
Learning User Communities for Improving the Services of Information Providers (GP, CP, VK, CDS, VM), pp. 367–383.
CSEETCSEET-1998-Hislop #education #network
Teaching Via Asynchronous Learning Networks (GWH), pp. 16–35.
ITiCSEITiCSE-1998-AbunawassMN #design #distance #education
An integratable unit based computer science distance learning curriculum design for the ACM/IEEE curricula 1991 (AMA, MM, KN), pp. 18–20.
ITiCSEITiCSE-1998-Casey #education #modelling #web
Learning “from” or “through” the Web: models of Web based education (DC), pp. 51–54.
ITiCSEITiCSE-1998-DavidovicT
Open learning environment and instruction system (OLEIS) (AD, ET), pp. 69–73.
ITiCSEITiCSE-1998-Ellis #development #internet #multi #problem #using
Group 1 (working group): development and use of multimedia and Internet resources for a problem based learning environment (AE), p. 269.
ITiCSEITiCSE-1998-GrayBS #java
A constructivist learning environment implemented in Java (JG, TB, CS), pp. 94–97.
ITiCSEITiCSE-1998-LewisM #comparison #compilation
A comparison between novice and experienced compiler users in a learning environment (SL, GM), pp. 157–161.
ITiCSEITiCSE-1998-TiwariH #collaboration #student #using
Learning groupware through using groupware-computer supported collaborative learning with face to face students (AT, CH), pp. 236–238.
ITiCSEITiCSE-1998-WhitehurstPI #distance #student
Utilising the student model in distance learning (RAW, CLP, JSI), pp. 254–256.
CHICHI-1998-ChinR #collaboration #design #evolution #staged
Progressive Design: Staged Evolution of Scenarios in the Design of a Collaborative Science Learning Environment (GCJ, MBR), pp. 611–618.
CHICHI-1998-JacksonKS #adaptation #design #interactive
The Design of Guided Learner-Adaptable Scaffolding in Interactive Learning Environments (SLJ, JK, ES), pp. 187–194.
CHICHI-1998-RoseDMBN #community #design #implementation
Building an Electronic Learning Community: From Design to Implementation (AR, WD, GM, JBJ, VN), pp. 203–210.
CHICHI-1998-Strommen #interface
When the Interface is a Talking Dinosaur: Learning Across Media with ActiMates Barney (ES), pp. 288–295.
CHICHI-1998-SumnerT #case study #design #experience
New Media, New Practices: Experiences in Open Learning Course Design (TS, JT), pp. 432–439.
CIKMCIKM-1998-DumaisPHS #algorithm #categorisation #induction
Inductive Learning Algorithms and Representations for Text Categorization (STD, JCP, DH, MS), pp. 148–155.
CIKMCIKM-1998-HongL #fuzzy
Learning Fuzzy Knowledge from Training Examples (TPH, CYL), pp. 161–166.
CIKMCIKM-1998-YuL #adaptation #algorithm #online
A New On-Line Learning Algorithm for Adaptive Text Filtering (KLY, WL), pp. 156–160.
ICMLICML-1998-AbeM #query #using
Query Learning Strategies Using Boosting and Bagging (NA, HM), pp. 1–9.
ICMLICML-1998-AlerBI #approach #multi #programming #search-based
Genetic Programming and Deductive-Inductive Learning: A Multi-Strategy Approach (RA, DB, PI), pp. 10–18.
ICMLICML-1998-AnglanoGBS #concept #evaluation
An Experimental Evaluation of Coevolutive Concept Learning (CA, AG, GLB, LS), pp. 19–27.
ICMLICML-1998-BillsusP #collaboration
Learning Collaborative Information Filters (DB, MJP), pp. 46–54.
ICMLICML-1998-BonetG #sorting
Learning Sorting and Decision Trees with POMDPs (BB, HG), pp. 73–81.
ICMLICML-1998-Dietterich
The MAXQ Method for Hierarchical Reinforcement Learning (TGD), pp. 118–126.
ICMLICML-1998-DzeroskiRB #relational
Relational Reinforcement Learning (SD, LDR, HB), pp. 136–143.
ICMLICML-1998-Freitag #information management #multi
Multistrategy Learning for Information Extraction (DF), pp. 161–169.
ICMLICML-1998-FriessCC #algorithm #kernel #performance
The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines (TTF, NC, CC), pp. 188–196.
ICMLICML-1998-GaborKS #multi
Multi-criteria Reinforcement Learning (ZG, ZK, CS), pp. 197–205.
ICMLICML-1998-GarciaN #algorithm #analysis
A Learning Rate Analysis of Reinforcement Learning Algorithms in Finite-Horizon (FG, SMN), pp. 215–223.
ICMLICML-1998-Heskes #approach #multi
Solving a Huge Number of Similar Tasks: A Combination of Multi-Task Learning and a Hierarchical Bayesian Approach (TH), pp. 233–241.
ICMLICML-1998-HuW #algorithm #framework #multi
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm (JH, MPW), pp. 242–250.
ICMLICML-1998-JuilleP #case study
Coevolutionary Learning: A Case Study (HJ, JBP), pp. 251–259.
ICMLICML-1998-KearnsS
Near-Optimal Reinforcement Learning in Polynominal Time (MJK, SPS), pp. 260–268.
ICMLICML-1998-KimuraK #algorithm #analysis #using
An Analysis of Actor/Critic Algorithms Using Eligibility Traces: Reinforcement Learning with Imperfect Value Function (HK, SK), pp. 278–286.
ICMLICML-1998-KollerF #approximate #probability #process #using
Using Learning for Approximation in Stochastic Processes (DK, RF), pp. 287–295.
ICMLICML-1998-LittmanJK #corpus #independence #representation
Learning a Language-Independent Representation for Terms from a Partially Aligned Corpus (MLL, FJ, GAK), pp. 314–322.
ICMLICML-1998-MargaritisT #3d #image #sequence
Learning to Locate an Object in 3D Space from a Sequence of Camera Images (DM, ST), pp. 332–340.
ICMLICML-1998-MaronR #classification #multi
Multiple-Instance Learning for Natural Scene Classification (OM, ALR), pp. 341–349.
ICMLICML-1998-McCallumN #classification
Employing EM and Pool-Based Active Learning for Text Classification (AM, KN), pp. 350–358.
ICMLICML-1998-MooreSBL #named #optimisation
Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions (AWM, JGS, JAB, MSL), pp. 386–394.
ICMLICML-1998-Ng #feature model #on the
On Feature Selection: Learning with Exponentially Many Irrelevant Features as Training Examples (AYN), pp. 404–412.
ICMLICML-1998-PendrithM #analysis #markov
An Analysis of Direct Reinforcement Learning in Non-Markovian Domains (MDP, MM), pp. 421–429.
ICMLICML-1998-RandlovA #using
Learning to Drive a Bicycle Using Reinforcement Learning and Shaping (JR, PA), pp. 463–471.
ICMLICML-1998-ReddyT #first-order #source code
Learning First-Order Acyclic Horn Programs from Entailment (CR, PT), pp. 472–480.
ICMLICML-1998-RyanP #architecture #composition #named
RL-TOPS: An Architecture for Modularity and Re-Use in Reinforcement Learning (MRKR, MDP), pp. 481–487.
ICMLICML-1998-SamuelCV
An Investigation of Transformation-Based Learning in Discourse (KS, SC, KVS), pp. 497–505.
ICMLICML-1998-SaundersGV #algorithm
Ridge Regression Learning Algorithm in Dual Variables (CS, AG, VV), pp. 515–521.
ICMLICML-1998-StuartB
Learning the Grammar of Dance (JMS, EB), pp. 547–555.
ICMLICML-1998-SuttonPS
Intra-Option Learning about Temporally Abstract Actions (RSS, DP, SPS), pp. 556–564.
ICPRICPR-1998-BukerK #hybrid
Learning in an active hybrid vision system (UB, BK), pp. 178–181.
ICPRICPR-1998-ConnellJ #online #prototype
Learning prototypes for online handwritten digits (SDC, AKJ), pp. 182–184.
ICPRICPR-1998-DayP #modelling
A projection filter for use with parameterised learning models (MJSD, JSP), pp. 867–869.
ICPRICPR-1998-DutaJ #concept #image
Learning the human face concept in black and white images (ND, AKJ), pp. 1365–1367.
ICPRICPR-1998-HickinbothamHA
Learning feature characteristics (SJH, ERH, JA), pp. 1160–1164.
ICPRICPR-1998-KeglKN #classification #network #parametricity
Radial basis function networks in nonparametric classification and function learning (BK, AK, HN), pp. 565–570.
ICPRICPR-1998-KnutssonBL #multi
Learning multidimensional signal processing (HK, MB, TL), pp. 1416–1420.
ICPRICPR-1998-LamOX #classification
Application of Bayesian Ying-Yang criteria for selecting the number of hidden units with backpropagation learning to electrocardiogram classification (WKL, NO, LX), pp. 1686–1688.
ICPRICPR-1998-Mizutani #classification #fault
Discriminative learning for minimum error and minimum reject classification (HM), pp. 136–140.
ICPRICPR-1998-Nagy #estimation #persistent
Persistent issues in learning and estimation (GN), pp. 561–564.
ICPRICPR-1998-PengB #recognition
Local reinforcement learning for object recognition (JP, BB), pp. 272–274.
ICPRICPR-1998-SatoY #classification #using
A formulation of learning vector quantization using a new misclassification measure (AS, KY), pp. 322–325.
ICPRICPR-1998-WengH #recognition #sequence
Sensorimotor action sequence learning with application to face recognition under discourse (J(W, WSH), pp. 252–254.
KDDKDD-1998-AndersonM #performance
ADtrees for Fast Counting and for Fast Learning of Association Rules (BSA, AWM), pp. 134–138.
KDDKDD-1998-ChanS #case study #detection #scalability #towards
Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection (PKC, SJS), pp. 164–168.
KDDKDD-1998-GrecuB #data mining #distributed #mining
Coactive Learning for Distributed Data Mining (DLG, LAB), pp. 209–213.
KDDKDD-1998-HandleyLR #predict
Learning to Predict the Duration of an Automobile Trip (SH, PL, FAR), pp. 219–223.
KDDKDD-1998-LaneB #concept #identification #online #security
Approaches to Online Learning and Concept Drift for User Identification in Computer Security (TL, CEB), pp. 259–263.
KDDKDD-1998-MoodyS
Reinforcement Learning for Trading Systems and Portfolios (JEM, MS), pp. 279–283.
KDDKDD-1998-WeissH #predict #sequence
Learning to Predict Rare Events in Event Sequences (GMW, HH), pp. 359–363.
SIGIRSIGIR-1998-Callan
Learning While Filtering Focuments (JPC), pp. 224–231.
FSEFSE-1998-MasudaSU #design pattern
Applying Design Patterns to Decision Tree Learning System (GM, NS, KU), pp. 111–120.
ICSEICSE-1998-HanakawaMM #development #simulation
A Learning Curve Based Simulation Model for Software Development (NH, SM, KiM), pp. 350–359.
SACSAC-1998-BillardL #automaton #behaviour #distributed #simulation
Simulation of period-doubling behavior in distributed learning automata (EB, SL), pp. 690–695.
SACSAC-1998-ChungC #interactive #multi
A multimedia system for interactive learning of organ literature (SC, SC), pp. 117–121.
DACDAC-1998-El-MalehKR #performance
A Fast Sequential Learning Technique for Real Circuits with Application to Enhancing ATPG Performance (AHEM, MK, JR), pp. 625–631.
STOCSTOC-1998-Bshouty #algorithm #composition #theorem
A New Composition Theorem for Learning Algorithms (NHB), pp. 583–589.
STOCSTOC-1998-Damaschke #adaptation
Adaptive versus Nonadaptive Attribute-Efficient Learning (PD), pp. 590–596.
DL-1997-MarchioniniNWDBRGEH #community
Content + Connectivity => Community: Digital Resources for a Learning Community (GM, VN, HW, WD, JBJ, AR, AG, EE, LH), pp. 212–220.
ICDARICDAR-1997-JunkerH #classification #documentation
Evaluating OCR and Non-OCR Text Representations for Learning Document Classifiers (MJ, RH), pp. 1060–1066.
ICDARICDAR-1997-WaizumiKSN #classification #using
High speed rough classification for handwritten characters using hierarchical learning vector quantization (YW, NK, KS, YN), pp. 23–27.
ICDARICDAR-1997-YamauchiIT #multi #recognition
Shape based Learning for a Multi-Template Method, and its Application to Handprinted Numeral Recognition (TY, YI, JT), pp. 495–498.
ITiCSEITiCSE-1997-Boulet #distance
Distance learning of the management of software projects (MMB), pp. 136–138.
ITiCSEITiCSE-1997-Carswell #communication #distance #education #internet #student
Teaching via the Internet: the impact of the Internet as a communication medium on distance learning introductory computing students (LC), pp. 1–5.
ITiCSEITiCSE-1997-DankelH #distance #using
The use of the WWW to support distance learning through NTU (DDDI, JH), pp. 8–10.
ITiCSEITiCSE-1997-Janser #algorithm #interactive #visualisation
An interactive learning system visualizing computer graphics algorithms (AWJ), pp. 21–23.
ITiCSEITiCSE-1997-Makkonen #collaboration #hypermedia #question
Does collaborative hypertext support better engagement in learning of the basics in informatics? (PM), pp. 130–132.
ITiCSEITiCSE-1997-Moser #game studies #what #why
A fantasy adventure game as a learning environment: why learning to program is so difficult and what can be done about it (RM), pp. 114–116.
ITiCSEITiCSE-1997-RoblesFPA #communication #distance #multi #using
Using multimedia communication technologies in distance learning (TR, DF, EP, SA), pp. 6–7.
ITiCSEITiCSE-WGR-1997-LawheadABCCDDFS #distance #web #what
The Web and distance learning: what is appropriate and what is not (report of the ITiCSE 1997 working group on the web and distance learning) (PBL, EA, CGB, LC, DC, JD, MD, ERF, KS), pp. 27–37.
ITiCSEITiCSE-WGR-1997-Maurer #distributed #education
The emergence of sophisticated distributed teaching and learning environments (HM), pp. 112–113.
DLTDLT-1997-DavidES #string
Learning String Adjunct and Tree Adjunct Languages (NGD, JDE, KGS), pp. 411–427.
CHICHI-1997-RappinGRL #interface #usability
Balancing Usability and Learning in an Interface (NR, MG, MR, PL), pp. 479–486.
CHICHI-1997-ScaifeRAD #design #interactive
Designing For or Designing With? Informant Design For Interactive Learning Environments (MS, YR, FA, MD), pp. 343–350.
HCIHCI-SEC-1997-DasaiKY #collaboration #distance
A Collaborative Distance Learning System and its Experimental Results (TD, HK, KY), pp. 165–168.
HCIHCI-SEC-1997-EnyedyVG #design #interactive
Designing Interactions for Guided Inquiry Learning Environments (NE, PV, BG), pp. 157–160.
HCIHCI-SEC-1997-Keating
Computer Based Learning: GroupSystems[R] in the Wireless Classroom (CCK), pp. 119–122.
HCIHCI-SEC-1997-MurphyKG #interface
Enhancing the Interface to Provide Intelligent Computer Aided Language Learning (MM, AK, AG), pp. 149–152.
HCIHCI-SEC-1997-Neal #distance #multi #using
Using Multiple Technologies for Distance Learning (LN), pp. 111–114.
HCIHCI-SEC-1997-PatelK #design #interactive #interface
Granular Interface Design: Decomposing Learning Tasks and Enhancing Tutoring Interaction (AP, K), pp. 161–164.
HCIHCI-SEC-1997-WilliamsFSTE #education #named #student
PEBBLES: Providing Education by Bringing Learning Environments to Students (LAW, DIF, GS, JT, RE), pp. 115–118.
CIKMCIKM-1997-ChengBL #approach #network
Learning Belief Networks from Data: An Information Theory Based Approach (JC, DAB, WL), pp. 325–331.
ICMLICML-1997-AtkesonS
Robot Learning From Demonstration (CGA, SS), pp. 12–20.
ICMLICML-1997-Auer #approach #empirical #evaluation #multi #on the
On Learning From Multi-Instance Examples: Empirical Evaluation of a Theoretical Approach (PA), pp. 21–29.
ICMLICML-1997-BottaGP #first-order #logic #named
FONN: Combining First Order Logic with Connectionist Learning (MB, AG, RP), pp. 46–56.
ICMLICML-1997-DattaK #prototype
Learning Symbolic Prototypes (PD, DFK), pp. 75–82.
ICMLICML-1997-Decatur #classification #induction
PAC Learning with Constant-Partition Classification Noise and Applications to Decision Tree Induction (SED), pp. 83–91.
ICMLICML-1997-Fiechter #bound #online
Expected Mistake Bound Model for On-Line Reinforcement Learning (CNF), pp. 116–124.
ICMLICML-1997-Friedman #network
Learning Belief Networks in the Presence of Missing Values and Hidden Variables (NF), pp. 125–133.
ICMLICML-1997-KimuraMK #approximate
Reinforcement Learning in POMDPs with Function Approximation (HK, KM, SK), pp. 152–160.
ICMLICML-1997-PrecupS
Exponentiated Gradient Methods for Reinforcement Learning (DP, RSS), pp. 272–277.
ICMLICML-1997-ReddyT #using
Learning Goal-Decomposition Rules using Exercises (CR, PT), pp. 278–286.
ICMLICML-1997-RistadY #distance #edit distance #string
Learning String Edit Distance (ESR, PNY), pp. 287–295.
ICMLICML-1997-SakrLCHG #data access #memory management #modelling #multi #predict
Predicting Multiprocessor Memory Access Patterns with Learning Models (MFS, SPL, DMC, BGH, CLG), pp. 305–312.
ICMLICML-1997-Schapire #multi #problem #using
Using output codes to boost multiclass learning problems (RES), pp. 313–321.
ICMLICML-1997-SuematsuHL #approach #markov
A Bayesian Approach to Model Learning in Non-Markovian Environments (NS, AH, SL), pp. 349–357.
ICMLICML-1997-TadepalliD
Hierarchical Explanation-Based Reinforcement Learning (PT, TGD), pp. 358–366.
KDDKDD-1997-Hekanaho #concept
GA-Based Rule Enhancement in Concept Learning (JH), pp. 183–186.
KDDKDD-1997-PazzaniMS
Beyond Concise and Colorful: Learning Intelligible Rules (MJP, SM, WRS), pp. 235–238.
KDDKDD-1997-RubinsteinH
Discriminative vs Informative Learning (YDR, TH), pp. 49–53.
KDDKDD-1997-Soderland #web
Learning to Extract Text-Based Information from the World Wide Web (SS), pp. 251–254.
KDDKDD-1997-ZighedRF #multi
Optimal Multiple Intervals Discretization of Continuous Attributes for Supervised Learning (DAZ, RR, FF), pp. 295–298.
SIGIRSIGIR-1997-NgGL #case study #categorisation #feature model #usability
Feature Selection, Perceptron Learning, and a Usability Case Study for Text Categorization (HTN, WBG, KLL), pp. 67–73.
SIGIRSIGIR-1997-SinghalMB #query
Learning Routing Queries in a Query Zone (AS, MM, CB), pp. 25–32.
SACSAC-1997-Goldberg
Virtual teams virtual projects = real learning (AG), p. 1.
SACSAC-1997-SolowayN #education #future of #lessons learnt
The future of computers in education: learning 10 lessons from the past (ES, CAN), p. 2.
STOCSTOC-1997-AuerLS #approximate #pseudo #set
Approximating Hyper-Rectangles: Learning and Pseudo-Random Sets (PA, PML, AS), pp. 314–323.
STOCSTOC-1997-Ben-DavidBK #algorithm #composition #concept #geometry #theorem
A Composition Theorem for Learning Algorithms with Applications to Geometric Concept Classes (SBD, NHB, EK), pp. 324–333.
CADECADE-1997-KolbeB #named #proving
Plagiator — A Learning Prover (TK, JB), pp. 256–259.
ITiCSEITiCSE-1996-BrodlieWW #novel #visualisation
Scientific visualization — some novel approaches to learning (KB, JDW, HW), pp. 28–32.
ITiCSEITiCSE-1996-CaoLLPZ #education #information management
Integrating CSCW in a cooperative learning environment to teach information systems (NVC, AL, ML, OP, CZ), pp. 125–129.
ITiCSEITiCSE-1996-FinkelW
Computer supported peer learning in an introductory computer science course (DF, CEW), pp. 55–56.
ITiCSEITiCSE-1996-JohansenKB #interactive
Interactive learning with gateway labs (MJ, JK, DB), p. 232.
ITiCSEITiCSE-1996-LeesC #natural language #operating system
Applying natural language technology to the learning of operating systems functions (BL, JC), pp. 11–13.
ITiCSEITiCSE-1996-McConnell
Active learning and its use in computer science (JJM), pp. 52–54.
ITiCSEITiCSE-1996-Prey #education
Cooperative learning and closed laboratories in an undergraduate computer science curriculum (JCP), pp. 23–24.
ITiCSEITiCSE-1996-Tjaden #how #student #visual notation
How visual software influences learning in college students (BJT), p. 229.
CHICHI-1996-SolowayJKQRSSSES #case study #design
Learning Theory in Practice: Case Studies of Learner-Centered Design (ES, SLJ, JK, CQ, JR, JS, SJS, SS, JE, NS), pp. 189–196.
CSCWCSCW-1996-HiltzT #collaboration #network #online #theory and practice
Asynchronous Learning Networks: The Theory and Practice of Collaborative Learning Online (SRH, MT), p. 5.
KDDAKDDM-1996-HsuK #induction #optimisation #query #semantics #using
Using Inductive Learning To Generate Rules for Semantic Query Optimization (CNH, CAK), pp. 425–445.
CIKMCIKM-1996-Huffman
Learning to Extract Information From Text Based on User-Provided Examples (SBH), pp. 154–163.
ICMLICML-1996-AbeL #modelling #using #word
Learning Word Association Norms Using Tree Cut Pair Models (NA, HL), pp. 3–11.
ICMLICML-1996-BlanzieriK #network #online
Learning Radial Basis Function Networks On-line (EB, PK), pp. 37–45.
ICMLICML-1996-BoyanM #evaluation #scalability
Learning Evaluation Functions for Large Acyclic Domains (JAB, AWM), pp. 63–70.
ICMLICML-1996-Caruana #algorithm #multi
Algorithms and Applications for Multitask Learning (RC), pp. 87–95.
ICMLICML-1996-DietterichKM #framework
Applying the Waek Learning Framework to Understand and Improve C4.5 (TGD, MJK, YM), pp. 96–104.
ICMLICML-1996-EmdeW #relational
Relational Instance-Based Learning (WE, DW), pp. 122–130.
ICMLICML-1996-EzawaSN #network #risk management
Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management (KJE, MS, SWN), pp. 139–147.
ICMLICML-1996-FriedmanG #network
Discretizing Continuous Attributes While Learning Bayesian Networks (NF, MG), pp. 157–165.
ICMLICML-1996-GeibelW #concept #relational
Learning Relational Concepts with Decision Trees (PG, FW), pp. 166–174.
ICMLICML-1996-GoetzKM #adaptation #online
On-Line Adaptation of a Signal Predistorter through Dual Reinforcement Learning (PG, SK, RM), pp. 175–181.
ICMLICML-1996-GordonS #parametricity #statistics
Nonparametric Statistical Methods for Experimental Evaluations of Speedup Learning (GJG, AMS), pp. 200–206.
ICMLICML-1996-GreinerGR #classification
Learning Active Classifiers (RG, AJG, DR), pp. 207–215.
ICMLICML-1996-Hekanaho #concept
Background Knowledge in GA-based Concept Learning (JH), pp. 234–242.
ICMLICML-1996-JappyNG #horn clause #robust #source code
Negative Robust Learning Results from Horn Clause Programs (PJ, RN, OG), pp. 258–265.
ICMLICML-1996-KoenigS #distance #navigation
Passive Distance Learning for Robot Navigation (SK, RGS), pp. 266–274.
ICMLICML-1996-Mahadevan
Sensitive Discount Optimality: Unifying Discounted and Average Reward Reinforcement Learning (SM), pp. 328–336.
ICMLICML-1996-Moore
Reinforcement Learning in Factories: The Auton Project (AWM0), p. 556.
ICMLICML-1996-Munos #algorithm #convergence
A Convergent Reinforcement Learning Algorithm in the Continuous Case: The Finite-Element Reinforcement Learning (RM), pp. 337–345.
ICMLICML-1996-OliverBW #using
Unsupervised Learning Using MML (JJO, RAB, CSW), pp. 364–372.
ICMLICML-1996-PendrithR #difference
Actual Return Reinforcement Learning versus Temporal Differences: Some Theoretical and Experimental Results (MDP, MRKR), pp. 373–381.
ICMLICML-1996-Perez #representation
Representing and Learning Quality-Improving Search Control Knowledge (MAP), pp. 382–390.
ICMLICML-1996-PerezR #concept
Learning Despite Concept Variation by Finding Structure in Attribute-based Data (EP, LAR), pp. 391–399.
ICMLICML-1996-ReddyTR #composition #empirical
Theory-guided Empirical Speedup Learning of Goal Decomposition Rules (CR, PT, SR), pp. 409–417.
ICMLICML-1996-Saerens #fault
Non Mean Square Error Criteria for the Training of Learning Machines (MS), pp. 427–434.
ICMLICML-1996-SinghP #classification #network #performance
Efficient Learning of Selective Bayesian Network Classifiers (MS, GMP), pp. 453–461.
ICMLICML-1996-Suzuki #algorithm #network #performance #using
Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: An Efficient Algorithm Using the B & B Technique (JS), pp. 462–470.
ICMLICML-1996-TadepalliO #approximate #domain model #modelling #scalability
Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function (PT, DO), pp. 471–479.
ICMLICML-1996-ThrunO #algorithm #multi
Discovering Structure in Multiple Learning Tasks: The TC Algorithm (ST, JO), pp. 489–497.
ICMLICML-1996-TirriKM
Prababilistic Instance-Based Learning (HT, PK, PM), pp. 507–515.
ICMLICML-1996-ZuckerG #performance #representation
Representation Changes for Efficient Learning in Structural Domains (JDZ, JGG), pp. 543–551.
ICPRICPR-1996-AizenbergAK #image #multi #network #pattern matching #pattern recognition #recognition
Multi-valued and universal binary neurons: mathematical model, learning, networks, application to image processing and pattern recognition (NNA, INA, GAK), pp. 185–189.
ICPRICPR-1996-AlquezarS #context-sensitive grammar #regular expression
Learning of context-sensitive languages described by augmented regular expressions (RA, AS), pp. 745–749.
ICPRICPR-1996-BebisGLS #modelling #recognition
Learning affine transformations of the plane for model-based object recognition (GB, MG, NdVL, MS), pp. 60–64.
ICPRICPR-1996-Bobrowski #classification #set
Piecewise-linear classifiers, formal neurons and separability of the learning sets (LB), pp. 224–228.
ICPRICPR-1996-BurgeBM #component #polymorphism #recognition
Recognition and learning with polymorphic structural components (MB, WB, WM), pp. 19–23.
ICPRICPR-1996-FrankH #approach
Pretopological approach for supervised learning (FL, HE), pp. 256–260.
ICPRICPR-1996-HoogsB #modelling
Model-based learning of segmentations (AH, RB), pp. 494–499.
ICPRICPR-1996-KositskyU
Learning class regions by the union of ellipsoids (MK, SU), pp. 750–757.
ICPRICPR-1996-LuettinTB96a
Learning to recognise talking faces (JL, NAT, SWB), pp. 55–59.
ICPRICPR-1996-Muraki #fault #statistics
Error correction scheme augmented with statistical and lexical learning capability, for Japanese OCR (KM), pp. 560–564.
ICPRICPR-1996-MuraseN #approach #generative #recognition
Learning by a generation approach to appearance-based object recognition (HM, SKN), pp. 24–29.
ICPRICPR-1996-PelilloF #network
Autoassociative learning in relaxation labeling networks (MP, AMF), pp. 105–110.
ICPRICPR-1996-PengB #recognition
Delayed reinforcement learning for closed-loop object recognition (JP, BB), pp. 310–314.
ICPRICPR-1996-SainzS #context-sensitive grammar #modelling #using
Learning bidimensional context-dependent models using a context-sensitive language (MS, AS), pp. 565–569.
ICPRICPR-1996-Stoyanov #network
An improved backpropagation neural network learning (IPS), pp. 586–588.
ICPRICPR-1996-WengC #incremental #navigation
Incremental learning for vision-based navigation (JW, SC), pp. 45–49.
ICPRICPR-1996-Yamakawa #feature model #recognition
Matchability-oriented feature selection for recognition structure learning (HY), pp. 123–127.
ICPRICPR-1996-ZanardiHC #interactive #mobile
Mutual learning or unsupervised interactions between mobile robots (CZ, JYH, PC), pp. 40–44.
ICPRICPR-1996-ZhengB #adaptation #detection
Adaptive object detection based on modified Hebbian learning (YJZ, BB), pp. 164–168.
KDDKDD-1996-Feelders #modelling #using
Learning from Biased Data Using Mixture Models (AJF), pp. 102–107.
KDDKDD-1996-Musick #network
Rethinking the Learning of Belief Network Probabilities (RM), pp. 120–125.
KDDKDD-1996-Sahami #classification #dependence
Learning Limited Dependence Bayesian Classifiers (MS), pp. 335–338.
KDDKDD-1996-StolorzC #markov #monte carlo #visual notation
Harnessing Graphical Structure in Markov Chain Monte Carlo Learning (PES, PCC), pp. 134–139.
KDDKDD-1996-TeranoI #induction #information management #interactive #using
Interactive Knowledge Discovery from Marketing Questionnaire Using Simulated Breeding and Inductive Learning Methods (TT, YI), pp. 279–282.
KRKR-1996-Ghallab #on the #online #recognition #representation
On Chronicles: Representation, On-line Recognition and Learning (MG), pp. 597–606.
SIGIRSIGIR-1996-CohenS #categorisation
Context-sensitive Learning Methods for Text Categorization (WWC, YS), pp. 307–315.
AdaTRI-Ada-1996-NebeshF #ada #component #html #using
Learning to Use Ada 95 Components Using HTML Linking (BN, MBF), pp. 207–210.
AdaTRI-Ada-1996-ParrishCLM #ada #assessment #process #re-engineering
Active Learning and Process Assessment: Two Experiments in an Ada-Based Software Engineering Course (ASP, DC, CL, DM), pp. 157–161.
STOCSTOC-1996-BergadanoCV #query
Learning Sat-k-DNF Formulas from Membership Queries (FB, DC, SV), pp. 126–130.
STOCSTOC-1996-BshoutyGMST #concept #geometry
Noise-Tolerant Distribution-Free Learning of General Geometric Concepts (NHB, SAG, HDM, SS, HT), pp. 151–160.
STOCSTOC-1996-Cesa-BianchiDFS #bound
Noise-Tolerant Learning Near the Information-Theoretic Bound (NCB, ED, PF, HUS), pp. 141–150.
STOCSTOC-1996-KearnsM #algorithm #on the #top-down
On the Boosting Ability of Top-Down Decision Tree Learning Algorithms (MJK, YM), pp. 459–468.
CADECADE-1996-DenzingerS #proving #theorem proving
Learning Domain Knowledge to Improve Theorem Proving (JD, SS), pp. 62–76.
ICDARICDAR-v1-1995-TakasuSK #documentation #image
A rule learning method for academic document image processing (AT, SS, EK), pp. 239–242.
ICDARICDAR-v2-1995-MatsunagaK #case study #classification #statistics
An experimental study of learning curves for statistical pattern classifiers (TM, HK), pp. 1103–1106.
CSEETCSEE-1995-DickJ #education #industrial
Industry Involvement in Undergraduate Curricula: Reinforcing Learning by Applying the Principles (GND, SFJ), pp. 51–63.
CSEETCSEE-1995-Mahy #re-engineering
From TRAINING to LEARNING: The Reengineering of Training at DMR Group Inc. (IM), p. 433.
ICALPICALP-1995-FortnowFGKKSS
Measure, Category and Learning Theory (LF, RF, WIG, MK, SAK, CHS, FS), pp. 558–569.
CHICHI-1995-AalstCM #analysis #design #framework #user interface
Design Space Analysis as “Training Wheels” in a Framework for Learning User Interface Design (JWvA, TTC, DLM), pp. 154–161.
CHICHI-1995-BauerJ #interactive #modelling
Modeling Time-Constrained Learning in a Highly Interactive Task (MIB, BEJ), pp. 19–26.
CHICHI-1995-JohnP #approach #case study #using
Learning and Using the Cognitive Walkthrough Method: A Case Study Approach (BEJ, HP), pp. 429–436.
CHICHI-1995-MitchellPB #using
Learning to Write Together Using Groupware (AM, IP, RB), pp. 288–295.
CIKMCIKM-1995-ChenM #information management
Learning Subjective Relevance to Facilitate Information Access (JRC, NM), pp. 218–225.
ICMLICML-1995-AbeLN #2d #algorithm #online #using
On-line Learning of Binary Lexical Relations Using Two-dimensional Weighted Majority Algorithms (NA, HL, AN), pp. 3–11.
ICMLICML-1995-AlmuallimAK #on the
On Handling Tree-Structured Attributed in Decision Tree Learning (HA, YA, SK), pp. 12–20.
ICMLICML-1995-Baird #algorithm #approximate
Residual Algorithms: Reinforcement Learning with Function Approximation (LCBI), pp. 30–37.
ICMLICML-1995-Benson #induction #modelling
Inductive Learning of Reactive Action Models (SB), pp. 47–54.
ICMLICML-1995-CichoszM #difference #performance
Fast and Efficient Reinforcement Learning with Truncated Temporal Differences (PC, JJM), pp. 99–107.
ICMLICML-1995-Cohen95a #categorisation #relational
Text Categorization and Relational Learning (WWC), pp. 124–132.
ICMLICML-1995-Cussens #algorithm #analysis #finite
A Bayesian Analysis of Algorithms for Learning Finite Functions (JC), pp. 142–149.
ICMLICML-1995-DattaK #concept #prototype
Learning Prototypical Concept Descriptions (PD, DFK), pp. 158–166.
ICMLICML-1995-DietterichF #perspective
Explanation-Based Learning and Reinforcement Learning: A Unified View (TGD, NSF), pp. 176–184.
ICMLICML-1995-Fuchs #adaptation #heuristic #parametricity #proving
Learning Proof Heuristics by Adaptive Parameters (MF), pp. 235–243.
ICMLICML-1995-GambardellaD #approach #named #problem
Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem (LMG, MD), pp. 252–260.
ICMLICML-1995-Heckerman #network
Learning With Bayesian Networks (DH), p. 588.
ICMLICML-1995-Hekanaho #concept #multimodal
Symbiosis in Multimodal Concept Learning (JH), pp. 278–285.
ICMLICML-1995-KimuraYK #probability
Reinforcement Learning by Stochastic Hill Climbing on Discounted Reward (HK, MY, SK), pp. 295–303.
ICMLICML-1995-KrishnanLV
Learning to Make Rent-to-Buy Decisions with Systems Applications (PK, PML, JSV), pp. 233–330.
ICMLICML-1995-Lang #named
NewsWeeder: Learning to Filter Netnews (KL), pp. 331–339.
ICMLICML-1995-Littlestone #algorithm
Comparing Several Linear-threshold Learning Algorithms on Tasks Involving Superfluous Attributes (NL), pp. 353–361.
ICMLICML-1995-LittmanCK #policy #scalability
Learning Policies for Partially Observable Environments: Scaling Up (MLL, ARC, LPK), pp. 362–370.
ICMLICML-1995-MaassW #performance
Efficient Learning with Virtual Threshold Gates (WM, MKW), pp. 378–386.
ICMLICML-1995-McCallum
Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State (AM), pp. 387–395.
ICMLICML-1995-MoriartyM #evolution #performance
Efficient Learning from Delayed Rewards through Symbiotic Evolution (DEM, RM), pp. 396–404.
ICMLICML-1995-Niyogi #complexity
Free to Choose: Investigating the Sample Complexity of Active Learning of Real Valued Functions (PN), pp. 405–412.
ICMLICML-1995-NockG #on the
On Learning Decision Committees (RN, OG), pp. 413–420.
ICMLICML-1995-Pomerleau
Learning for Automotive Collision Avoidance and Autonomous Control (DP), p. 589.
ICMLICML-1995-SalganicoffU #multi #using
Active Exploration and Learning in real-Valued Spaces using Multi-Armed Bandit Allocation Indices (MS, LHU), pp. 480–487.
ICMLICML-1995-StreetMW #approach #induction #predict
An Inductive Learning Approach to Prognostic Prediction (WNS, OLM, WHW), pp. 522–530.
ICMLICML-1995-TowellVGJ #information retrieval
Learning Collection FUsion Strategies for Information Retrieval (GGT, EMV, NKG, BJL), pp. 540–548.
ICMLICML-1995-Wang #approach #incremental
Learning by Observation and Practice: An Incremental Approach for Planning Operator Acquisition (XW), pp. 549–557.
ICMLICML-1995-Weiss
Learning with Rare Cases and Small Disjuncts (GMW), pp. 558–565.
ICMLICML-1995-YamazakiPM #ambiguity #natural language
Learning Hierarchies from Ambiguous Natural Language Data (TY, MJP, CJM), pp. 575–583.
KDDKDD-1995-AugierVK #algorithm #first-order #logic #search-based
Learning First Order Logic Rules with a Genetic Algorithm (SA, GV, YK), pp. 21–26.
KDDKDD-1995-CortesJC #quality
Limits on Learning Machine Accuracy Imposed by Data Quality (CC, LDJ, WPC), pp. 57–62.
KDDKDD-1995-HuC #database #set #similarity
Rough Sets Similarity-Based Learning from Databases (XH, NC), pp. 162–167.
KDDKDD-1995-SpirtesM #network
Learning Bayesian Networks with Discrete Variables from Data (PS, CM), pp. 294–299.
SEKESEKE-1995-LiangT #domain model #modelling
Apprenticeship Learning of Domain Models (YL, GT), pp. 54–62.
SIGIRSIGIR-1995-VoorheesGJ
Learning Collection Fusion Strategies (EMV, NKG, BJL), pp. 172–179.
ICSEICSE-1995-HenningerLR #analysis #approach
An Organizational Learning Approach to Domain Analysis (SH, KL, AR), pp. 95–104.
SACSAC-1995-GuzdialRC #collaboration #education #interactive #multi
Collaborative and multimedia interactive learning environment for engineering education (MG, NR, DC), pp. 5–9.
SACSAC-1995-Tschichold-Gurman #classification #fuzzy #generative #incremental #using
Generation and improvement of fuzzy classifiers with incremental learning using fuzzy RuleNet (NNTG), pp. 466–470.
DACDAC-1995-JainMF #verification
Advanced Verification Techniques Based on Learning (JJ, RM, MF), pp. 420–426.
ICLPICLP-1995-Sato #logic programming #semantics #source code #statistics
A Statistical Learning Method for Logic Programs with Distribution Semantics (TS), pp. 715–729.
CSEETCSEE-1994-MooreP #experience #re-engineering
Learning by Doing: Goals & Experience of Two Software Engineering Project Courses (MMM, CP), pp. 151–164.
CHICHI-1994-KurtenbachB94a #performance
User learning and performance with marking menus (GK, WB), pp. 258–264.
CSCWCSCW-1994-WanJ #approach #collaboration #using
Computer Supported Collaborative Learning Using CLARE: The Approach and Experimental Findings (DW, PMJ), pp. 187–198.
CIKMCIKM-1994-LamirelC #approach #database #design #interactive #online
Application of a Symbolico-Connectionist Approach for the Design of a Highly Interactive Documentary Database Interrogation System with On-Line Learning Capabilities (JCL, MC), pp. 155–163.
ICMLICML-1994-AhaLLM #recursion #set
Learning Recursive Relations with Randomly Selected Small Training Sets (DWA, SL, CXL, SM), pp. 12–18.
ICMLICML-1994-Elomaa
In Defense of C4.5: Notes Learning One-Level Decision Trees (TE), pp. 62–69.
ICMLICML-1994-GervasioD #approach #incremental
An Incremental Learning Approach for Completable Planning (MTG, GD), pp. 78–86.
ICMLICML-1994-Gil #incremental #refinement
Learning by Experimentation: Incremental Refinement of Incomplete Planning Domains (YG), pp. 87–95.
ICMLICML-1994-GiordanaSZ #algorithm #concept #search-based
Learning Disjunctive Concepts by Means of Genetic Algorithms (AG, LS, FZ), pp. 96–104.
ICMLICML-1994-Heger
Consideration of Risk in Reinformance Learning (MH), pp. 105–111.
ICMLICML-1994-LewisC #nondeterminism
Heterogenous Uncertainty Sampling for Supervised Learning (DDL, JC), pp. 148–156.
ICMLICML-1994-Littman #framework #game studies #markov #multi
Markov Games as a Framework for Multi-Agent Reinforcement Learning (MLL), pp. 157–163.
ICMLICML-1994-Mahadevan #case study
To Discount or Not to Discount in Reinforcement Learning: A Case Study Comparing R Learning and Q Learning (SM), pp. 164–172.
ICMLICML-1994-Mataric
Reward Functions for Accelerated Learning (MJM), pp. 181–189.
ICMLICML-1994-SchapireW #algorithm #analysis #on the #worst-case
On the Worst-Case Analysis of Temporal-Difference Learning Algorithms (RES, MKW), pp. 266–274.
ICMLICML-1994-SinghJJ #markov #process
Learning Without State-Estimation in Partially Observable Markovian Decision Processes (SPS, TSJ, MIJ), pp. 284–292.
ICMLICML-1994-TchoumatchenkoG #framework
A Baysian Framework to Integrate Symbolic and Neural Learning (IT, JGG), pp. 302–308.
ICMLICML-1994-ZuckerG #concept
Selective Reformulation of Examples in Concept Learning (JDZ, JGG), pp. 352–360.
KDDKDD-1994-Furnkranz #comparison #concept #relational
A Comparison of Pruning Methods for Relational Concept Learning (JF), pp. 371–382.
KDDKDD-1994-HeckermanGC #network #statistics
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data (DH, DG, DMC), pp. 85–96.
KDDKDD-1994-HuCX #database
Learning Data Trend Regularities From Databases in a Dynamic Environment (XH, NC, JX), pp. 323–334.
KDDKDD-1994-Kaufman #development #multi #tool support #using
Comparing International Development Patterns Using Multi-Operator Learning and Discovery Tools (KAK), pp. 431–440.
KDDKDD-1994-ShenMOZ #database #deduction #induction #using
Using Metagueries to Integrate Inductive Learning and Deductive Database Technology (WMS, BGM, KO, CZ), pp. 335–346.
KRKR-1994-Carbonell #information management #representation
Knowledge Representation Issues in Integrated Planning and Learning Systems (JGC), p. 633.
KRKR-1994-CohenH #logic
Learning the Classic Description Logic: Theoretical and Experimental Results (WWC, HH), pp. 121–133.
SEKESEKE-1994-AbranDMMS #analysis #hypermedia #using
Structured hypertext for using and learning function point analysis (AA, JMD, DM, MM, DSP), pp. 164–171.
SEKESEKE-1994-ReynoldsZ #algorithm #using
Learning to understand software from examples using cultural algorithms (RGR, EZ), pp. 188–192.
SIGIRSIGIR-1994-Allen #information retrieval #performance
Perceptual Speed, Learning and Information Retrieval Performance (BA), pp. 71–80.
SIGIRSIGIR-1994-ApteDW #automation #categorisation #independence #modelling #towards
Towards Language Independent Automated Learning of Text Categorisation Models (CA, FD, SMW), pp. 23–30.
SIGIRSIGIR-1994-Yang #categorisation #effectiveness #network #performance #retrieval
Expert Network: Effective and Efficient Learning from Human Decisions in Text Categorization and Retrieval (YY), pp. 13–22.
OOPSLAOOPSLA-1994-RobertsonCMRAK #design #named #object-oriented #self
ODE: A Self-Guided, Scenario-Based Learning Environment for Object-Oriented Design Principles (SPR, JMC, RLM, MBR, SRA, JKB), pp. 51–64.
LOPSTRLOPSTR-1994-SemeraroEMBP #case study #logic #source code
Avoiding Non-Termination when Learning Logical Programs: A Case Study with FOIL and FOCL (GS, FE, DM, CB, MJP), pp. 183–198.
SACSAC-1994-Chen
Application of Boolean expression minimization to learning via hierarchical generalization (JC), pp. 303–307.
SACSAC-1994-HughesWK
Virtual space learning: creating text-based learning environments (BH, JW, BK), pp. 578–582.
SACSAC-1994-Janikow #algorithm #fuzzy #search-based
A genetic algorithm for learning fuzzy controllers (CZJ), pp. 232–236.
SACSAC-1994-RothermelT #logic #overview
Test Review: a new method of computer-assisted learning to promote careful reading and logical skills (DR, GT), pp. 573–577.
SACSAC-1994-WongM #specification #verification
Specification and verification of learning (KWW, RAM), pp. 6–9.
STOCSTOC-1994-ApsitisFS #approach
Choosing a learning team: a topological approach (KA, RF, CHS), pp. 283–289.
STOCSTOC-1994-AuerL #simulation
Simulating access to hidden information while learning (PA, PML), pp. 263–272.
STOCSTOC-1994-BlumFJKMR #analysis #fourier #query #statistics #using
Weakly learning DNF and characterizing statistical query learning using Fourier analysis (AB, MLF, JCJ, MJK, YM, SR), pp. 253–262.
STOCSTOC-1994-Sitharam #algorithm #generative #pseudo
Pseudorandom generators and learning algorithms for AC (MS), pp. 478–486.
ICDARICDAR-1993-Dengel #documentation
Initial learning of document structure (AD), pp. 86–90.
ICDARICDAR-1993-Ho #independence #recognition
Recognition of handwritten digits by combining independent learning vector quantizations (TKH), pp. 818–821.
ICDARICDAR-1993-Kawatani #polynomial #recognition
Handprinted numeral recognition with the learning quadratic discriminant function (TK), pp. 14–17.
ICDARICDAR-1993-KuritaK #database #image #visual notation
Learning of personal visual impression for image database systems (TK, TK), pp. 547–552.
ICDARICDAR-1993-LebourgeoisH
A contextual processing for an OCR system, based on pattern learning (FL, JLH), pp. 862–865.
ICDARICDAR-1993-SatohMS #comprehension #image
Drawing image understanding system with capability of rule learning (SS, HM, MS), pp. 119–124.
HCIHCI-SHI-1993-HutchingsHC #hypermedia
A Model of Learning with Hypermedia Systems (GH, WH, CJC), pp. 494–499.
HCIHCI-SHI-1993-LeclercM #natural language
Natural Language as Object and Medium in Computer-Based Learning (SL, SdM), pp. 373–378.
HCIHCI-SHI-1993-NogamiYYM #development
Development of a Simulation-Based Intelligent Tutoring System for Assisting PID Control Learning (TN, YY, IY, SM), pp. 814–818.
HCIHCI-SHI-1993-RizzoPCB
Control of Complex System by Situated Knowledge: The Role of Implicit Learning (AR, OP, CC, SB), pp. 855–860.
HCIHCI-SHI-1993-YoungM #approach #assessment #problem
A Situated Cognition Approach to Problem Solving with Implications for Computer-Based Learning and Assessment (MFY, MDM), pp. 825–830.
CHIINTERCHI-1993-NilsenJOBRM #performance
The growth of software skill: a longitudinal look at learning & performance (EN, HSJ, JSO, KB, HHR, SM), pp. 149–156.
CHIINTERCHI-1993-StaskoBL #algorithm #analysis #animation #empirical
Do algorithm animations assist learning?: an empirical study and analysis (JTS, ANB, CL), pp. 61–66.
CIKMCIKM-1993-ChanS #multi
Experiments on Multi-Strategy Learning by Meta-Learning (PKC, SJS), pp. 314–323.
CIKMCIKM-1993-EickJ #algorithm #classification #search-based
Learning Bayesian Classification Rules through Genetic Algorithms (CFE, DJ), pp. 305–313.
ICMLICML-1993-BrezellecS #bottom-up #named
ÉLÉNA: A Bottom-Up Learning Method (PB, HS), pp. 9–16.
ICMLICML-1993-Cardie #using
Using Decision Trees to Improve Case-Based Learning (CC), pp. 25–32.
ICMLICML-1993-Caruana #bias #induction #knowledge-based #multi
Multitask Learning: A Knowledge-Based Source of Inductive Bias (RC), pp. 41–48.
ICMLICML-1993-ClarkM #induction #modelling #using
Using Qualitative Models to Guide Inductive Learning (PC, SM), pp. 49–56.
ICMLICML-1993-CravenS #network #using
Learning Symbolic Rules Using Artificial Neural Networks (MC, JWS), pp. 73–80.
ICMLICML-1993-DanylukP #fault #network
Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network (APD, FJP), pp. 81–88.
ICMLICML-1993-DattaK #concept #multi
Concept Sharing: A Means to Improve Multi-Concept Learning (PD, DFK), pp. 89–96.
ICMLICML-1993-FrazierP
Learning From Entailment: An Application to Propositional Horn Sentences (MF, LP), pp. 120–127.
ICMLICML-1993-GratchCD #network #scheduling
Learning Search Control Knowledge for Deep Space Network Scheduling (JG, SAC, GD), pp. 135–142.
ICMLICML-1993-HuffmanL #interactive #natural language
Learning Procedures from Interactive Natural Language Instructions (SBH, JEL), pp. 143–150.
ICMLICML-1993-JordanJ #approach #divide and conquer #statistics
Supervised Learning and Divide-and-Conquer: A Statistical Approach (MIJ, RAJ), pp. 159–166.
ICMLICML-1993-Kaelbling #probability
Hierarchical Learning in Stochastic Domains: Preliminary Results (LPK), pp. 167–173.
ICMLICML-1993-KimR
Constraining Learning with Search Control (JK, PSR), pp. 174–181.
ICMLICML-1993-Lin #scalability
Scaling Up Reinforcement Learning for Robot Control (LJL), pp. 182–189.
ICMLICML-1993-MitchellT #comparison #network
Explanation Based Learning: A Comparison of Symbolic and Neural Network Approaches (TMM, ST), pp. 197–204.
ICMLICML-1993-Mladenic #combinator #concept #induction #optimisation
Combinatorial Optimization in Inductive Concept Learning (DM), pp. 205–211.
ICMLICML-1993-NortonH #probability
Learning DNF Via Probabilistic Evidence Combination (SWN, HH), pp. 220–227.
ICMLICML-1993-Quinlan #modelling
Combining Instance-Based and Model-Based Learning (JRQ), pp. 236–243.
ICMLICML-1993-RagavanR #concept #lookahead
Lookahead Feature Construction for Learning Hard Concepts (HR, LAR), pp. 252–259.
ICMLICML-1993-Salganicoff #adaptation
Density-Adaptive Learning and Forgetting (MS), pp. 276–283.
ICMLICML-1993-Schwartz
A Reinforcement Learning Method for Maximizing Undiscounted Rewards (AS), pp. 298–305.
ICMLICML-1993-SuttonW #online #random
Online Learning with Random Representations (RSS, SDW), pp. 314–321.
ICMLICML-1993-Tadepalli #bias #query
Learning from Queries and Examples with Tree-structured Bias (PT), pp. 322–329.
ICMLICML-1993-Tan #independence #multi
Multi-Agent Reinforcement Learning: Independent versus Cooperative Agents (MT), pp. 330–337.
FSEFSE-1993-Bergadano #generative #testing
Test Case Generation by Means of Learning Techniques (FB), pp. 149–162.
SACSAC-1993-GallionSCB #algorithm
Dynamic ID3: A Symbolic Learning Algorithm for Many-Valued Attribute Domains (RG, CLS, DCSC, WEB), pp. 14–20.
SACSAC-1993-KountanisS #concept #graph
Graphs as a Language to Describe Learning System Concepts (DIK, ES), pp. 469–475.
SACSAC-1993-VaidyanathanL #analysis #bound
Analysis of Upper Bound in Valiant’s Model for Learning Bounded CNF Expressions (SV, SL), pp. 754–761.
DACDAC-1993-PomeranzR #generative #incremental #named #testing
INCREDYBLE-TG: INCREmental DYnamic test generation based on LEarning (IP, SMR), pp. 80–85.
HPDCHPDC-1993-FletcherO #distributed #network #parallel
Parallel and Distributed Systems for Constructive Neural Network Learning (JF, ZO), pp. 174–178.
STOCSTOC-1993-FreundKRRSS #automaton #finite #performance #random
Efficient learning of typical finite automata from random walks (YF, MJK, DR, RR, RES, LS), pp. 315–324.
STOCSTOC-1993-Kearns #performance #query #statistics
Efficient noise-tolerant learning from statistical queries (MJK), pp. 392–401.
STOCSTOC-1993-Kharitonov #encryption
Cryptographic hardness of distribution-specific learning (MK), pp. 372–381.
STOCSTOC-1993-Maass #bound #complexity
Bounds for the computational power and learning complexity of analog neural nets (WM), pp. 335–344.
HTHT-ECHT-1992-Eco #education #hypermedia #multi
Hypermedia for Teaching and Learning: A Multimedia Guide to the History of European Civilization (MuG) (UE), p. 288.
PODSPODS-1992-Greiner #performance #query
Learning Efficient Query Processing Strategies (RG), pp. 33–46.
CHICHI-1992-Clancey #overview #research
Overview of the Institute for Research on Learning (WJC), pp. 571–572.
CHICHI-1992-Spohrer #case study #experience #prototype
Simulation-based learning systems: prototypes and experiences (AJ, JCS), pp. 523–524.
CSCWCSCW-1992-BerlinJ #collaboration #problem
Consultants and Apprentices: Observations about Learning and Collaborative Problem Solving (LMB, RJ), pp. 130–137.
CSCWCSCW-1992-Orlikowski #implementation
Learning from Notes: Organizational Issues in Groupware Implementation (WJO), pp. 362–369.
KRKR-1992-GreinerS #approximate
Learning Useful Horn Approximations (RG, DS), pp. 383–392.
ICMLML-1992-AlmuallimD #concept #on the
On Learning More Concepts (HA, TGD), pp. 11–19.
ICMLML-1992-Bhatnagar
Learning by Incomplete Explanation-Based Learning (NB), pp. 37–42.
ICMLML-1992-Chen
Improving Path Planning with Learning (PCC), pp. 55–61.
ICMLML-1992-Christiansen #nondeterminism #predict
Learning to Predict in Uncertain Continuous Tasks (ADC), pp. 72–81.
ICMLML-1992-ClouseU #education
A Teaching Method for Reinforcement Learning (JAC, PEU), pp. 92–110.
ICMLML-1992-ConverseH
Learning to Satisfy Conjunctive Goals (TMC, KJH), pp. 117–122.
ICMLML-1992-CoxR #multi
Multistrategy Learning with Introspective Meta-Explanations (MTC, AR), pp. 123–128.
ICMLML-1992-Etzioni #analysis
An Asymptotic Analysis of Speedup Learning (OE), pp. 129–136.
ICMLML-1992-GiordanaS #algorithm #concept #search-based #using
Learning Structured Concepts Using Genetic Algorithms (AG, CS), pp. 169–178.
ICMLML-1992-GratchD #analysis #problem
An Analysis of Learning to Plan as a Search Problem (JG, GD), pp. 179–188.
ICMLML-1992-GrefenstetteR #approach
An Approach to Anytime Learning (JJG, CLR), pp. 189–195.
ICMLML-1992-HirschbergP #analysis #concept
Average Case Analysis of Learning κ-CNF Concepts (DSH, MJP), pp. 206–211.
ICMLML-1992-HoggerB #approach #heuristic #logic programming #source code
The MENTLE Approach to Learning Heuristics for the Control of Logic Programs (EIH, KB), pp. 212–217.
ICMLML-1992-Janikow #contest #induction
Combining Competition and Cooperation in Supervised Inductive Learning (CZJ), pp. 241–248.
ICMLML-1992-KononenkoK #generative #multi #optimisation #probability
Learning as Optimization: Stochastic Generation of Multiple Knowledge (IK, MK), pp. 257–262.
ICMLML-1992-Mahadevan #modelling #probability
Enhancing Transfer in Reinforcement Learning by Building Stochastic Models of Robot Actions (SM), pp. 290–299.
ICMLML-1992-Mao #named
THOUGHT: An Integrated Learning System for Acquiring Knowledge Structure (CM), pp. 300–309.
ICMLML-1992-Markov #approach #concept
An Approach to Concept Learning Based on Term Generalization (ZM), pp. 310–315.
ICMLML-1992-McCallum #performance #proximity #using
Using Transitional Proximity for Faster Reinforcement Learning (AM), pp. 316–321.
ICMLML-1992-RubyK #optimisation
Learning Episodes for Optimization (DR, DFK), pp. 379–384.
ICMLML-1992-SammutHKM
Learning to Fly (CS, SH, DK, DM), pp. 385–393.
ICMLML-1992-Singh #algorithm #modelling #scalability
Scaling Reinforcement Learning Algorithms by Learning Variable Temporal Resolution Models (SPS), pp. 406–415.
ICMLML-1992-Tesauro #difference
Temporal Difference Learning of Backgammon Strategy (GT), pp. 451–457.
ICMLML-1992-Zhang
Selecting Typical Instances in Instance-Based Learning (JZ), pp. 470–479.
OOPSLAOOPSLA-1992-LiuGG #object-oriented #question #what
What Contributes to Successful Object-Oriented Learning? (CL, SG, BG), pp. 77–86.
STOCSTOC-1992-Angluin #overview
Computational Learning Theory: Survey and Selected Bibliography (DA), pp. 351–369.
STOCSTOC-1992-BlumR #performance #query
Fast Learning of k-Term DNF Formulas with Queries (AB, SR), pp. 382–389.
STOCSTOC-1992-BshoutyHH
Learning Arithmetic Read-Once Formulas (NHB, TRH, LH), pp. 370–381.
CHICHI-1991-PalmiterE #evaluation
An evaluation of animated demonstrations of learning computer-based tasks (SP, JE), pp. 257–263.
KDDKDD-1991-BergadanoGSBM
Integrated Learning in a Real Domain (FB, AG, LS, FB, DDM), pp. 277–288.
KDDKDD-1991-UthurusamyFS
Learning Useful Rules from Inconclusive Data (RU, UMF, WSS), pp. 141–158.
ICMLML-1991-Bain
Experiments in Non-Monotonic Learning (MB), pp. 380–384.
ICMLML-1991-Berenji #approximate #refinement
Refinement of Approximate Reasoning-based Controllers by Reinforcement Learning (HRB), pp. 475–479.
ICMLML-1991-BottaRSS #abduction #using
Improving Learning Using Causality and Abduction (MB, SR, LS, SBS), pp. 480–484.
ICMLML-1991-Brand
Decision-Theoretic Learning in an Action System (MB), pp. 283–287.
ICMLML-1991-BratkoMV #modelling
Learning Qualitative Models of Dynamic Systems (IB, SM, AV), pp. 385–388.
ICMLML-1991-BrunkP #algorithm #concept #relational
An Investigation of Noise-Tolerant Relational Concept Learning Algorithms (CB, MJP), pp. 389–393.
ICMLML-1991-ChienGD #on the
On Becoming Decreasingly Reactive: Learning to Deliberate Minimally (SAC, MTG, GD), pp. 288–292.
ICMLML-1991-CobbG #persistent
Learning the Persistence of Actions in Reactive Control Rules (HGC, JJG), pp. 292–297.
ICMLML-1991-Day #csp #heuristic #problem
Learning Variable Descriptors for Applying Heuristics Across CSP Problems (DSD), pp. 127–131.
ICMLML-1991-desJardins #bias #probability
Probabilistic Evaluating of Bias for Learning Systems (Md), pp. 495–499.
ICMLML-1991-DzeroskiL #comparison #empirical
Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL (SD, NL), pp. 399–402.
ICMLML-1991-Goel #formal method #incremental
Model Revision: A Theory of Incremental Model Learning (AKG), pp. 605–609.
ICMLML-1991-GokerM #incremental #information retrieval
Incremental Learning in a Probalistic Information Retrieval System (AG, TLM), pp. 255–259.
ICMLML-1991-HastingsLL #word
Learning Words From Context (PMH, SLL, RKL), pp. 55–59.
ICMLML-1991-Herrmann
Learning Analytical Knowledge About VLSI-Design from Observation (JH), pp. 610–614.
ICMLML-1991-HirakiGYA #image
Learning Spatial Relations from Images (KH, JHG, YY, YA), pp. 407–411.
ICMLML-1991-HsuS #evaluation
Learning Football Evaluation for a Walking Robot (GTH, RGS), pp. 303–307.
ICMLML-1991-JordanR #modelling
Internal World Models and Supervised Learning (MIJ, DER), pp. 70–74.
ICMLML-1991-Kadie #induction
Quantifying the Value of Constructive Induction, Knowledge, and Noise Filtering on Inductive Learning (CMK), pp. 153–157.
ICMLML-1991-Kadie91a #concept #set
Continous Conceptual Set Covering: Learning Robot Operators From Examples (CMK), pp. 615–619.
ICMLML-1991-KijsirikulNS #logic programming #performance #source code
Efficient Learning of Logic Programs with Non-determinant, Non-discriminating Literals (BK, MN, MS), pp. 417–421.
ICMLML-1991-KokarR
Learning to Select a Model in a Changing World (MMK, SAR), pp. 313–317.
ICMLML-1991-Krulwich
Learning from Deliberated Reactivity (BK), pp. 318–322.
ICMLML-1991-Kwok #adaptation #architecture #query #using
Query Learning Using an ANN with Adaptive Architecture (KLK), pp. 260–264.
ICMLML-1991-LeckieZ #approach #induction
Learning Search Control Rules for Planning: An Inductive Approach (CL, IZ), pp. 422–426.
ICMLML-1991-Lewis #information retrieval
Learning in Intelligent Information Retrieval (DDL), pp. 235–239.
ICMLML-1991-Lin #education #self
Self-improvement Based on Reinforcement Learning, Planning and Teaching (LJL), pp. 323–327.
ICMLML-1991-MahadevanC #architecture #scalability
Scaling Reinforcement Learning to Robotics by Exploiting the Subsumption Architecture (SM, JC), pp. 328–332.
ICMLML-1991-MartinB #bias #variability
Variability Bias and Category Learning (JDM, DB), pp. 90–94.
ICMLML-1991-Maza #concept #prototype
A Prototype Based Symbolic Concept Learning System (MdlM), pp. 41–45.
ICMLML-1991-MillanT
Learning to Avoid Obstacles Through Reinforcement (JdRM, CT), pp. 298–302.
ICMLML-1991-OliveiraS #concept #network
Learning Concepts by Synthesizing Minimal Threshold Gate Networks (ALO, ALSV), pp. 193–197.
ICMLML-1991-PageF
Learning Constrained Atoms (CDPJ, AMF), pp. 427–431.
ICMLML-1991-PazzaniBS #approach #concept #relational
A Knowledge-intensive Approach to Learning Relational Concepts (MJP, CB, GS), pp. 432–436.
ICMLML-1991-Pierce #set
Learning a Set of Primitive Actions with an Uninterpreted Sensorimotor Apparatus (DRP), pp. 338–342.
ICMLML-1991-RagavanR #empirical
Relations, Knowledge and Empirical Learning (HR, LAR), pp. 188–192.
ICMLML-1991-Reich #design
Design Integrated Learning Systems for Engineering Design (YR), pp. 635–639.
ICMLML-1991-Schlimmer #consistency #database #induction
Database Consistency via Inductive Learning (JCS), pp. 640–644.
ICMLML-1991-SilversteinP #induction #relational
Relational Clichés: Constraining Induction During Relational Learning (GS, MJP), pp. 203–207.
ICMLML-1991-Singh #composition
Transfer of Learning Across Compositions of Sequentail Tasks (SPS), pp. 348–352.
ICMLML-1991-SuttonM #polynomial
Learning Polynomial Functions by Feature Construction (RSS, CJM), pp. 208–212.
ICMLML-1991-Tadepalli
Learning with Incrutable Theories (PT), pp. 544–548.
ICMLML-1991-Tan #representation
Learning a Cost-Sensitive Internal Representation for Reinforcement Learning (MT), pp. 358–362.
ICMLML-1991-TecuciM #adaptation #multi
A Method for Multistrategy Task-Adaptive Learning Based on Plausible Justifications (GT, RSM), pp. 549–553.
ICMLML-1991-VanLehnJ #correctness #physics
Learning Physics Via Explanation-Based Learning of Correctness and Analogical Search Control (KV, RMJ), pp. 110–114.
ICMLML-1991-WhitehallL #case study #how #knowledge-based
A Study of How Domain Knowledge Improves Knowledge-Based Learning Systems (BLW, SCYL), pp. 559–563.
ICMLML-1991-Wixson #composition #scalability
Scaling Reinforcement Learning Techniques via Modularity (LEW), pp. 3368–372.
ICMLML-1991-YamanishiK #probability #search-based #sequence
Learning Stochastic Motifs from Genetic Sequences (KY, AK), pp. 467–471.
ECOOPECOOP-1991-BergsteinL #incremental #optimisation #taxonomy
Incremental Class Dictionary Learning and Optimization (PLB, KJL), pp. 377–396.
LOPSTRLOPSTR-1991-Eusterbrock #abstraction #logic programming #source code
Speed-up Transformations of Logic Programs by Abstraction and Learning (JE), pp. 167–182.
SASWSA-1991-Breuer #analysis #synthesis
An Analysis/Synthesis Language with Learning Strategies (PTB), pp. 202–209.
STOCSTOC-1991-KushilevitzM #fourier #using
Learning Decision Trees Using the Fourier Sprectrum (EK, YM), pp. 455–464.
STOCSTOC-1991-LittlestoneLW #linear #online
On-Line Learning of Linear Functions (NL, PML, MKW), pp. 465–475.
ICALPICALP-1990-JainS
Language Learning by a “Team” (SJ, AS), pp. 153–166.
ICALPICALP-1990-Watanabe #formal method #query
A Formal Study of Learning via Queries (OW0), pp. 139–152.
CHICHI-1990-CarrollSBA #smalltalk
A view matcher for learning Smalltalk (JMC, JAS, RKEB, SRA), pp. 431–437.
CHICHI-1990-HowesP #analysis #semantics
Semantic analysis during exploratory learning (AH, SJP), pp. 399–406.
CSCWCSCW-1990-BullenB #experience #user interface
Learning from User Experience with Groupware (CVB, JLB), pp. 291–302.
ICMLML-1990-ArunkumarY #information management #representation #using
Knowledge Acquisition from Examples using Maximal Representation Learning (SA, SY), pp. 2–8.
ICMLML-1990-BergadanoGSMB
Integrated Learning in a real Domain (FB, AG, LS, DDM, FB), pp. 322–329.
ICMLML-1990-ChanW #analysis #induction #performance #probability
Performance Analysis of a Probabilistic Inductive Learning System (KCCC, AKCW), pp. 16–23.
ICMLML-1990-Cohen #analysis #concept #representation
An Analysis of Representation Shift in Concept Learning (WWC), pp. 104–112.
ICMLML-1990-Cohen90a #approximate
Learning Approximate Control Rules of High Utility (WWC), pp. 268–276.
ICMLML-1990-Epstein
Learning Plans for Competitive Domains (SLE), pp. 190–197.
ICMLML-1990-GenestMP #approach
Explanation-Based Learning with Incomplete Theories: A Three-step Approach (JG, SM, BP), pp. 286–294.
ICMLML-1990-Hammond #process
Learning and Enforcement: Stabilizing Environments to Facilitate Activity (KJH), pp. 204–210.
ICMLML-1990-Hirsh #bound #consistency #nondeterminism
Learning from Data with Bounded Inconsistency (HH), pp. 32–39.
ICMLML-1990-Hume #induction
Learning Procedures by Environment-Driven Constructive Induction (DVH), pp. 113–121.
ICMLML-1990-Kaelbling
Learning Functions in k-DNF from Reinforcement (LPK), pp. 162–169.
ICMLML-1990-KoMT #string
Learning String Patterns and Tree Patterns from Examples (KIK, AM, WGT), pp. 384–391.
ICMLML-1990-Lehman
A General Method for Learning Idiosyncratic Grammars (JFL), pp. 368–376.
ICMLML-1990-LytinenM #comparison
A Comparison of Learning Techniques in Second Language Learning (SLL, CEM), pp. 377–383.
ICMLML-1990-ObradovicP #multi
Learning with Discrete Multi-Valued Neurons (ZO, IP), pp. 392–399.
ICMLML-1990-PazzaniS #algorithm #analysis
Average Case Analysis of Conjunctive Learning Algorithms (MJP, WS), pp. 339–347.
ICMLML-1990-Ram #incremental
Incremental Learning of Explanation Patterns and Their Indices (AR), pp. 313–320.
ICMLML-1990-RamseyGS #contest #difference
Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment (CLR, JJG, ACS), pp. 211–215.
ICMLML-1990-SammutC #performance #question
Is Learning Rate a Good Performance Criterion for Learning? (CS, JC), pp. 170–178.
ICMLML-1990-SchoenauerS #incremental
Incremental Learning of Rules and Meta-rules (MS, MS), pp. 49–57.
ICMLML-1990-Segen #clustering #graph
Graph Clustering and Model Learning by Data Compression (JS), pp. 93–101.
ICMLML-1990-SilverFIVB #framework #multi
A Framework for Multi-Paradigmatic Learning (BS, WJF, GAI, JV, KB), pp. 348–356.
ICMLML-1990-Sutton #approximate #architecture #programming
Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming (RSS), pp. 216–224.
ICMLML-1990-WhiteheadB
Active Perception and Reinforcement Learning (SDW, DHB), pp. 179–188.
SEKESEKE-1990-EstevaR #induction #reuse
Learning to Recognize Reusable Software by Induction (JCE, RGR), pp. 19–24.
SEKESEKE-1990-Mazurov #parallel #process
Parallel Processes of Decision Making and Multivalued Interpretation of Contradictory Data by Learning Neuron Machines (VDM), p. 165.
STOCSTOC-1990-Blum #infinity
Learning Boolean Functions in an Infinite Atribute Space (AB), pp. 64–72.
CHICHI-1989-BlackBMC #effectiveness #online #question #what
On-line tutorials: What kind of inference leads to the most effective learning? (JBB, JSB, MM, JMC), pp. 81–83.
CHICHI-1989-LeePB #metric
Learning and transfer of measurement tasks (AYL, PGP, WAB), pp. 115–120.
ICMLML-1989-Aha #concept #incremental #independence
Incremental, Instance-Based Learning of Independent and Graded Concept Descriptions (DWA), pp. 387–391.
ICMLML-1989-Anderson #network
Tower of Hanoi with Connectionist Networks: Learning New Features (CWA), pp. 345–349.
ICMLML-1989-BarlettaK #empirical
Improving Explanation-Based Indexing with Empirical Learning (RB, RK), pp. 84–86.
ICMLML-1989-BergadanoGP #deduction #induction #top-down
Deduction in Top-Down Inductive Learning (FB, AG, SP), pp. 23–25.
ICMLML-1989-Buntine #classification #using
Learning Classification Rules Using Bayes (WLB), pp. 94–98.
ICMLML-1989-Chan #induction
Inductive Learning with BCT (PKC), pp. 104–108.
ICMLML-1989-Chien
Learning by Analyzing Fortuitous Occurrences (SAC), pp. 249–251.
ICMLML-1989-ClearwaterCHB #incremental
Incremental Batch Learning (SHC, TPC, HH, BGB), pp. 366–370.
ICMLML-1989-ConverseHM
Learning from Opportunity (TMC, KJH, MM), pp. 246–248.
ICMLML-1989-Cornuejols #incremental
An Exploration Into Incremental Learning: the INFLUENCE System (AC), pp. 383–386.
ICMLML-1989-Diederich
“Learning by Instruction” in connectionist Systems (JD), pp. 66–68.
ICMLML-1989-Dietterich #induction
Limitations on Inductive Learning (TGD), pp. 124–128.
ICMLML-1989-Fawcett
Learning from Plausible Explanations (TF), pp. 37–39.
ICMLML-1989-FisherMMST
Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems (DHF, KBM, RJM, JWS, GGT), pp. 169–173.
ICMLML-1989-Flann #abstraction #problem
Learning Appropriate Abstractions for Planning in Formation Problems (NSF), pp. 235–239.
ICMLML-1989-Fogarty #algorithm #incremental #realtime #search-based
An Incremental Genetic Algorithm for Real-Time Learning (TCF), pp. 416–419.
ICMLML-1989-FriedrichN #algorithm #induction #using
Using Domain Knowledge to Improve Inductive Learning Algorithms for Diagnosis (GF, WN), pp. 75–77.
ICMLML-1989-GamsK #empirical
New Empirical Learning Mechanisms Perform Significantly Better in Real Life Domains (MG, AK), pp. 99–103.
ICMLML-1989-GervasioD
Explanation-Based Learning of Reactive Operations (MTG, GD), pp. 252–254.
ICMLML-1989-Grefenstette #algorithm #incremental #search-based
Incremental Learning of Control Strategies with Genetic algorithms (JJG), pp. 340–344.
ICMLML-1989-Haines
Explanation Based Learning as Constrained Search (DH), pp. 43–45.
ICMLML-1989-HilliardLRP #approach #classification #hybrid #problem #scheduling
Learning Decision Rules for scheduling Problems: A Classifier Hybrid Approach (MRH, GEL, GR, MRP), pp. 188–190.
ICMLML-1989-Hirsh #empirical
Combining Empirical and Analytical Learning with Version Spaces (HH), pp. 29–33.
ICMLML-1989-Jones #problem
Learning to Retrieve Useful Information for Problem Solving (RMJ), pp. 212–214.
ICMLML-1989-Kaelbling #embedded #framework
A Formal Framework for Learning in Embedded Systems (LPK), pp. 350–353.
ICMLML-1989-Katz #network
Integrating Learning in a Neural Network (BFK), pp. 69–71.
ICMLML-1989-Keller #compilation #performance
Compiling Learning Vocabulary from a Performance System Description (RMK), pp. 482–495.
ICMLML-1989-Knoblock #abstraction
Learning Hierarchies of Abstraction Spaces (CAK), pp. 241–245.
ICMLML-1989-LambertTL #algorithm #concept #hybrid #recursion
Generalized Recursive Splitting Algorithms for Learning Hybrid Concepts (BLL, DKT, SCYL), pp. 496–498.
ICMLML-1989-Langley #empirical
Unifying Themes in Empirical and Explanation-Based Learning (PL), pp. 2–4.
ICMLML-1989-LeviPS
Learning Tactical Plans for Pilot Aiding (KRL, DLP, VLS), pp. 191–193.
ICMLML-1989-Marie #bias #dependence
Building A Learning Bias from Perceived Dependencies (CdSM), pp. 501–502.
ICMLML-1989-Martin
Reducing Redundant Learning (JDM), pp. 396–399.
ICMLML-1989-MasonCM
Experiments in Robot Learning (MTM, ADC, TMM), pp. 141–145.
ICMLML-1989-MatwinM
Learning Procedural Knowledge in the EBG Context (SM, JM), pp. 197–199.
ICMLML-1989-MooneyO #aspect-oriented #concept #induction
Induction Over the Unexplained: Integrated Learning of Concepts with Both Explainable and Conventional Aspects (RJM, DO), pp. 5–7.
ICMLML-1989-Morris
Reducing Search and Learning Goal Preferences (SM), pp. 46–48.
ICMLML-1989-NumaoS #similarity
Explanation-Based Acceleration of Similarity-Based Learning (MN, MS), pp. 58–60.
ICMLML-1989-ORorkeCO
Learning to Recognize Plans Involving Affect (PO, TC, AO), pp. 209–211.
ICMLML-1989-Paredis #behaviour
Learning the Behavior of Dynamical Systems form Examples (JP), pp. 137–140.
ICMLML-1989-Pazzani
Explanation-Based Learning with Week Domain Theories (MJP), pp. 72–74.
ICMLML-1989-Puget #invariant
Learning Invariants from Explanations (JFP), pp. 200–204.
ICMLML-1989-RasZ #concept
Imprecise Concept Learning within a Growing Language (ZWR, MZ), pp. 314–319.
ICMLML-1989-Redmond #reasoning
Combining Case-Based Reasoning, Explanation-Based Learning, and Learning form Instruction (MR), pp. 20–22.
ICMLML-1989-RudyK
Learning to Plan in Complex Domains (DR, DFK), pp. 180–182.
ICMLML-1989-SarrettP #algorithm #empirical
One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning (WS, MJP), pp. 26–28.
ICMLML-1989-ScottM #case study #experience #nondeterminism
Uncertainty Based Selection of Learning Experiences (PDS, SM), pp. 358–361.
ICMLML-1989-Selfridge #adaptation #case study #contest
Atoms of Learning II: Adaptive Strategies A Study of Two-Person Zero-Sum Competition (OGS), pp. 412–415.
ICMLML-1989-Shavlik #analysis #empirical
An Empirical Analysis of EBL Approaches for Learning Plan Schemata (JWS), pp. 183–187.
ICMLML-1989-ShavlikT #network
Combining Explanation-Based Learning and Artificial Neural Networks (JWS, GGT), pp. 90–93.
ICMLML-1989-SobekL #using
Using Learning to Recover Side-Effects of Operators in Robotics (RPS, JPL), pp. 205–208.
ICMLML-1989-Spackman #detection #induction #tool support
Signal Detection Theory: Valuable Tools for Evaluating Inductive Learning (KAS), pp. 160–163.
ICMLML-1989-TanS #approach #concept #recognition
Cost-Sensitive Concept Learning of Sensor Use in Approach ad Recognition (MT, JCS), pp. 392–395.
ICMLML-1989-TecuciK #multi
Multi-Strategy Learning in Nonhomongeneous Domain Theories (GT, YK), pp. 14–16.
ICMLML-1989-Utgoff #incremental
Improved Training Via Incremental Learning (PEU), pp. 362–365.
ICMLML-1989-WefaldR #adaptation
Adaptive Learning of Decision-Theoretic Search Control Knowledge (EW, SJR), pp. 408–411.
ICMLML-1989-Widmer #deduction #integration
A Tight Integration of Deductive Learning (GW), pp. 11–13.
ICMLML-1989-Wollowski
A Schema for an Integrated Learning System (MW), pp. 87–89.
ICMLML-1989-YagerF
Participatory Learning: A Constructivist Model (RRY, KMF), pp. 420–425.
ICMLML-1989-ZhangM
A Description of Preference Criterion in Constructive Learning: A Discussion of Basis Issues (JZ, RSM), pp. 17–19.
STOCSTOC-1989-KearnsV #automaton #encryption #finite
Cryptographic Limitations on Learning Boolean Formulae and Finite Automata (MJK, LGV), pp. 433–444.
CSEETSEI-1988-Stevens
SEI Demonstration: Advanced Learning Technologies Project (SS), p. 120.
CSCWCSCW-1988-Hiltz #collaboration
Collaborative Learning in a Virtual Classroom: Highlights of Findings (SRH), pp. 282–290.
ICMLML-1988-Amsterdam
Extending the Valiant Learning Model (JA), pp. 381–394.
ICMLML-1988-Carpineto #approach #generative
An Approach Based on Integrated Learning to Generating Stories (CC), pp. 298–304.
ICMLML-1988-Cohen #multi
Generalizing Number and Learning from Multiple Examples in Explanation Based Learning (WWC), pp. 256–269.
ICMLML-1988-Etzioni #approach #reliability
Hypothesis Filtering: A Practical Approach to Reliable Learning (OE), pp. 416–429.
ICMLML-1988-Gross #concept #incremental #multi #using
Incremental Multiple Concept Learning Using Experiments (KPG), pp. 65–72.
ICMLML-1988-Helft #first-order
Learning Systems of First-Order Rules (NH), pp. 395–401.
ICMLML-1988-Hirsh #reasoning
Reasoning about Operationality for Explanation-Based Learning (HH), pp. 214–220.
ICMLML-1988-IbaWL #concept #incremental
Trading Off Simplicity and Coverage in Incremental concept Learning (WI, JW, PL), pp. 73–79.
ICMLML-1988-JongS #game studies #using
Using Experience-Based Learning in Game Playing (KADJ, ACS), pp. 284–290.
ICMLML-1988-Kadie #named
Diffy-S: Learning Robot Operator Schemata from Examples (CMK), pp. 430–436.
ICMLML-1988-Lynne
Competitive Reinforcement Learning (KJL), pp. 188–199.
ICMLML-1988-MahadevanT #on the
On the Tractability of Learning from Incomplete Theories (SM, PT), pp. 235–241.
ICMLML-1988-MarkovitchS
The Role of Forgetting in Learning (SM, PDS), pp. 459–465.
ICMLML-1988-NatarajanT #framework
Two New Frameworks for Learning (BKN, PT), pp. 402–415.
ICMLML-1988-Pazzani
Integrated Learning with Incorrect and Incomplete Theories (MJP), pp. 291–297.
ICMLML-1988-Segen #graph #modelling
Learning Graph Models of Shape (JS), pp. 29–35.
ICMLML-1988-Spackman #category theory
Learning Categorical Decision Criteria in Biomedical Domains (KAS), pp. 36–46.
ICMLML-1988-Tesauro
Connectionist Learning of Expert Backgammon Evaluations (GT), pp. 200–206.
ICMLML-1988-Williams
Learning to Program by Examining and Modifying Cases (RSW), pp. 318–324.
ICMLML-1988-WisniewskiA #induction
Some Interesting Properties of a Connectionist Inductive Learning System (EJW, JAA), pp. 181–187.
SIGIRSIGIR-1988-YuM #information retrieval
Two Learning Schemes in Information Retrieval (CTY, HM), pp. 201–218.
PPoPPPPEALS-1988-TambeKGFMN #named #parallel
Soar/PSM-E: Investigating Match Parallelism in a Learning Production System (MT, DK, AG, CF, BM, AN), pp. 146–160.
STOCSTOC-1988-KearnsL #fault
Learning in the Presence of Malicious Errors (MJK, ML), pp. 267–280.
CADECADE-1988-DonatW #higher-order #using
Learning and Applying Generalised Solutions using Higher Order Resolution (MRD, LAW), pp. 41–60.
ICALPICALP-1987-PittS #probability
Probability and Plurality for Aggregations of Learning Machines (LP, CHS), pp. 1–10.
ICALPICALP-1987-Valiant #formal method
Recent Developments in the Theory of Learning (LGV), p. 563.
HCIHCI-CE-1987-Bosser #evaluation
The Evaluation of Learning Requirement of IT Systems (TB), pp. 45–52.
SIGIRSIGIR-1987-OommenM #automaton #clustering #performance #probability #using
Fast Object Partitioning Using Stochastic Learning Automata (BJO, DCYM), pp. 111–122.
STOCSTOC-1987-Natarajan #on the
On Learning Boolean Functions (BKN), pp. 296–304.
CSLCSL-1987-RinnS #fault
Learning by Teams from Examples with Errors (RR, BS), pp. 223–234.
SIGIRSIGIR-1986-DeogunR #clustering #documentation #framework #information retrieval
User-Oriented Document Clustering: A Framework for Learning in Information Retrieval (JSD, VVR), pp. 157–163.
VLDBVLDB-1985-BorgidaW #database #exception
Accommodating Exceptions in Databases, and Refining the Schema by Learning from them (AB, KEW), pp. 72–81.
SIGIRSIGIR-1985-Gordon #algorithm #documentation
A Learning Algorithm Applied to Document Description (MG), pp. 179–186.
SIGIRSIGIR-1984-Allan #information retrieval
Computerised Information Retrieval Systems for Open Learning (BA), pp. 325–341.

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