BibSLEIGH — learning tag

learning

learning

Tag #learning

5675 papers:

PADL-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.
ASPLOS-2020-AngstadtJW #automaton #bound #kernel #legacy #string: Accelerating Legacy String Kernels via Bounded Automata Learning (KA, JBJ, WW), pp. 235–249.
ASPLOS-2020-HuangJ0 #gpu #memory management #named: SwapAdvisor: Pushing Deep Learning Beyond the GPU Memory Limit via Smart Swapping (CCH, GJ, JL0), pp. 1341–1355.
ASPLOS-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.
ASPLOS-2020-MireshghallahTR #named #privacy: Shredder: Learning Noise Distributions to Protect Inference Privacy (FM, MT, PR, AJ, DMT, HE), pp. 3–18.
ASPLOS-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.
CC-2020-BrauckmannGEC #graph #modelling: Compiler-based graph representations for deep learning models of code (AB, AG, SE, JC), pp. 201–211.
CGO-2020-Haj-AliAWSAS #named: NeuroVectorizer: end-to-end vectorization with deep reinforcement learning (AHA, NKA, TLW, YSS, KA, IS), pp. 242–255.
EDM-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).
EDM-2019-AusinABC #induction #policy: Leveraging Deep Reinforcement Learning for Pedagogical Policy Induction in an Intelligent Tutoring System (MSA, HA, TB, MC).
EDM-2019-BroisinH #automation #design #evaluation #programming #semantics: Design and evaluation of a semantic indicator for automatically supporting programming learning (JB, CH).
EDM-2019-CaoPB #analysis #performance: Incorporating Prior Practice Difficulty into Performance Factor Analysis to Model Mandarin Tone Learning (MC, PIPJ, GMB).
EDM-2019-ChoffinPBV #distributed #modelling #named #scheduling #student: DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills (BC, FP, YB, JJV).
EDM-2019-ChopraKMG #difference #gender: Gender Differences in Work-Integrated Learning Assessments (SC, AK, MM, LG).
EDM-2019-DavisRF #difference #student: Individual Differences in Student Learning Aid Usage (AKD, YJR, DF).
EDM-2019-EmondV #3d #performance #predict #visualisation: Visualizing Learning Performance Data and Model Predictions as Objects in a 3D Space (BE, JJV).
EDM-2019-Furr #clustering #interactive #online #visualisation: Visualization and clustering of learner pathways in an interactive online learning environment (DF).
EDM-2019-GagnonLBD: Filtering non-relevant short answers in peer learning applications (VG, AL, SB, MCD).
EDM-2019-GuthrieC #behaviour #online #quality #student: Adding duration-based quality labels to learning events for improved description of students' online learning behavior (MWG, ZC).
EDM-2019-HarmonW #education #online: Measuring Item Teaching Value in an Online Learning Environment (JH, RW).
EDM-2019-HarrakBLB #automation #identification #self: Automatic identification of questions in MOOC forums and association with self-regulated learning (FH, FB, VL, RB).
EDM-2019-Ikeda #analysis #education #quality #using: Learning Feature Analysis for Quality Improvement of Web-Based Teaching Materials Using Mouse Cursor Tracking (MI).
EDM-2019-JiangIDLW #student: Measuring students' thermal comfort and its impact on learning (HJ, MI, SVD, SL, JW).
EDM-2019-JiangP: Binary Q-matrix Learning with dAFM (NJ, ZAP).
EDM-2019-JoYKL #analysis #comparative #education #effectiveness #online #word: A Comparative Analysis of Emotional Words for Learning Effectiveness in Online Education (JJ, YY, GK, HL).
EDM-2019-JuZABC #identification: Identifying Critical Pedagogical Decisions through Adversarial Deep Reinforcement Learning (SJ, GZ, HA, TB, MC).
EDM-2019-KraussMA #modelling #recommendation: Smart Learning Object Recommendations based on Time-Dependent Learning Need Models (CK, AM, SA).
EDM-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).
EDM-2019-MussackFSC #behaviour #problem #similarity #towards: Towards discovering problem similarity through deep learning: combining problem features and user behavior (DM, RF, PS, PCL).
EDM-2019-NazaretskyHA #clustering #education: Kappa Learning: A New Item-Similarity Method for Clustering Educational Items from Response Data (TN, SH, GA).
EDM-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).
EDM-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).
EDM-2019-Reddick #algorithm #using: Using a Glicko-based Algorithm to Measure In-Course Learning (RR).
EDM-2019-ReillyD #assessment: Exploring Stealth Assessment via Deep Learning in an Open-Ended Virtual Environment (JMR, CD).
EDM-2019-Sher #mobile #using: Anatomy of mobile learners: Using learning analytics to unveil learning in presence of mobile devices (VS).
EDM-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).
EDM-2019-ShimadaMTOTK #optimisation #process #student: Optimizing Assignment of Students to Courses based on Learning Activity Analytics (AS, KM, YT, HO, RiT, SK).
EDM-2019-WeitekampHMRK #predict #student #towards #using: Toward Near Zero-Parameter Prediction Using a Computational Model of Student Learning (DWI, EH, CJM, NR, KRK).
EDM-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).
EDM-2019-YangBSHL #detection #student: Active Learning for Student Affect Detection (TYY, RSB, CS, NTH, ASL).
EDM-2019-Yeung #named #using: Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory (CKY).
EDM-2019-ZaidiCDMBR #modelling #student #using: Accurate Modelling of Language Learning Tasks and Students Using Representations of Grammatical Proficiency (AHZ, AC, CD, RM, PB, AR).
EDM-2019-ZhangDYS #student: Student Knowledge Diagnosis on Response Data via the Model of Sparse Factor Learning (YZ, HD, YY, XS).
ICPC-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.
ICPC-2019-XieQMZ #named #programming #visual notation: DeepVisual: a visual programming tool for deep learning systems (CX, HQ, LM0, JZ), pp. 130–134.
ICSME-2019-BarbezKG #anti #metric: Deep Learning Anti-Patterns from Code Metrics History (AB, FK, YGG), pp. 114–124.
ICSME-2019-Ha0 #configuration management #fourier: Performance-Influence Model for Highly Configurable Software with Fourier Learning and Lasso Regression (HH, HZ0), pp. 470–480.
ICSME-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.
ICSME-2019-OumazizF0BK #product line: Handling Duplicates in Dockerfiles Families: Learning from Experts (MAO, JRF, XB0, TFB, JK), pp. 524–535.
ICSME-2019-PalacioMMBPS #identification #network #using: Learning to Identify Security-Related Issues Using Convolutional Neural Networks (DNP, DM, KM, CBC, DP, CS), pp. 140–144.
ICSME-2019-TufanoWBPWP #how #source code: Learning How to Mutate Source Code from Bug-Fixes (MT, CW, GB, MDP, MW, DP), pp. 301–312.
MSR-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.
MSR-2019-PerezC #abstract syntax tree #clone detection #detection #syntax: Cross-language clone detection by learning over abstract syntax trees (DP, SC), pp. 518–528.
MSR-2019-TheetenVC #library: Import2vec learning embeddings for software libraries (BT, FV, TVC), pp. 18–28.
SANER-2019-MaJXLLLZ #combinator #named #testing: DeepCT: Tomographic Combinatorial Testing for Deep Learning Systems (LM0, FJX, MX, BL0, LL0, YL0, JZ), pp. 614–618.
SANER-2019-WhiteTMMP #program repair #sorting: Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities (MW, MT, MM, MM, DP), pp. 479–490.
SANER-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.
SANER-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.
SANER-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.
FM-2019-Sheinvald #automaton #infinity: Learning Deterministic Variable Automata over Infinite Alphabets (SS), pp. 633–650.
FM-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.
SEFM-2019-AvellanedaP #approach #automaton #satisfiability: Learning Minimal DFA: Taking Inspiration from RPNI to Improve SAT Approach (FA, AP), pp. 243–256.
AIIDE-2019-BontragerKASST #game studies #network: “Superstition” in the Network: Deep Reinforcement Learning Plays Deceptive Games (PB, AK, DA, MS, CS, JT), pp. 10–16.
AIIDE-2019-FrazierR: Improving Deep Reinforcement Learning in Minecraft with Action Advice (SF, MR), pp. 146–152.
AIIDE-2019-GaoKHT #case study #on the: On Hard Exploration for Reinforcement Learning: A Case Study in Pommerman (CG, BK, PHL, MET), pp. 24–30.
AIIDE-2019-Hernandez-LealK #modelling: Agent Modeling as Auxiliary Task for Deep Reinforcement Learning (PHL, BK, MET), pp. 31–37.
AIIDE-2019-KartalHT #predict: Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning (BK, PHL, MET), pp. 38–44.
AIIDE-2019-KartalHT19a: Action Guidance with MCTS for Deep Reinforcement Learning (BK, PHL, MET), pp. 153–159.
AIIDE-2019-LinXR #named #semantics: GenerationMania: Learning to Semantically Choreograph (ZL, KX, MR), pp. 52–58.
AIIDE-2019-Marino #game studies #programming #realtime #search-based: Learning Strategies for Real-Time Strategy Games with Genetic Programming (JRHM), pp. 219–220.
AIIDE-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.
AIIDE-2019-XuKZHLS #metaprogramming: Macro Action Selection with Deep Reinforcement Learning in StarCraft (SX, HK, ZZ, RH, YL, HS), pp. 94–99.
CoG-2019-AshleyCKB #evolution: Learning to Select Mates in Evolving Non-playable Characters (DRA, VC, BK, VB), pp. 1–8.
CoG-2019-ChenL #game studies #metaprogramming: Macro and Micro Reinforcement Learning for Playing Nine-ball Pool (YC, YL), pp. 1–4.
CoG-2019-ChenYL #abstraction #game studies #object-oriented #video: Object-Oriented State Abstraction in Reinforcement Learning for Video Games (YC, HY, YL), pp. 1–4.
CoG-2019-DockhornLVBGL #game studies #modelling: Learning Local Forward Models on Unforgiving Games (AD, SML, VV, IB, RDG, DPL), pp. 1–4.
CoG-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.
CoG-2019-GainaS #game studies #video: “Did You Hear That?” Learning to Play Video Games from Audio Cues (RDG, MS), pp. 1–4.
CoG-2019-GeorgiadisLBW #assessment: Learning Analytics Should Analyse the Learning: Proposing a Generic Stealth Assessment Tool (KG, GvL, KB, WW), pp. 1–8.
CoG-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.
CoG-2019-IlhanGP #education #multi: Teaching on a Budget in Multi-Agent Deep Reinforcement Learning (EI, JG, DPL), pp. 1–8.
CoG-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.
CoG-2019-KamaldinovM #game studies: Deep Reinforcement Learning in Match-3 Game (IK, IM), pp. 1–4.
CoG-2019-KanagawaK #challenge #named: Rogue-Gym: A New Challenge for Generalization in Reinforcement Learning (YK, TK), pp. 1–8.
CoG-2019-KanervistoH #named: ToriLLE: Learning Environment for Hand-to-Hand Combat (AK, VH), pp. 1–8.
CoG-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.
CoG-2019-KeehlS: Monster Carlo 2: Integrating Learning and Tree Search for Machine Playtesting (OK, AMS), pp. 1–8.
CoG-2019-KhaustovBM #game studies: Pass in Human Style: Learning Soccer Game Patterns from Spatiotemporal Data (VK, GMB, MM), pp. 1–2.
CoG-2019-Konen #education #game studies #research: General Board Game Playing for Education and Research in Generic AI Game Learning (WK), pp. 1–8.
CoG-2019-LiapisKMSY #multimodal: Fusing Level and Ruleset Features for Multimodal Learning of Gameplay Outcomes (AL, DK, KM, KS, GNY), pp. 1–8.
CoG-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.
CoG-2019-NaderiBRH #approach: A Reinforcement Learning Approach To Synthesizing Climbing Movements (KN, AB, SR, PH), pp. 1–7.
CoG-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.
CoG-2019-NamI #game studies #generative #using: Generation of Diverse Stages in Turn-Based Role-Playing Game using Reinforcement Learning (SN, KI), pp. 1–8.
CoG-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.
CoG-2019-PleinesZB: Action Spaces in Deep Reinforcement Learning to Mimic Human Input Devices (MP, FZ, VPB), pp. 1–8.
CoG-2019-RebstockSB #policy: Learning Policies from Human Data for Skat (DR, CS, MB), pp. 1–8.
CoG-2019-SoemersPSB #policy #self: Learning Policies from Self-Play with Policy Gradients and MCTS Value Estimates (DJNJS, ÉP, MS, CB), pp. 1–8.
CoG-2019-SpickCW #generative #using: Procedural Generation using Spatial GANs for Region-Specific Learning of Elevation Data (RJS, PC, JAW), pp. 1–8.
CoG-2019-ZhangPFAJ #game studies #lr: 1GBDT, LR & Deep Learning for Turn-based Strategy Game AI (LZ, HP, QF, CA, YJ), pp. 1–8.
CoG-2019-ZuinV #game studies: Learning a Resource Scale for Collectible Card Games (GLZ, AV), pp. 1–8.
DiGRA-2019-KultimaL #game studies: Sami Game Jam - Learning, Exploring, Reflecting and Sharing Indigenous Culture through Game Jamming (AK, OL).
FDG-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.
FDG-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.
FDG-2019-KarthS: Addressing the fundamental tension of PCGML with discriminative learning (IK, AMS), p. 9.
FDG-2019-Ruch #development #education #game studies: Trans-pacific project-based learning: game production curriculum development (AWR), p. 9.
FDG-2019-WangCYPTA #game studies #synthesis: Goal-based progression synthesis in a korean learning game (SW, BC, SY, JYP, NT, EA), p. 9.
VS-Games-2019-Hohl #architecture #game studies #interactive #visualisation: Game-Based Learning - Developing a Business Game for Interactive Architectural Visualization (WH), pp. 1–4.
VS-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.
CIKM-2019-00090S #representation: Hyper-Path-Based Representation Learning for Hyper-Networks (JH0, XL0, YS), pp. 449–458.
CIKM-2019-BhutaniZJ #composition #knowledge base #query: Learning to Answer Complex Questions over Knowledge Bases with Query Composition (NB, XZ, HVJ), pp. 739–748.
CIKM-2019-BoiarovT #metric #recognition #scalability: Large Scale Landmark Recognition via Deep Metric Learning (AB, ET), pp. 169–178.
CIKM-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.
CIKM-2019-ChengLCHHCMH #named: DIRT: Deep Learning Enhanced Item Response Theory for Cognitive Diagnosis (SC, QL0, EC, ZH, ZH, YC, HM, GH), pp. 2397–2400.
CIKM-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.
CIKM-2019-ChenTL #query #social: Query Embedding Learning for Context-based Social Search (YCC, YCT, CTL), pp. 2441–2444.
CIKM-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.
CIKM-2019-EladGNKR #personalisation: Learning to Generate Personalized Product Descriptions (GE, IG, SN, BK, KR), pp. 389–398.
CIKM-2019-ElMS #data analysis #named: ATENA: An Autonomous System for Data Exploration Based on Deep Reinforcement Learning (OBE, TM, AS), pp. 2873–2876.
CIKM-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.
CIKM-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.
CIKM-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.
CIKM-2019-GuHDM #analysis #named: LinkRadar: Assisting the Analysis of Inter-app Page Links via Transfer Learning (DG, ZH, SD, YM0), pp. 2077–2080.
CIKM-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.
CIKM-2019-HosseiniH #feature model #kernel #multi #prototype #representation: Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection (BH, BH), pp. 1863–1872.
CIKM-2019-HuangSZWC #network #self: Similarity-Aware Network Embedding with Self-Paced Learning (CH0, BS, XZ, XW, NVC), pp. 2113–2116.
CIKM-2019-HuangYX #detection #graph: System Deterioration Detection and Root Cause Learning on Time Series Graphs (HH, SY, YX), pp. 2537–2545.
CIKM-2019-JenkinsFWL #multimodal #representation: Unsupervised Representation Learning of Spatial Data via Multimodal Embedding (PJ, AF, SW, ZL), pp. 1993–2002.
CIKM-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.
CIKM-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.
CIKM-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.
CIKM-2019-KangHLY #recommendation: Semi-Supervised Learning for Cross-Domain Recommendation to Cold-Start Users (SK, JH, DL, HY), pp. 1563–1572.
CIKM-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.
CIKM-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.
CIKM-2019-LiuWSL: Loopless Semi-Stochastic Gradient Descent with Less Hard Thresholding for Sparse Learning (XL, BW, FS, HL), pp. 881–890.
CIKM-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.
CIKM-2019-LiuZYCY #generative #refinement: Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System (YL, CZ, XY, YC, PSY), pp. 1643–1652.
CIKM-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.
CIKM-2019-LuoSAZ0 #multi #retrieval: Cross-modal Image-Text Retrieval with Multitask Learning (JL, YS, XA, ZZ, MY0), pp. 2309–2312.
CIKM-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.
CIKM-2019-LuYGWLC #clustering #realtime: Reinforcement Learning with Sequential Information Clustering in Real-Time Bidding (JL, CY, XG, LW, CL, GC), pp. 1633–1641.
CIKM-2019-MaAWSCTY #data analysis #graph #similarity: Deep Graph Similarity Learning for Brain Data Analysis (GM, NKA, TLW, DS, MWC, NBTB, PSY), pp. 2743–2751.
CIKM-2019-MaoSSSS #process: Investigating the Learning Process in Job Search: A Longitudinal Study (JM, DS, SS, FS, MS), pp. 2461–2464.
CIKM-2019-NeutatzMA #detection #fault #named: ED2: A Case for Active Learning in Error Detection (FN, MM, ZA), pp. 2249–2252.
CIKM-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.
CIKM-2019-RizosHS #classification: Augment to Prevent: Short-Text Data Augmentation in Deep Learning for Hate-Speech Classification (GR, KH, BWS), pp. 991–1000.
CIKM-2019-ShenTB #graph #representation: GRLA 2019: The first International Workshop on Graph Representation Learning and its Applications (HS, JT, PB), pp. 2997–2998.
CIKM-2019-ShresthaMAV #behaviour #graph #interactive #social: Learning from Dynamic User Interaction Graphs to Forecast Diverse Social Behavior (PS, SM, DA, SV), pp. 2033–2042.
CIKM-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.
CIKM-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.
CIKM-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.
CIKM-2019-TrittenbachB #detection #multi: One-Class Active Learning for Outlier Detection with Multiple Subspaces (HT, KB), pp. 811–820.
CIKM-2019-Wang0C #graph #reasoning #recommendation: Learning and Reasoning on Graph for Recommendation (XW, XH0, TSC), pp. 2971–2972.
CIKM-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.
CIKM-2019-WangL #behaviour #network: Spotting Terrorists by Learning Behavior-aware Heterogeneous Network Embedding (PCW, CTL), pp. 2097–2100.
CIKM-2019-WangRCR0R #graph #predict: Order-free Medicine Combination Prediction with Graph Convolutional Reinforcement Learning (SW, PR, ZC, ZR, JM0, MdR), pp. 1623–1632.
CIKM-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.
CIKM-2019-WuLZQ #recommendation: Long- and Short-term Preference Learning for Next POI Recommendation (YW, KL, GZ, XQ), pp. 2301–2304.
CIKM-2019-WuPDTZD #distance #graph #network: Long-short Distance Aggregation Networks for Positive Unlabeled Graph Learning (MW, SP, LD, IWT, XZ, BD), pp. 2157–2160.
CIKM-2019-WuWZJ #effectiveness #performance #recommendation: Learning to Effectively Estimate the Travel Time for Fastest Route Recommendation (NW, JW, WXZ, YJ), pp. 1923–1932.
CIKM-2019-XiaoLM #collaboration: Dynamic Collaborative Recurrent Learning (TX, SL, ZM), pp. 1151–1160.
CIKM-2019-XiaoRMSL #metric #personalisation: Dynamic Bayesian Metric Learning for Personalized Product Search (TX, JR, ZM, HS, SL), pp. 1693–1702.
CIKM-2019-XiaWY #comprehension #multi: Incorporating Relation Knowledge into Commonsense Reading Comprehension with Multi-task Learning (JX, CW, MY), pp. 2393–2396.
CIKM-2019-XiongZXL: Learning Traffic Signal Control from Demonstrations (YX, GZ, KX, ZL), pp. 2289–2292.
CIKM-2019-XuHY #graph #network #scalability: Scalable Causal Graph Learning through a Deep Neural Network (CX, HH, SY), pp. 1853–1862.
CIKM-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.
CIKM-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.
CIKM-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.
CIKM-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.
CIKM-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.
CIKM-2019-ZhaoCY #comprehension #e-commerce #query: A Dynamic Product-aware Learning Model for E-commerce Query Intent Understanding (JZ, HC, DY), pp. 1843–1852.
CIKM-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.
CIKM-2019-ZhengXZFWZLXL #contest: Learning Phase Competition for Traffic Signal Control (GZ, YX, XZ, JF, HW, HZ, YL0, KX, ZL), pp. 1963–1972.
CIKM-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.
CIKM-2019-ZouK: Learning to Ask: Question-based Sequential Bayesian Product Search (JZ, EK), pp. 369–378.
CIKM-2019-ZouLAWZ #multi #named #rank: MarlRank: Multi-agent Reinforced Learning to Rank (SZ, ZL, MA, JW0, PZ), pp. 2073–2076.
ECIR-p1-2019-BalikasDMAA #semantics #using: Learning Lexical-Semantic Relations Using Intuitive Cognitive Links (GB, GD, RM, HA, MRA), pp. 3–18.
ECIR-p1-2019-FlorescuJ #graph #representation: A Supervised Keyphrase Extraction System Based on Graph Representation Learning (CF, WJ), pp. 197–212.
ECIR-p2-2019-Landin #recommendation: Learning User and Item Representations for Recommender Systems (AL), pp. 337–342.
ECIR-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.
ICML-2019-0002CZG #adaptation #invariant #on the: On Learning Invariant Representations for Domain Adaptation (HZ0, RTdC, KZ0, GJG), pp. 7523–7532.
ICML-2019-0002H: Target-Based Temporal-Difference Learning (DL0, NH), pp. 3713–3722.
ICML-2019-0002VBB #performance: Provably Efficient Imitation Learning from Observation Alone (WS0, AV, BB, DB), pp. 6036–6045.
ICML-2019-0002VY #constraints #policy: Batch Policy Learning under Constraints (HML0, CV, YY), pp. 3703–3712.
ICML-2019-AbelsRLNS #multi: Dynamic Weights in Multi-Objective Deep Reinforcement Learning (AA, DMR, TL, AN, DS), pp. 11–20.
ICML-2019-AcharyaSFS #communication #distributed #sublinear: Distributed Learning with Sublinear Communication (JA, CDS, DJF, KS), pp. 40–50.
ICML-2019-AdamsJWS #fault #metric #modelling: Learning Models from Data with Measurement Error: Tackling Underreporting (RA, YJ, XW, SS), pp. 61–70.
ICML-2019-AdelW #approach #named #visual notation: TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning (TA, AW), pp. 71–81.
ICML-2019-AgarwalLS0: Learning to Generalize from Sparse and Underspecified Rewards (RA, CL, DS, MN0), pp. 130–140.
ICML-2019-Allen-ZhuLS #convergence: A Convergence Theory for Deep Learning via Over-Parameterization (ZAZ, YL, ZS), pp. 242–252.
ICML-2019-AllenSST #infinity #prototype: Infinite Mixture Prototypes for Few-shot Learning (KRA, ES, HS, JBT), pp. 232–241.
ICML-2019-AssranLBR #distributed #probability: Stochastic Gradient Push for Distributed Deep Learning (MA, NL, NB, MR), pp. 344–353.
ICML-2019-BalduzziGB0PJG #game studies #symmetry: Open-ended learning in symmetric zero-sum games (DB, MG, YB, WC0, JP, MJ, TG), pp. 434–443.
ICML-2019-BaranchukPSB #graph #similarity: Learning to Route in Similarity Graphs (DB, DP, AS, AB), pp. 475–484.
ICML-2019-BehpourLZ #predict #probability: Active Learning for Probabilistic Structured Prediction of Cuts and Matchings (SB, AL, BDZ), pp. 563–572.
ICML-2019-BelilovskyEO: Greedy Layerwise Learning Can Scale To ImageNet (EB, ME, EO), pp. 583–593.
ICML-2019-BenzingGMMS #approximate #realtime: Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning (FB, MMG, AM, AM, AS), pp. 604–613.
ICML-2019-BhagojiCMC #lens: Analyzing Federated Learning through an Adversarial Lens (ANB, SC, PM, SBC), pp. 634–643.
ICML-2019-BibautMVL #evaluation #performance: More Efficient Off-Policy Evaluation through Regularized Targeted Learning (AB, IM, NV, MJvdL), pp. 654–663.
ICML-2019-BrownGNN: Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations (DSB, WG, PN, SN), pp. 783–792.
ICML-2019-BunneA0J #generative #modelling: Learning Generative Models across Incomparable Spaces (CB, DAM, AK0, SJ), pp. 851–861.
ICML-2019-ByrdL #question #what: What is the Effect of Importance Weighting in Deep Learning? (JB, ZCL), pp. 872–881.
ICML-2019-CaoS #multi #problem: Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit Problem (JC, WS), pp. 912–920.
ICML-2019-ChandakTKJT: Learning Action Representations for Reinforcement Learning (YC, GT, JK, SMJ, PST), pp. 941–950.
ICML-2019-CharoenphakdeeL #on the #symmetry: On Symmetric Losses for Learning from Corrupted Labels (NC, JL, MS), pp. 961–970.
ICML-2019-ChatterjiPB #kernel #online: Online learning with kernel losses (NSC, AP, PLB), pp. 971–980.
ICML-2019-Chen0LJQS #generative #recommendation: Generative Adversarial User Model for Reinforcement Learning Based Recommendation System (XC, SL0, HL, SJ, YQ, LS), pp. 1052–1061.
ICML-2019-ChengVOCYB: Control Regularization for Reduced Variance Reinforcement Learning (RC, AV, GO, SC, YY, JB), pp. 1141–1150.
ICML-2019-ChenJ: Information-Theoretic Considerations in Batch Reinforcement Learning (JC, NJ), pp. 1042–1051.
ICML-2019-ChuBG #functional #probability: Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning (CC, JHB, PWG), pp. 1213–1222.
ICML-2019-CobbeKHKS: Quantifying Generalization in Reinforcement Learning (KC, OK, CH, TK, JS), pp. 1282–1289.
ICML-2019-CohenKM: Learning Linear-Quadratic Regulators Efficiently with only √T Regret (AC, TK, YM), pp. 1300–1309.
ICML-2019-ColasOSFC #composition #motivation #multi #named: CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning (CC, PYO, OS, PF, MC), pp. 1331–1340.
ICML-2019-CortesDGMY #feedback #graph #online: Online Learning with Sleeping Experts and Feedback Graphs (CC, GD, CG, MM, SY), pp. 1370–1378.
ICML-2019-CortesDMZG #graph: Active Learning with Disagreement Graphs (CC, GD, MM, NZ, CG), pp. 1379–1387.
ICML-2019-CreagerMJWSPZ #representation: Flexibly Fair Representation Learning by Disentanglement (EC, DM, JHJ, MAW, KS, TP, RSZ), pp. 1436–1445.
ICML-2019-CutkoskyS #online: Matrix-Free Preconditioning in Online Learning (AC, TS), pp. 1455–1464.
ICML-2019-CvitkovicK #statistics: Minimal Achievable Sufficient Statistic Learning (MC, GK), pp. 1465–1474.
ICML-2019-CvitkovicSA #source code: Open Vocabulary Learning on Source Code with a Graph-Structured Cache (MC, BS, AA), pp. 1475–1485.
ICML-2019-DadashiBTRS: The Value Function Polytope in Reinforcement Learning (RD, MGB, AAT, NLR, DS), pp. 1486–1495.
ICML-2019-Dann0WB #policy #towards: Policy Certificates: Towards Accountable Reinforcement Learning (CD, LL0, WW, EB), pp. 1507–1516.
ICML-2019-DaoGERR #algorithm #linear #performance #using: Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations (TD, AG, ME, AR, CR), pp. 1517–1527.
ICML-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.
ICML-2019-DiaconuW #approach: Learning to Convolve: A Generalized Weight-Tying Approach (ND, DEW), pp. 1586–1595.
ICML-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.
ICML-2019-DoerrVTTD: Trajectory-Based Off-Policy Deep Reinforcement Learning (AD, MV, MT, ST, CD), pp. 1636–1645.
ICML-2019-Duetting0NPR: Optimal Auctions through Deep Learning (PD, ZF0, HN, DCP, SSR), pp. 1706–1715.
ICML-2019-DuklerLLM #generative #modelling: Wasserstein of Wasserstein Loss for Learning Generative Models (YD, WL, ATL, GM), pp. 1716–1725.
ICML-2019-DuN: Task-Agnostic Dynamics Priors for Deep Reinforcement Learning (YD, KN), pp. 1696–1705.
ICML-2019-DunckerBBS #modelling #probability: Learning interpretable continuous-time models of latent stochastic dynamical systems (LD, GB, JB, MS), pp. 1726–1734.
ICML-2019-ElfekiCRE #named #process #using: GDPP: Learning Diverse Generations using Determinantal Point Processes (ME, CC, MR, ME), pp. 1774–1783.
ICML-2019-FatemiSSK: Dead-ends and Secure Exploration in Reinforcement Learning (MF, SS, HvS, SEK), pp. 1873–1881.
ICML-2019-Feige #invariant #multi #representation: Invariant-Equivariant Representation Learning for Multi-Class Data (IF), pp. 1882–1891.
ICML-2019-FoersterSHBDWBB #multi: Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning (JNF, HFS, EH, NB, ID, SW, MB, MB), pp. 1942–1951.
ICML-2019-FranceschiNPH #graph #network: Learning Discrete Structures for Graph Neural Networks (LF, MN, MP, XH), pp. 1972–1982.
ICML-2019-FrancP #nondeterminism #on the #predict: On discriminative learning of prediction uncertainty (VF, DP), pp. 1963–1971.
ICML-2019-FujimotoMP: Off-Policy Deep Reinforcement Learning without Exploration (SF, DM, DP), pp. 2052–2062.
ICML-2019-GamrianG: Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation (SG, YG), pp. 2063–2072.
ICML-2019-GaoJWWYZ #generative: Deep Generative Learning via Variational Gradient Flow (YG, YJ, YW, YW0, CY, SZ), pp. 2093–2101.
ICML-2019-GeladaKBNB #modelling #named #representation: DeepMDP: Learning Continuous Latent Space Models for Representation Learning (CG, SK, JB, ON, MGB), pp. 2170–2179.
ICML-2019-GhadikolaeiGFS #big data #dataset: Learning and Data Selection in Big Datasets (HSG, HGG, CF, MS), pp. 2191–2200.
ICML-2019-GhaziPW #composition #recursion #sketching: Recursive Sketches for Modular Deep Learning (BG, RP, JRW), pp. 2211–2220.
ICML-2019-GilboaB0 #performance #taxonomy: Efficient Dictionary Learning with Gradient Descent (DG, SB, JW0), pp. 2252–2259.
ICML-2019-GillickREEB #sequence: Learning to Groove with Inverse Sequence Transformations (JG, AR, JHE, DE, DB), pp. 2269–2279.
ICML-2019-GolovnevPS: The information-theoretic value of unlabeled data in semi-supervised learning (AG, DP, BS), pp. 2328–2336.
ICML-2019-GreenfeldGBYK #multi: Learning to Optimize Multigrid PDE Solvers (DG, MG, RB, IY, RK), pp. 2415–2423.
ICML-2019-GreffKKWBZMBL #multi #representation: Multi-Object Representation Learning with Iterative Variational Inference (KG, RLK, RK, NW, CB, DZ, LM, MB, AL), pp. 2424–2433.
ICML-2019-GuoSH #dependence #graph #relational: Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs (LG, ZS, WH0), pp. 2505–2514.
ICML-2019-HacohenW #education #network #on the #power of: On The Power of Curriculum Learning in Training Deep Networks (GH, DW), pp. 2535–2544.
ICML-2019-HafnerLFVHLD: Learning Latent Dynamics for Planning from Pixels (DH, TPL, IF, RV, DH, HL, JD), pp. 2555–2565.
ICML-2019-HanS: Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning (SH, YS), pp. 2586–2595.
ICML-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.
ICML-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.
ICML-2019-HendrickxOS #graph: Graph Resistance and Learning from Pairwise Comparisons (JMH, AO, VS), pp. 2702–2711.
ICML-2019-HoferKND #persistent #representation: Connectivity-Optimized Representation Learning via Persistent Homology (CDH, RK, MN, MD), pp. 2751–2760.
ICML-2019-HoLCSA #performance #policy: Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules (DH, EL, XC0, IS, PA), pp. 2731–2741.
ICML-2019-HoulsbyGJMLGAG: Parameter-Efficient Transfer Learning for NLP (NH, AG, SJ, BM, QdL, AG, MA, SG), pp. 2790–2799.
ICML-2019-HuangDGZ: Unsupervised Deep Learning by Neighbourhood Discovery (JH, QD0, SG, XZ), pp. 2849–2858.
ICML-2019-InnesL #problem: Learning Structured Decision Problems with Unawareness (CI, AL), pp. 2941–2950.
ICML-2019-IqbalS #multi: Actor-Attention-Critic for Multi-Agent Reinforcement Learning (SI, FS), pp. 2961–2970.
ICML-2019-IshidaNMS #modelling: Complementary-Label Learning for Arbitrary Losses and Models (TI, GN, AKM, MS), pp. 2971–2980.
ICML-2019-JacqGPP: Learning from a Learner (AJ, MG, AP, OP), pp. 2990–2999.
ICML-2019-JagielskiKMORSU: Differentially Private Fair Learning (MJ, MJK, JM, AO, AR0, SSM, JU), pp. 3000–3008.
ICML-2019-JangLHS #what: Learning What and Where to Transfer (YJ, HL, SJH, JS), pp. 3030–3039.
ICML-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.
ICML-2019-JayRGST #internet: A Deep Reinforcement Learning Perspective on Internet Congestion Control (NJ, NHR, BG, MS, AT), pp. 3050–3059.
ICML-2019-JeongS19a: Learning Discrete and Continuous Factors of Data via Alternating Disentanglement (YJ, HOS), pp. 3091–3099.
ICML-2019-JiangL #logic: Neural Logic Reinforcement Learning (ZJ, SL), pp. 3110–3119.
ICML-2019-KaplanisSC #policy: Policy Consolidation for Continual Reinforcement Learning (CK, MS, CC), pp. 3242–3251.
ICML-2019-KaplanMMS #concept #geometry: Differentially Private Learning of Geometric Concepts (HK, YM, YM, US), pp. 3233–3241.
ICML-2019-KempkaKW #adaptation #algorithm #invariant #linear #modelling #online: Adaptive Scale-Invariant Online Algorithms for Learning Linear Models (MK, WK, MKW), pp. 3321–3330.
ICML-2019-KhadkaMNDTMLT #collaboration: Collaborative Evolutionary Reinforcement Learning (SK, SM, TN, ZD, ET, SM, YL, KT), pp. 3341–3350.
ICML-2019-KipfLDZSGKB #composition #execution #named: CompILE: Compositional Imitation Learning and Execution (TK, YL, HD, VFZ, ASG, EG, PK, PWB), pp. 3418–3428.
ICML-2019-KonstantinovL #robust: Robust Learning from Untrusted Sources (NK, CL), pp. 3488–3498.
ICML-2019-LawLSZ #distance: Lorentzian Distance Learning for Hyperbolic Representations (MTL, RL, JS, RSZ), pp. 3672–3681.
ICML-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.
ICML-2019-LiDMMHH #named #network: LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning (HYL, WD, XM, CM, FH, BGH), pp. 3825–3834.
ICML-2019-LiGDVK #graph #network #similarity: Graph Matching Networks for Learning the Similarity of Graph Structured Objects (YL, CG, TD, OV, PK), pp. 3835–3845.
ICML-2019-LiLS #online #rank: Online Learning to Rank with Features (SL, TL, CS), pp. 3856–3865.
ICML-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.
ICML-2019-LimA #kernel #markov #process #robust: Kernel-Based Reinforcement Learning in Robust Markov Decision Processes (SHL, AA), pp. 3973–3981.
ICML-2019-LiSK #physics: Adversarial camera stickers: A physical camera-based attack on deep learning systems (JL0, FRS, JZK), pp. 3896–3904.
ICML-2019-LiSSG #exponential #kernel #product line: Learning deep kernels for exponential family densities (WL, DJS, HS, AG), pp. 6737–6746.
ICML-2019-LiuS #multi: Sparse Extreme Multi-label Learning with Oracle Property (WL, XS0), pp. 4032–4041.
ICML-2019-LiuSH: The Implicit Fairness Criterion of Unconstrained Learning (LTL, MS, MH), pp. 4051–4060.
ICML-2019-LiuSX #performance: Taming MAML: Efficient unbiased meta-reinforcement learning (HL, RS, CX), pp. 4061–4071.
ICML-2019-LiZWSX #framework: Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting (XL, YZ, TW, RS, CX), pp. 3925–3934.
ICML-2019-LocatelloBLRGSB: Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations (FL, SB, ML, GR, SG, BS, OB), pp. 4114–4124.
ICML-2019-MahloujifarMM #multi: Data Poisoning Attacks in Multi-Party Learning (SM, MM, AM), pp. 4274–4283.
ICML-2019-MalikKSNSE #modelling: Calibrated Model-Based Deep Reinforcement Learning (AM, VK, JS, DN, HS, SE), pp. 4314–4323.
ICML-2019-MannGGHJLS #recommendation: Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems (TAM, SG, AG, HH, RJ, BL, PS), pp. 4324–4332.
ICML-2019-MaryCK: Fairness-Aware Learning for Continuous Attributes and Treatments (JM, CC, NEK), pp. 4382–4391.
ICML-2019-MavrinYKWY #performance: Distributional Reinforcement Learning for Efficient Exploration (BM, HY, LK, KW, YY), pp. 4424–4434.
ICML-2019-MenschBP #geometry: Geometric Losses for Distributional Learning (AM, MB, GP), pp. 4516–4525.
ICML-2019-MetelliGR #configuration management: Reinforcement Learning in Configurable Continuous Environments (AMM, EG, MR), pp. 4546–4555.
ICML-2019-MishneCC: Co-manifold learning with missing data (GM, ECC, RRC), pp. 4605–4614.
ICML-2019-MohriSS: Agnostic Federated Learning (MM, GS, ATS), pp. 4615–4625.
ICML-2019-NabiMS #policy: Learning Optimal Fair Policies (RN, DM, IS), pp. 4674–4682.
ICML-2019-NaganoY0K: A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning (YN, SY, YF0, MK), pp. 4693–4702.
ICML-2019-NamKMPSF #classification #multi #permutation: Learning Context-dependent Label Permutations for Multi-label Classification (JN, YBK, ELM, SP, RS, JF), pp. 4733–4742.
ICML-2019-NedelecKP: Learning to bid in revenue-maximizing auctions (TN, NEK, VP), pp. 4781–4789.
ICML-2019-Nguyen #on the #set: On Connected Sublevel Sets in Deep Learning (QN), pp. 4790–4799.
ICML-2019-NiekerkJER: Composing Value Functions in Reinforcement Learning (BvN, SJ, ACE, BR), pp. 6401–6409.
ICML-2019-NyeHTS #sketching: Learning to Infer Program Sketches (MIN, LBH, JBT, ASL), pp. 4861–4870.
ICML-2019-OglicG #kernel #scalability: Scalable Learning in Reproducing Kernel Krein Spaces (DO, TG0), pp. 4912–4921.
ICML-2019-OymakS #question: Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path? (SO, MS), pp. 4951–4960.
ICML-2019-PaulOW #optimisation #policy #robust: Fingerprint Policy Optimisation for Robust Reinforcement Learning (SP, MAO, SW), pp. 5082–5091.
ICML-2019-PengHSS: Domain Agnostic Learning with Disentangled Representations (XP, ZH, XS, KS), pp. 5102–5112.
ICML-2019-PingPSZRW #normalisation #representation: Differentiable Dynamic Normalization for Learning Deep Representation (LP, ZP, WS, RZ, JR, LW), pp. 4203–4211.
ICML-2019-QuMX: Nonlinear Distributional Gradient Temporal-Difference Learning (CQ, SM, HX), pp. 5251–5260.
ICML-2019-RadanovicDPS #markov #process: Learning to Collaborate in Markov Decision Processes (GR, RD, DCP, AS), pp. 5261–5270.
ICML-2019-RakellyZFLQ #performance #probability: Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables (KR, AZ, CF, SL, DQ), pp. 5331–5340.
ICML-2019-ReslerM #online: Adversarial Online Learning with noise (AR, YM), pp. 5429–5437.
ICML-2019-RollandKISC #performance #probability #testing: Efficient learning of smooth probability functions from Bernoulli tests with guarantees (PR, AK, AI, AS, VC), pp. 5459–5467.
ICML-2019-RowlandDKMBD #statistics: Statistics and Samples in Distributional Reinforcement Learning (MR, RD, SK, RM, MGB, WD), pp. 5528–5536.
ICML-2019-SaunshiPAKK #analysis #representation: A Theoretical Analysis of Contrastive Unsupervised Representation Learning (NS, OP, SA, MK, HK), pp. 5628–5637.
ICML-2019-SchroeterSM #locality: Weakly-Supervised Temporal Localization via Occurrence Count Learning (JS, KAS, ADM), pp. 5649–5659.
ICML-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.
ICML-2019-ShaniEM #revisited: Exploration Conscious Reinforcement Learning Revisited (LS, YE, SM), pp. 5680–5689.
ICML-2019-ShenLL: Learning to Clear the Market (WS, SL, RPL), pp. 5710–5718.
ICML-2019-ShenS: Learning with Bad Training Data via Iterative Trimmed Loss Minimization (YS, SS), pp. 5739–5748.
ICML-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.
ICML-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.
ICML-2019-SongK0 #named #robust: SELFIE: Refurbishing Unclean Samples for Robust Deep Learning (HS, MK, JGL0), pp. 5907–5915.
ICML-2019-SonKKHY #multi #named: QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning (KS, DK, WJK, DH, YY), pp. 5887–5896.
ICML-2019-Stickland0 #adaptation #multi #performance: BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning (ACS, IM0), pp. 5986–5995.
ICML-2019-Streeter #linear: Learning Optimal Linear Regularizers (MS), pp. 5996–6004.
ICML-2019-SundinSSVSK: Active Learning for Decision-Making from Imbalanced Observational Data (IS, PS, ES, AV, SS, SK), pp. 6046–6055.
ICML-2019-SuW #distance #metric #sequence: Learning Distance for Sequences by Learning a Ground Metric (BS, YW), pp. 6015–6025.
ICML-2019-SuWSJ #adaptation #evaluation #named #policy: CAB: Continuous Adaptive Blending for Policy Evaluation and Learning (YS, LW, MS, TJ), pp. 6005–6014.
ICML-2019-TesslerEM #robust: Action Robust Reinforcement Learning and Applications in Continuous Control (CT, YE, SM), pp. 6215–6224.
ICML-2019-ThulasidasanBBC #using: Combating Label Noise in Deep Learning using Abstention (ST, TB, JAB, GC, JMY), pp. 6234–6243.
ICML-2019-TranDRC #generative: Bayesian Generative Active Deep Learning (TT, TTD, IDR0, GC), pp. 6295–6304.
ICML-2019-TrouleauEGKT #process: Learning Hawkes Processes Under Synchronization Noise (WT, JE, MG, NK, PT), pp. 6325–6334.
ICML-2019-VarmaSHRR #dependence #modelling: Learning Dependency Structures for Weak Supervision Models (PV, FS, AH, AR, CR), pp. 6418–6427.
ICML-2019-VinayakKVK #estimation #parametricity: Maximum Likelihood Estimation for Learning Populations of Parameters (RKV, WK, GV, SMK), pp. 6448–6457.
ICML-2019-VorobevUGS #ranking: Learning to select for a predefined ranking (AV, AU, GG, PS), pp. 6477–6486.
ICML-2019-Wang0 #modelling: Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models (DW, QL0), pp. 6576–6585.
ICML-2019-WangCAD #estimation #policy #random: Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation (RW, CC, PVA, YD), pp. 6536–6544.
ICML-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.
ICML-2019-WangZ0Q #random #recommendation #robust: Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random (XW, RZ0, YS0, JQ0), pp. 6638–6647.
ICML-2019-WangZXS #on the: On the Generalization Gap in Reparameterizable Reinforcement Learning (HW, SZ, CX, RS), pp. 6648–6658.
ICML-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.
ICML-2019-WonXL: Projection onto Minkowski Sums with Application to Constrained Learning (JHW, JX, KL), pp. 3642–3651.
ICML-2019-WuCBTS: Imitation Learning from Imperfect Demonstration (YHW, NC, HB, VT, MS), pp. 6818–6827.
ICML-2019-WuDSYHSRK #matrix #metric: Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling (SW, AD, SS, FXY, DNHR, DS, AR, SK), pp. 6828–6839.
ICML-2019-XuLZC #graph: Gromov-Wasserstein Learning for Graph Matching and Node Embedding (HX, DL, HZ, LC), pp. 6932–6941.
ICML-2019-XuRDLF: Learning a Prior over Intent via Meta-Inverse Reinforcement Learning (KX, ER, ADD, SL, CF), pp. 6952–6962.
ICML-2019-YangD #proving #theorem: Learning to Prove Theorems via Interacting with Proof Assistants (KY, JD), pp. 6984–6994.
ICML-2019-YinCRB #distributed: Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning (DY, YC0, KR, PLB), pp. 7074–7084.
ICML-2019-YoonSM #adaptation #named #network: TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning (SWY, JS, JM), pp. 7115–7123.
ICML-2019-YoungBN #generative #modelling #synthesis: Learning Neurosymbolic Generative Models via Program Synthesis (HY, OB, MN), pp. 7144–7153.
ICML-2019-YuCGY #graph #named #network: DAG-GNN: DAG Structure Learning with Graph Neural Networks (YY, JC, TG, MY), pp. 7154–7163.
ICML-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.
ICML-2019-YurochkinAGGHK #network #parametricity: Bayesian Nonparametric Federated Learning of Neural Networks (MY, MA, SG, KHG, TNH, YK), pp. 7252–7261.
ICML-2019-YuSE #multi: Multi-Agent Adversarial Inverse Reinforcement Learning (LY, JS, SE), pp. 7194–7201.
ICML-2019-YuTRKSAZL #distributed #network: Distributed Learning over Unreliable Networks (CY, HT, CR, SK, AS, DA, CZ, JL0), pp. 7202–7212.
ICML-2019-ZablockiBSPG #recognition: Context-Aware Zero-Shot Learning for Object Recognition (EZ, PB, LS, BP, PG), pp. 7292–7303.
ICML-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.
ICML-2019-ZengLLY #convergence #coordination: Global Convergence of Block Coordinate Descent in Deep Learning (JZ, TTKL, SL, YY0), pp. 7313–7323.
ICML-2019-ZhangHY #named #performance #recognition #visual notation: LatentGNN: Learning Efficient Non-local Relations for Visual Recognition (SZ, XH, SY), pp. 7374–7383.
ICML-2019-ZhangL #incremental #kernel #online #random #sketching: Incremental Randomized Sketching for Online Kernel Learning (XZ, SL), pp. 7394–7403.
ICML-2019-ZhangS #network: Co-Representation Network for Generalized Zero-Shot Learning (FZ, GS), pp. 7434–7443.
ICML-2019-ZhangVSA0L #modelling #named: SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning (MZ, SV, LS, PA, MJJ0, SL), pp. 7444–7453.
ICML-2019-ZhangYT #novel #policy: Learning Novel Policies For Tasks (YZ, WY, GT), pp. 7483–7492.
ICML-2019-ZhaoST #multi: Maximum Entropy-Regularized Multi-Goal Reinforcement Learning (RZ, XS0, VT), pp. 7553–7562.
ICML-2019-ZhuangCO #online #optimisation #probability: Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization (ZZ, AC, FO), pp. 7664–7672.
ICML-2019-ZhuSLHB #fault tolerance #graph: Improved Dynamic Graph Learning through Fault-Tolerant Sparsification (CJZ, SS, KyL, SH, JB), pp. 7624–7633.
ICML-2019-ZhuWS #classification: Learning Classifiers for Target Domain with Limited or No Labels (PZ, HW, VS), pp. 7643–7653.
KDD-2019-BabaevSTU: E.T.-RNN: Applying Deep Learning to Credit Loan Applications (DB, MS, AT, DU), pp. 2183–2190.
KDD-2019-CenZZYZ0 #multi #network #representation: Representation Learning for Attributed Multiplex Heterogeneous Network (YC, XZ, JZ, HY, JZ, JT0), pp. 1358–1368.
KDD-2019-ChenreddyPNCA #named #optimisation: SMOILE: A Shopper Marketing Optimization and Inverse Learning Engine (ARC, PP, SN, RC, RA), pp. 2934–2942.
KDD-2019-Chien #comprehension #mining: Deep Bayesian Mining, Learning and Understanding (JTC), pp. 3197–3198.
KDD-2019-DengRN #graph #predict #social: Learning Dynamic Context Graphs for Predicting Social Events (SD, HR, YN), pp. 1007–1016.
KDD-2019-deWetO: Finding Users Who Act Alike: Transfer Learning for Expanding Advertiser Audiences (Sd, JO), pp. 2251–2259.
KDD-2019-DiSC: Relation Extraction via Domain-aware Transfer Learning (SD, YS, LC), pp. 1348–1357.
KDD-2019-EsfandiariWAR #online #optimisation: Optimizing Peer Learning in Online Groups with Affinities (ME, DW, SAY, SBR), pp. 1216–1226.
KDD-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.
KDD-2019-FeiTL #multi #predict #word: Hierarchical Multi-Task Word Embedding Learning for Synonym Prediction (HF, ST, PL0), pp. 834–842.
KDD-2019-GaoJ #graph #network #representation: Graph Representation Learning via Hard and Channel-Wise Attention Networks (HG, SJ), pp. 741–749.
KDD-2019-HaldarARXYDZBTC: Applying Deep Learning to Airbnb Search (MH, MA, PR, TX, SY, HD, QZ, NBW, BCT, BMC, TL), pp. 1927–1935.
KDD-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.
KDD-2019-HeLLH #network: Learning Network-to-Network Model for Content-rich Network Embedding (ZH, JL0, NL, YH), pp. 1037–1045.
KDD-2019-HeXZMZY #multi: Off-policy Learning for Multiple Loggers (LH, LX, WZ, ZMM, YZ, DY), pp. 1184–1193.
KDD-2019-HossainR #process #recognition: Active Deep Learning for Activity Recognition with Context Aware Annotator Selection (HMSH, NR), pp. 1862–1870.
KDD-2019-HouCLCY #framework #graph #representation: A Representation Learning Framework for Property Graphs (YH, HC, CL, JC, MCY), pp. 65–73.
KDD-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.
KDD-2019-HuFS #network: Adversarial Learning on Heterogeneous Information Networks (BH, YF0, CS), pp. 120–129.
KDD-2019-HughesCZ #generative: Generating Better Search Engine Text Advertisements with Deep Reinforcement Learning (JWH, KhC, RZ), pp. 2269–2277.
KDD-2019-HuH #named #network #set: Sets2Sets: Learning from Sequential Sets with Neural Networks (HH, XH0), pp. 1491–1499.
KDD-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.
KDD-2019-InabaFKZ #approach #distance #energy #metric: A Free Energy Based Approach for Distance Metric Learning (SI, CTF, RVK, KZ), pp. 5–13.
KDD-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.
KDD-2019-JiaSSB #graph: Graph-based Semi-Supervised & Active Learning for Edge Flows (JJ, MTS, SS, ARB), pp. 761–771.
KDD-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.
KDD-2019-KillianWSCDT #using: Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data (JAK, BW, AS, VC, BD, MT), pp. 2430–2438.
KDD-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.
KDD-2019-LiuFWWBL #automation #multi: Automating Feature Subspace Exploration via Multi-Agent Reinforcement Learning (KL, YF, PW, LW, RB, XL), pp. 207–215.
KDD-2019-LiuLDCG #named #recommendation: DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation (DL, JL0, BD, JC, RG), pp. 344–352.
KDD-2019-LiuTLZCMW #adaptation: Exploiting Cognitive Structure for Adaptive Learning (QL0, ST, CL, HZ, EC, HM, SW), pp. 627–635.
KDD-2019-LiZY #effectiveness #performance: Efficient and Effective Express via Contextual Cooperative Reinforcement Learning (YL, YZ, QY), pp. 510–519.
KDD-2019-MingXQR #prototype #sequence: Interpretable and Steerable Sequence Learning via Prototypes (YM, PX, HQ, LR), pp. 903–913.
KDD-2019-OhI #detection #using: Sequential Anomaly Detection using Inverse Reinforcement Learning (MhO, GI), pp. 1480–1490.
KDD-2019-PanLW00Z #predict #using: Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning (ZP, YL, WW, YY0, YZ0, JZ), pp. 1720–1730.
KDD-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.
KDD-2019-ParkLHHLC #quality: Learning Sleep Quality from Daily Logs (SP, CTL, SH, CH, SWL, MC), pp. 2421–2429.
KDD-2019-Qin0Y: Deep Reinforcement Learning with Applications in Transportation (Z(Q, JT0, JY), pp. 3201–3202.
KDD-2019-RawatLY #multi #using: Naranjo Question Answering using End-to-End Multi-task Learning Model (BPSR, FL, HY0), pp. 2547–2555.
KDD-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.
KDD-2019-Salakhutdinov: Integrating Domain-Knowledge into Deep Learning (RS), p. 3176.
KDD-2019-ShangYLQMY #re-engineering #recommendation: Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation (WS, YY, QL, ZQ, YM, JY), pp. 566–576.
KDD-2019-ShenVAAHN #monitoring #smarttech #using: Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning (YS, MV, AA, AA, AYH, AYN), pp. 1909–1916.
KDD-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.
KDD-2019-SuzukiWN #scheduling: TV Advertisement Scheduling by Learning Expert Intentions (YS, WMW, IN), pp. 3071–3081.
KDD-2019-TangXWZL #multi: Retaining Privileged Information for Multi-Task Learning (FT, CX, FW, JZ, LWHL), pp. 1369–1377.
KDD-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.
KDD-2019-WangFXL #mobile #profiling #representation: Adversarial Substructured Representation Learning for Mobile User Profiling (PW, YF, HX, XL), pp. 130–138.
KDD-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.
KDD-2019-WangLZ #adaptation #ambiguity #graph: Adaptive Graph Guided Disambiguation for Partial Label Learning (DBW, LL0, MLZ), pp. 83–91.
KDD-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.
KDD-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.
KDD-2019-WangYCZ #convergence #performance: ADMM for Efficient Deep Learning with Global Convergence (JW, FY, XC0, LZ0), pp. 111–119.
KDD-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.
KDD-2019-XieH: Learning Class-Conditional GANs with Active Sampling (MKX, SJH), pp. 998–1006.
KDD-2019-XuTZ #kernel #multi: Isolation Set-Kernel and Its Application to Multi-Instance Learning (BCX, KMT, ZHZ), pp. 941–949.
KDD-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.
KDD-2019-YaoCC #clustering #multi #robust: Robust Task Grouping with Representative Tasks for Clustered Multi-Task Learning (YY, JC0, HC), pp. 1408–1417.
KDD-2019-YoshidaTK #graph #metric #mining: Learning Interpretable Metric between Graphs: Convex Formulation and Computation with Graph Mining (TY, IT, MK), pp. 1026–1036.
KDD-2019-YuGNCPH #constraints #incremental: Tackle Balancing Constraint for Incremental Semi-Supervised Support Vector Learning (SY, BG, KN, HC, JP, HH), pp. 1587–1595.
KDD-2019-ZhaiWTPR #visual notation: Learning a Unified Embedding for Visual Search at Pinterest (AZ, HYW, ET, DHP, CR), pp. 2412–2420.
KDD-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.
KDD-2019-ZhangYY #robust: Adversarial Variational Embedding for Robust Semi-supervised Learning (XZ0, LY, FY), pp. 139–147.
KDD-2019-ZhangZJZ: Learning from Incomplete and Inaccurate Supervision (ZyZ, PZ, YJ0, ZHZ), pp. 1017–1025.
KDD-2019-ZhaoDSZLX #multi #network #relational: Multiple Relational Attention Network for Multi-task Learning (JZ, BD, LS, FZ, WL, HX), pp. 1123–1131.
KDD-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.
KDD-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.
KDD-2019-ZhouM0H #education #optimisation: Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching (YZ, FM, JG0, JH), pp. 3231–3232.
KDD-2019-ZouXDS0Y #recommendation: Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems (LZ, LX, ZD, JS, WL0, DY), pp. 2810–2818.
MoDELS-2019-BencomoP #modelling #named #ram #runtime #using: RaM: Causally-Connected and Requirements-Aware Runtime Models using Bayesian Learning (NB, LHGP), pp. 216–226.
Onward-2019-BaniassadBHKA #design: Learning to listen for design (ELAB, IB, RH, GK, MA), pp. 179–186.
Onward-2019-CambroneroDV0WR #re-engineering: Active learning for software engineering (JPC, THYD, NV, JS0, JW, MCR), pp. 62–78.
OOPSLA-2019-BaderSP0 #automation #debugging #named: Getafix: learning to fix bugs automatically (JB, AS, MP, SC0), p. 27.
OOPSLA-2019-CambroneroR #named #source code: AL: autogenerating supervised learning programs (JPC, MCR), p. 28.
OOPSLA-2019-ChenWFBD #relational #using #verification: Relational verification using reinforcement learning (JC, JW, YF, OB, ID), p. 30.
OOPSLA-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.
OOPSLA-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.
PLATEAU-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.
PLDI-2019-0001R #database #modelling #using: Using active learning to synthesize models of applications that access databases (JS0, MCR), pp. 269–285.
PLDI-2019-AstorgaMSWX #generative: Learning stateful preconditions modulo a test generator (AA, PM, SS, SW, TX0), pp. 775–787.
PLDI-2019-EberhardtSRV #alias #api #specification: Unsupervised learning of API aliasing specifications (JE, SS, VR, MTV), pp. 745–759.
PLDI-2019-ZhuXMJ #framework #induction #synthesis: An inductive synthesis framework for verifiable reinforcement learning (HZ0, ZX, SM, SJ), pp. 686–701.
POPL-2019-AlonZLY #distributed #named: code2vec: learning distributed representations of code (UA0, MZ, OL, EY), p. 29.
SAS-2019-NeiderS0M #algorithm #invariant #named: Sorcar: Property-Driven Algorithms for Learning Conjunctive Invariants (DN, SS, PG0, PM), pp. 323–346.
ASE-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.
ASE-2019-Hu0XY0Z #framework #mutation testing #testing: DeepMutation++: A Mutation Testing Framework for Deep Learning Systems (QH, LM0, XX, BY, YL0, JZ), pp. 1158–1161.
ASE-2019-NejadgholiY #approximate #case study #library #testing: A Study of Oracle Approximations in Testing Deep Learning Libraries (MN, JY0), pp. 785–796.
ASE-2019-SaifullahAR #api: Learning from Examples to Find Fully Qualified Names of API Elements in Code Snippets (CMKS, MA, CKR), pp. 243–254.
ASE-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.
ASE-2019-ZhangC #adaptation #approach #modelling #named: Apricot: A Weight-Adaptation Approach to Fixing Deep Learning Models (HZ, WKC), pp. 376–387.
ASE-2019-ZhangYFSL0 #modelling #named #visualisation: NeuralVis: Visualizing and Interpreting Deep Learning Models (XZ, ZY, YF0, QS, JL, ZC0), pp. 1106–1109.
ASE-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-FSE-2019-BuiYJ #api #named: SAR: learning cross-language API mappings with little knowledge (NDQB, YY, LJ), pp. 796–806.
ESEC-FSE-2019-CambroneroLKS0 #code search: When deep learning met code search (JC, HL, SK, KS, SC0), pp. 964–974.
ESEC-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-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-FSE-2019-IslamNPR #debugging: A comprehensive study on deep learning bug characteristics (MJI, GN, RP, HR), pp. 510–520.
ESEC-FSE-2019-Kwiatkowska #robust #safety: Safety and robustness for deep learning with provable guarantees (keynote) (MK), p. 2.
ESEC-FSE-2019-MesbahRJGA #compilation #fault #named: DeepDelta: learning to repair compilation errors (AM, AR, EJ, NG, EA), pp. 925–936.
ESEC-FSE-2019-WuJYBSPX #grammar inference #named: REINAM: reinforcement learning for input-grammar inference (ZW, EJ, WY0, OB, DS, JP, TX0), pp. 488–498.
ESEC-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.
ASPLOS-2019-ChoOPJL #named: FA3C: FPGA-Accelerated Deep Reinforcement Learning (HC, PO, JP, WJ, JL), pp. 499–513.
ASPLOS-2019-SivathanuCSZ #named #predict: Astra: Exploiting Predictability to Optimize Deep Learning (MS, TC, SSS, LZ), pp. 909–923.
CASE-2019-AyoobiCVV #using: Handling Unforeseen Failures Using Argumentation-Based Learning (HA, MC0, RV, BV), pp. 1699–1704.
CASE-2019-CronrathAL: Enhancing Digital Twins through Reinforcement Learning (CC, ARA, BL), pp. 293–298.
CASE-2019-FarooquiF #modelling #synthesis #using: Synthesis of Supervisors for Unknown Plant Models Using Active Learning (AF, MF), pp. 502–508.
CASE-2019-FoxBSG #automation #multi: Multi-Task Hierarchical Imitation Learning for Home Automation (RF, RB, IS, KG), pp. 1–8.
CASE-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.
CASE-2019-GaoZ0 #behaviour #modelling #navigation: Modeling Socially Normative Navigation Behaviors from Demonstrations with Inverse Reinforcement Learning (XG, XZ, MT0), pp. 1333–1340.
CASE-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.
CASE-2019-Huang0C #policy: Machine Preventive Replacement Policy for Serial Production Lines Based on Reinforcement Learning (JH0, QC0, NC), pp. 523–528.
CASE-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.
CASE-2019-KazmiNVRC #detection #recognition #using: Vehicle tire (tyre) detection and text recognition using deep learning (WK, IN, GV, PR, AC), pp. 1074–1079.
CASE-2019-LaiL #detection #using: Video-Based Windshield Rain Detection and Wiper Control Using Holistic-View Deep Learning (CCL, CHGL), pp. 1060–1065.
CASE-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.
CASE-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.
CASE-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.
CASE-2019-QianAX0 #performance: Improved Production Performance Through Manufacturing System Learning (YQ, JA, GX, QC0), pp. 517–522.
CASE-2019-RazaL #approach #multi #policy: Constructive Policy: Reinforcement Learning Approach for Connected Multi-Agent Systems (SJAR, ML), pp. 257–262.
CASE-2019-ShkorutaCMR: Iterative learning control for power profile shaping in selective laser melting (AS, WC, SM, SR), pp. 655–660.
CASE-2019-SoniGAS #hybrid #named: HMC: A Hybrid Reinforcement Learning Based Model Compression for Healthcare Applications (RS, JG, GA, VRS), pp. 146–151.
CASE-2019-WangY0 #approach #monitoring: A Deep Learning Approach for Heating and Cooling Equipment Monitoring (YW, CY, WS0), pp. 228–234.
CASE-2019-WuZQX #precise: Deep Reinforcement Learning of Robotic Precision Insertion Skill Accelerated by Demonstrations (XW, DZ, FQ, DX), pp. 1651–1656.
CASE-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.
CASE-2019-XuMZLKZ #adaptation #word: An Adaptive Wordpiece Language Model for Learning Chinese Word Embeddings (BX, LM, LZ, HL, QK, MZ), pp. 812–817.
CASE-2019-YangLYK #classification #realtime: Investigation of Deep Learning for Real-Time Melt Pool Classification in Additive Manufacturing (ZY, YL, HY, SK), pp. 640–647.
CASE-2019-ZhangCZXL #detection #fault: Weld Defect Detection Based on Deep Learning Method (HZ, ZC, CZ, JX, XL), pp. 1574–1579.
CASE-2019-ZhangLGWL #algorithm #classification #taxonomy: A Shapelet Dictionary Learning Algorithm for Time Series Classification (JZ, XL, LG0, LW, GL), pp. 299–304.
CASE-2019-ZhangLWGG #fault #network #using: Fault Diagnosis Using Unsupervised Transfer Learning Based on Adversarial Network (ZZ, XL, LW, LG0, YG), pp. 305–310.
CASE-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.
CADE-2019-ChenWAZKZ #named: NIL: Learning Nonlinear Interpolants (MC, JW0, JA, BZ, DK, NZ), pp. 178–196.
CADE-2019-FioriW #modelling: SCL Clause Learning from Simple Models (AF, CW), pp. 233–249.
ICST-2019-KooS0B #automation #generative #named #testing #worst-case: PySE: Automatic Worst-Case Test Generation by Reinforcement Learning (JK, CS, MK0, SB), pp. 136–147.
ICST-2019-WangWZK #alloy: Learning to Optimize the Alloy Analyzer (WW, KW, MZ, SK), pp. 228–239.
ICST-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.
ICTSS-2019-ArcainiGR #regular expression #testing: Regular Expression Learning with Evolutionary Testing and Repair (PA, AG, ER), pp. 22–40.
TAP-2019-AichernigPSW #case study #predict #testing: Predicting and Testing Latencies with Deep Learning: An IoT Case Study (BKA, FP, RS, AW), pp. 93–111.
TAP-2019-PetrenkoA #communication #state machine: Learning Communicating State Machines (AP, FA), pp. 112–128.
JCDL-2018-ColeASSCJ #design #framework #platform #research: Designing a Research Platform for Engaged Learning (NC, AAR, RS, CES, SC, RJ), pp. 315–316.
JCDL-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.
EDM-2018-AguerrebereCW #deployment #online #process #student: Estimating the Treatment Effect of New Device Deployment on Uruguayan Students' Online Learning Activity (CA, CC, JW).
EDM-2018-AkramMWMBL #assessment #game studies: Improving Stealth Assessment in Game-based Learning with LSTM-based Analytics (BA, WM, ENW, BWM, KB, JCL).
EDM-2018-CarvalhoGMK #online #process: Analyzing the relative learning benefits of completing required activities and optional readings in online courses (PFC, MG, BM, KK).
EDM-2018-ChenLCBC #analysis #behaviour #scalability: Behavioral Analysis at Scale: Learning Course Prerequisite Structures from Learner Clickstreams (WC, ASL, DC, CGB, MC).
EDM-2018-ChopraG #mining: Job Description Mining to Understand Work-Integrated Learning (SC, LG).
EDM-2018-DuDP #analysis #behaviour #named: ELBA: Exceptional Learning Behavior Analysis (XD, WD, MP).
EDM-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).
EDM-2018-KarumbaiahBS #game studies #predict #student: Predicting Quitting in Students Playing a Learning Game (SK, RSB, VJS).
EDM-2018-KimVG #named #performance #predict #student: GritNet: Student Performance Prediction with Deep Learning (BHK, EV, VG).
EDM-2018-MatayoshiGDUC #adaptation #assessment #testing: Forgetting curves and testing effect in an adaptive learning and assessment system (JM, UG, CD, HU, EC).
EDM-2018-RajendranKCLB #behaviour #predict: Predicting Learning by Analyzing Eye-Gaze Data of Reading Behavior (RR, AK, KEC, DTL, GB).
EDM-2018-ReillyRS #collaboration #multi #using: Exploring Collaboration Using Motion Sensors and Multi-Modal Learning Analytics (JMR, MR, BS).
EDM-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).
EDM-2018-SinghSCD #behaviour #modelling #multi #student: Modeling Hint-Taking Behavior and Knowledge State of Students with Multi-Task Learning (HS, SKS, RC, PD).
EDM-2018-TranLCGSBM #design #documentation #generative: Document Chunking and Learning Objective Generation for Instruction Design (KNT, JHL, DC, UG, BS, CJB, MKM).
EDM-2018-WinchellMLGP #predict #student: Textbook annotations as an early predictor of student learning (AW, MM, ASL, PG, HP).
ICPC-2018-LiNJWHW #behaviour #evolution #named: Logtracker: learning log revision behaviors proactively from software evolution history (SL, XN, ZJ, JW, HH, TW0), pp. 178–188.
ICPC-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.
MSR-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.
MSR-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.
MSR-2018-TufanoWBPWP08 #source code: Deep learning similarities from different representations of source code (MT, CW, GB, MDP, MW, DP), pp. 542–553.
MSR-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.
SANER-2018-FakhouryANKA #detection #question #smell: Keep it simple: Is deep learning good for linguistic smell detection? (SF, VA, CN, FK, GA), pp. 602–611.
SANER-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.
FM-2018-AkazakiLYDH #cyber-physical #using: Falsification of Cyber-Physical Systems Using Deep Reinforcement Learning (TA, SL, YY, YD, JH), pp. 456–465.
SEFM-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.
AIIDE-2018-LeeTZXDA #architecture #composition: Modular Architecture for StarCraft II with Deep Reinforcement Learning (DL, HT, JOZ, HX, TD, PA), pp. 187–193.
AIIDE-2018-PackardO #case study #user study: A User Study on Learning from Human Demonstration (BP, SO), pp. 208–214.
CIG-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.
CIG-2018-AungBDCKYW #dataset #predict #scalability: Predicting Skill Learning in a Large, Longitudinal MOBA Dataset (MA, VB, AD, PIC, AVK, CY, ARW), pp. 1–7.
CIG-2018-BulitkoD #heuristic #realtime: Anxious Learning in Real-Time Heuristic Search (VB, KD), pp. 1–4.
CIG-2018-DockhornA #approximate #game studies #video: Forward Model Approximation for General Video Game Learning (AD, DA), pp. 1–8.
CIG-2018-GlavinM #experience #using: Skilled Experience Catalogue: A Skill-Balancing Mechanism for Non-Player Characters using Reinforcement Learning (FGG, MGM), pp. 1–8.
CIG-2018-GudmundssonEPNP: Human-Like Playtesting with Deep Learning (SFG, PE, EP, AN, SP, BK, RM, LC), pp. 1–8.
CIG-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.
CIG-2018-JustesenR #automation #education: Automated Curriculum Learning by Rewarding Temporally Rare Events (NJ, SR), pp. 1–8.
CIG-2018-KaczmarekP #interactive #motivation: Promotion of Learning Motivation through Individualization of Learner-Game Interaction (SK, SP), pp. 1–8.
CIG-2018-KowalskiK #regular expression: Regular Language Inference for Learning Rules of Simplified Boardgames (JK, AK), pp. 1–8.
CIG-2018-ShaoZLZ: Learning Battles in ViZDoom via Deep Reinforcement Learning (KS, DZ, NL, YZ), pp. 1–4.
CIG-2018-SpyrouVPAL #personalisation: Exploiting IoT Technologies for Personalized Learning (ES, NV, AP, SA, HCL), pp. 1–8.
CIG-2018-SwiechowskiTJ #algorithm: Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms (MS, TT, AJ), pp. 1–8.
CIG-2018-TavaresC #game studies #realtime: Tabular Reinforcement Learning in Real-Time Strategy Games via Options (ART, LC), pp. 1–8.
CIG-2018-TorradoBT0P #game studies #video: Deep Reinforcement Learning for General Video Game AI (RRT, PB, JT, JL0, DPL), pp. 1–8.
CIG-2018-WoofC #game studies #network: Learning to Play General Video-Games via an Object Embedding Network (WW, KC), pp. 1–8.
CIG-2018-YangO #evaluation #game studies #independence #realtime: Learning Map-Independent Evaluation Functions for Real-Time Strategy Games (ZY, SO), pp. 1–7.
DiGRA-2018-RichardMA #collaboration #contest: Collegiate eSports as Learning Ecologies: Investigating Collaborative Learning and Cognition During Competitions (GTR, ZAM, RWA).
DiGRA-2018-Wu #education #game studies #video: Video Games, Learning, and the Shifting Educational Landscape (HAW).
FDG-2018-Maureira #game studies #named #tool support: CURIO: a game-based learning toolkit for fostering curiosity (MAGM), p. 6.
VS-Games-2018-KutunS #game studies: Rallye Game: Learning by Playing with Racing Cars (BK, WS), pp. 1–2.
VS-Games-2018-Perez-ColadoRFM #game studies #multi: Multi-Level Game Learning Analytics for Serious Games (IJPC, DCR, MFM, IMO, BFM), pp. 1–4.
VS-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.
CIKM-2018-0013H #consistency #interactive #modelling #multi: Interactions Modeling in Multi-Task Multi-View Learning with Consistent Task Diversity (XL0, JH), pp. 853–861.
CIKM-2018-AiMLC #rank #theory and practice: Unbiased Learning to Rank: Theory and Practice (QA, JM, YL, WBC), pp. 2305–2306.
CIKM-2018-BiessmannSSSL: “Deep” Learning for Missing Value Imputationin Tables with Non-Numerical Data (FB, DS, SS, PS, DL), pp. 2017–2025.
CIKM-2018-DaveZHAK #approach #recommendation #representation: A Combined Representation Learning Approach for Better Job and Skill Recommendation (VSD, BZ, MAH, KA, MK), pp. 1997–2005.
CIKM-2018-DingTZ #generative #graph: Semi-supervised Learning on Graphs with Generative Adversarial Nets (MD, JT, JZ), pp. 913–922.
CIKM-2018-FerroLM0 #continuation #education #rank: Continuation Methods and Curriculum Learning for Learning to Rank (NF0, CL, MM, RP0), pp. 1523–1526.
CIKM-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.
CIKM-2018-JinSLGWZ #multi #realtime: Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising (JJ, CS, HL, KG, JW0, WZ0), pp. 2193–2201.
CIKM-2018-KimLCCK #comprehension #scheduling: Learning User Preferences and Understanding Calendar Contexts for Event Scheduling (DK, JL, DC, JC, JK), pp. 337–346.
CIKM-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.
CIKM-2018-LiuZHL #representation #visual notation: Adversarial Learning of Answer-Related Representation for Visual Question Answering (YL, XZ0, FH, ZL), pp. 1013–1022.
CIKM-2018-LoyolaGS #debugging #locality #rank: Bug Localization by Learning to Rank and Represent Bug Inducing Changes (PL, KG, FS), pp. 657–665.
CIKM-2018-LuoWHYZ #segmentation #semantics: Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning (YL, ZW, ZH, YY0, CZ), pp. 237–246.
CIKM-2018-MedinaVY #online #testing: Online Learning for Non-Stationary A/B Tests (AMM, SV, DY), pp. 317–326.
CIKM-2018-MelidisSN: Learning under Feature Drifts in Textual Streams (DPM, MS, EN), pp. 527–536.
CIKM-2018-MoraesPH #process: Contrasting Search as a Learning Activity with Instructor-designed Learning (FM, SRP, CH), pp. 167–176.
CIKM-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.
CIKM-2018-OhSL #graph #multi: Knowledge Graph Completion by Context-Aware Convolutional Learning with Multi-Hop Neighborhoods (BO, SS, KHL), pp. 257–266.
CIKM-2018-OosterhuisR #online #rank: Differentiable Unbiased Online Learning to Rank (HO, MdR), pp. 1293–1302.
CIKM-2018-PandeyKS #recommendation #using: Recommending Serendipitous Items using Transfer Learning (GP0, DK, AS), pp. 1771–1774.
CIKM-2018-PauleMMO #fine-grained #twitter: Learning to Geolocalise Tweets at a Fine-Grained Level (JDGP, YM, CM, IO), pp. 1675–1678.
CIKM-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.
CIKM-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.
CIKM-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.
CIKM-2018-SongZWTZJC #graph #named #rank: TGNet: Learning to Rank Nodes in Temporal Graphs (QS, BZ, YW, LAT, HZ, GJ, HC), pp. 97–106.
CIKM-2018-SuLK #distributed #hybrid #metric: Communication-Efficient Distributed Deep Metric Learning with Hybrid Synchronization (YS, MRL, IK), pp. 1463–1472.
CIKM-2018-WuCYWTZXG: Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising (DW, XC, XY, HW, QT, XZ, JX, KG), pp. 1443–1451.
CIKM-2018-WuLZ #retrieval #semantics #taxonomy: Joint Dictionary Learning and Semantic Constrained Latent Subspace Projection for Cross-Modal Retrieval (JW, ZL, HZ), pp. 1663–1666.
CIKM-2018-WuWL #classification #multi #sentiment: Imbalanced Sentiment Classification with Multi-Task Learning (FW, CW, JL), pp. 1631–1634.
CIKM-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.
CIKM-2018-WuZA #classification #graph: A Quest for Structure: Jointly Learning the Graph Structure and Semi-Supervised Classification (XW, LZ, LA), pp. 87–96.
CIKM-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.
CIKM-2018-YangS #multi #named #performance: FALCON: A Fast Drop-In Replacement of Citation KNN for Multiple Instance Learning (SY, XS), pp. 67–76.
CIKM-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.
CIKM-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.
CIKM-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.
ECIR-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.
ECIR-2018-AgrawalA #detection #multi #platform #social #social media: Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms (SA, AA), pp. 141–153.
ECIR-2018-HerreraPP #microblog #retrieval: Learning to Leverage Microblog Information for QA Retrieval (JMH, BP, DP), pp. 507–520.
ECIR-2018-Jalan0V #classification #using: Medical Forum Question Classification Using Deep Learning (RSJ, MG0, VV), pp. 45–58.
ECIR-2018-McDonaldMO #overview #perspective: Active Learning Strategies for Technology Assisted Sensitivity Review (GM, CM, IO), pp. 439–453.
ECIR-2018-NiculaRR #multi: Improving Deep Learning for Multiple Choice Question Answering with Candidate Contexts (BN, SR, TR), pp. 678–683.
ECIR-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.
ECIR-2018-WilkensZF #documentation #ranking: Document Ranking Applied to Second Language Learning (RW, LZ, CF), pp. 618–624.
ICML-2018-0001JADYD: Hierarchical Imitation and Reinforcement Learning (HML0, NJ, AA, MD, YY, HDI), pp. 2923–2932.
ICML-2018-AbelALL #abstraction: State Abstractions for Lifelong Reinforcement Learning (DA, DA, LL, MLL), pp. 10–19.
ICML-2018-AbelJGKL #policy: Policy and Value Transfer in Lifelong Reinforcement Learning (DA, YJ, SYG, GDK, MLL), pp. 20–29.
ICML-2018-AchlioptasDMG #3d #generative #modelling: Learning Representations and Generative Models for 3D Point Clouds (PA, OD, IM, LJG), pp. 40–49.
ICML-2018-AlaaS18a #automation #kernel #modelling #named #optimisation: AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning (AMA, MvdS), pp. 139–148.
ICML-2018-AlmahairiRSBC: Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data (AA, SR, AS, PB, ACC), pp. 195–204.
ICML-2018-AsadiML #modelling: Lipschitz Continuity in Model-based Reinforcement Learning (KA, DM, MLL), pp. 264–273.
ICML-2018-BalcanDSV #branch: Learning to Branch (MFB, TD, TS, EV), pp. 353–362.
ICML-2018-BalestrieroCGB: Spline Filters For End-to-End Deep Learning (RB, RC, HG, RGB), pp. 373–382.
ICML-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.
ICML-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.
ICML-2018-BelkinMM #kernel: To Understand Deep Learning We Need to Understand Kernel Learning (MB, SM, SM), pp. 540–548.
ICML-2018-CalandrielloKLV #graph #scalability: Improved Large-Scale Graph Learning through Ridge Spectral Sparsification (DC, IK, AL, MV), pp. 687–696.
ICML-2018-CaoGWSHT #coordination: Adversarial Learning with Local Coordinate Coding (JC, YG, QW, CS, JH, MT), pp. 706–714.
ICML-2018-CharlesP #algorithm: Stability and Generalization of Learning Algorithms that Converge to Global Optima (ZBC, DSP), pp. 744–753.
ICML-2018-Chatterjee: Learning and Memorization (SC), pp. 754–762.
ICML-2018-ChengDH #rank: Extreme Learning to Rank via Low Rank Assumption (MC, ID, CJH), pp. 950–959.
ICML-2018-ChenLW #scalability #using: Scalable Bilinear Learning Using State and Action Features (YC, LL0, MW), pp. 833–842.
ICML-2018-ChenMS: Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations (TC0, MRM, YS), pp. 853–862.
ICML-2018-ChenSWJ: Learning to Explain: An Information-Theoretic Perspective on Model Interpretation (JC, LS, MJW, MIJ), pp. 882–891.
ICML-2018-ChenXG #multi: End-to-End Learning for the Deep Multivariate Probit Model (DC, YX, CPG), pp. 931–940.
ICML-2018-Chierichetti0T #multi: Learning a Mixture of Two Multinomial Logits (FC, RK0, AT), pp. 960–968.
ICML-2018-ChowNG #consistency: Path Consistency Learning in Tsallis Entropy Regularized MDPs (YC, ON, MG), pp. 978–987.
ICML-2018-Co-ReyesLGEAL #self: Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings (JDCR, YL, AG0, BE, PA, SL), pp. 1008–1017.
ICML-2018-ColasSO #algorithm #named: GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms (CC, OS, PYO), pp. 1038–1047.
ICML-2018-CorneilGB #performance: Efficient ModelBased Deep Reinforcement Learning with Variational State Tabulation (DSC, WG, JB), pp. 1057–1066.
ICML-2018-CortesDGMY #online: Online Learning with Abstention (CC, GD, CG, MM, SY), pp. 1067–1075.
ICML-2018-CzarneckiJJHTHO #education: Mix & Match Agent Curricula for Reinforcement Learning (WMC, SMJ, MJ, LH, YWT, NH, SO, RP), pp. 1095–1103.
ICML-2018-DabneyOSM #network: Implicit Quantile Networks for Distributional Reinforcement Learning (WD, GO, DS, RM), pp. 1104–1113.
ICML-2018-DaiKDSS #algorithm #graph: Learning Steady-States of Iterative Algorithms over Graphs (HD, ZK, BD, AJS, LS), pp. 1114–1122.
ICML-2018-DaiS0XHLCS #approximate #convergence #named: SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation (BD, AS, LL0, LX, NH, ZL0, JC, LS), pp. 1133–1142.
ICML-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.
ICML-2018-DibangoyeB #distributed: Learning to Act in Decentralized Partially Observable MDPs (JSD, OB), pp. 1241–1250.
ICML-2018-DietterichTC: Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning (TGD, GT, ZC), pp. 1261–1269.
ICML-2018-DimakopoulouR #concurrent #coordination: Coordinated Exploration in Concurrent Reinforcement Learning (MD, BVR), pp. 1270–1278.
ICML-2018-EfroniDSM #approach: Beyond the One-Step Greedy Approach in Reinforcement Learning (YE, GD, BS, SM), pp. 1386–1395.
ICML-2018-FalahatgarJOPR #ranking: The Limits of Maxing, Ranking, and Preference Learning (MF, AJ, AO, VP, VR), pp. 1426–1435.
ICML-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.
ICML-2018-FlorensaHGA #automation #generative: Automatic Goal Generation for Reinforcement Learning Agents (CF, DH, XG, PA), pp. 1514–1523.
ICML-2018-FruitPLO #performance: Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning (RF, MP, AL, RO), pp. 1573–1581.
ICML-2018-GaneaBH: Hyperbolic Entailment Cones for Learning Hierarchical Embeddings (OEG, GB, TH), pp. 1632–1641.
ICML-2018-GaninKBEV #image #source code #using: Synthesizing Programs for Images using Reinforced Adversarial Learning (YG, TK, IB, SMAE, OV), pp. 1652–1661.
ICML-2018-GaoW #network #parallel: Parallel Bayesian Network Structure Learning (TG, DW), pp. 1671–1680.
ICML-2018-GarciaCEd #predict: Structured Output Learning with Abstention: Application to Accurate Opinion Prediction (AG0, CC, SE, FdB), pp. 1681–1689.
ICML-2018-Georgogiannis #fault #taxonomy: The Generalization Error of Dictionary Learning with Moreau Envelopes (AG), pp. 1710–1718.
ICML-2018-GhassamiSKB #design #empirical: Budgeted Experiment Design for Causal Structure Learning (AG, SS, NK, EB), pp. 1719–1728.
ICML-2018-GhoshalH #modelling #polynomial #predict: Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time (AG, JH), pp. 1749–1757.
ICML-2018-GhoshYD #network: Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors (SG, JY, FDV), pp. 1739–1748.
ICML-2018-GilraG #network: Non-Linear Motor Control by Local Learning in Spiking Neural Networks (AG, WG), pp. 1768–1777.
ICML-2018-GoelKM: Learning One Convolutional Layer with Overlapping Patches (SG, ARK, RM), pp. 1778–1786.
ICML-2018-GroverAGBE #multi #policy: Learning Policy Representations in Multiagent Systems (AG, MAS, JKG, YB, HE), pp. 1797–1806.
ICML-2018-GuezWASVWMS: Learning to Search with MCTSnets (AG, TW, IA, KS, OV, DW, RM, DS), pp. 1817–1826.
ICML-2018-HaarnojaHAL #policy: Latent Space Policies for Hierarchical Reinforcement Learning (TH, KH, PA, SL), pp. 1846–1855.
ICML-2018-HaarnojaZAL #probability: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (TH, AZ, PA, SL), pp. 1856–1865.
ICML-2018-HammN #optimisation #performance: K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning (JH, YKN), pp. 1876–1884.
ICML-2018-HashemiSSALCKR #data access #memory management: Learning Memory Access Patterns (MH, KS, JAS, GA, HL, JC, CK, PR), pp. 1924–1933.
ICML-2018-HeinonenYMIL #modelling #process: Learning unknown ODE models with Gaussian processes (MH, CY, HM, JI, HL), pp. 1964–1973.
ICML-2018-Huang0S #markov #modelling #topic: Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling (KH, XF0, NDS), pp. 2073–2082.
ICML-2018-HuangA0S #using: Learning Deep ResNet Blocks Sequentially using Boosting Theory (FH, JTA, JL0, RES), pp. 2063–2072.
ICML-2018-HuNSS #classification #question #robust: Does Distributionally Robust Supervised Learning Give Robust Classifiers? (WH, GN, IS, MS), pp. 2034–2042.
ICML-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.
ICML-2018-IglZLWW: Deep Variational Reinforcement Learning for POMDPs (MI, LMZ, TAL, FW, SW), pp. 2122–2131.
ICML-2018-IlseTW #multi: Attention-based Deep Multiple Instance Learning (MI, JMT, MW), pp. 2132–2141.
ICML-2018-JaffeWCKN #approach #modelling: Learning Binary Latent Variable Models: A Tensor Eigenpair Approach (AJ, RW, SC, YK, BN), pp. 2201–2210.
ICML-2018-JawanpuriaM #framework #matrix #rank: A Unified Framework for Structured Low-rank Matrix Learning (PJ, BM), pp. 2259–2268.
ICML-2018-JeongS #performance: Efficient end-to-end learning for quantizable representations (YJ, HOS), pp. 2269–2278.
ICML-2018-JiangEL: Feedback-Based Tree Search for Reinforcement Learning (DRJ, EE, HL), pp. 2289–2298.
ICML-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.
ICML-2018-JinKL18a: Regret Minimization for Partially Observable Deep Reinforcement Learning (PHJ, KK, SL), pp. 2347–2356.
ICML-2018-Johnson0 #functional #generative #modelling: Composite Functional Gradient Learning of Generative Adversarial Models (RJ, TZ0), pp. 2376–2384.
ICML-2018-KalimerisSSW #using: Learning Diffusion using Hyperparameters (DK, YS, KS, UW), pp. 2425–2433.
ICML-2018-KalyanLKB #multi: Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations (AK, SL, AK, DB), pp. 2454–2463.
ICML-2018-KamnitsasCFWTRG #clustering: Semi-Supervised Learning via Compact Latent Space Clustering (KK, DCC, LLF, IW, RT, DR, BG, AC, AVN), pp. 2464–2473.
ICML-2018-KaplanisSC: Continual Reinforcement Learning with Complex Synapses (CK, MS, CC), pp. 2502–2511.
ICML-2018-KatharopoulosF: Not All Samples Are Created Equal: Deep Learning with Importance Sampling (AK, FF), pp. 2530–2539.
ICML-2018-KearnsNRW: Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness (MJK, SN, AR0, ZSW), pp. 2569–2577.
ICML-2018-KennamerKIS #classification #named: ContextNet: Deep learning for Star Galaxy Classification (NK, DK, ATI, FJSL), pp. 2587–2595.
ICML-2018-KhanNTLGS #performance #scalability: Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam (MEK, DN, VT, WL, YG, AS), pp. 2616–2625.
ICML-2018-KuleshovFE #nondeterminism #using: Accurate Uncertainties for Deep Learning Using Calibrated Regression (VK, NF, SE), pp. 2801–2809.
ICML-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.
ICML-2018-LeeYH #multi #symmetry: Deep Asymmetric Multi-task Feature Learning (HL, EY, SJH), pp. 2962–2970.
ICML-2018-LehtinenMHLKAA #image #named: Noise2Noise: Learning Image Restoration without Clean Data (JL, JM, JH, SL, TK, MA, TA), pp. 2971–2980.
ICML-2018-LiangLNMFGGJS #abstraction #distributed #named: RLlib: Abstractions for Distributed Reinforcement Learning (EL, RL, RN, PM, RF, KG, JG, MIJ, IS), pp. 3059–3068.
ICML-2018-LiaoC18a #approach #matrix #random: The Dynamics of Learning: A Random Matrix Approach (ZL, RC), pp. 3078–3087.
ICML-2018-LiGD #bias #induction #network: Explicit Inductive Bias for Transfer Learning with Convolutional Networks (XL0, YG, FD), pp. 2830–2839.
ICML-2018-LiH #approach #network: An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks (QL, SH), pp. 2991–3000.
ICML-2018-LinC #distributed #multi #probability: Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods (JL, VC), pp. 3098–3107.
ICML-2018-LongLMD #named: PDE-Net: Learning PDEs from Data (ZL, YL, XM, BD0), pp. 3214–3222.
ICML-2018-LuoSZLZW: End-to-end Active Object Tracking via Reinforcement Learning (WL, PS, FZ, WL0, TZ0, YW), pp. 3292–3301.
ICML-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.
ICML-2018-MadrasCPZ: Learning Adversarially Fair and Transferable Representations (DM, EC, TP, RSZ), pp. 3381–3390.
ICML-2018-MalikPFHRD #performance: An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning (DM, MP, JFF, DHM, SJR, ADD), pp. 3391–3399.
ICML-2018-MaWHZEXWB: Dimensionality-Driven Learning with Noisy Labels (XM, YW0, MEH, SZ0, SME, STX, SNRW, JB0), pp. 3361–3370.
ICML-2018-MeyersonM #multi #pseudo: Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing-and Back (EM, RM), pp. 3508–3517.
ICML-2018-MhamdiGR #distributed: The Hidden Vulnerability of Distributed Learning in Byzantium (EMEM, RG, SR), pp. 3518–3527.
ICML-2018-MishchenkoIMA #algorithm #distributed: A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning (KM, FI, JM, MRA), pp. 3584–3592.
ICML-2018-Nachum0TS #policy: Smoothed Action Value Functions for Learning Gaussian Policies (ON, MN0, GT, DS), pp. 3689–3697.
ICML-2018-NguyenSH #on the: On Learning Sparsely Used Dictionaries from Incomplete Samples (TVN, AS, CH), pp. 3766–3775.
ICML-2018-NickelK #geometry: Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry (MN, DK), pp. 3776–3785.
ICML-2018-OglicG #kernel: Learning in Reproducing Kernel Krein Spaces (DO, TG0), pp. 3856–3864.
ICML-2018-OhGSL #self: Self-Imitation Learning (JO, YG, SS, HL), pp. 3875–3884.
ICML-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.
ICML-2018-OsamaZS #locality #modelling #streaming: Learning Localized Spatio-Temporal Models From Streaming Data (MO, DZ, TBS), pp. 3924–3932.
ICML-2018-Oymak #network: Learning Compact Neural Networks with Regularization (SO), pp. 3963–3972.
ICML-2018-PaassenGMH #adaptation #distance #edit distance: Tree Edit Distance Learning via Adaptive Symbol Embeddings (BP, CG, AM, BH), pp. 3973–3982.
ICML-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.
ICML-2018-PanS #predict: Learning to Speed Up Structured Output Prediction (XP, VS), pp. 3993–4002.
ICML-2018-PanZD #analysis: Theoretical Analysis of Image-to-Image Translation with Adversarial Learning (XP, MZ, DD), pp. 4003–4012.
ICML-2018-ParascandoloKRS #independence: Learning Independent Causal Mechanisms (GP, NK, MRC, BS), pp. 4033–4041.
ICML-2018-PardoTLK: Time Limits in Reinforcement Learning (FP, AT, VL, PK), pp. 4042–4051.
ICML-2018-PearceBZN #approach #predict: High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach (TP, AB, MZ, AN), pp. 4072–4081.
ICML-2018-PretoriusKK #linear: Learning Dynamics of Linear Denoising Autoencoders (AP, SK, HK), pp. 4138–4147.
ICML-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.
ICML-2018-RaeDDL #parametricity #performance: Fast Parametric Learning with Activation Memorization (JWR, CD, PD, TPL), pp. 4225–4234.
ICML-2018-RaghuIAKLK #game studies #question: Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games? (MR, AI, JA, RK, QVL, JMK), pp. 4235–4243.
ICML-2018-RaileanuDSF #modelling #multi #using: Modeling Others using Oneself in Multi-Agent Reinforcement Learning (RR, ED, AS, RF), pp. 4254–4263.
ICML-2018-RashidSWFFW #multi #named: QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning (TR, MS, CSdW, GF, JNF, SW), pp. 4292–4301.
ICML-2018-RavuriMRV #generative #modelling: Learning Implicit Generative Models with the Method of Learned Moments (SVR, SM, MR, OV), pp. 4311–4320.
ICML-2018-RenZYU #robust: Learning to Reweight Examples for Robust Deep Learning (MR, WZ, BY, RU), pp. 4331–4340.
ICML-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.
ICML-2018-RobertsERHE #music: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music (AR, JHE, CR, CH, DE), pp. 4361–4370.
ICML-2018-RosenfeldBGS #combinator: Learning to Optimize Combinatorial Functions (NR, EB, AG, YS), pp. 4371–4380.
ICML-2018-SahooLM #equation: Learning Equations for Extrapolation and Control (SSS, CHL, GM), pp. 4439–4447.
ICML-2018-SchmitJ: Learning with Abandonment (SS, RJ), pp. 4516–4524.
ICML-2018-SchwabKMMSK #multi: Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care (PS, EK, CM, DJM, CS, WK), pp. 4525–4534.
ICML-2018-Schwarz0LGTPH #framework #scalability: Progress & Compress: A scalable framework for continual learning (JS, WC0, JL, AGB, YWT, RP, RH), pp. 4535–4544.
ICML-2018-ShazeerS #adaptation #memory management #named #sublinear: Adafactor: Adaptive Learning Rates with Sublinear Memory Cost (NS, MS), pp. 4603–4611.
ICML-2018-SheldonWS #automation #difference #integer #modelling: Learning in Integer Latent Variable Models with Nested Automatic Differentiation (DS, KW, DS), pp. 4622–4630.
ICML-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.
ICML-2018-ShiarlisWSWP #composition #named: TACO: Learning Task Decomposition via Temporal Alignment for Control (KS, MW, SS, SW, IP), pp. 4661–4670.
ICML-2018-SibliniMK #clustering #multi #performance #random: CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning (WS, FM, PK), pp. 4671–4680.
ICML-2018-SmithHP #policy: An Inference-Based Policy Gradient Method for Learning Options (MS, HvH, JP), pp. 4710–4719.
ICML-2018-SrinivasJALF #network: Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control (AS, AJ, PA, SL, CF), pp. 4739–4748.
ICML-2018-SroujiZS: Structured Control Nets for Deep Reinforcement Learning (MS, JZ, RS), pp. 4749–4758.
ICML-2018-SunZWZLG #composition #kernel #process: Differentiable Compositional Kernel Learning for Gaussian Processes (SS, GZ, CW, WZ, JL, RBG), pp. 4835–4844.
ICML-2018-SuW: Learning Low-Dimensional Temporal Representations (BS, YW), pp. 4768–4777.
ICML-2018-Talvitie: Learning the Reward Function for a Misspecified Model (ET), pp. 4845–4854.
ICML-2018-ThomasDB: Decoupling Gradient-Like Learning Rules from Representations (PST, CD, EB), pp. 4924–4932.
ICML-2018-TianZZ #named: CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions (KT, TZ, JZ), pp. 4933–4942.
ICML-2018-TirinzoniSPR: Importance Weighted Transfer of Samples in Reinforcement Learning (AT, AS, MP, MR), pp. 4943–4952.
ICML-2018-TrinhDLL #dependence: Learning Longer-term Dependencies in RNNs with Auxiliary Losses (THT, AMD, TL, QVL), pp. 4972–4981.
ICML-2018-TschannenKA #multi #named: StrassenNets: Deep Learning with a Multiplication Budget (MT, AK, AA), pp. 4992–5001.
ICML-2018-TuckerBGTGL: The Mirage of Action-Dependent Baselines in Reinforcement Learning (GT, SB, SG, RET, ZG, SL), pp. 5022–5031.
ICML-2018-TuR #difference #linear #polynomial: Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator (ST, BR), pp. 5012–5021.
ICML-2018-VermaMSKC: Programmatically Interpretable Reinforcement Learning (AV, VM, RS, PK, SC), pp. 5052–5061.
ICML-2018-VogelBC #optimisation #probability #similarity: A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization (RV, AB, SC), pp. 5062–5071.
ICML-2018-WagnerGKM #data type: Semi-Supervised Learning on Data Streams via Temporal Label Propagation (TW, SG, SPK, NM), pp. 5082–5091.
ICML-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.
ICML-2018-WangK #multi: Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations (XW, DK), pp. 5130–5138.
ICML-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.
ICML-2018-WeinshallCA #education #network: Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks (DW, GC, DA), pp. 5235–5243.
ICML-2018-WeiZHY: Transfer Learning via Learning to Transfer (YW, YZ, JH, QY), pp. 5072–5081.
ICML-2018-XiaTTQYL: Model-Level Dual Learning (YX, XT, FT, TQ, NY, TYL), pp. 5379–5388.
ICML-2018-XieWZX #analysis #distance #metric: Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis (PX, WW, YZ, EPX), pp. 5399–5408.
ICML-2018-XieZCC #adaptation #semantics: Learning Semantic Representations for Unsupervised Domain Adaptation (SX, ZZ, LC0, CC), pp. 5419–5428.
ICML-2018-XuCZ #process: Learning Registered Point Processes from Idiosyncratic Observations (HX, LC, HZ), pp. 5439–5448.
ICML-2018-XuLTSKJ #graph #network #representation: Representation Learning on Graphs with Jumping Knowledge Networks (KX, CL, YT, TS, KiK, SJ), pp. 5449–5458.
ICML-2018-XuLZP: Learning to Explore via Meta-Policy Gradient (TX, QL0, LZ, JP0), pp. 5459–5468.
ICML-2018-XuZFLB #semantics: A Semantic Loss Function for Deep Learning with Symbolic Knowledge (JX, ZZ, TF, YL, GVdB), pp. 5498–5507.
ICML-2018-YanCJ: Active Learning with Logged Data (SY, KC, TJ), pp. 5517–5526.
ICML-2018-YangKU #equivalence #graph: Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions (KDY, AK, CU), pp. 5537–5546.
ICML-2018-YangLLZZW #multi: Mean Field Multi-Agent Reinforcement Learning (YY, RL, ML, MZ, WZ0, JW0), pp. 5567–5576.
ICML-2018-YenKYHKR #composition #performance #scalability: Loss Decomposition for Fast Learning in Large Output Spaces (IEHY, SK, FXY, DNHR, SK, PR), pp. 5626–5635.
ICML-2018-YinCRB #distributed #statistics #towards: Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates (DY, YC0, KR, PLB), pp. 5636–5645.
ICML-2018-YonaR #approximate: Probably Approximately Metric-Fair Learning (GY, GNR), pp. 5666–5674.
ICML-2018-ZanetteB #bound #identification #problem: Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs (AZ, EB), pp. 5732–5740.
ICML-2018-ZhangLSD #dependence #fourier: Learning Long Term Dependencies via Fourier Recurrent Units (JZ, YL, ZS, ISD), pp. 5810–5818.
ICML-2018-ZhangYL0B #distributed #multi: Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents (KZ, ZY, HL0, TZ0, TB), pp. 5867–5876.
ICML-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.
ICML-2018-ZhaoDBZ #topic #word: Inter and Intra Topic Structure Learning with Word Embeddings (HZ, LD, WLB, MZ), pp. 5887–5896.
ICPR-2018-AfonsoPSP #classification #using: Improving Optimum- Path Forest Classification Using Unsupervised Manifold Learning (LCSA, DCGP, ANdS, JPP), pp. 560–565.
ICPR-2018-Aldana-LopezCZG #approach #network: Dynamic Learning Rate for Neural Networks: A Fixed-Time Stability Approach (RAL, LECM, JZ, DGG, AC), pp. 1378–1383.
ICPR-2018-BiFW #constraints #metric: Cayley- Klein Metric Learning with Shrinkage-Expansion Constraints (YB, BF, FW), pp. 43–48.
ICPR-2018-CaoCHP #identification #metric: Region-specific Metric Learning for Person Re-identification (MC, CC0, XH, SP), pp. 794–799.
ICPR-2018-CaoGWXW #detection: Gaze-Aided Eye Detection via Appearance Learning (LC, CG, KW, GX, FYW0), pp. 1965–1970.
ICPR-2018-CaoLL0JJC #detection #image: Deep Learning Based Bioresorbable Vascular Scaffolds Detection in IVOCT Images (YC, YL, JL, RZ0, QJ, JJ, YC), pp. 3778–3783.
ICPR-2018-CuiB00JH #graph #hybrid #kernel #network: A Deep Hybrid Graph Kernel Through Deep Learning Networks (LC, LB0, LR0, YW0, YJ0, ERH), pp. 1030–1035.
ICPR-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.
ICPR-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.
ICPR-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.
ICPR-2018-DuCWP #distributed #named #representation: Zone2Vec: Distributed Representation Learning of Urban Zones (JD, YC, YW0, JP), pp. 880–885.
ICPR-2018-EleziTVP #network: Transductive Label Augmentation for Improved Deep Network Learning (IE, AT, SV, MP), pp. 1432–1437.
ICPR-2018-FuGA #detection #scalability: Simultaneous Context Feature Learning and Hashing for Large Scale Loop Closure Detection (ZF, YG, WA), pp. 1689–1694.
ICPR-2018-GaoDS: Discernibility Matrix-Based Ensemble Learning (SG, JD, HS), pp. 952–957.
ICPR-2018-GaolLH0W #automation #multi #predict: Automatic Facial Attractiveness Prediction by Deep Multi-Task Learning (LG, WL, ZH, DH0, YW), pp. 3592–3597.
ICPR-2018-GrelssonF #exponential #linear #network: Improved Learning in Convolutional Neural Networks with Shifted Exponential Linear Units (ShELUs) (BG, MF), pp. 517–522.
ICPR-2018-GuptaMSM #image #order #ranking #similarity: Learning an Order Preserving Image Similarity through Deep Ranking (NG, SM, SS, SM), pp. 1115–1120.
ICPR-2018-HailatK0: Deep Semi-Supervised Learning (ZH, AK, XwC0), pp. 2154–2159.
ICPR-2018-HanXW #generative #multi #network #representation: Learning Multi-view Generator Network for Shared Representation (TH0, XX, YNW), pp. 2062–2068.
ICPR-2018-HanXZL #composition #image #network: Learning Intrinsic Image Decomposition by Deep Neural Network with Perceptual Loss (GH, XX, WSZ, JL), pp. 91–96.
ICPR-2018-HaoDWT #fine-grained #named #representation #retrieval: DeepFirearm: Learning Discriminative Feature Representation for Fine-grained Firearm Retrieval (JH, JD0, WW0, TT), pp. 3335–3340.
ICPR-2018-HeGG #network: Structure Learning of Bayesian Networks by Finding the Optimal Ordering (CCH, XGG, ZgG), pp. 177–182.
ICPR-2018-HuangWDSL #image #lightweight: Lightweight Deep Residue Learning for Joint Color Image Demosaicking and Denoising (TH, FW, WD, GS, XL0), pp. 127–132.
ICPR-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.
ICPR-2018-JiangLSWZW #identification #similarity: Orientation-Guided Similarity Learning for Person Re-identification (NJ, JL, CS, YW, ZZ, WW), pp. 2056–2061.
ICPR-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.
ICPR-2018-LiCQWW #adaptation #network #semantics: Cross-domain Semantic Feature Learning via Adversarial Adaptation Networks (RL, WmC0, SQ, HSW, SW), pp. 37–42.
ICPR-2018-LiL #metric: Riemannian Metric Learning based on Curvature Flow (YL, RL), pp. 806–811.
ICPR-2018-LingLZG #classification #image #network: Semi-Supervised Learning via Convolutional Neural Network for Hyperspectral Image Classification (ZL, XL, WZ, SG), pp. 1–6.
ICPR-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.
ICPR-2018-LiWK18a #framework #image #using: Infrared and Visible Image Fusion using a Deep Learning Framework (HL0, XJW, JK), pp. 2705–2710.
ICPR-2018-LuoZLW #clustering #graph #image: Graph Embedding-Based Ensemble Learning for Image Clustering (XL, LZ0, FL, BW), pp. 213–218.
ICPR-2018-LyuYCZZ #classification #detection: Learning Fixation Point Strategy for Object Detection and Classification (JL0, ZY, DC, YZ, HZ), pp. 2081–2086.
ICPR-2018-MaBCX0 #collaboration #visual notation: Learning Collaborative Model for Visual Tracking (DM, WB, YC, YX, XW0), pp. 2582–2587.
ICPR-2018-MadapanaW #gesture #recognition: Hard Zero Shot Learning for Gesture Recognition (NM, JPW), pp. 3574–3579.
ICPR-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.
ICPR-2018-ManessiR: Learning Combinations of Activation Functions (FM, AR), pp. 61–66.
ICPR-2018-NguyenNSADF #recognition: Meta Transfer Learning for Facial Emotion Recognition (DNT, KN0, SS, IA, DD, CF), pp. 3543–3548.
ICPR-2018-NguyenTL #data-driven #using: Are French Really That Different? Recognizing Europeans from Faces Using Data-Driven Learning (VDN, MT, JL), pp. 2729–2734.
ICPR-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.
ICPR-2018-NiuHSC #named: SynRhythm: Learning a Deep Heart Rate Estimator from General to Specific (XN, HH, SS, XC), pp. 3580–3585.
ICPR-2018-NiuS0 #graph: Enhancing Knowledge Graph Completion with Positive Unlabeled Learning (JN, ZS, WZ0), pp. 296–301.
ICPR-2018-PangDWH: Dynamic Ensemble Active Learning: A Non-Stationary Bandit with Expert Advice (KP, MD, YW, TMH), pp. 2269–2276.
ICPR-2018-PeiFR #multi: Learning with Latent Label Hierarchy from Incomplete Multi-Label Data (YP, XZF, RR), pp. 2075–2080.
ICPR-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.
ICPR-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.
ICPR-2018-RibaFLF #graph #message passing #network: Learning Graph Distances with Message Passing Neural Networks (PR, AF0, JL0, AF), pp. 2239–2244.
ICPR-2018-RoyT #higher-order #using: Learning to Learn Second-Order Back-Propagation for CNNs Using LSTMs (AR, ST), pp. 97–102.
ICPR-2018-RuedaF #process #recognition #representation: Learning Attribute Representation for Human Activity Recognition (FMR, GAF), pp. 523–528.
ICPR-2018-SahaVJ #named: Class2Str: End to End Latent Hierarchy Learning (SS, GV, CVJ), pp. 1000–1005.
ICPR-2018-SiddiquiV0 #approach #recognition: Face Recognition for Newborns, Toddlers, and Pre-School Children: A Deep Learning Approach (SS, MV, RS0), pp. 3156–3161.
ICPR-2018-SuiZYC #detection #framework #novel #recognition: A Novel Integrated Framework for Learning both Text Detection and Recognition (WS, QZ, JY, WC), pp. 2233–2238.
ICPR-2018-SunCWX #coordination #metric #online #parallel #rank: Online Low-Rank Metric Learning via Parallel Coordinate Descent Method (GS, YC, QW0, XX), pp. 207–212.
ICPR-2018-SunZJLWY #adaptation #robust #taxonomy: Robust Discriminative Projective Dictionary Pair Learning by Adaptive Representations (YS, ZZ0, WJ, GL, MW0, SY), pp. 621–626.
ICPR-2018-SunZWJ #behaviour #detection: Weak Supervised Learning Based Abnormal Behavior Detection (XS, SZ, SW, XYJ), pp. 1580–1585.
ICPR-2018-TayanovKS #classification #predict #using: Prediction-based classification using learning on Riemannian manifolds (VT, AK, CYS), pp. 591–596.
ICPR-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.
ICPR-2018-WangHJ #using: Focus on Scene Text Using Deep Reinforcement Learning (HW, SH, LJ), pp. 3759–3765.
ICPR-2018-WangSSL #metric: Nonlinear Metric Learning through Geodesic Interpolation within Lie Groups (ZW, BS, CDS, JL), pp. 898–903.
ICPR-2018-WangWCK #classification #image #metric #multi #set: Multiple Manifolds Metric Learning with Application to Image Set Classification (RW, XJW, KXC, JK), pp. 627–632.
ICPR-2018-WangWL #education #performance: Weakly- and Semi-supervised Faster R-CNN with Curriculum Learning (JW, XW, WL0), pp. 2416–2421.
ICPR-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.
ICPR-2018-WitmerB #classification #image #multi #using: Multi-label Classification of Stem Cell Microscopy Images Using Deep Learning (AW, BB), pp. 1408–1413.
ICPR-2018-WuLCW #multi #semantics: Learning a Hierarchical Latent Semantic Model for Multimedia Data (SHW, YSL, SHC, JCW), pp. 2995–3000.
ICPR-2018-WuYSZ #identification #ranking: Joint Semi-supervised Learning and Re-ranking for Vehicle Re-identification (FW, SY, JSS, BZ), pp. 278–283.
ICPR-2018-XuCG #modelling #multi #random #using: Common Random Subgraph Modeling Using Multiple Instance Learning (TX, DKYC, IG), pp. 1205–1210.
ICPR-2018-XuWK #correlation #representation: Non-negative Subspace Representation Learning Scheme for Correlation Filter Based Tracking (TX, XJW, JK), pp. 1888–1893.
ICPR-2018-XuZL18a #incremental #kernel #linear #online: A Linear Incremental Nyström Method for Online Kernel Learning (SX, XZ, SL), pp. 2256–2261.
ICPR-2018-YangDWL: Masked Label Learning for Optical Flow Regression (GY, ZD, SW, ZL), pp. 1139–1144.
ICPR-2018-YanWSLZ #image #network #using: Image Captioning using Adversarial Networks and Reinforcement Learning (SY, FW, JSS, WL, BZ), pp. 248–253.
ICPR-2018-Ye0 #classification #image #invariant: Rotational Invariant Discriminant Subspace Learning For Image Classification (QY, ZZ0), pp. 1217–1222.
ICPR-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.
ICPR-2018-YuanWXZ #empirical #estimation #multi: Multiple- Instance Learning with Empirical Estimation Guided Instance Selection (LY, XW, HX, LZ), pp. 770–775.
ICPR-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.
ICPR-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.
ICPR-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.
ICPR-2018-ZhangWG #adaptation #multi #representation: Adaptive Latent Representation for Multi-view Subspace Learning (YZ, XW, XG), pp. 1229–1234.
ICPR-2018-ZhangWGWXL #detection #effectiveness #network: An Effective Deep Learning Based Scheme for Network Intrusion Detection (HZ, CQW, SG, ZW, YX, YL), pp. 682–687.
ICPR-2018-ZhaoPL0DWQ #locality #semantics #topic #using: Learning Topics Using Semantic Locality (ZZ, KP, SL, ZL0, CD, YW, QQ), pp. 3710–3715.
ICPR-2018-ZhouL0LL #estimation: Scale and Orientation Aware EPI-Patch Learning for Light Field Depth Estimation (WZ, LL, HZ, AL, LL), pp. 2362–2367.
ICPR-2018-ZhouMMB #detection: Learning Training Samples for Occlusion Edge Detection and Its Application in Depth Ordering Inference (YZ0, JM, AM, XB), pp. 541–546.
ICPR-2018-ZhouWD #online #realtime #robust: Online Learning of Spatial-Temporal Convolution Response for Robust Real-Time Tracking (JZ, RW, JD), pp. 1821–1826.
ICPR-2018-Zhuang0CW #classification #multi: Multi-task Learning of Cascaded CNN for Facial Attribute Classification (NZ, YY0, SC, HW), pp. 2069–2074.
ICPR-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.
ICPR-2018-ZhuX #approximate #graph #scalability: Scalable Semi-Supervised Learning by Graph Construction with Approximate Anchors Embedding (HZ, MX), pp. 1331–1336.
ICPR-2018-ZhuZZ: Learning Evasion Strategy in Pursuit-Evasion by Deep Q-network (JZ, WZ, ZZ), pp. 67–72.
ICPR-2018-ZhuZZ18b #recognition #representation: End-to-end Video-level Representation Learning for Action Recognition (JZ, ZZ, WZ), pp. 645–650.
KDD-2018-0009QG0H #realtime: Deep Reinforcement Learning for Sponsored Search Real-time Bidding (JZ0, GQ, ZG, WZ0, XH), pp. 1021–1030.
KDD-2018-BaiZEV #representation: Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time (TB, SZ, BLE, SV), pp. 43–51.
KDD-2018-CaiWGSJ #multi: Deep Adversarial Learning for Multi-Modality Missing Data Completion (LC, ZW, HG, DS, SJ), pp. 1158–1166.
KDD-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.
KDD-2018-Chen0DTHT #online #recommendation: Stabilizing Reinforcement Learning in Dynamic Environment with Application to Online Recommendation (SYC, YY0, QD, JT, HKH, HHT), pp. 1187–1196.
KDD-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.
KDD-2018-DiPSC #morphism: Transfer Learning via Feature Isomorphism Discovery (SD, JP, YS, LC), pp. 1301–1309.
KDD-2018-DonnatZHL: Learning Structural Node Embeddings via Diffusion Wavelets (CD, MZ, DH, JL), pp. 1320–1329.
KDD-2018-FoxAJPW #multi #predict: Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories (IF, LA, MJ, RPB, JW), pp. 1387–1395.
KDD-2018-FuWHW #approximate #fault #reduction #scalability: Scalable Active Learning by Approximated Error Reduction (WF, MW, SH, XW0), pp. 1396–1405.
KDD-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.
KDD-2018-GorovitsGPB #community #named: LARC: Learning Activity-Regularized Overlapping Communities Across Time (AG, EG, EEP, PB), pp. 1465–1474.
KDD-2018-GuYCH #algorithm #incremental: New Incremental Learning Algorithm for Semi-Supervised Support Vector Machine (BG, XTY, SC, HH), pp. 1475–1484.
KDD-2018-HanSSZ #collaboration #multi #semistructured data: Multi-label Learning with Highly Incomplete Data via Collaborative Embedding (YH, GS, YS, XZ0), pp. 1494–1503.
KDD-2018-HongCL #kernel: Disturbance Grassmann Kernels for Subspace-Based Learning (JH, HC, FL), pp. 1521–1530.
KDD-2018-HuaiMLSSZ #metric #probability: Metric Learning from Probabilistic Labels (MH, CM, YL, QS, LS, AZ), pp. 1541–1550.
KDD-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.
KDD-2018-Janakiraman #multi #safety #using: Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning (VMJ), pp. 406–415.
KDD-2018-JeongJ #multi: Variable Selection and Task Grouping for Multi-Task Learning (JYJ, CHJ), pp. 1589–1598.
KDD-2018-KumagaiI #bound: Learning Dynamics of Decision Boundaries without Additional Labeled Data (AK, TI), pp. 1627–1636.
KDD-2018-Le0V #memory management: Dual Memory Neural Computer for Asynchronous Two-view Sequential Learning (HL, TT0, SV), pp. 1637–1645.
KDD-2018-LeeAVN #collaboration #comprehension #metric #video: Collaborative Deep Metric Learning for Video Understanding (JL, SAEH, BV, AN), pp. 481–490.
KDD-2018-LiaoZWMCYGW #predict #sequence: Deep Sequence Learning with Auxiliary Information for Traffic Prediction (BL, JZ, CW0, DM, TC, SY, YG, FW), pp. 537–546.
KDD-2018-LiFWSYL #estimation #multi #representation: Multi-task Representation Learning for Travel Time Estimation (YL, KF, ZW, CS, JY, YL0), pp. 1695–1704.
KDD-2018-LinZXZ #multi #performance #scalability: Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning (KL, RZ, ZX, JZ), pp. 1774–1783.
KDD-2018-LiuZC #metric #performance: Efficient Similar Region Search with Deep Metric Learning (YL, KZ0, GC), pp. 1850–1859.
KDD-2018-LiY #classification #network #policy: Learning Adversarial Networks for Semi-Supervised Text Classification via Policy Gradient (YL, JY), pp. 1715–1723.
KDD-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.
KDD-2018-LiZY #approach: Dynamic Bike Reposition: A Spatio-Temporal Reinforcement Learning Approach (YL, YZ, QY), pp. 1724–1733.
KDD-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.
KDD-2018-LuoCTSLCY #information management #invariant #named #network: TINET: Learning Invariant Networks via Knowledge Transfer (CL, ZC, LAT, AS, ZL, HC, JY), pp. 1890–1899.
KDD-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.
KDD-2018-NieHL #multi: Calibrated Multi-Task Learning (FN, ZH, XL), pp. 2012–2021.
KDD-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.
KDD-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.
KDD-2018-PangCCL #detection #random: Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection (GP, LC, LC, HL), pp. 2041–2050.
KDD-2018-QiuTMDW0 #named #predict #social: DeepInf: Social Influence Prediction with Deep Learning (JQ, JT, HM, YD, KW, JT0), pp. 2110–2119.
KDD-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.
KDD-2018-SamelM: Active Deep Learning to Tune Down the Noise in Labels (KS, XM), pp. 685–694.
KDD-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.
KDD-2018-ShiZGZ0 #network: Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks (YS, QZ, FG, CZ0, JH0), pp. 2190–2199.
KDD-2018-SureshGG #multi: Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU (HS, JJG, JVG), pp. 802–810.
KDD-2018-TangW #modelling #performance #ranking #recommendation: Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System (JT, KW), pp. 2289–2298.
KDD-2018-Teh #big data #on the #problem: On Big Data Learning for Small Data Problems (YWT), p. 3.
KDD-2018-VandalKDGNG #nondeterminism: Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning (TV, EK, JGD, SG, RRN, ARG), pp. 2377–2386.
KDD-2018-WangFY: Learning to Estimate the Travel Time (ZW, KF, JY), pp. 858–866.
KDD-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.
KDD-2018-WangJZEC #behaviour #multi: Multi-Type Itemset Embedding for Learning Behavior Success (DW, MJ0, QZ, ZE, NVC), pp. 2397–2406.
KDD-2018-WangOWW #modelling: Learning Credible Models (JW, JO, HW, JW), pp. 2417–2426.
KDD-2018-WangZ #problem #towards: Towards Mitigating the Class-Imbalance Problem for Partial Label Learning (JW, MLZ), pp. 2427–2436.
KDD-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.
KDD-2018-WangZHZ #network #recommendation: Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation (LW, WZ0, XH, HZ), pp. 2447–2456.
KDD-2018-WeiZYL #approach #named: IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control (HW, GZ, HY, ZL), pp. 2496–2505.
KDD-2018-WuYC #realtime: Deep Censored Learning of the Winning Price in the Real Time Bidding (WCHW, MYY, MSC), pp. 2526–2535.
KDD-2018-WuYYZ #process: Decoupled Learning for Factorial Marked Temporal Point Processes (WW, JY, XY, HZ), pp. 2516–2525.
KDD-2018-XuLDH #metric #robust #using: New Robust Metric Learning Model Using Maximum Correntropy Criterion (JX0, LL, CD, HH), pp. 2555–2564.
KDD-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.
KDD-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.
KDD-2018-YoshidaTK #distance #metric: Safe Triplet Screening for Distance Metric Learning (TY, IT, MK), pp. 2653–2662.
KDD-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.
KDD-2018-YuZCASZCW #network: Learning Deep Network Representations with Adversarially Regularized Autoencoders (WY, CZ, WC, CCA, DS, BZ, HC, WW0), pp. 2663–2671.
KDD-2018-ZangC0 #empirical: Learning and Interpreting Complex Distributions in Empirical Data (CZ, PC0, WZ0), pp. 2682–2691.
KDD-2018-ZhangWLTYY #matrix #self: Discrete Ranking-based Matrix Factorization with Self-Paced Learning (YZ0, HW, DL, IWT, HY, GY), pp. 2758–2767.
KDD-2018-ZhangZCMHWT #adaptation #online #symmetry: Online Adaptive Asymmetric Active Learning for Budgeted Imbalanced Data (YZ0, PZ, JC, WM, JH, QW, MT), pp. 2768–2777.
KDD-2018-ZhaoLSY #e-commerce #representation: Learning and Transferring IDs Representation in E-commerce (KZ, YL, ZS, CY), pp. 1031–1039.
KDD-2018-ZhaoZDXTY #feedback #recommendation: Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning (XZ, LZ, ZD, LX, JT, DY), pp. 1040–1048.
KDD-2018-ZhuLZLHLG #recommendation: Learning Tree-based Deep Model for Recommender Systems (HZ, XL, PZ, GL, JH, HL, KG), pp. 1079–1088.
ECOOP-2018-ChenHZHK0 #execution #program transformation #symbolic computation: Learning to Accelerate Symbolic Execution via Code Transformation (JC0, WH, LZ, DH, SK, LZ0), p. 27.
Onward-2018-RinardSM #source code: Active learning for inference and regeneration of computer programs that store and retrieve data (MCR, JS0, VM), pp. 12–28.
OOPSLA-2018-EzudheenND0M #contract #invariant: Horn-ICE learning for synthesizing invariants and contracts (PE, DN, DD, PG0, PM), p. 25.
OOPSLA-2018-PradelS #approach #debugging #detection #named: DeepBugs: a learning approach to name-based bug detection (MP, KS), p. 25.
PLDI-2018-Bastani0AL #points-to #specification: Active learning of points-to specifications (OB, RS0, AA, PL), pp. 678–692.
PLDI-2018-FengMBD #synthesis #using: Program synthesis using conflict-driven learning (YF, RM, OB, ID), pp. 420–435.
SAS-2018-PrabhuMV #behaviour #proving #safety: Efficiently Learning Safety Proofs from Appearance as well as Behaviours (SP, KM, RV), pp. 326–343.
ASE-2018-ChaLO #online #testing: Template-guided concolic testing via online learning (SC, SL, HO), pp. 408–418.
ASE-2018-GaoYFJS #named #platform #semantics: VulSeeker: a semantic learning based vulnerability seeker for cross-platform binary (JG, XY, YF, YJ0, JS), pp. 896–899.
ASE-2018-HabibP #documentation #graph #thread #using: Is this class thread-safe? inferring documentation using graph-based learning (AH, MP), pp. 41–52.
ASE-2018-HanYL #debugging #named #performance: PerfLearner: learning from bug reports to understand and generate performance test frames (XH, TY, DL0), pp. 17–28.
ASE-2018-LiuXZ #detection: Deep learning based feature envy detection (HL, ZX, YZ), pp. 385–396.
ASE-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.
ASE-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.
ASE-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-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-FSE-2018-GuoJZCS #difference #fuzzing #named #testing: DLFuzz: differential fuzzing testing of deep learning systems (JG, YJ0, YZ, QC, JS), pp. 739–743.
ESEC-FSE-2018-HellendoornBBA #type inference: Deep learning type inference (VJH, CB, ETB, MA), pp. 152–162.
ESEC-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-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-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.
ASPLOS-2018-MishraILH #energy #latency #named #predict: CALOREE: Learning Control for Predictable Latency and Low Energy (NM, CI, JDL, HH), pp. 184–198.
CASE-2018-BanerjeeRP #towards: A Step Toward Learning to Control Tens of Optically Actuated Microrobots in Three Dimensions (AGB, KR, BP), pp. 1460–1465.
CASE-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.
CASE-2018-FarooquiFF #automation #modelling #simulation #towards: Towards Automatic Learning of Discrete-Event Models from Simulations (AF, PF, MF), pp. 857–862.
CASE-2018-HuaH #concept #induction #logic programming #semantics: Concept Learning in AutomationML with Formal Semantics and Inductive Logic Programming (YH, BH), pp. 1542–1547.
CASE-2018-JiKPFG #2d: Learning 2D Surgical Camera Motion From Demonstrations (JJJ, SK, VP, DF, KG), pp. 35–42.
CASE-2018-LeeLFG #constraints #estimation: Constraint Estimation and Derivative-Free Recovery for Robot Learning from Demonstrations (JL, ML, RF, KG), pp. 270–277.
CASE-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.
CASE-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.
CASE-2018-ParkHGS #process: Robot Model Learning with Gaussian Process Mixture Model (SP, YH, CFG, KS), pp. 1263–1268.
CASE-2018-RenWLG #behaviour #online #video: Learning Traffic Behaviors by Extracting Vehicle Trajectories from Online Video Streams (XR, DW, ML, KG), pp. 1276–1283.
CASE-2018-SeichterESG #detection #how: How to Improve Deep Learning based Pavement Distress Detection while Minimizing Human Effort (DS, ME0, RS, HMG), pp. 63–70.
CASE-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.
CASE-2018-TanGCC #analysis #automation: Learning with Corrosion Feature: For Automated Quantitative Risk Analysis of Corrosion Mechanism (WCT, PCG, KHC, IMC), pp. 1290–1295.
CASE-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.
CASE-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.
CASE-2018-YangZCTK: Intelligent Diagnosis of Forging Die based on Deep Learning (HCY, CHZ, YZC, CMT, YCK), pp. 199–204.
ESOP-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.
CAV-2018-DreossiJS #semantics: Semantic Adversarial Deep Learning (TD, SJ, SAS), pp. 3–26.
CAV-2018-KelmendiKKW #algorithm #game studies #probability: Value Iteration for Simple Stochastic Games: Stopping Criterion and Learning Algorithm (EK, JK, JK, MW), pp. 623–642.
CAV-2018-SinghPV #performance #program analysis: Fast Numerical Program Analysis with Reinforcement Learning (GS, MP, MTV), pp. 211–229.
CAV-2018-WangADM #abstraction #synthesis: Learning Abstractions for Program Synthesis (XW0, GA, ID, KLM), pp. 407–426.
CAV-2018-ZhouL: Safety-Aware Apprenticeship Learning (WZ, WL), pp. 662–680.
ICTSS-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.
IJCAR-2018-PiotrowskiU #feedback #named: ATPboost: Learning Premise Selection in Binary Setting with ATP Feedback (BP, JU), pp. 566–574.
VMCAI-2018-LiTZS #automaton: Learning to Complement Büchi Automata (YL0, AT, LZ0, SS), pp. 313–335.
JCDL-2017-WeihsE #metric #predict: Learning to Predict Citation-Based Impact Measures (LW, OE), pp. 49–58.
JCDL-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.
CSEET-2017-BinderNRM #challenge #development #education #mobile: Challenge Based Learning Applied to Mobile Software Development Teaching (FVB, MN, SSR, AM), pp. 57–64.
CSEET-2017-LeildeR #assessment #process: Does Process Assessment Drive Process Learning? The Case of a Bachelor Capstone Project (VL, VR), pp. 197–201.
EDM-2017-AgrawalNM #student: Grouping Students for Maximizing Learning from Peers (RA, SN, NMM).
EDM-2017-BaoCH #multi #on the #online: On the Prevalence of Multiple-Account Cheating in Massive Open Online Learning (YB, GC, CH).
EDM-2017-BeckCB #data mining #education #mining: Workshop proposal: deep learning for educational data mining (JB, MC, RSB).
EDM-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).
EDM-2017-DongB #behaviour #modelling #student: An Extended Learner Modeling Method to Assess Students' Learning Behaviors (YD, GB).
EDM-2017-EkambaramMDKSN #physics: Tell Me More: Digital Eyes to the Physical World for Early Childhood Learning (VE, RSM, PD, RK, AKS, SVN).
EDM-2017-FangNPXGH #online #persistent: Online Learning Persistence and Academic Achievement (YF, BN, PIPJ, YX, ACG, XH).
EDM-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).
EDM-2017-HongB #predict #using: A Prediction and Early Alert Model Using Learning Management System Data and Grounded in Learning Science Theory (WJH, MLB).
EDM-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).
EDM-2017-LiuK #automation #data-driven: Closing the loop: Automated data-driven cognitive model discoveries lead to improved instruction and learning (RL0, KRK).
EDM-2017-MaM #composition: Intelligent Composition of Test Papers based on MOOC Learning Data (LM, YM).
EDM-2017-NamFC #predict #semantics #word: Predicting Short- and Long-Term Vocabulary Learning via Semantic Features of Partial Word Knowledge (SN, GAF, KCT).
EDM-2017-RomeroEGGM #automation #classification #towards: Towards Automatic Classification of Learning Objects: Reducing the Number of Used Features (CR, PGE, EG, AZG, VHM).
EDM-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).
EDM-2017-SuprajaHTK #automation #towards: Toward the Automatic Labeling of Course Questions for Ensuring their Alignment with Learning Outcomes (SS, KH, ST, AWHK).
EDM-2017-ThanasuanCW #mining #student: Emerging Patterns in Student's Learning Attributes through Text Mining (KT, WC, CW).
EDM-2017-WangSLP #programming #student #using: Learning to Represent Student Knowledge on Programming Exercises Using Deep Learning (LW, AS, LL, CP).
EDM-2017-WatersGLB: Short-Answer Responses to STEM Exercises: Measuring Response Validity and Its Impact on Learning (AEW, PG, ASL, RGB).
EDM-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).
EDM-2017-ZhouWLC #policy #towards: Towards Closing the Loop: Bridging Machine-induced Pedagogical Policies to Learning Theories (GZ, JW, CL, MC).
ICPC-2017-LamNNN #debugging #information retrieval #locality: Bug localization with combination of deep learning and information retrieval (ANL, ATN0, HAN, TNN), pp. 218–229.
ICSME-2017-DeshmukhMPSD #debugging #retrieval #towards #using: Towards Accurate Duplicate Bug Retrieval Using Deep Learning Techniques (JD, KMA, SP, SS, ND), pp. 115–124.
ICSME-2017-HanLXLF #predict #using: Learning to Predict Severity of Software Vulnerability Using Only Vulnerability Description (ZH, XL0, ZX, HL, ZF0), pp. 125–136.
ICSME-2017-LiJZZ #fault #kernel #multi #predict: Heterogeneous Defect Prediction Through Multiple Kernel Learning and Ensemble Learning (ZL0, XYJ, XZ, HZ0), pp. 91–102.
ICSME-2017-VerwerH #automaton #named: flexfringe: A Passive Automaton Learning Package (SV, CAH), pp. 638–642.
ICSME-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.
SANER-2017-GoerFM #execution #named: scat: Learning from a single execution of a binary (FdG, CF, LM), pp. 492–496.
SANER-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.
SEFM-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.
AIIDE-2017-BarrigaSB #game studies #realtime: Combining Strategic Learning with Tactical Search in Real-Time Strategy Games (NAB, MS, MB), pp. 9–15.
AIIDE-2017-CampbellV: Learning Combat in NetHack (JC, CV), pp. 16–22.
AIIDE-2017-SigurdsonB #algorithm #heuristic #realtime: Deep Learning for Real-Time Heuristic Search Algorithm Selection (DS, VB), pp. 108–114.
CHI-PLAY-2017-ArroyoMCOHR #game studies #multi #smarttech: Wearable Learning: Multiplayer Embodied Games for Math (IA, MM, JC, EO, TH, MMTR), pp. 205–216.
CHI-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-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.
CIG-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.
CIG-2017-JustesenR #using: Learning macromanagement in starcraft from replays using deep learning (NJ, SR), pp. 162–169.
CIG-2017-MinK #game studies #using #visual notation: Learning to play visual doom using model-free episodic control (BJM, KJK), pp. 223–225.
CIG-2017-NguyenRGM #automation #network: Automated learning of hierarchical task networks for controlling minecraft agents (CN, NR, SG, HMA), pp. 226–231.
CIG-2017-OonishiI #game studies #using: Improving generalization ability in a puzzle game using reinforcement learning (HO, HI), pp. 232–239.
CIG-2017-OsbornSM #automation #design #game studies: Automated game design learning (JCO, AS, MM), pp. 240–247.
CIG-2017-PhucNK #behaviour #statistics #using: Learning human-like behaviors using neuroevolution with statistical penalties (LHP, KN, KI), pp. 207–214.
CIG-2017-PoulsenTFR #named #visual notation: DLNE: A hybridization of deep learning and neuroevolution for visual control (APP, MT, MHF, SR), pp. 256–263.
CIG-2017-ZhangB #policy: Improving hearthstone AI by learning high-level rollout policies and bucketing chance node events (SZ, MB), pp. 309–316.
DiGRA-2017-Loban #game studies #video: Digitising Diplomacy: Grand Strategy Video Games as an Introductory Tool for Learning Diplomacy and International Relations (RL).
DiGRA-2017-TyackW #adaptation #design #game studies: Adapting Epic Theatre Principles for the Design of Games for Learning (AT, PW).
FDG-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.
FDG-2017-KaravolosLY #game studies #multi: Learning the patterns of balance in a multi-player shooter game (DK, AL, GNY), p. 10.
FDG-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.
FDG-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.
ICGJ-2017-PollockMY #development #game studies: Brain jam: STEAM learning through neuroscience-themed game development (IP, JM, BY), pp. 15–21.
VS-Games-2017-BlomeDRBM #artificial reality: VReanimate - Non-verbal guidance and learning in virtual reality (TB, AD, SR, KB, SvM), pp. 23–30.
VS-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.
VS-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.
VS-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.
VS-Games-2017-TsatsouVD #adaptation #case study #experience #modelling #multi: Modelling learning experiences in adaptive multi-agent learning environments (DT, NV, PD), pp. 193–200.
CIKM-2017-0001KGR #constraints #named: TaCLe: Learning Constraints in Tabular Data (SP0, SK, TG, LDR), pp. 2511–2514.
CIKM-2017-0002L #representation: Region Representation Learning via Mobility Flow (HW0, ZL), pp. 237–246.
CIKM-2017-Abu-El-HaijaPA #rank #symmetry: Learning Edge Representations via Low-Rank Asymmetric Projections (SAEH, BP, RAR), pp. 1787–1796.
CIKM-2017-BiegaGFGW #community #online: Learning to Un-Rank: Quantifying Search Exposure for Users in Online Communities (AJB, AG, HF, KPG, GW), pp. 267–276.
CIKM-2017-BouadjenekVZ #biology #sequence #using: Learning Biological Sequence Types Using the Literature (MRB, KV, JZ), pp. 1991–1994.
CIKM-2017-CavallariZCCC #community #detection #graph: Learning Community Embedding with Community Detection and Node Embedding on Graphs (SC, VWZ, HC, KCCC, EC), pp. 377–386.
CIKM-2017-ChaiLTS #multi: Compact Multiple-Instance Learning (JC, WL0, IWT, XBS), pp. 2007–2010.
CIKM-2017-ChenDWXCCM #detection #spreadsheet: Spreadsheet Property Detection With Rule-assisted Active Learning (ZC, SD, RW, GX, DC, MJC, JDM), pp. 999–1008.
CIKM-2017-DangCWZC #classification #kernel: Unsupervised Matrix-valued Kernel Learning For One Class Classification (SD, XC, YW0, JZ, FC0), pp. 2031–2034.
CIKM-2017-DehghaniRAF #query: Learning to Attend, Copy, and Generate for Session-Based Query Suggestion (MD0, SR, EA, PF), pp. 1747–1756.
CIKM-2017-EnsanBZK #empirical #rank: An Empirical Study of Embedding Features in Learning to Rank (FE, EB, AZ, AK), pp. 2059–2062.
CIKM-2017-FanGLXPC #visual notation #web: Learning Visual Features from Snapshots for Web Search (YF, JG, YL, JX0, LP, XC), pp. 247–256.
CIKM-2017-FuLL #named #network #representation: HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning (TYF, WCL, ZL), pp. 1797–1806.
CIKM-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.
CIKM-2017-LeiLLZ #personalisation #ranking: Alternating Pointwise-Pairwise Learning for Personalized Item Ranking (YL, WL0, ZL, MZ), pp. 2155–2158.
CIKM-2017-LiCY #graph #recommendation: Learning Graph-based Embedding For Time-Aware Product Recommendation (YL, WC, HY), pp. 2163–2166.
CIKM-2017-LiDHTCL #network: Attributed Network Embedding for Learning in a Dynamic Environment (JL, HD, XH, JT, YC, HL0), pp. 387–396.
CIKM-2017-LiTZYW #recommendation #representation: Content Recommendation by Noise Contrastive Transfer Learning of Feature Representation (YL, GT, WZ0, YY0, JW0), pp. 1657–1665.
CIKM-2017-Liu0MLLM: A Two-step Information Accumulation Strategy for Learning from Highly Imbalanced Data (BL, MZ0, WM, XL0, YL, SM), pp. 1289–1298.
CIKM-2017-LyuHLP #collaboration #privacy #process #recognition: Privacy-Preserving Collaborative Deep Learning with Application to Human Activity Recognition (LL, XH, YWL, MP), pp. 1219–1228.
CIKM-2017-MansouriZRO0 #ambiguity #query #web: Learning Temporal Ambiguity in Web Search Queries (BM, MSZ, MR, FO, RC0), pp. 2191–2194.
CIKM-2017-MehrotraY #query #using: Task Embeddings: Learning Query Embeddings using Task Context (RM, EY), pp. 2199–2202.
CIKM-2017-Moon0S #graph: Learning Entity Type Embeddings for Knowledge Graph Completion (CM, PJ0, NFS), pp. 2215–2218.
CIKM-2017-Ni0ZYM #fine-grained #metric #similarity #using: Fine-grained Patient Similarity Measuring using Deep Metric Learning (JN, JL0, CZ, DY, ZM), pp. 1189–1198.
CIKM-2017-OosterhuisR17a #information retrieval #online #quality #rank: Balancing Speed and Quality in Online Learning to Rank for Information Retrieval (HO, MdR), pp. 277–286.
CIKM-2017-PangXCZ #category theory #detection: Selective Value Coupling Learning for Detecting Outliers in High-Dimensional Categorical Data (GP, HX, LC, WZ), pp. 807–816.
CIKM-2017-QianPS #scalability: Active Learning for Large-Scale Entity Resolution (KQ0, LP0, PS), pp. 1379–1388.
CIKM-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.
CIKM-2017-SahaJHH #modelling #representation: Regularized and Retrofitted models for Learning Sentence Representation with Context (TKS, SRJ, NH, MAH), pp. 547–556.
CIKM-2017-ShiPW #modelling #student: Modeling Student Learning Styles in MOOCs (YS, ZP, HW), pp. 979–988.
CIKM-2017-TanZW #graph #representation #scalability: Representation Learning of Large-Scale Knowledge Graphs via Entity Feature Combinations (ZT, XZ0, WW0), pp. 1777–1786.
CIKM-2017-TengLW #detection #multi #network #using: Anomaly Detection in Dynamic Networks using Multi-view Time-Series Hypersphere Learning (XT, YRL, XW), pp. 827–836.
CIKM-2017-XiangJ #multimodal #network: Common-Specific Multimodal Learning for Deep Belief Network (CX, XJ), pp. 2387–2390.
CIKM-2017-XiaoMZLM #personalisation #recommendation #social: Learning and Transferring Social and Item Visibilities for Personalized Recommendation (XL0, MZ0, YZ, YL, SM), pp. 337–346.
CIKM-2017-XuLLX #rank: Learning to Rank with Query-level Semi-supervised Autoencoders (BX0, HL, YL0, KX), pp. 2395–2398.
CIKM-2017-Yang: When Deep Learning Meets Transfer Learning (QY), p. 5.
CIKM-2017-ZhangACC #recommendation #representation: Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources (YZ, QA, XC, WBC), pp. 1449–1458.
CIKM-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.
CIKM-2017-ZhangXKZ #graph #interactive: Learning Node Embeddings in Interaction Graphs (YZ, YX, XK, YZ), pp. 397–406.
CIKM-2017-ZhaoWLL: Missing Value Learning (ZLZ, CDW, KYL, JHL), pp. 2427–2430.
CIKM-2017-ZhaoXYYZFQ #image: Dual Learning for Cross-domain Image Captioning (WZ, WX, MY0, JY, ZZ, YF, YQ), pp. 29–38.
CIKM-2017-ZhouZL0: Learning Knowledge Embeddings by Combining Limit-based Scoring Loss (XZ, QZ, PL, LG0), pp. 1009–1018.
CIKM-2017-ZhuZHWZZY #collaboration #multi #recommendation: Broad Learning based Multi-Source Collaborative Recommendation (JZ, JZ, LH0, QW, BZ0, CZ, PSY), pp. 1409–1418.
CIKM-2017-ZohrevandGTSSS #framework: Deep Learning Based Forecasting of Critical Infrastructure Data (ZZ, UG, MAT, HYS, MS, AYS), pp. 1129–1138.
ECIR-2017-AlkhawaldehPJY #clustering #information retrieval #named #query: LTRo: Learning to Route Queries in Clustered P2P IR (RSA, DP0, JMJ, FY), pp. 513–519.
ECIR-2017-AyadiKHDJ #image #using: Learning to Re-rank Medical Images Using a Bayesian Network-Based Thesaurus (HA, MTK, JXH, MD, MBJ), pp. 160–172.
ECIR-2017-GuptaS: Learning to Classify Inappropriate Query-Completions (PG, JS), pp. 548–554.
ECIR-2017-RomeoMBM #approach #multi #ranking: A Multiple-Instance Learning Approach to Sentence Selection for Question Ranking (SR, GDSM, ABC, AM), pp. 437–449.
ECIR-2017-SoldainiG #approach #health #rank #semantics: Learning to Rank for Consumer Health Search: A Semantic Approach (LS, NG), pp. 640–646.
ICML-2017-0001N #composition #modelling #scalability: Relative Fisher Information and Natural Gradient for Learning Large Modular Models (KS0, FN), pp. 3289–3298.
ICML-2017-0004K: Follow the Moving Leader in Deep Learning (SZ0, JTK), pp. 4110–4119.
ICML-2017-0007MW #effectiveness: Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible (KZ0, WM, LW0), pp. 4130–4139.
ICML-2017-AgarwalS #difference #online #privacy: The Price of Differential Privacy for Online Learning (NA, KS), pp. 32–40.
ICML-2017-AlaaHS #process: Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis (AMA, SH, MvdS), pp. 60–69.
ICML-2017-AllamanisCKS #semantics: Learning Continuous Semantic Representations of Symbolic Expressions (MA, PC, PK, CAS), pp. 80–88.
ICML-2017-Allen-ZhuL17b #online #performance: Follow the Compressed Leader: Faster Online Learning of Eigenvectors and Faster MMWU (ZAZ, YL), pp. 116–125.
ICML-2017-AndreasKL #composition #multi #policy #sketching: Modular Multitask Reinforcement Learning with Policy Sketches (JA, DK, SL), pp. 166–175.
ICML-2017-AnschelBS #named #reduction: Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning (OA, NB, NS), pp. 176–185.
ICML-2017-AsadiL: An Alternative Softmax Operator for Reinforcement Learning (KA, MLL), pp. 243–252.
ICML-2017-AzarOM #bound: Minimax Regret Bounds for Reinforcement Learning (MGA, IO, RM), pp. 263–272.
ICML-2017-BachHRR #generative #modelling: Learning the Structure of Generative Models without Labeled Data (SHB, BDH, AR, CR), pp. 273–282.
ICML-2017-BachmanST #algorithm: Learning Algorithms for Active Learning (PB, AS, AT), pp. 301–310.
ICML-2017-BalleM #finite #policy: Spectral Learning from a Single Trajectory under Finite-State Policies (BB, OAM), pp. 361–370.
ICML-2017-BaramACM: End-to-End Differentiable Adversarial Imitation Learning (NB, OA, IC, SM), pp. 390–399.
ICML-2017-BarmannPS #online #optimisation: Emulating the Expert: Inverse Optimization through Online Learning (AB, SP, OS), pp. 400–410.
ICML-2017-BelangerYM #energy #network #predict: End-to-End Learning for Structured Prediction Energy Networks (DB, BY, AM), pp. 429–439.
ICML-2017-BelilovskyKVB #modelling #visual notation: Learning to Discover Sparse Graphical Models (EB, KK, GV, MBB), pp. 440–448.
ICML-2017-BellemareDM: A Distributional Perspective on Reinforcement Learning (MGB, WD, RM), pp. 449–458.
ICML-2017-BelloZVL: Neural Optimizer Search with Reinforcement Learning (IB, BZ, VV, QVL), pp. 459–468.
ICML-2017-BergmannJV: Learning Texture Manifolds with the Periodic Spatial GAN (UB, NJ, RV), pp. 469–477.
ICML-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.
ICML-2017-BeygelzimerOZ #multi #online #performance: Efficient Online Bandit Multiclass Learning with Õ(√T) Regret (AB, FO, CZ), pp. 488–497.
ICML-2017-BojanowskiJ #predict: Unsupervised Learning by Predicting Noise (PB, AJ), pp. 517–526.
ICML-2017-BotevRB #optimisation: Practical Gauss-Newton Optimisation for Deep Learning (AB, HR, DB), pp. 557–565.
ICML-2017-ChebotarHZSSL #modelling: Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning (YC, KH, MZ, GSS, SS, SL), pp. 703–711.
ICML-2017-ChenHCDLBF: Learning to Learn without Gradient Descent by Gradient Descent (YC, MWH, SGC, MD, TPL, MB, NdF), pp. 748–756.
ICML-2017-ChenZLHH: Learning to Aggregate Ordinal Labels by Maximizing Separating Width (GC, SZ, DL, HH0, PAH), pp. 787–796.
ICML-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.
ICML-2017-CortesGKMY #adaptation #named #network: AdaNet: Adaptive Structural Learning of Artificial Neural Networks (CC, XG, VK, MM, SY), pp. 874–883.
ICML-2017-DevlinUBSMK #named: RobustFill: Neural Program Learning under Noisy I/O (JD, JU, SB, RS, ArM, PK), pp. 990–998.
ICML-2017-FoersterNFATKW #experience #multi: Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning (JNF, NN, GF, TA, PHST, PK, SW), pp. 1146–1155.
ICML-2017-FutomaHH #classification #detection #multi #process: Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier (JF, SH, KAH), pp. 1174–1182.
ICML-2017-GalIG #image: Deep Bayesian Active Learning with Image Data (YG, RI, ZG), pp. 1183–1192.
ICML-2017-GaoFC #network: Local-to-Global Bayesian Network Structure Learning (TG, KPF, MC), pp. 1193–1202.
ICML-2017-GehringAGYD #sequence: Convolutional Sequence to Sequence Learning (JG, MA, DG, DY, YND), pp. 1243–1252.
ICML-2017-GravesBMMK #automation #education #network: Automated Curriculum Learning for Neural Networks (AG, MGB, JM, RM, KK), pp. 1311–1320.
ICML-2017-HaarnojaTAL #energy #policy: Reinforcement Learning with Deep Energy-Based Policies (TH, HT, PA, SL), pp. 1352–1361.
ICML-2017-HarandiSH #geometry #metric #reduction: Joint Dimensionality Reduction and Metric Learning: A Geometric Take (MTH, MS, RIH), pp. 1404–1413.
ICML-2017-HigginsPRMBPBBL #named: DARLA: Improving Zero-Shot Transfer in Reinforcement Learning (IH, AP, AAR, LM, CB, AP, MB, CB, AL), pp. 1480–1490.
ICML-2017-Hoffman #markov #modelling #monte carlo: Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo (MDH), pp. 1510–1519.
ICML-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.
ICML-2017-HuMTMS #self: Learning Discrete Representations via Information Maximizing Self-Augmented Training (WH, TM, ST, EM, MS), pp. 1558–1567.
ICML-2017-JabbariJKMR: Fairness in Reinforcement Learning (SJ, MJ, MJK, JM, AR0), pp. 1617–1626.
ICML-2017-JainMR #generative #modelling #multi #scalability: Scalable Generative Models for Multi-label Learning with Missing Labels (VJ, NM, PR), pp. 1636–1644.
ICML-2017-JerniteCS #classification #estimation: Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation (YJ, AC, DAS), pp. 1665–1674.
ICML-2017-KattOA #monte carlo: Learning in POMDPs with Monte Carlo Tree Search (SK, FAO, CA), pp. 1819–1827.
ICML-2017-KhasanovaF #graph #invariant #representation: Graph-based Isometry Invariant Representation Learning (RK, PF), pp. 1847–1856.
ICML-2017-KimCKLK #generative #network: Learning to Discover Cross-Domain Relations with Generative Adversarial Networks (TK, MC, HK, JKL, JK), pp. 1857–1865.
ICML-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.
ICML-2017-KocaogluDV #graph: Cost-Optimal Learning of Causal Graphs (MK, AD, SV), pp. 1875–1884.
ICML-2017-KrishnamurthyAH #classification: Active Learning for Cost-Sensitive Classification (AK, AA, TKH, HDI, JL0), pp. 1915–1924.
ICML-2017-LawUZ #clustering: Deep Spectral Clustering Learning (MTL, RU, RSZ), pp. 1985–1994.
ICML-2017-LeeHPS #multi: Confident Multiple Choice Learning (KL, CH, KP, JS), pp. 2014–2023.
ICML-2017-LevyW #source code: Learning to Align the Source Code to the Compiled Object Code (DL, LW), pp. 2043–2051.
ICML-2017-LeY0L #coordination #multi: Coordinated Multi-Agent Imitation Learning (HML0, YY, PC0, PL), pp. 1995–2003.
ICML-2017-LivniCG #infinity #kernel #network: Learning Infinite Layer Networks Without the Kernel Trick (RL, DC, AG), pp. 2198–2207.
ICML-2017-LongZ0J #adaptation #network: Deep Transfer Learning with Joint Adaptation Networks (ML, HZ, JW0, MIJ), pp. 2208–2217.
ICML-2017-Luo #architecture #network: Learning Deep Architectures via Generalized Whitened Neural Networks (PL0), pp. 2238–2246.
ICML-2017-LvJL: Learning Gradient Descent: Better Generalization and Longer Horizons (KL, SJ, JL), pp. 2247–2255.
ICML-2017-MacGlashanHLPWR #feedback #interactive: Interactive Learning from Policy-Dependent Human Feedback (JM, MKH, RTL, BP, GW, DLR, MET, MLL), pp. 2285–2294.
ICML-2017-MachadoBB #framework: A Laplacian Framework for Option Discovery in Reinforcement Learning (MCM, MGB, MHB), pp. 2295–2304.
ICML-2017-MaystreG #approach #effectiveness #exclamation: Just Sort It! A Simple and Effective Approach to Active Preference Learning (LM, MG), pp. 2344–2353.
ICML-2017-MirhoseiniPLSLZ #optimisation: Device Placement Optimization with Reinforcement Learning (AM, HP, QVL, BS, RL0, YZ, NK, MN0, SB, JD), pp. 2430–2439.
ICML-2017-MohajerSE #rank: Active Learning for Top-K Rank Aggregation from Noisy Comparisons (SM, CS, AE), pp. 2488–2497.
ICML-2017-OhSLK #multi: Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning (JO, SPS, HL, PK), pp. 2661–2670.
ICML-2017-OmidshafieiPAHV #distributed #multi: Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability (SO, JP, CA, JPH, JV), pp. 2681–2690.
ICML-2017-OsbandR #question #why: Why is Posterior Sampling Better than Optimism for Reinforcement Learning? (IO, BVR), pp. 2701–2710.
ICML-2017-OsogamiKS #bidirectional #modelling: Bidirectional Learning for Time-series Models with Hidden Units (TO, HK, TS), pp. 2711–2720.
ICML-2017-PadSCTU #taxonomy: Dictionary Learning Based on Sparse Distribution Tomography (PP, FS, LEC, PT, MU), pp. 2731–2740.
ICML-2017-PentinaL #multi: Multi-task Learning with Labeled and Unlabeled Tasks (AP, CHL), pp. 2807–2816.
ICML-2017-PintoDSG #robust: Robust Adversarial Reinforcement Learning (LP, JD, RS, AG0), pp. 2817–2826.
ICML-2017-RiquelmeGL #estimation #linear #modelling: Active Learning for Accurate Estimation of Linear Models (CR, MG, AL), pp. 2931–2939.
ICML-2017-Shalev-ShwartzS: Failures of Gradient-Based Deep Learning (SSS, OS, SS), pp. 3067–3075.
ICML-2017-ShamirS #feedback #online #permutation: Online Learning with Local Permutations and Delayed Feedback (OS, LS), pp. 3086–3094.
ICML-2017-ShrikumarGK #difference: Learning Important Features Through Propagating Activation Differences (AS, PG, AK), pp. 3145–3153.
ICML-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.
ICML-2017-SunRMW #named: meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting (XS0, XR, SM, HW), pp. 3299–3308.
ICML-2017-SunVGBB #predict: Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction (WS0, AV, GJG, BB, JAB), pp. 3309–3318.
ICML-2017-TandonLDK #distributed: Gradient Coding: Avoiding Stragglers in Distributed Learning (RT, QL, AGD, NK), pp. 3368–3376.
ICML-2017-TanM #modelling: Partitioned Tensor Factorizations for Learning Mixed Membership Models (ZT, SM0), pp. 3358–3367.
ICML-2017-ToshD: Diameter-Based Active Learning (CT, SD), pp. 3444–3452.
ICML-2017-UmlauftH #probability: Learning Stable Stochastic Nonlinear Dynamical Systems (JU, SH), pp. 3502–3510.
ICML-2017-UrschelBMR #process: Learning Determinantal Point Processes with Moments and Cycles (JU, VEB, AM, PR), pp. 3511–3520.
ICML-2017-VaswaniKWGLS #independence #online: Model-Independent Online Learning for Influence Maximization (SV, BK, ZW, MG, LVSL, MS), pp. 3530–3539.
ICML-2017-VezhnevetsOSHJS #network: FeUdal Networks for Hierarchical Reinforcement Learning (ASV, SO, TS, NH, MJ, DS, KK), pp. 3540–3549.
ICML-2017-VillegasYZSLL #predict: Learning to Generate Long-term Future via Hierarchical Prediction (RV, JY, YZ, SS, XL, HL), pp. 3560–3569.
ICML-2017-VorontsovTKP #dependence #network #on the #orthogonal: On orthogonality and learning recurrent networks with long term dependencies (EV, CT, SK, CP), pp. 3570–3578.
ICML-2017-WangKS0 #distributed #performance: Efficient Distributed Learning with Sparsity (JW, MK, NS, TZ0), pp. 3636–3645.
ICML-2017-WangLJK #kernel #optimisation: Batched High-dimensional Bayesian Optimization via Structural Kernel Learning (ZW, CL, SJ, PK), pp. 3656–3664.
ICML-2017-White #specification: Unifying Task Specification in Reinforcement Learning (MW), pp. 3742–3750.
ICML-2017-XiaQCBYL: Dual Supervised Learning (YX, TQ, WC0, JB0, NY, TYL), pp. 3789–3798.
ICML-2017-XieDZKYZX #constraints #modelling: Learning Latent Space Models with Angular Constraints (PX, YD, YZ, AK, YY, JZ, EPX), pp. 3799–3810.
ICML-2017-XuLZ #process #sequence: Learning Hawkes Processes from Short Doubly-Censored Event Sequences (HX, DL, HZ), pp. 3831–3840.
ICML-2017-YangFSH #clustering #towards: Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering (BY, XF0, NDS, MH), pp. 3861–3870.
ICML-2017-ZenkePG: Continual Learning Through Synaptic Intelligence (FZ, BP, SG), pp. 3987–3995.
ICML-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.
ICML-2017-ZhangHTC: Re-revisiting Learning on Hypergraphs: Confidence Interval and Subgradient Method (CZ, SH, ZGT, THHC), pp. 4026–4034.
ICML-2017-ZhangZZHZ #distributed #network #online: Projection-free Distributed Online Learning in Networks (WZ0, PZ, WZ0, SCHH, TZ), pp. 4054–4062.
ICML-2017-ZhaoSE #generative #modelling: Learning Hierarchical Features from Deep Generative Models (SZ, JS, SE), pp. 4091–4099.
ICML-2017-ZhaoYKJB #architecture: Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture (MZ, SY, DK, TSJ, MTB), pp. 4100–4109.
ICML-2017-ZoghiTGKSW #modelling #online #probability #rank: Online Learning to Rank in Stochastic Click Models (MZ, TT, MG, BK, CS, ZW), pp. 4199–4208.
KDD-2017-0013H #paradigm #predict: Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics (XL0, JH), pp. 285–294.
KDD-2017-AmandH #composition #metric: Sparse Compositional Local Metric Learning (JSA, JH), pp. 1097–1104.
KDD-2017-AngelinoLASR: Learning Certifiably Optimal Rule Lists (EA, NLS, DA, MS, CR), pp. 35–44.
KDD-2017-ChoiBSSS #graph #named #representation: GRAM: Graph-based Attention Model for Healthcare Representation Learning (EC, MTB, LS, WFS, JS), pp. 787–795.
KDD-2017-DadkhahiM #detection #embedded #network: Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices (HD, BMM), pp. 1773–1781.
KDD-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.
KDD-2017-DongCS #named #network #representation #scalability: metapath2vec: Scalable Representation Learning for Heterogeneous Networks (YD, NVC, AS), pp. 135–144.
KDD-2017-EmraniMX #multi #using: Prognosis and Diagnosis of Parkinson's Disease Using Multi-Task Learning (SE, AM, WX), pp. 1457–1466.
KDD-2017-How #nondeterminism #theory and practice: Planning and Learning under Uncertainty: Theory and Practice (JPH), p. 19.
KDD-2017-IosifidisN #scalability #sentiment: Large Scale Sentiment Learning with Limited Labels (VI, EN), pp. 1823–1832.
KDD-2017-LabutovHBH #mining: Semi-Supervised Techniques for Mining Learning Outcomes and Prerequisites (IL, YH0, PB, DH), pp. 907–915.
KDD-2017-LiuPH #distributed #multi: Distributed Multi-Task Relationship Learning (SL, SJP, QH), pp. 937–946.
KDD-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.
KDD-2017-OvadiaHKLNPZS: Learning to Count Mosquitoes for the Sterile Insect Technique (YO, YH, DK, JL, DN, RP, TZ, DS), pp. 1943–1949.
KDD-2017-RibeiroSF #named: struc2vec: Learning Node Representations from Structural Identity (LFRR, PHPS, DRF), pp. 385–394.
KDD-2017-ShenHGC #comprehension #named: ReasoNet: Learning to Stop Reading in Machine Comprehension (YS, PSH, JG, WC), pp. 1047–1055.
KDD-2017-SpringS #random #scalability: Scalable and Sustainable Deep Learning via Randomized Hashing (RS, AS), pp. 445–454.
KDD-2017-TangW0M: End-to-end Learning for Short Text Expansion (JT, YW, KZ0, QM), pp. 1105–1113.
KDD-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.
KDD-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.
KDD-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.
KDD-2017-XiaoGVT #behaviour: Learning Temporal State of Diabetes Patients via Combining Behavioral and Demographic Data (HX, JG0, LHV, DST), pp. 2081–2089.
KDD-2017-XieBLZ #distributed #multi #privacy: Privacy-Preserving Distributed Multi-Task Learning with Asynchronous Updates (LX, IMB, KL, JZ), pp. 1195–1204.
KDD-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.
KDD-2017-YeZMPB #network: Learning from Labeled and Unlabeled Vertices in Networks (WY0, LZ, DM, CP, CB), pp. 1265–1274.
KDD-2017-YouX0T #education #multi #network: Learning from Multiple Teacher Networks (SY, CX0, CX0, DT), pp. 1285–1294.
KDD-2017-ZhangCTSS #effectiveness #multi #named: LEAP: Learning to Prescribe Effective and Safe Treatment Combinations for Multimorbidity (YZ, RC, JT0, WFS, JS), pp. 1315–1324.
KDD-2017-ZhanZ #induction #multi: Inductive Semi-supervised Multi-Label Learning with Co-Training (WZ, MLZ), pp. 1305–1314.
KDD-2017-ZhengBLL #detection #metric: Contextual Spatial Outlier Detection with Metric Learning (GZ, SLB, TL, ZL), pp. 2161–2170.
KDD-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.
OOPSLA-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.
OOPSLA-2017-SantolucitoZDSP #specification: Synthesizing configuration file specifications with association rule learning (MS, EZ, RD, AS, RP), p. 20.
OOPSLA-2017-SeidelSCWJ #data-driven #fault: Learning to blame: localizing novice type errors with data-driven diagnosis (ELS, HS, KC, WW, RJ), p. 27.
OOPSLA-2017-WuCC #error message: Learning user friendly type-error messages (BW, JPCI, SC0), p. 29.
PADL-2017-Vennekens #api #declarative #programming #python: Lowering the Learning Curve for Declarative Programming: A Python API for the IDP System (JV), pp. 86–102.
POPL-2017-MoermanS0KS #automaton: Learning nominal automata (JM, MS, AS0, BK, MS), pp. 613–625.
PPDP-2017-HoweRK #symmetry: Theory learning with symmetry breaking (JMH, ER, AK), pp. 85–96.
SAS-2017-BrockschmidtCKK #analysis: Learning Shape Analysis (MB, YC, PK, SK, DT), pp. 66–87.
ASE-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.
ASE-2017-Krishna #effectiveness: Learning effective changes for software projects (RK), pp. 1002–1005.
ASE-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-FSE-2017-FuM #case study: Easy over hard: a case study on deep learning (WF0, TM), pp. 49–60.
ESEC-FSE-2017-FuM17a #fault #predict: Revisiting unsupervised learning for defect prediction (WF0, TM), pp. 72–83.
ESEC-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-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.
GPCE-2017-MartiniH #automation #case study #experience #generative: Automatic generation of virtual learning spaces driven by CaVaDSL: an experience report (RGM, PRH), pp. 233–245.
ASPLOS-2017-LiCCZ #modelling #named #topic: SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs (KL, JC0, WC, JZ0), pp. 497–509.
CASE-2017-ChuckLKJFG #automation #statistics: Statistical data cleaning for deep learning of automation tasks from demonstrations (CC, ML, SK, RJ, RF, KG), pp. 1142–1149.
CASE-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.
CASE-2017-HanPM #approach #linear #modelling: Model-based reinforcement learning approach for deformable linear object manipulation (HH, GP, TM), pp. 750–755.
CASE-2017-KapadiaSJG #named: EchoBot: Facilitating data collection for robot learning with the Amazon echo (RK, SS, LJ, KG), pp. 159–165.
CASE-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.
CASE-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.
CASE-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.
CASE-2017-LiXZ #analysis #complexity: Complexity analysis of reinforcement learning and its application to robotics (BL, LX, QZ), pp. 1425–1426.
CASE-2017-LuRSW #detection #visual notation: Visual guided deep learning scheme for fall detection (NL, XR, JS, YW), pp. 801–806.
CASE-2017-PengZH #distributed #fault: Distributed fault diagnosis with shared-basis and B-splines-based matched learning (CP, YZ, QH), pp. 536–541.
CASE-2017-RenWJ #equivalence: Engineering effect equivalence enabled transfer learning (JR, HW, XJ), pp. 1174–1179.
CASE-2017-SunLZJ #framework #functional #using: Exploring functional variant using a deep learning framework (TS, ZL, XMZ, RJ), pp. 98–99.
CASE-2017-ZhaoCDW #fault #multi #taxonomy: TQWT-based multi-scale dictionary learning for rotating machinery fault diagnosis (ZZ, XC, BD, SW), pp. 554–559.
CASE-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.
CASE-2017-ZhouWY #approach: Dynamic dispatching for re-entrant production lines - A deep learning approach (FYZ, CHW, CJY), pp. 1026–1031.
CGO-2017-OgilvieP0L #compilation #cost analysis: Minimizing the cost of iterative compilation with active learning (WFO, PP, ZW0, HL), pp. 245–256.
CAV-2017-BielikRV: Learning a Static Analyzer from Data (PB, VR, MTV), pp. 233–253.
CAV-2017-Vazquez-Chanlatte #clustering #logic: Logical Clustering and Learning for Time-Series Data (MVC, JVD, XJ, SAS), pp. 305–325.
CSL-2017-AngluinAF #polynomial #query: Query Learning of Derived Omega-Tree Languages in Polynomial Time (DA, TA, DF), p. 21.
CSL-2017-HeerdtS0 #automaton #category theory #framework #named: CALF: Categorical Automata Learning Framework (GvH, MS, AS0), p. 24.
ICST-2017-TapplerAB #automaton #communication #modelling #testing: Model-Based Testing IoT Communication via Active Automata Learning (MT, BKA, RB), pp. 276–287.
ICTSS-2017-MaAYE #execution #testing: Fragility-Oriented Testing with Model Execution and Reinforcement Learning (TM, SA0, TY0, ME), pp. 3–20.
CSEET-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.
CSEET-2016-FreitasSM #student #using: Using an Active Learning Environment to Increase Students' Engagement (SAAdF, WCMPS, GM), pp. 232–236.
CSEET-2016-GeorgasPM #architecture #runtime #using #visualisation: Supporting Software Architecture Learning Using Runtime Visualization (JCG, JDP, MJM), pp. 101–110.
CSEET-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.
CSEET-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.
CSEET-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.
EDM-2016-BhartiyaCBSM #documentation #segmentation: Document Segmentation for Labeling with Academic Learning Objectives (DB, DC, SB, BS, MKM), pp. 282–287.
EDM-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.
EDM-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.
EDM-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.
EDM-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.
EDM-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.
EDM-2016-DavisCHH: Gauging MOOC Learners' Adherence to the Designed Learning Path (DD, GC, CH, GJH), pp. 54–61.
EDM-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.
EDM-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.
EDM-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.
EDM-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.
EDM-2016-FeildLZRE #automation #feedback #framework #platform #scalability: A Scalable Learning Analytics Platform for Automated Writing Feedback (JLF, NL, NLZ, MR, AE), pp. 688–693.
EDM-2016-HuangB #framework #modelling #student #towards: Towards Modeling Chunks in a Knowledge Tracing Framework for Students' Deep Learning (YH0, PB), pp. 666–668.
EDM-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.
EDM-2016-JoL #how #online: How to Judge Learning on Online Learning: Minimum Learning Judgment System (JJ, HL), pp. 597–598.
EDM-2016-JoTFRG #behaviour #modelling #social: Expediting Support for Social Learning with Behavior Modeling (YJ, GT, OF, CPR, DG), pp. 400–405.
EDM-2016-Kay #people: Enabling people to harness and control EDM for lifelong, life-wide learning (JK), p. 4.
EDM-2016-Kay16a #people: Enabling people to harness and control EDM for lifelong, life-wide learning (JK), pp. 10–20.
EDM-2016-LabutovL #web: Web as a textbook: Curating Targeted Learning Paths through the Heterogeneous Learning Resources on the Web (IL, HL), pp. 110–118.
EDM-2016-LanB #framework #personalisation: A Contextual Bandits Framework for Personalized Learning Action Selection (ASL, RGB), pp. 424–429.
EDM-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.
EDM-2016-Linn: WISE Ways to Strengthen Inquiry Science Learning (MCL), p. 3.
EDM-2016-MacLellanHPK #architecture #education: The Apprentice Learner architecture: Closing the loop between learning theory and educational data (CJM, EH, RP, KRK), pp. 151–158.
EDM-2016-Nam #adaptation #behaviour #predict: Predicting Off-task Behaviors for Adaptive Vocabulary Learning System (SN), pp. 672–674.
EDM-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.
EDM-2016-NiuNZWKY #algorithm #clustering: A Coupled User Clustering Algorithm for Web-based Learning Systems (KN, ZN, XZ, CW, KK, MY), pp. 175–182.
EDM-2016-QuigleyDSHPSAP: Equity of Learning Opportunities in the Chicago City of Learning Program (DQ, OD, MAS, KVH, WRP, TS, UA, NP), pp. 618–619.
EDM-2016-Rau #concept #mining #physics #social: Pattern mining uncovers social prompts of conceptual learning with physical and virtual representations (MAR), pp. 478–483.
EDM-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.
EDM-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.
EDM-2016-RoweAEHBBE #game studies #metric #validation: Validating Game-based Measures of Implicit Science Learning (ER, JAC, ME, AH, TB, RB, TE), pp. 490–495.
EDM-2016-Sande #component #multi #problem: Learning Curves for Problems with Multiple Knowledge Components (BvdS), pp. 523–526.
EDM-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.
EDM-2016-ShenC #feature model #modelling: Aim Low: Correlation-based Feature Selection for Model-based Reinforcement Learning (SS, MC), pp. 507–512.
EDM-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.
EDM-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.
EDM-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.
EDM-2016-SunY #community #online #personalisation: Personalization of Learning Paths in Online Communities of Creators (MS, SY), pp. 513–516.
EDM-2016-Wang #concept #design #interactive #personalisation: Designing Interactive and Personalized Concept Mapping Learning Environments (SW0), pp. 678–680.
EDM-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.
EDM-2016-YadavSKSD #framework #named #platform: TutorSpace: Content-centric Platform for Enabling Blended Learning in Developing Countries (KY, KS, RK, SS, OD), pp. 705–706.
EDM-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.
EDM-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.
EDM-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.
EDM-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.
ICPC-2016-TianWLG #debugging #rank #recommendation: Learning to rank for bug report assignee recommendation (YT0, DW, DL0, CLG), pp. 1–10.
ICSME-2016-YeXFLK #api #natural language: Learning to Extract API Mentions from Informal Natural Language Discussions (DY, ZX, CYF, JL0, NK), pp. 389–399.
FM-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.
FM-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.
AIIDE-2016-HarrisonR #crowdsourcing #using: Learning From Stories: Using Crowdsourced Narratives to Train Virtual Agents (BH, MOR), pp. 183–189.
AIIDE-2016-SummervilleM #design: Mystical Tutor: A Magic: The Gathering Design Assistant via Denoising Sequence-to-Sequence Learning (AJS, MM), pp. 86–92.
CHI-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.
CIG-2016-Bursztein #statistics #using: I am a legend: Hacking hearthstone using statistical learning methods (EB), pp. 1–8.
CIG-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.
CIG-2016-ChuIHT #game studies #video: Position-based reinforcement learning biased MCTS for General Video Game Playing (CYC, SI, TH, RT), pp. 1–8.
CIG-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.
CIG-2016-Shaker #framework #generative #motivation: Intrinsically motivated reinforcement learning: A promising framework for procedural content generation (NS), pp. 1–8.
CIG-2016-ShakerA #experience #predict: Transfer learning for cross-game prediction of player experience (NS, MAZ), pp. 1–8.
CIG-2016-ShiC #generative #online: Online level generation in Super Mario Bros via learning constructive primitives (PS, KC0), pp. 1–8.
CIG-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.
CIG-2016-SungurS #algorithm #behaviour: Voluntary behavior on cortical learning algorithm based agents (AKS, ES), pp. 1–7.
DiGRA-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).
VS-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.
VS-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.
VS-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.
VS-Games-2016-PatinoRP #analysis #game studies: Analysis of Game and Learning Mechanics According to the Learning Theories (AP, MR, JNP), pp. 1–4.
VS-Games-2016-RamosP #game studies: Program with Ixquic: Educative Games and Learning in Augmented and Virtual Environments (CR, TP), pp. 1–2.
CIKM-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.
CIKM-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.
CIKM-2016-AmandH: Discriminative View Learning for Single View Co-Training (JSA, JH), pp. 2221–2226.
CIKM-2016-BaruahZGLSV #optimisation: Optimizing Nugget Annotations with Active Learning (GB, HZ0, RG, JJL, MDS, OV), pp. 2359–2364.
CIKM-2016-ChenOX #recommendation: Learning Points and Routes to Recommend Trajectories (DC, CSO, LX), pp. 2227–2232.
CIKM-2016-CheungL #rank #robust #scalability: Scalable Spectral k-Support Norm Regularization for Robust Low Rank Subspace Learning (YmC, JL), pp. 1151–1160.
CIKM-2016-CormackG #classification #reliability #scalability: Scalability of Continuous Active Learning for Reliable High-Recall Text Classification (GVC, MRG), pp. 1039–1048.
CIKM-2016-DeveaudMN #rank: Learning to Rank System Configurations (RD, JM, JYN), pp. 2001–2004.
CIKM-2016-FengXZ #distributed: Distributed Deep Learning for Question Answering (MF, BX, BZ), pp. 2413–2416.
CIKM-2016-GyselRK: Learning Latent Vector Spaces for Product Search (CVG, MdR, EK), pp. 165–174.
CIKM-2016-HanSBW: Routing an Autonomous Taxi with Reinforcement Learning (MH, PS, SB, HW0), pp. 2421–2424.
CIKM-2016-HeTOKYC #query: Learning to Rewrite Queries (YH, JT, HO, CK, DY, YC), pp. 1443–1452.
CIKM-2016-KhabsaCAZAW #metric: Learning to Account for Good Abandonment in Search Success Metrics (MK, ACC, AHA, IZ, TA, KW), pp. 1893–1896.
CIKM-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.
CIKM-2016-MartinoBR0M #community #using: Learning to Re-Rank Questions in Community Question Answering Using Advanced Features (GDSM, ABC, SR, AU0, AM), pp. 1997–2000.
CIKM-2016-RenZRZYW #optimisation #performance: User Response Learning for Directly Optimizing Campaign Performance in Display Advertising (KR, WZ0, YR, HZ, YY0, JW0), pp. 679–688.
CIKM-2016-SilvaGAG #rank: Compression-Based Selective Sampling for Learning to Rank (RMS, GdCMG, MSA, MAG), pp. 247–256.
CIKM-2016-SousaCRMG #feature model #rank: Incorporating Risk-Sensitiveness into Feature Selection for Learning to Rank (DXdS, SDC, TCR, WSM, MAG), pp. 257–266.
CIKM-2016-TymoshenkoBM #rank #web: Learning to Rank Non-Factoid Answers: Comment Selection in Web Forums (KT, DB, AM), pp. 2049–2052.
CIKM-2016-WangJYJM #using: Learning to Extract Conditional Knowledge for Question Answering using Dialogue (PW, LJ, JY0, LJ, WYM), pp. 277–286.
CIKM-2016-WangWW: Learning Hidden Features for Contextual Bandits (HW, QW, HW), pp. 1633–1642.
CIKM-2016-XieWY: Active Zero-Shot Learning (SX, SW, PSY), pp. 1889–1892.
CIKM-2016-XieYWXCW #graph #recommendation: Learning Graph-based POI Embedding for Location-based Recommendation (MX, HY, HW, FX, WC, SW), pp. 15–24.
CIKM-2016-YuanGJCYZ #named #ranking #using: LambdaFM: Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates (FY, GG, JMJ, LC0, HY, WZ0), pp. 227–236.
CIKM-2016-ZhaoK #online #rank #reliability: Constructing Reliable Gradient Exploration for Online Learning to Rank (TZ, IK), pp. 1643–1652.
CIKM-2016-ZhengC #classification #constraints #probability: Regularizing Structured Classifier with Conditional Probabilistic Constraints for Semi-supervised Learning (VWZ, KCCC), pp. 1029–1038.
CIKM-2016-ZhengW #graph #multi: Graph-Based Multi-Modality Learning for Clinical Decision Support (ZZ, XW0), pp. 1945–1948.
CIKM-2016-ZhuangLPXH #adaptation: Ensemble of Anchor Adapters for Transfer Learning (FZ, PL0, SJP, HX, QH), pp. 2335–2340.
ECIR-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.
ECIR-2016-BotevaGSR #dataset #information retrieval #rank: A Full-Text Learning to Rank Dataset for Medical Information Retrieval (VB, DGG, AS, SR), pp. 716–722.
ECIR-2016-CroceB #kernel #scalability: Large-Scale Kernel-Based Language Learning Through the Ensemble Nystr đdoto o ¨ m Methods (DC, RB0), pp. 100–112.
ECIR-2016-IencoRRRT #mining #modelling #multi: MultiLingMine 2016: Modeling, Learning and Mining for Cross/Multilinguality (DI, MR, SR, PR, AT), pp. 869–873.
ECIR-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.
ECIR-2016-MiottoLD #health #predict: Deep Learning to Predict Patient Future Diseases from the Electronic Health Records (RM, LL0, JTD), pp. 768–774.
ECIR-2016-MustoSGL #recommendation #wiki #word: Learning Word Embeddings from Wikipedia for Content-Based Recommender Systems (CM, GS, MdG, PL), pp. 729–734.
ECIR-2016-NiuLC #approach #named #twitter: LExL: A Learning Approach for Local Expert Discovery on Twitter (WN, ZL, JC), pp. 803–809.
ECIR-2016-WangGLXC #multi #predict #representation: Multi-task Representation Learning for Demographic Prediction (PW, JG, YL, JX0, XC), pp. 88–99.
ECIR-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.
ICML-2016-AJFMS #cumulative #predict: Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control (PLA, CJ, MCF0, SIM, CS), pp. 1406–1415.
ICML-2016-AkrourNAA #optimisation: Model-Free Trajectory Optimization for Reinforcement Learning (RA, GN, HA, AA), pp. 2961–2970.
ICML-2016-AroraMM #multi #optimisation #probability #representation #using: Stochastic Optimization for Multiview Representation Learning using Partial Least Squares (RA, PM, TVM), pp. 1786–1794.
ICML-2016-BaiRWS #classification #difference #geometry: Differential Geometric Regularization for Supervised Learning of Classifiers (QB, SR, ZW, SS), pp. 1879–1888.
ICML-2016-BalkanskiMKS #combinator: Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization (EB, BM, AK0, YS), pp. 2207–2216.
ICML-2016-CohenHK #feedback #graph #online: Online Learning with Feedback Graphs Without the Graphs (AC, TH, TK), pp. 811–819.
ICML-2016-DaneshmandLH #adaptation: Starting Small - Learning with Adaptive Sample Sizes (HD, AL, TH), pp. 1463–1471.
ICML-2016-DuanCHSA #benchmark #metric: Benchmarking Deep Reinforcement Learning for Continuous Control (YD, XC0, RH, JS, PA), pp. 1329–1338.
ICML-2016-FernandoG #classification #video: Learning End-to-end Video Classification with Rank-Pooling (BF, SG), pp. 1187–1196.
ICML-2016-FinnLA #optimisation #policy: Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization (CF, SL, PA), pp. 49–58.
ICML-2016-FriesenD #modelling #theorem: The Sum-Product Theorem: A Foundation for Learning Tractable Models (ALF, PMD), pp. 1909–1918.
ICML-2016-GalG #approximate #nondeterminism #representation: Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning (YG, ZG), pp. 1050–1059.
ICML-2016-GlaudeP #automaton #probability: PAC learning of Probabilistic Automaton based on the Method of Moments (HG, OP), pp. 820–829.
ICML-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.
ICML-2016-HammCB #multi: Learning privately from multiparty data (JH, YC, MB), pp. 555–563.
ICML-2016-HashimotoGJ #generative: Learning Population-Level Diffusions with Generative RNNs (TBH, DKG, TSJ), pp. 2417–2426.
ICML-2016-HeB #modelling: Opponent Modeling in Deep Reinforcement Learning (HH0, JLBG), pp. 1804–1813.
ICML-2016-HoGE #optimisation #policy: Model-Free Imitation Learning with Policy Optimization (JH, JKG, SE), pp. 2760–2769.
ICML-2016-JiangL #evaluation #robust: Doubly Robust Off-policy Value Evaluation for Reinforcement Learning (NJ, LL0), pp. 652–661.
ICML-2016-JohanssonSS: Learning Representations for Counterfactual Inference (FDJ, US, DAS), pp. 3020–3029.
ICML-2016-Kasiviswanathan #empirical #performance: Efficient Private Empirical Risk Minimization for High-dimensional Learning (SPK, HJ), pp. 488–497.
ICML-2016-KatariyaKSW #multi #rank: DCM Bandits: Learning to Rank with Multiple Clicks (SK, BK, CS, ZW), pp. 1215–1224.
ICML-2016-KawakitaT: Barron and Cover's Theory in Supervised Learning and its Application to Lasso (MK, JT), pp. 1958–1966.
ICML-2016-LeeYH #multi #symmetry: Asymmetric Multi-task Learning based on Task Relatedness and Confidence (GL, EY, SJH), pp. 230–238.
ICML-2016-LeKYC #online #predict #sequence: Smooth Imitation Learning for Online Sequence Prediction (HML0, AK, YY, PC0), pp. 680–688.
ICML-2016-LererGF #physics: Learning Physical Intuition of Block Towers by Example (AL, SG, RF), pp. 430–438.
ICML-2016-LiuSSF #markov #network: Structure Learning of Partitioned Markov Networks (SL0, TS, MS, KF), pp. 439–448.
ICML-2016-LiuY #multi: Cross-Graph Learning of Multi-Relational Associations (HL, YY), pp. 2235–2243.
ICML-2016-LiZALH #optimisation #probability: Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning (XL, TZ, RA, HL0, JDH), pp. 917–925.
ICML-2016-LiZZ #memory management: Learning to Generate with Memory (CL, JZ0, BZ0), pp. 1177–1186.
ICML-2016-LouizosW #matrix #performance: Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors (CL, MW), pp. 1708–1716.
ICML-2016-MenschMTV #matrix #taxonomy: Dictionary Learning for Massive Matrix Factorization (AM, JM, BT, GV), pp. 1737–1746.
ICML-2016-MnihBMGLHSK: Asynchronous Methods for Deep Reinforcement Learning (VM, APB, MM, AG, TPL, TH, DS, KK), pp. 1928–1937.
ICML-2016-MussmannE: Learning and Inference via Maximum Inner Product Search (SM, SE), pp. 2587–2596.
ICML-2016-NiepertAK #graph #network: Learning Convolutional Neural Networks for Graphs (MN, MA, KK), pp. 2014–2023.
ICML-2016-OswalCRRN #network #similarity: Representational Similarity Learning with Application to Brain Networks (UO, CRC, MALR, TTR, RDN), pp. 1041–1049.
ICML-2016-Papakonstantinou #on the: On the Power and Limits of Distance-Based Learning (PAP, JX0, GY), pp. 2263–2271.
ICML-2016-PatriniNNC #robust: Loss factorization, weakly supervised learning and label noise robustness (GP, FN, RN, MC), pp. 708–717.
ICML-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.
ICML-2016-ScheinZBW #composition: Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations (AS, MZ, DMB, HMW), pp. 2810–2819.
ICML-2016-SchnabelSSCJ #evaluation #recommendation: Recommendations as Treatments: Debiasing Learning and Evaluation (TS, AS, AS, NC, TJ), pp. 1670–1679.
ICML-2016-ShahamCDJNCK #approach: A Deep Learning Approach to Unsupervised Ensemble Learning (US, XC, OD, AJ, BN, JTC, YK), pp. 30–39.
ICML-2016-ShahG #correlation: Pareto Frontier Learning with Expensive Correlated Objectives (AS, ZG), pp. 1919–1927.
ICML-2016-SinglaTK #elicitation: Actively Learning Hemimetrics with Applications to Eliciting User Preferences (AS, ST, AK0), pp. 412–420.
ICML-2016-SongGC #network #sequence: Factored Temporal Sigmoid Belief Networks for Sequence Learning (JS, ZG, LC), pp. 1272–1281.
ICML-2016-SuLCC #modelling #statistics #visual notation: Nonlinear Statistical Learning with Truncated Gaussian Graphical Models (QS, XL, CC, LC), pp. 1948–1957.
ICML-2016-SunVBB #predict: Learning to Filter with Predictive State Inference Machines (WS0, AV, BB, JAB), pp. 1197–1205.
ICML-2016-SyrgkanisKS #algorithm #performance: Efficient Algorithms for Adversarial Contextual Learning (VS, AK, RES), pp. 2159–2168.
ICML-2016-ThomasB #evaluation #policy: Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning (PST, EB), pp. 2139–2148.
ICML-2016-UstinovskiyFGS: Meta-Gradient Boosted Decision Tree Model for Weight and Target Learning (YU, VF, GG, PS), pp. 2692–2701.
ICML-2016-WangSHHLF #architecture #network: Dueling Network Architectures for Deep Reinforcement Learning (ZW0, TS, MH, HvH, ML, NdF), pp. 1995–2003.
ICML-2016-XieZX #modelling: Diversity-Promoting Bayesian Learning of Latent Variable Models (PX, JZ0, EPX), pp. 59–68.
ICML-2016-XuFZ #process: Learning Granger Causality for Hawkes Processes (HX, MF, HZ), pp. 1717–1726.
ICML-2016-YangCS #graph: Revisiting Semi-Supervised Learning with Graph Embeddings (ZY, WWC, RS), pp. 40–48.
ICML-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.
ICML-2016-YaoK #performance #product line: Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity (QY, JTK), pp. 2645–2654.
ICML-2016-YuL #multi #performance: Learning from Multiway Data: Simple and Efficient Tensor Regression (RY, YL0), pp. 373–381.
ICML-2016-ZadehHS #geometry #metric: Geometric Mean Metric Learning (PZ, RH, SS), pp. 2464–2471.
ICML-2016-ZarembaMJF #algorithm: Learning Simple Algorithms from Examples (WZ, TM, AJ, RF), pp. 421–429.
ICML-2016-ZhaoPX #modelling: Learning Mixtures of Plackett-Luce Models (ZZ, PP, LX), pp. 2906–2914.
ICPR-2016-AbdicFBARMS #approach #detection: Detecting road surface wetness from audio: A deep learning approach (IA, LF, DEB, WA, BR, EM, BWS), pp. 3458–3463.
ICPR-2016-AfridiRS #framework #latency #named: L-CNN: Exploiting labeling latency in a CNN learning framework (MJA, AR, EMS), pp. 2156–2161.
ICPR-2016-AgustssonTG: Regressor Basis Learning for anchored super-resolution (EA, RT, LVG), pp. 3850–3855.
ICPR-2016-AhmedK #multi #taxonomy: Coupled multiple dictionary learning based on edge sharpness for single-image super-resolution (JA, RK), pp. 3838–3843.
ICPR-2016-BalaziaS16a #recognition #robust: Learning robust features for gait recognition by Maximum Margin Criterion (MB, PS), pp. 901–906.
ICPR-2016-BarddalGGBE #nearest neighbour: Overcoming feature drifts via dynamic feature weighted k-nearest neighbor learning (JPB, HMG, JG, AdSBJ, FE), pp. 2186–2191.
ICPR-2016-BayramogluKH #classification #image #independence: Deep learning for magnification independent breast cancer histopathology image classification (NB, JK, JH), pp. 2440–2445.
ICPR-2016-BorgaAL #image #segmentation: Semi-supervised learning of anatomical manifolds for atlas-based segmentation of medical images (MB, TA, ODL), pp. 3146–3149.
ICPR-2016-CaoN #fine-grained #process #recognition: Exploring deep learning based solutions in fine grained activity recognition in the wild (SC, RN), pp. 384–389.
ICPR-2016-CarbonneauGG #identification #multi #random #using: Witness identification in multiple instance learning using random subspaces (MAC, EG, GG), pp. 3639–3644.
ICPR-2016-ChenWHF #detection #estimation: Deep learning for integrated hand detection and pose estimation (TYC, MYW, YHH, LCF), pp. 615–620.
ICPR-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.
ICPR-2016-DasguptaYO #sequence: Regularized dynamic Boltzmann machine with Delay Pruning for unsupervised learning of temporal sequences (SD, TY, TO), pp. 1201–1206.
ICPR-2016-DevanneWDBBP #analysis: Learning shape variations of motion trajectories for gait analysis (MD, HW, MD, SB, ADB, PP), pp. 895–900.
ICPR-2016-FanWH #adaptation #multi: Multi-stage multi-task feature learning via adaptive threshold (YF, YW, TZH), pp. 1666–1671.
ICPR-2016-FengLL #effectiveness #using: Learning effective Gait features using LSTM (YF0, YL, JL), pp. 325–330.
ICPR-2016-Forstner #modelling #semantics: A future for learning semantic models of man-made environments (WF), pp. 2475–2485.
ICPR-2016-GhaderiA #network: Selective unsupervised feature learning with Convolutional Neural Network (S-CNN) (AG, VA), pp. 2486–2490.
ICPR-2016-GonzalezVT #classification #invariant: Learning rotation invariant convolutional filters for texture classification (DM, MV, DT), pp. 2012–2017.
ICPR-2016-GuoCL #multi: Multi-label learning with globAl densiTy fusiOn Mapping features (YG, FC, GL0), pp. 462–467.
ICPR-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.
ICPR-2016-HouXX0 #classification #graph: Semi-supervised learning competence of classifiers based on graph for dynamic classifier selection (CH, YX, ZX, JS0), pp. 3650–3654.
ICPR-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.
ICPR-2016-HuLL #named #representation #semantics #video: Video2vec: Learning semantic spatio-temporal embeddings for video representation (ShH, YL, BL), pp. 811–816.
ICPR-2016-JafariKNSSWN #image #segmentation #using: Skin lesion segmentation in clinical images using deep learning (MHJ, NK, ENE, SS, SMRS, KRW, KN), pp. 337–342.
ICPR-2016-JenckelBD #documentation #named #sequence: anyOCR: A sequence learning based OCR system for unlabeled historical documents (MJ, SSB, AD0), pp. 4035–4040.
ICPR-2016-JiaoZ #multi #taxonomy #using: Multiple Instance Dictionary Learning using Functions of Multiple Instances (CJ, AZ), pp. 2688–2693.
ICPR-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.
ICPR-2016-KalraSRT #network #using: Learning opposites using neural networks (SK, AS, SR, HRT), pp. 1213–1218.
ICPR-2016-KanehiraSH #multi #scalability: True-negative label selection for large-scale multi-label learning (AK, AS, TH), pp. 3673–3678.
ICPR-2016-KaremF #concept #multi: Multiple Instance Learning with multiple positive and negative target concepts (AK, HF), pp. 474–479.
ICPR-2016-KaurDCM #hybrid #image: Hybrid deep learning for Reflectance Confocal Microscopy skin images (PK, KJD, GOC, MCM), pp. 1466–1471.
ICPR-2016-KawanishiDIMF #classification #robust: Misclassification tolerable learning for robust pedestrian orientation classification (YK, DD, II, HM, HF), pp. 486–491.
ICPR-2016-KhanH #adaptation #polynomial #using: Adapting instance weights for unsupervised domain adaptation using quadratic mutual information and subspace learning (MNAK, DRH), pp. 1560–1565.
ICPR-2016-KhodabandehMVMP #segmentation #video: Unsupervised learning of supervoxel embeddings for video Segmentation (MK, SM, AV, NM, EMP, SS, GM), pp. 2392–2397.
ICPR-2016-Kobayashi #data-driven #image #similarity: Learning data-driven image similarity measure (TK), pp. 3679–3684.
ICPR-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.
ICPR-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.
ICPR-2016-LiaoQL #image #multi: Semisupervised manifold learning for color transfer between multiview images (DL, YQ, ZNL), pp. 259–264.
ICPR-2016-Liu16a #classification #multi #network #scalability: Hierarchical learning for large multi-class network classification (LL), pp. 2307–2312.
ICPR-2016-LoogY #consistency #empirical #nondeterminism: An empirical investigation into the inconsistency of sequential active learning (ML, YY), pp. 210–215.
ICPR-2016-MaoZCLHY16a #collaboration #recognition #taxonomy: Group and collaborative dictionary pair learning for face recognition (MM, ZZ, ZC, HL, XH, RY), pp. 4107–4111.
ICPR-2016-MarkusPA #optimisation: Learning local descriptors by optimizing the keypoint-correspondence criterion (NM, ISP, JA), pp. 2380–2385.
ICPR-2016-MoutafisLK #metric: Regression-based metric learning (PM, ML, IAK), pp. 2700–2705.
ICPR-2016-NahaW16a #segmentation #using: Object figure-ground segmentation using zero-shot learning (SN, YW0), pp. 2842–2847.
ICPR-2016-Nilsson #consistency #taxonomy: Sparse coding with unity range codes and label consistent discriminative dictionary learning (MN), pp. 3186–3191.
ICPR-2016-NogueiraMCSS #image #semantics: Learning to semantically segment high-resolution remote sensing images (KN, MDM, JC, WRS, JAdS), pp. 3566–3571.
ICPR-2016-OhY #algorithm #graph: Enhancing label inference algorithms considering vertex importance in graph-based semi-supervised learning (BO, JY), pp. 1671–1676.
ICPR-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.
ICPR-2016-PalCGCC #multi #using: Severity grading of psoriatic plaques using deep CNN based multi-task learning (AP, AC, UG, AC, RC), pp. 1478–1483.
ICPR-2016-PassalisT #embedded #retrieval #word: Bag of Embedded Words learning for text retrieval (NP, AT), pp. 2416–2421.
ICPR-2016-PengRP #network #recognition #using: Learning face recognition from limited training data using deep neural networks (XP, NKR, SP), pp. 1442–1447.
ICPR-2016-PironkovDD #automation #multi #recognition #speech: Speaker-aware Multi-Task Learning for automatic speech recognition (GP, SD, TD), pp. 2900–2905.
ICPR-2016-QianCKNM: Deep structured-output regression learning for computational color constancy (YQ, KC0, JKK, JN, JM), pp. 1899–1904.
ICPR-2016-QuachtranHS #detection #using: Detection of Intracranial Hypertension using Deep Learning (BQ, RBH, FS), pp. 2491–2496.
ICPR-2016-QuLFT #effectiveness #retrieval: Improving PGF retrieval effectiveness with active learning (JQ, XL, SF, ZT), pp. 1125–1130.
ICPR-2016-RaytchevKKTK: Ensemble-based local learning for high-dimensional data regression (BR, YK, MK, TT, KK), pp. 2640–2645.
ICPR-2016-RedkoB #kernel: Kernel alignment for unsupervised transfer learning (IR, YB), pp. 525–530.
ICPR-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.
ICPR-2016-RoyTL #network: Context-regularized learning of fully convolutional networks for scene labeling (AR, ST, LJL), pp. 3751–3756.
ICPR-2016-Saha0PV #problem #visual notation: Transfer learning for rare cancer problems via Discriminative Sparse Gaussian Graphical model (BS, SG0, DQP, SV), pp. 537–542.
ICPR-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.
ICPR-2016-ShankarDG #network: Reinforcement Learning via Recurrent Convolutional Neural Networks (TS, SKD, PG), pp. 2592–2597.
ICPR-2016-ShwetaE0B #architecture #identification #interactive: A deep learning architecture for protein-protein Interaction Article identification (S, AE, SS0, PB), pp. 3128–3133.
ICPR-2016-SoleymaniGF: Loss factors for learning Boosting ensembles from imbalanced data (RS, EG, GF), pp. 204–209.
ICPR-2016-SousaB #consistency: Constrained Local and Global Consistency for semi-supervised learning (CARdS, GEAPAB), pp. 1689–1694.
ICPR-2016-SouzaSC #comprehension #semantics: Building semantic understanding beyond deep learning from sound and vision (FDMdS, SS, GCC), pp. 2097–2102.
ICPR-2016-SunBTTH #detection #locality #using: Tattoo detection and localization using region-based deep learning (ZS, JB, PT, MT, AH), pp. 3055–3060.
ICPR-2016-SunHLK #multi #network #recognition: Multiple Instance Learning Convolutional Neural Networks for object recognition (MS, TXH, MCL, AKR), pp. 3270–3275.
ICPR-2016-SvobodaMB #recognition: Palmprint recognition via discriminative index learning (JS, JM, MMB), pp. 4232–4237.
ICPR-2016-TairaTO #robust #synthesis: Robust feature matching by learning descriptor covariance with viewpoint synthesis (HT, AT, MO), pp. 1953–1958.
ICPR-2016-TounsiMA #framework #recognition #taxonomy: Supervised dictionary learning in BoF framework for Scene Character recognition (MT, IM, AMA), pp. 3987–3992.
ICPR-2016-Triantafyllidou #detection #incremental #network: Face detection based on deep convolutional neural networks exploiting incremental facial part learning (DT, AT), pp. 3560–3565.
ICPR-2016-TzelepiT #image #retrieval: Exploiting supervised learning for finetuning deep CNNs in content based image retrieval (MT, AT), pp. 2918–2923.
ICPR-2016-UlmB: Learning tubes (MU, NB), pp. 3655–3660.
ICPR-2016-WangHG #classification #novel: A novel fingerprint classification method based on deep learning (RW, CH, TG), pp. 931–936.
ICPR-2016-WangLLCL #visual notation: Visual tracking via sparsity pattern learning (YW, YL0, ZL, LFC, HL), pp. 2716–2721.
ICPR-2016-WangZWGSH #identification #metric #similarity: Contextual Similarity Regularized Metric Learning for person re-identification (JW0, JZ, ZW, CG, NS, RH0), pp. 2048–2053.
ICPR-2016-WeiLSKM #taxonomy: Joint learning dictionary and discriminative features for high dimensional data (XW, YL, HS, MK, YLM), pp. 366–371.
ICPR-2016-WichtFH #keyword: Deep learning features for handwritten keyword spotting (BW, AF0, JH), pp. 3434–3439.
ICPR-2016-WuWJ #multi #recognition: Multiple Facial Action Unit recognition by learning joint features and label relations (SW, SW, QJ), pp. 2246–2251.
ICPR-2016-XueB #multi: Multi-task learning for one-class SVM with additional new features (YX, PB), pp. 1571–1576.
ICPR-2016-XuSARS #multi #recognition #retrieval #taxonomy: Multi-Paced Dictionary Learning for cross-domain retrieval and recognition (DX0, JS, XAP, ER0, NS), pp. 3228–3233.
ICPR-2016-XuT #3d #network: Beam search for learning a deep Convolutional Neural Network of 3D shapes (XX, ST), pp. 3506–3511.
ICPR-2016-YangJPL #image #taxonomy: Enhancement of Low Light Level Images with coupled dictionary learning (JY, XJ, CP, CLL), pp. 751–756.
ICPR-2016-YangL #nondeterminism #using: Active learning using uncertainty information (YY, ML), pp. 2646–2651.
ICPR-2016-ZhaoZWJ #multi: Multilingual articulatory features augmentation learning (YZ, RZ, XW0, QJ), pp. 2895–2899.
ICPR-2016-ZhengYYY #feature model #robust: Robust unsupervised feature selection by nonnegative sparse subspace learning (WZ, HY, JY0, JY), pp. 3615–3620.
ICPR-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.
KDD-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.
KDD-2016-ChangZTYCHH #network #streaming: Positive-Unlabeled Learning in Streaming Networks (SC, YZ0, JT, DY, YC, MAHJ, TSH), pp. 755–764.
KDD-2016-ChoiBSCTBTS #concept #multi #representation: Multi-layer Representation Learning for Medical Concepts (EC, MTB, ES, CC, MT, JB, JTS, JS), pp. 1495–1504.
KDD-2016-FeiW0 #cumulative #information management: Learning Cumulatively to Become More Knowledgeable (GF, SW, BL0), pp. 1565–1574.
KDD-2016-Freitas #composition #network: Learning to Learn and Compositionality with Deep Recurrent Neural Networks: Learning to Learn and Compositionality (NdF), p. 3.
KDD-2016-GroverL #named #network #scalability: node2vec: Scalable Feature Learning for Networks (AG, JL), pp. 855–864.
KDD-2016-Herbrich #modelling #scalability: Learning Sparse Models at Scale (RH), p. 407.
KDD-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.
KDD-2016-LiGHZ #recommendation: Point-of-Interest Recommendations: Learning Potential Check-ins from Friends (HL, YG, RH, HZ), pp. 975–984.
KDD-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.
KDD-2016-LinXBJZ #feature model #interactive #multi: Multi-Task Feature Interaction Learning (KL, JX, IMB, SJ, JZ), pp. 1735–1744.
KDD-2016-LiWYR #analysis #multi: A Multi-Task Learning Formulation for Survival Analysis (YL, JW0, JY, CKR), pp. 1715–1724.
KDD-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.
KDD-2016-NingMRR #modelling #multi: Modeling Precursors for Event Forecasting via Nested Multi-Instance Learning (YN, SM, HR, NR), pp. 1095–1104.
KDD-2016-PetitjeanW #modelling #scalability #visual notation: Scalable Learning of Graphical Models (FP, GIW), pp. 2131–2132.
KDD-2016-ReddyLBJ #bound #scheduling: Unbounded Human Learning: Optimal Scheduling for Spaced Repetition (SR, IL, SB, TJ), pp. 1815–1824.
KDD-2016-Schneider #embedded #optimisation: Bayesian Optimization and Embedded Learning Systems (JS), p. 413.
KDD-2016-XuT0 #multi #robust: Robust Extreme Multi-label Learning (CX0, DT, CX0), pp. 1275–1284.
KDD-2016-ZhaiCZZ #named #network #online: DeepIntent: Learning Attentions for Online Advertising with Recurrent Neural Networks (SZ, KhC, RZ, Z(Z), pp. 1295–1304.
KDD-2016-ZhangYS #online #symmetry: Online Asymmetric Active Learning with Imbalanced Data (XZ, TY, PS), pp. 2055–2064.
KDD-2016-ZhangZL #ambiguity: Partial Label Learning via Feature-Aware Disambiguation (MLZ, BBZ, XYL), pp. 1335–1344.
KDD-2016-ZhangZWX: Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising (WZ0, TZ, JW0, JX), pp. 665–674.
KDD-2016-ZhaoYCLR #multi: Hierarchical Incomplete Multi-source Feature Learning for Spatiotemporal Event Forecasting (LZ0, JY, FC0, CTL, NR), pp. 2085–2094.
KDD-2016-ZhengYC #invariant #performance #taxonomy: Efficient Shift-Invariant Dictionary Learning (GZ, YY, JGC), pp. 2095–2104.
MoDELS-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.
PLDI-2016-HeuleS0A #automation #set #synthesis: Stratified synthesis: automatically learning the x86-64 instruction set (SH, ES, RS0, AA), pp. 237–250.
PLDI-2016-ZhuPJ #automation #specification: Automatically learning shape specifications (HZ0, GP, SJ), pp. 491–507.
POPL-2016-0001NMR #invariant #using: Learning invariants using decision trees and implication counterexamples (PG0, DN, PM, DR), pp. 499–512.
POPL-2016-LongR #automation #generative: Automatic patch generation by learning correct code (FL, MR), pp. 298–312.
POPL-2016-RaychevBVK #semistructured data #source code: Learning programs from noisy data (VR, PB, MTV, AK0), pp. 761–774.
SAS-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.
ASE-2016-ChenCXX #retrieval: Learning a dual-language vector space for domain-specific cross-lingual question retrieval (GC, CC, ZX, BX), pp. 744–755.
ASE-2016-KrishnaMF #automation: Too much automation? the bellwether effect and its implications for transfer learning (RK, TM, WF), pp. 122–131.
ASE-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.
ASE-2016-WhiteTVP #clone detection #detection: Deep learning code fragments for code clone detection (MW, MT, CV, DP), pp. 87–98.
FSE-2016-BusjaegerX #case study #industrial: Learning for test prioritization: an industrial case study (BB, TX), pp. 975–980.
FSE-2016-GuZZK #api: Deep API learning (XG, HZ0, DZ, SK0), pp. 631–642.
FSE-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.
CASE-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.
CASE-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.
CASE-2016-LiuS #kernel #online #recognition #taxonomy: Online kernel dictionary learning for object recognition (HL0, FS), pp. 268–273.
CAV-2016-Fiterau-Brostean #implementation #model checking: Combining Model Learning and Model Checking to Analyze TCP Implementations (PFB, RJ, FWV), pp. 454–471.
CAV-2016-SantolucitoZP #automation #probability: Probabilistic Automated Language Learning for Configuration Files (MS, EZ, RP), pp. 80–87.
CSL-2016-Silva #algebra: Coalgebraic Learning (AS0), p. 1.
ICTSS-2016-ReichstallerEKR #testing #using: Risk-Based Interoperability Testing Using Reinforcement Learning (AR, BE, AK, WR, MG), pp. 52–69.
ECSA-2015-KiwelekarW #architecture: Learning Objectives for a Course on Software Architecture (AWK, HSW), pp. 169–180.
DRR-2015-FuLLQT #diagrams #multi #retrieval: A diagram retrieval method with multi-label learning (SF, XL, LL, JQ, ZT).
HT-2015-KirchnerR #collaboration #in the cloud: Collaborative Learning in the Cloud: A Cross-Cultural Perspective of Collaboration (KK, LR), pp. 333–336.
HT-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.
JCDL-2015-KananZMF #big data #problem #summary: Big Data Text Summarization for Events: A Problem Based Learning Course (TK, XZ, MM, EAF), pp. 87–90.
SIGMOD-2015-KumarNP #linear #modelling #normalisation: Learning Generalized Linear Models Over Normalized Data (AK, JFN, JMP), pp. 1969–1984.
VLDB-2015-QianGJ #adaptation #comparison: Learning User Preferences By Adaptive Pairwise Comparison (LQ, JG, HVJ), pp. 1322–1333.
EDM-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.
EDM-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.
EDM-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.
EDM-2015-ChandrasekaranK: Learning Instructor Intervention from MOOC Forums: Early Results and Issues (MKC, MYK, BCYT, KR), pp. 218–225.
EDM-2015-ChenBD #detection: Video-Based Affect Detection in Noninteractive Learning Environments (YC, NB, SKD), pp. 440–443.
EDM-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.
EDM-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.
EDM-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.
EDM-2015-KeshtkarCKC #interactive #student: Analyzing Students' Interaction Based on Their Response to Determine Their Learning Outcomes (FK, JC, BK, AC), pp. 588–589.
EDM-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.
EDM-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.
EDM-2015-MacLellanLK #modelling #student: Accounting for Slipping and Other False Negatives in Logistic Models of Student Learning (CJM, RL0, KRK), pp. 53–60.
EDM-2015-MostowGEG #automation #identification #word: Automatic Identification of Nutritious Contexts for Learning Vocabulary Words (JM, DG, RE, RG), pp. 266–273.
EDM-2015-Olivares-Rodriguez #mining #student #word: Learning the Creative Potential of Students by Mining a Word Association Task (COR, MG), pp. 400–403.
EDM-2015-OlsenAR #collaboration #performance #predict #student: Predicting Student Performance In a Collaborative Learning Environment (JKO, VA, NR), pp. 211–217.
EDM-2015-Ostrow #adaptation #motivation #student: Enhancing Student Motivation and Learning Within Adaptive Tutors (KO), pp. 668–670.
EDM-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.
EDM-2015-Pelanek15b #modelling #question #student: Modeling Student Learning: Binary or Continuous Skill? (RP), pp. 560–561.
EDM-2015-Rasanen #education: Educational Neuroscience as a Tool to Understand Learning and Learning Disabilities in Mathematics (PR), p. 7.
EDM-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.
EDM-2015-RitterF: Carnegie Learning's Cognitive Tutor (SR, SF), pp. 633–634.
EDM-2015-RoweBA #game studies: Strategic Game Moves Mediate Implicit Science Learning (ER, RSB, JAC), pp. 432–435.
EDM-2015-SiemensBG #graph: Personal Knowledge/Learning Graph (GS, RSB, DG), p. 5.
EDM-2015-Streeter #modelling: Mixture Modeling of Individual Learning Curves (MJS), pp. 45–52.
EDM-2015-Tibbles #data mining #mining: Exploring the Impact of Spacing in Mathematics Learning through Data Mining (RT), pp. 590–591.
EDM-2015-Truong #adaptation: Integrating Learning Styles into Adaptive e-Learning System (HMT), pp. 645–647.
EDM-2015-VossSMS #approach #dataset #matrix: A Transfer Learning Approach for Applying Matrix Factorization to Small ITS Datasets (LV, CS, CM, LST), pp. 372–375.
EDM-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.
EDM-2015-YeKSB #behaviour #multi #process #sequence: Learning Behavior Characterization with Multi-Feature, Hierarchical Activity Sequences (CY, JSK, JRS, GB), pp. 380–383.
EDM-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.
ITiCSE-2015-AlshammariAH #adaptation #education #security: The Impact of Learning Style Adaptivity in Teaching Computer Security (MA, RA, RJH), pp. 135–140.
ITiCSE-2015-Annamaa #ide #programming #python: Thonny, : a Python IDE for Learning Programming (AA), p. 343.
ITiCSE-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.
ITiCSE-2015-Hamilton #education: Learning and Teaching Computing Sustainability (MH), p. 338.
ITiCSE-2015-Harms #community #source code: Department Programs to Encourage and Support Service Learning and Community Engagement (DEH), p. 330.
ITiCSE-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.
ITiCSE-2015-QuinsonO #education #programming: A Teaching System to Learn Programming: the Programmer’s Learning Machine (MQ, GO), pp. 260–265.
ITiCSE-2015-SantosSFN #agile #development #framework #mobile: Combining Challenge-Based Learning and Scrum Framework for Mobile Application Development (ARS, AS, PF, MN), pp. 189–194.
ITiCSE-2015-SettleLS #community: A Computer Science Linked-courses Learning Community (AS, JL, TS), pp. 123–128.
ITiCSE-2015-TarmazdiVSFF #using #visualisation: Using Learning Analytics to Visualise Computer Science Teamwork (HT, RV, CS, KEF, NJGF), pp. 165–170.
ITiCSE-2015-Tudor #optimisation #query #xml: Virtual Learning Laboratory about Query Optimization against XML Data (LNT), p. 348.
SIGITE-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.
SIGITE-2015-Miller #evaluation #usability: Usability Evaluation: Learning When Method Findings Converge-And When They Don’t (CSM), pp. 167–172.
SIGITE-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.
SIGITE-2015-NicolaiNHW #education #industrial: Experiential Learning Business/Industry and Education Wants and Needs (BN, DN, CHJ, CW), pp. 95–96.
SIGITE-2015-SettleLS #community #development: Evaluating a Linked-courses Learning Community for Development Majors (AS, JL, TS), pp. 127–132.
ICSME-2015-CorleyDK #feature model #using: Exploring the use of deep learning for feature location (CSC, KD, NAK), pp. 556–560.
MSR-2015-WhiteVVP #repository #towards: Toward Deep Learning Software Repositories (MW, CV, MLV, DP), pp. 334–345.
LATA-2015-Yoshinaka #boolean grammar #grammar inference: Learning Conjunctive Grammars and Contextual Binary Feature Grammars (RY), pp. 623–635.
SEFM-2015-Muhlberg0DLP #source code #verification: Learning Assertions to Verify Linked-List Programs (JTM, DHW, MD, GL, FP), pp. 37–52.
ICFP-2015-ZhuNJ #refinement: Learning refinement types (HZ, AVN, SJ), pp. 400–411.
AIIDE-2015-UriarteO #automation #game studies #modelling: Automatic Learning of Combat Models for RTS Games (AU, SO), pp. 212–219.
CHI-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-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-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.
CIG-2015-DannZT #approach: An improved approach to reinforcement learning in Computer Go (MD, FZ, JT), pp. 169–176.
CIG-2015-DobreL #game studies #mining #online: Online learning and mining human play in complex games (MSD, AL), pp. 60–67.
CIG-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.
CIG-2015-HuangW: Learning overtaking and blocking skills in simulated car racing (HHH, TW), pp. 439–445.
CIG-2015-IvanovoRZL #monte carlo: Combining Monte Carlo tree search and apprenticeship learning for capture the flag (JI, WLR, FZ, XL0), pp. 154–161.
CIG-2015-KamekoMT #game studies #generative: Learning a game commentary generator with grounded move expressions (HK, SM, YT), pp. 177–184.
CIG-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.
CIG-2015-QuiterioPM #approach #geometry: A reinforcement learning approach for the circle agent of geometry friends (JQ, RP, FSM), pp. 423–430.
CIG-2015-Yao #game studies #speech: Keynote speech I: Co-evolutionary learning in game-playing (XY0), p. 16.
FDG-2015-KaoH15a #game studies #named: Mazzy: A STEM Learning Game (DK, DFH).
FDG-2015-KaoH15b #game studies #using: Exploring the Construction, Play, Use of Virtual Identities in a STEM Learning Game (DK, DFH).
FDG-2015-PackardO #behaviour #metric #similarity: Learning Behavior form Demonstration in Minecraft via Symbolic Similarity Measures (BP, SO).
FDG-2015-Pirker #collaboration: Learning in Collaborative and Motivational Virtual Environments (JP).
FDG-2015-ShakerAS #modelling: Active Learning for Player Modeling (NS, MAZ, MS).
FDG-2015-SummervilleBMJ #data-driven #game studies #generative: The Learning of Zelda: Data-Driven Level Generation for Action Role Playing Games (AS, MB, MM, AJ).
VS-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.
VS-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.
VS-Games-2015-PanzoliPL #communication #game studies: Communication and Knowledge Sharing in an Immersive Learning Game (DP, CPL, PL), pp. 1–8.
VS-Games-2015-YohannisP #algorithm #gamification #sorting #visualisation: Sort Attack: Visualization and Gamification of Sorting Algorithm Learning (AY, YP), pp. 1–8.
CHI-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.
CHI-2015-DavisK #student: Investigating High School Students’ Perceptions of Digital Badges in Afterschool Learning (KD, EK), pp. 4043–4046.
CHI-2015-KardanC #adaptation #evaluation #interactive #simulation: Providing Adaptive Support in an Interactive Simulation for Learning: An Experimental Evaluation (SK, CC), pp. 3671–3680.
CHI-2015-Noble #self: Resilience Ex Machina: Learning a Complex Medical Device for Haemodialysis Self-Treatment (PJN), pp. 4147–4150.
CHI-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.
CHI-2015-ShovmanBSS #3d #interface: Twist and Learn: Interface Learning in 3DOF Exploration of 3D Scatterplots (MMS, JLB, AS, KCSB), pp. 313–316.
CHI-2015-StrohmayerCB #people: Exploring Learning Ecologies among People Experiencing Homelessness (AS, RC, MB), pp. 2275–2284.
CHI-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.
CHI-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.
CSCW-2015-CoetzeeLFHH #interactive #scalability: Structuring Interactions for Large-Scale Synchronous Peer Learning (DC, SL, AF, BH, MAH), pp. 1139–1152.
CSCW-2015-DornSS #collaboration: Piloting TrACE: Exploring Spatiotemporal Anchored Collaboration in Asynchronous Learning (BD, LBS, AS), pp. 393–403.
CSCW-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.
DHM-HM-2015-NishimuraK #case study: A Study on Learning Effects of Marking with Highlighter Pen (HN, NK), pp. 357–367.
DUXU-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.
DUXU-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.
DUXU-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.
DUXU-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.
DUXU-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.
HCI-DE-2015-BakkeB #developer #proximity: The Closer the Better: Effects of Developer-User Proximity for Mutual Learning (SB, TB), pp. 14–26.
HCI-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.
HIMI-IKC-2015-AraiTA #development: Development of a Learning Support System for Class Structure Mapping Based on Viewpoint (TA, TT, TA), pp. 285–293.
HIMI-IKC-2015-HasegawaD #approach #framework #platform #ubiquitous: A Ubiquitous Lecture Archive Learning Platform with Note-Centered Approach (SH, JD), pp. 294–303.
HIMI-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.
HIMI-IKC-2015-Iwata #difference: Method to Generate an Operation Learning Support System by Shortcut Key Differences in Similar Software (HI), pp. 332–340.
HIMI-IKC-2015-KimitaMMNIS #education: Learning State Model for Value Co-Creative Education Services (KK, KM, SM, YN, TI, YS), pp. 341–349.
HIMI-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.
HIMI-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.
LCT-2015-BoonbrahmKB #artificial reality #student #using: Using Augmented Reality Technology in Assisting English Learning for Primary School Students (SB, CK, PB), pp. 24–32.
LCT-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.
LCT-2015-FonsecaRVG #3d #education: From Formal to Informal 3D Learning. Assesment of Users in the Education (DF, ER, FV, ODG), pp. 460–469.
LCT-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.
LCT-2015-GonzalezHGS #interactive #student #tool support: Exploring Student Interactions: Learning Analytics Tools for Student Tracking (MÁCG, ÁHG, FJGP, MLSE), pp. 50–61.
LCT-2015-HoffmannPLSMJ #student: Enhancing the Learning Success of Engineering Students by Virtual Experiments (MH, LP, LL, KS, TM, SJ), pp. 394–405.
LCT-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.
LCT-2015-KimCD #artificial reality #simulation: The Learning Effect of Augmented Reality Training in a Computer-Based Simulation Environment (JHK, TC, WD), pp. 406–414.
LCT-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.
LCT-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.
LCT-2015-OrehovackiB #game studies #programming #quality: Inspecting Quality of Games Designed for Learning Programming (TO, SB), pp. 620–631.
LCT-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.
LCT-2015-ShimizuO #design #implementation #novel #word: Design and Implementation of Novel Word Learning System “Überall” (RS, KO), pp. 148–159.
LCT-2015-TamuraTHN #generative #wiki: Generating Quizzes for History Learning Based on Wikipedia Articles (YT, YT, YH, YIN), pp. 337–346.
LCT-2015-VielRTP #design #interactive #multi: Design Solutions for Interactive Multi-video Multimedia Learning Objects (CCV, KRHR, CACT, MdGCP), pp. 160–171.
LCT-2015-YusoffK #design #game studies #interactive #persuasion: Game Rhetoric: Interaction Design Model of Persuasive Learning for Serious Games (ZY, AK), pp. 644–654.
ICEIS-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.
ICEIS-v1-2015-RibeiroTWBE: A Learning Model for Intelligent Agents Applied to Poultry Farming (RR, MT, ALW, APB, FE), pp. 495–503.
ICEIS-v1-2015-SouzaBGBE #online: Applying Ensemble-based Online Learning Techniques on Crime Forecasting (AJdS, APB, HMG, JPB, FE), pp. 17–24.
CIKM-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.
CIKM-2015-BuchA #approximate #string #using: Approximate String Matching by End-Users using Active Learning (LB, AA0), pp. 93–102.
CIKM-2015-CaoLX #graph #named: GraRep: Learning Graph Representations with Global Structural Information (SC, WL0, QX), pp. 891–900.
CIKM-2015-HaoZHM #data type #online #similarity: Learning Relative Similarity from Data Streams: Active Online Learning Approaches (SH, PZ, SCHH, CM), pp. 1181–1190.
CIKM-2015-HeLJ0 #graph: Learning to Represent Knowledge Graphs with Gaussian Embedding (SH, KL0, GJ, JZ0), pp. 623–632.
CIKM-2015-HongWW #classification #clustering: Clustering-based Active Learning on Sensor Type Classification in Buildings (DH, HW, KW), pp. 363–372.
CIKM-2015-JinLZHH #distributed #multi #online: Collaborating between Local and Global Learning for Distributed Online Multiple Tasks (XJ0, PL0, FZ, JH, QH), pp. 113–122.
CIKM-2015-JinZPDLH #classification #multi #semantics: Heterogeneous Multi-task Semantic Feature Learning for Classification (XJ0, FZ, SJP, CD, PL0, QH), pp. 1847–1850.
CIKM-2015-KangLHWNXP #rank #similarity: Cross-Modal Similarity Learning: A Low Rank Bilinear Formulation (CK, SL, YH, JW, WN, SX, CP), pp. 1251–1260.
CIKM-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.
CIKM-2015-LiuTL #matrix #multi #named #scalability: MF-Tree: Matrix Factorization Tree for Large Multi-Class Learning (LL, PNT, XL), pp. 881–890.
CIKM-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.
CIKM-2015-MishraH #clustering #multi #using: Learning Task Grouping using Supervised Task Space Partitioning in Lifelong Multitask Learning (MM, JH), pp. 1091–1100.
CIKM-2015-MunozTG #approach #ranking: A Soft Computing Approach for Learning to Aggregate Rankings (JAVM, RdST, MAG), pp. 83–92.
CIKM-2015-ShuL #adaptation: Transductive Domain Adaptation with Affinity Learning (LS, LJL), pp. 1903–1906.
CIKM-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.
CIKM-2015-WangSL #distance #summary #using: Update Summarization using Semi-Supervised Learning Based on Hellinger Distance (DW0, SS, TL0), pp. 1907–1910.
CIKM-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.
CIKM-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.
CIKM-2015-YinWW #clustering #multi: Incomplete Multi-view Clustering via Subspace Learning (QY, SW, LW0), pp. 383–392.
CIKM-2015-ZenginC #documentation #topic: Learning User Preferences for Topically Similar Documents (MZ, BC), pp. 1795–1798.
CIKM-2015-ZhangJRXCY #graph #modelling #query: Learning Entity Types from Query Logs via Graph-Based Modeling (JZ, LJ, AR, SX, YC, PSY), pp. 603–612.
ECIR-2015-HuynhHR #analysis #sentiment #strict: Learning Higher-Level Features with Convolutional Restricted Boltzmann Machines for Sentiment Analysis (TH, YH, SMR), pp. 447–452.
ECIR-2015-NicosiaBM #rank: Learning to Rank Aggregated Answers for Crossword Puzzles (MN, GB, AM), pp. 556–561.
ECIR-2015-PasinatoMZ #elicitation #rating: Active Learning Applied to Rating Elicitation for Incentive Purposes (MBP, CEM, GZ), pp. 291–302.
ECIR-2015-PelejaM #retrieval #sentiment: Learning Sentiment Based Ranked-Lexicons for Opinion Retrieval (FP, JM), pp. 435–440.
ICML-2015-AmidU #multi: Multiview Triplet Embedding: Learning Attributes in Multiple Maps (EA, AU), pp. 1472–1480.
ICML-2015-BachHBG #performance: Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs (SHB, BH, JLBG, LG), pp. 381–390.
ICML-2015-Bou-AmmarTE #policy #sublinear: Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret (HBA, RT, EE), pp. 2361–2369.
ICML-2015-ChangKADL #education: Learning to Search Better than Your Teacher (KWC, AK, AA, HDI, JL), pp. 2058–2066.
ICML-2015-ChenSYU #modelling: Learning Deep Structured Models (LCC, AGS, ALY, RU), pp. 1785–1794.
ICML-2015-CilibertoMPR #multi: Convex Learning of Multiple Tasks and their Structure (CC, YM, TAP, LR), pp. 1548–1557.
ICML-2015-CohenH #online: Following the Perturbed Leader for Online Structured Learning (AC, TH), pp. 1034–1042.
ICML-2015-DanielyGS #adaptation #online: Strongly Adaptive Online Learning (AD, AG, SSS), pp. 1405–1411.
ICML-2015-FetayaU #invariant: Learning Local Invariant Mahalanobis Distances (EF, SU), pp. 162–168.
ICML-2015-GarberHM #online: Online Learning of Eigenvectors (DG, EH, TM), pp. 560–568.
ICML-2015-GuptaAGN #precise: Deep Learning with Limited Numerical Precision (SG, AA, KG, PN), pp. 1737–1746.
ICML-2015-HallakSMM #modelling: Off-policy Model-based Learning under Unknown Factored Dynamics (AH, FS, TAM, SM), pp. 711–719.
ICML-2015-Hernandez-Lobato15b #network #probability #scalability: Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks (JMHL, RA), pp. 1861–1869.
ICML-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.
ICML-2015-HongYKH #network #online: Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network (SH, TY, SK, BH), pp. 597–606.
ICML-2015-HsiehND #matrix: PU Learning for Matrix Completion (CJH, NN, ISD), pp. 2445–2453.
ICML-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.
ICML-2015-JerniteRS #approach #markov #modelling #performance #random: A Fast Variational Approach for Learning Markov Random Field Language Models (YJ, AMR, DS), pp. 2209–2217.
ICML-2015-JiangKS #abstraction #modelling: Abstraction Selection in Model-based Reinforcement Learning (NJ, AK, SS), pp. 179–188.
ICML-2015-Kandemir #process #symmetry: Asymmetric Transfer Learning with Deep Gaussian Processes (MK), pp. 730–738.
ICML-2015-KvetonSWA #rank: Cascading Bandits: Learning to Rank in the Cascade Model (BK, CS, ZW, AA), pp. 767–776.
ICML-2015-LakshmananOR #bound: Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning (KL, RO, DR), pp. 524–532.
ICML-2015-LeC #metric #using: Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations (TL, MC), pp. 2002–2011.
ICML-2015-LiuY #graph #predict: Bipartite Edge Prediction via Transductive Learning over Product Graphs (HL, YY), pp. 1880–1888.
ICML-2015-LondonHG #approximate: The Benefits of Learning with Strongly Convex Approximate Inference (BL, BH, LG), pp. 410–418.
ICML-2015-LongC0J #adaptation #network: Learning Transferable Features with Deep Adaptation Networks (ML, YC, JW, MJ), pp. 97–105.
ICML-2015-Lopez-PazMST #towards: Towards a Learning Theory of Cause-Effect Inference (DLP, KM, BS, IT), pp. 1452–1461.
ICML-2015-MaclaurinDA #optimisation: Gradient-based Hyperparameter Optimization through Reversible Learning (DM, DKD, RPA), pp. 2113–2122.
ICML-2015-MarietS #algorithm #fixpoint #process: Fixed-point algorithms for learning determinantal point processes (ZM, SS), pp. 2389–2397.
ICML-2015-MenonROW #estimation: Learning from Corrupted Binary Labels via Class-Probability Estimation (AKM, BvR, CSO, BW), pp. 125–134.
ICML-2015-PerrotH #analysis #metric: A Theoretical Analysis of Metric Hypothesis Transfer Learning (MP, AH), pp. 1708–1717.
ICML-2015-PhamRFA #multi #novel: Multi-instance multi-label learning in the presence of novel class instances (ATP, RR, XZF, JPA), pp. 2427–2435.
ICML-2015-PiechHNPSG #feedback #student: Learning Program Embeddings to Propagate Feedback on Student Code (CP, JH, AN, MP, MS, LJG), pp. 1093–1102.
ICML-2015-PlessisNS: Convex Formulation for Learning from Positive and Unlabeled Data (MCdP, GN, MS), pp. 1386–1394.
ICML-2015-Romera-ParedesT #approach: An embarrassingly simple approach to zero-shot learning (BRP, PHST), pp. 2152–2161.
ICML-2015-SerrurierP #evaluation: Entropy evaluation based on confidence intervals of frequency estimates : Application to the learning of decision trees (MS, HP), pp. 1576–1584.
ICML-2015-SibonyCJ #ranking #statistics: MRA-based Statistical Learning from Incomplete Rankings (ES, SC, JJ), pp. 1432–1441.
ICML-2015-Sohl-DicksteinW #using: Deep Unsupervised Learning using Nonequilibrium Thermodynamics (JSD, EAW, NM, SG), pp. 2256–2265.
ICML-2015-SrivastavaMS #using #video: Unsupervised Learning of Video Representations using LSTMs (NS, EM, RS), pp. 843–852.
ICML-2015-SteinhardtL15a #modelling #predict: Learning Fast-Mixing Models for Structured Prediction (JS, PL), pp. 1063–1072.
ICML-2015-SwaminathanJ #feedback: Counterfactual Risk Minimization: Learning from Logged Bandit Feedback (AS, TJ), pp. 814–823.
ICML-2015-TangSX #network: Learning Scale-Free Networks by Dynamic Node Specific Degree Prior (QT, SS, JX), pp. 2247–2255.
ICML-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.
ICML-2015-VanseijenS: A Deeper Look at Planning as Learning from Replay (HV, RS), pp. 2314–2322.
ICML-2015-WangALB #multi #on the #representation: On Deep Multi-View Representation Learning (WW, RA, KL, JAB), pp. 1083–1092.
ICML-2015-WangWLCW #multi #segmentation: Multi-Task Learning for Subspace Segmentation (YW, DPW, QL, WC, IJW), pp. 1209–1217.
ICML-2015-WangY #matrix #multi: Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices (JW, JY), pp. 1747–1756.
ICML-2015-WeiIB #set: Submodularity in Data Subset Selection and Active Learning (KW, RKI, JAB), pp. 1954–1963.
ICML-2015-WeissN #alias: Learning Parametric-Output HMMs with Two Aliased States (RW, BN), pp. 635–644.
ICML-2015-WenKA #combinator #performance #scalability: Efficient Learning in Large-Scale Combinatorial Semi-Bandits (ZW, BK, AA), pp. 1113–1122.
ICML-2015-WuS #algorithm #modelling #online: An Online Learning Algorithm for Bilinear Models (YW, SS), pp. 890–898.
ICML-2015-YogatamaFDS #word: Learning Word Representations with Hierarchical Sparse Coding (DY, MF, CD, NAS), pp. 87–96.
ICML-2015-YuB: Learning Submodular Losses with the Lovasz Hinge (JY, MBB), pp. 1623–1631.
ICML-2015-YuCL #multi #online #rank: Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams (RY, DC, YL), pp. 238–247.
KDD-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.
KDD-2015-DuS #adaptation #feature model: Unsupervised Feature Selection with Adaptive Structure Learning (LD, YDS), pp. 209–218.
KDD-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.
KDD-2015-GleichM #algorithm #graph #using: Using Local Spectral Methods to Robustify Graph-Based Learning Algorithms (DFG, MWM), pp. 359–368.
KDD-2015-HanZ #multi: Learning Tree Structure in Multi-Task Learning (LH, YZ), pp. 397–406.
KDD-2015-JohanssonD #geometry #graph #similarity #using: Learning with Similarity Functions on Graphs using Matchings of Geometric Embeddings (FDJ, DPD), pp. 467–476.
KDD-2015-LanH #complexity #multi: Reducing the Unlabeled Sample Complexity of Semi-Supervised Multi-View Learning (CL, JH), pp. 627–634.
KDD-2015-MaoWGS #graph #reduction: Dimensionality Reduction Via Graph Structure Learning (QM, LW, SG, YS), pp. 765–774.
KDD-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.
KDD-2015-Papagiannopoulou #multi: Discovering and Exploiting Deterministic Label Relationships in Multi-Label Learning (CP, GT, IT), pp. 915–924.
KDD-2015-RiondatoU15a #algorithm #statistics: VC-Dimension and Rademacher Averages: From Statistical Learning Theory to Sampling Algorithms (MR, EU), pp. 2321–2322.
KDD-2015-SunAYMMBY #classification: Transfer Learning for Bilingual Content Classification (QS, MSA, BY, CM, VM, AB, JY), pp. 2147–2156.
KDD-2015-TanSZ0 #transitive: Transitive Transfer Learning (BT, YS, EZ, QY), pp. 1155–1164.
KDD-2015-VeeriahDQ #architecture #predict: Deep Learning Architecture with Dynamically Programmed Layers for Brain Connectome Prediction (VV, RD, GJQ), pp. 1205–1214.
KDD-2015-WangWY #collaboration #recommendation: Collaborative Deep Learning for Recommender Systems (HW, NW, DYY), pp. 1235–1244.
KDD-2015-XuSB #predict: Longitudinal LASSO: Jointly Learning Features and Temporal Contingency for Outcome Prediction (TX, JS, JB), pp. 1345–1354.
KDD-2015-YangH #multi: Model Multiple Heterogeneity via Hierarchical Multi-Latent Space Learning (PY, JH), pp. 1375–1384.
KDD-2015-YangSJWDY #visual notation: Structural Graphical Lasso for Learning Mouse Brain Connectivity (SY, QS, SJ, PW, ID, JY), pp. 1385–1394.
KDD-2015-YanRHC #distributed #modelling #optimisation #performance #scalability: Performance Modeling and Scalability Optimization of Distributed Deep Learning Systems (FY, OR, YH, TMC), pp. 1355–1364.
KDD-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.
KDD-2015-ZhaoSYCLR #multi: Multi-Task Learning for Spatio-Temporal Event Forecasting (LZ, QS, JY, FC, CTL, NR), pp. 1503–1512.
MLDM-2015-Chou #data-driven #geometry: Data Driven Geometry for Learning (EPC), pp. 395–402.
MLDM-2015-DhulekarNOY #graph #mining #predict: Seizure Prediction by Graph Mining, Transfer Learning, and Transformation Learning (ND, SN, BO, BY), pp. 32–52.
MLDM-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.
MLDM-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.
MLDM-2015-MoldovanM #data mining #mining #performance #using: Learning the Relationship Between Corporate Governance and Company Performance Using Data Mining (DM, SM), pp. 368–381.
RecSys-2015-AlmahairiKCC #collaboration #distributed: Learning Distributed Representations from Reviews for Collaborative Filtering (AA, KK, KC, ACC), pp. 147–154.
SEKE-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.
SEKE-2015-GoswamiWS #performance #using: Using Learning Styles of Software Professionals to Improve their Inspection Team Performance (AG, GSW, AS), pp. 680–685.
SEKE-2015-LiuXC #recommendation: Context-aware Recommendation System with Anonymous User Profile Learning (YL, YX, MC), pp. 93–98.
SEKE-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.
SEKE-2015-SampaioMLM #adaptation #approach #research: Reflecting, adapting and learning in small software organizations: an action research approach (SS, MM, AL, HPM), pp. 46–50.
SEKE-2015-TironiMRM #approach #identification: An approach to identify relevant subjects for supporting the Learning Scheme creation task (HT, ALAM, SSR, AM), pp. 506–511.
SEKE-2015-WanderleyP #detection #folksonomy: Learning Folksonomies for Trend Detection in Task-Oriented Dialogues (GW, ECP), pp. 483–488.
SEKE-2015-ZegarraCW #graph #visualisation: Facilitating Peer Learning and Knowledge Sharing in STEM Courses via Pattern Based Graph Visualization (EZ, SKC, JW), pp. 284–289.
SIGIR-2015-Arora: Promoting User Engagement and Learning in Amorphous Search Tasks (PA), p. 1051.
SIGIR-2015-CormackG #multi #overview #perspective: Multi-Faceted Recall of Continuous Active Learning for Technology-Assisted Review (GVC, MRG), pp. 763–766.
SIGIR-2015-FoleyBJ #web: Learning to Extract Local Events from the Web (JF, MB, VJ), pp. 423–432.
SIGIR-2015-HarveyHE #query: Learning by Example: Training Users with High-quality Query Suggestions (MH, CH, DE), pp. 133–142.
SIGIR-2015-Li15a #information retrieval: Transfer Learning for Information Retrieval (PL), p. 1061.
SIGIR-2015-LiuW #collaboration: Learning Context-aware Latent Representations for Context-aware Collaborative Filtering (XL, WW), pp. 887–890.
SIGIR-2015-MehrotraY #query #rank #using: Representative & Informative Query Selection for Learning to Rank using Submodular Functions (RM, EY), pp. 545–554.
SIGIR-2015-SeverynM #network #rank: Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks (AS, AM), pp. 373–382.
SIGIR-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.
SIGIR-2015-SpinaPR #microblog: Active Learning for Entity Filtering in Microblog Streams (DS, MHP, MdR), pp. 975–978.
SIGIR-2015-WangGLXWC #recommendation #representation: Learning Hierarchical Representation Model for NextBasket Recommendation (PW, JG, YL, JX, SW, XC), pp. 403–412.
SIGIR-2015-WangLWZZ #named: LBMCH: Learning Bridging Mapping for Cross-modal Hashing (YW, XL, LW, WZ, QZ), pp. 999–1002.
SIGIR-2015-XiaXLGC #evaluation #metric #optimisation: Learning Maximal Marginal Relevance Model via Directly Optimizing Diversity Evaluation Measures (LX, JX, YL, JG, XC), pp. 113–122.
SIGIR-2015-ZamaniMS #adaptation #evaluation #multi: Adaptive User Engagement Evaluation via Multi-task Learning (HZ, PM, AS), pp. 1011–1014.
SIGIR-2015-ZhengC #distributed: Learning to Reweight Terms with Distributed Representations (GZ, JC), pp. 575–584.
SKY-2015-Oliveira #using: Learning the Meaning of Language and using It Creatively (HGO), p. 3.
OOPSLA-2015-OhYY #adaptation #optimisation #program analysis: Learning a strategy for adapting a program analysis via bayesian optimisation (HO, HY, KY), pp. 572–588.
PLATEAU-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.
ASE-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.
ASE-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.
ASE-2015-ZhangGBC #configuration management #fourier #performance #predict: Performance Prediction of Configurable Software Systems by Fourier Learning (T) (YZ, JG, EB, KC), pp. 365–373.
ASE-2015-ZouYLM0 #rank #retrieval: Learning to Rank for Question-Oriented Software Text Retrieval (T) (YZ, TY, YL, JM, LZ), pp. 1–11.
ESEC-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-FSE-2015-SunXLLQ #abstraction #named #testing #validation: TLV: abstraction through testing, learning, and validation (JS, HX, YL, SWL, SQ), pp. 698–709.
ICSE-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.
ICSE-v1-2015-JiaCHP #combinator #generative #interactive #testing #using: Learning Combinatorial Interaction Test Generation Strategies Using Hyperheuristic Search (YJ, MBC, MH, JP), pp. 540–550.
ICSE-v1-2015-ZhuHFZLZ #developer: Learning to Log: Helping Developers Make Informed Logging Decisions (JZ, PH, QF, HZ, MRL, DZ), pp. 415–425.
ICSE-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.
ICSE-v2-2015-Honsel #evolution #mining #simulation #statistics: Statistical Learning and Software Mining for Agent Based Simulation of Software Evolution (VH), pp. 863–866.
ICSE-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.
ICSE-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.
ICSE-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.
ICSE-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.
ICSE-v2-2015-SedelmaierL #education #induction #re-engineering: Active and Inductive Learning in Software Engineering Education (YS, DL), pp. 418–427.
ICSE-v2-2015-WilkinsG #design #student: Drawing Insight from Student Perceptions of Reflective Design Learning (TVW, JCG), pp. 253–262.
SAC-2015-BarrosCMP #education #repository #reuse #using: Integrating educational repositories to improve the reuse of learning objects (HB, EC, JM, RP), pp. 270–272.
SAC-2015-Brefeld #multi: Multi-view learning with dependent views (UB), pp. 865–870.
SAC-2015-GomesBE #classification #data type: Pairwise combination of classifiers for ensemble learning on data streams (HMG, JPB, FE), pp. 941–946.
SAC-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.
SAC-2015-OmatuYI #classification #smell: Smell classification of wines by the learning vector quantization method (SO, MY, YI), pp. 195–200.
SAC-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.
SAC-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.
SAC-2015-Pesare #social: Smart learning environments for social learning (EP), pp. 273–274.
SAC-2015-ReadPB #data type: Deep learning in partially-labeled data streams (JR, FPC, AB), pp. 954–959.
SAC-2015-ReddySC #approach #aspect-oriented #incremental #performance #weaving: Incremental aspect weaving: an approach for faster AOP learning (YRR, AS, MC), pp. 1480–1485.
SAC-2015-RegoMP #approach #detection #folksonomy: A supervised learning approach to detect subsumption relations between tags in folksonomies (ASdCR, LBM, CESP), pp. 409–415.
SAC-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.
SAC-2015-SugiyamaS #multi: Meta-strategy for cooperative tasks with learning of environments in multi-agent continuous tasks (AS, TS), pp. 494–500.
SAC-2015-WanderleyP #folksonomy: Learning folksonomies from task-oriented dialogues (GMPW, ECP), pp. 360–367.
CASE-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.
CASE-2015-LiX #energy #multi: A multi-grid reinforcement learning method for energy conservation and comfort of HVAC in buildings (BL, LX), pp. 444–449.
CASE-2015-ParisACAR #behaviour #markov #smarttech #using: Using Hidden Semi-Markov Model for learning behavior in smarthomes (AP, SA, NC, AEA, NR), pp. 752–757.
CASE-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.
CASE-2015-ZhangWZZ #automaton #optimisation #performance: Incorporation of ordinal optimization into learning automata for high learning efficiency (JZ, CW, DZ, MZ), pp. 1206–1211.
CGO-2015-McAfeeO #framework #generative #multi #named: EMEURO: a framework for generating multi-purpose accelerators via deep learning (LCM, KO), pp. 125–135.
DATE-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.
DATE-2015-ChenM #distributed #manycore #optimisation #performance: Distributed reinforcement learning for power limited many-core system performance optimization (ZC, DM), pp. 1521–1526.
DATE-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.
DATE-2015-RenTB #detection #statistics: Detection of illegitimate access to JTAG via statistical learning in chip (XR, VGT, RD(B), pp. 109–114.
STOC-2015-BarakKS #composition #taxonomy: Dictionary Learning and Tensor Decomposition via the Sum-of-Squares Method (BB, JAK, DS), pp. 143–151.
STOC-2015-Bresler #graph #modelling: Efficiently Learning Ising Models on Arbitrary Graphs (GB), pp. 771–782.
STOC-2015-GeHK: Learning Mixtures of Gaussians in High Dimensions (RG, QH, SMK), pp. 761–770.
STOC-2015-HardtP #bound: Tight Bounds for Learning a Mixture of Two Gaussians (MH, EP), pp. 753–760.
STOC-2015-LiRSS #statistics: Learning Arbitrary Statistical Mixtures of Discrete Distributions (JL, YR, LJS, CS), pp. 743–752.
CAV-2015-BrazdilCCFK #markov #process: Counterexample Explanation by Learning Small Strategies in Markov Decision Processes (TB, KC, MC, AF, JK), pp. 158–177.
CAV-2015-GehrDV #commutative #specification: Learning Commutativity Specifications (TG, DD, MTV), pp. 307–323.
CAV-2015-IsbernerHS #automaton #framework #open source: The Open-Source LearnLib — A Framework for Active Automata Learning (MI, FH, BS), pp. 487–495.
CAV-2015-Saha0M #named: Alchemist: Learning Guarded Affine Functions (SS, PG, PM), pp. 440–446.
ICLP-2015-MartinezRIAT #modelling #probability: Learning Probabilistic Action Models from Interpretation Transitions (DM, TR, KI, GA, CT), pp. 114–127.
ICLP-J-2015-LawRB #constraints #programming #set: Learning weak constraints in answer set programming (ML, AR, KB), pp. 511–525.
SAT-2015-TuHJ #named #reasoning #satisfiability: QELL: QBF Reasoning with Extended Clause Learning and Levelized SAT Solving (KHT, TCH, JHRJ), pp. 343–359.
DRR-2014-CartonLC #interactive #named: LearnPos: a new tool for interactive learning positioning (CC, AL, BC), p. ?–12.
DRR-2014-TaoTX #documentation #random #using: Document page structure learning for fixed-layout e-books using conditional random fields (XT, ZT, CX), p. ?–9.
HT-2014-AbbasiTL #scalability #using: Scalable learning of users’ preferences using networked data (MAA, JT, HL), pp. 4–12.
JCDL-2014-BarrioSGG #framework #named: REEL: A Relation Extraction Learning framework (PB, GS, HG, LG), pp. 455–456.
JCDL-2014-ChakrabortyKGGM #approach #predict #towards: Towards a stratified learning approach to predict future citation counts (TC, SK, PG, NG, AM), pp. 351–360.
VLDB-2014-ZouJLGWX #framework #named #platform: Mariana: Tencent Deep Learning Platform and its Applications (YZ, XJ, YL, ZG, EW, BX), pp. 1772–1777.
VLDB-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.
CSEET-2014-Ackerman #re-engineering: An active learning module for an introduction to software engineering course (AFA), pp. 190–191.
CSEET-2014-BoeschS #automation: Automated mentor assignment in blended learning environments (CB, KS), pp. 94–98.
CSEET-2014-Ding #re-engineering #self: Self-guided learning environment for undergraduate software engineering (JD), pp. 188–189.
CSEET-2014-FranklBK #development: Learning and working together as prerequisites for the development of high-quality software (GF, SB, BK), pp. 154–157.
CSEET-2014-KroppMMZ #agile #collaboration #education: Teaching and learning agile collaboration (MK, AM, MM, CGZ), pp. 139–148.
CSEET-2014-PotterSDW #game studies #named: InspectorX: A game for software inspection training and learning (HP, MS, LD, VW), pp. 55–64.
CSEET-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.
EDM-2014-AdjeiSHPBK: Refining Learning Maps with Data Fitting Techniques: Searching for Better Fitting Learning Maps (SA, DS, NTH, ZAP, AB, NK), pp. 413–414.
EDM-2014-ColvinCLFP: Comparing Learning in a MOOC and a Blended, On-Campus Course (KFC, JC, AL, CF, DEP), pp. 343–344.
EDM-2014-Fancsali #algebra #behaviour #modelling: Causal Discovery with Models: Behavior, Affect, and Learning in Cognitive Tutor Algebra (SF), pp. 28–35.
EDM-2014-ForsythGPMS #predict: Discovering Theoretically Grounded Predictors of Shallow vs. Deep- level Learning (CF, ACG, PIPJ, KKM, BS), pp. 229–232.
EDM-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.
EDM-2014-KaserKG #analysis #parametricity #predict: Different parameters - same prediction: An analysis of learning curves (TK, KRK, MHG), pp. 52–59.
EDM-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.
EDM-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.
EDM-2014-LanSB #matrix #personalisation: Quantized Matrix Completion for Personalized Learning (ASL, CS, RGB), pp. 280–283.
EDM-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.
EDM-2014-LiuMBP #multi: Trading Off Scientific Knowledge and User Learning with Multi-Armed Bandits (YEL, TM, EB, ZP), pp. 161–168.
EDM-2014-MavrikisSPZ #adaptation #visualisation: Indicator Visualization for Adaptive Exploratory Learning Environments (MM, SGS, AP, ZZ), pp. 377–378.
EDM-2014-MorganBR #analysis #fault #validation: Error Analysis as a Validation of Learning Progressions (BM, WB, VR), pp. 245–248.
EDM-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.
EDM-2014-PechenizkiyT #education: Learning to Teach like a Bandit (MP, PAT), pp. 381–382.
EDM-2014-RoweBAKH #automation #detection: Building Automated Detectors of Gameplay Strategies to Measure Implicit Science Learning (ER, RSB, JAC, EK, WJH), pp. 337–338.
EDM-2014-SantosMP #collaboration #mining #student: Mining students' strategies to enable collaborative learning (SGS, MM, AP), pp. 397–398.
EDM-2014-Schneider #collaboration #detection #multimodal #towards: Toward Collaboration Sensing: Multimodal Detection of the Chameleon Effect in Collaborative Learning Settings (BS), pp. 435–437.
EDM-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.
EDM-2014-SnowJVDM #named: Entropy: A Stealth Measure of Agency in Learning Environments (ELS, MEJ, LKV, JD, DSM), pp. 241–244.
EDM-2014-SnowVM #analysis: Tracking Choices: Computational Analysis of Learning Trajectories (ELS, LKV, DSM), pp. 316–319.
EDM-2014-SunYIS #recursion: Alternating Recursive Method for Q-matrix Learning (YS, SY, SI, YS), pp. 14–20.
EDM-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.
EDM-2014-Wang #motivation: MOOC Leaner Motivation and Learning Pattern Discovery (YW), pp. 452–454.
EDM-2014-WorsleyB #multimodal #using: Using Multimodal Learning Analytics to Study Learning Mechanisms (MW, PB), pp. 431–432.
EDM-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.
EDM-2014-ZhengP #algorithm #using: Dynamic Re-Composition of Learning Groups Using PSO-Based Algorithms (ZZ, NP), pp. 357–358.
ITiCSE-2014-BerryK #game studies #programming: The state of play: a notional machine for learning programming (MB, MK), pp. 21–26.
ITiCSE-2014-EckerdalKTNSM #education: Teaching and learning with MOOCs: computing academics’ perspectives and engagement (AE, PK, NT, AN, JS, LM), pp. 9–14.
ITiCSE-2014-EllisH #open source #re-engineering: Structuring software engineering learning within open source software participation (HJCE, GWH), p. 326.
ITiCSE-2014-EllisJBPHD: Learning within a professional environment: shared ownership of an HFOSS project (HJCE, SJ, DB, LP, GWH, JD), p. 337.
ITiCSE-2014-FalknerVF #identification #self: Identifying computer science self-regulated learning strategies (KF, RV, NJGF), pp. 291–296.
ITiCSE-2014-GroverCP: Assessing computational learning in K-12 (SG, SC, RP), pp. 57–62.
ITiCSE-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.
ITiCSE-2014-Hijon-NeiraVPC #experience #game studies #programming: Game programming for improving learning experience (RBHN, JÁVI, CPR, LC), pp. 225–230.
ITiCSE-2014-Jasute #education #geometry #interactive #visualisation: An interactive visualization method of constructionist teaching and learning of geometry (EJ), p. 349.
ITiCSE-2014-KothiyalMI #question #scalability: Think-pair-share in a large CS1 class: does learning really happen? (AK, SM, SI), pp. 51–56.
ITiCSE-2014-Marcos-Abed #case study #effectiveness #programming: Learning computer programming: a study of the effectiveness of a COAC# (JMA), p. 333.
ITiCSE-2014-MedinaSGG #student #using: Learning outcomes using objectives with computer science students (JAM, JJS, EGL, AGC), p. 339.
ITiCSE-2014-PirkerRG #education #student: Motivational active learning: engaging university students in computer science education (JP, MRS, CG), pp. 297–302.
ITiCSE-2014-PriorCL #case study #experience: Things coming together: learning experiences in a software studio (JP, AC, JL), pp. 129–134.
ITiCSE-2014-Rogers #question: New technology, new learning? (YR), p. 1.
ITiCSE-2014-TaubBA #physics: The effect of computer science on the learning of computational physics (RT, MBA, MA), p. 352.
ITiCSE-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.
ITiCSE-2014-Verwaal: Team based learning in theoretical computer science (NV), p. 331.
ITiCSE-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.
ITiCSE-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.
SIGITE-2014-EllisJBPHD: Learning within a professional environment: shared ownership of an HFOSS project (HJCE, SJ, DB, LP, GWH, JD), pp. 95–100.
SIGITE-2014-RytikovaB #personalisation: A methodology for personalized competency-based learning in undergraduate courses (IR, MB), pp. 81–86.
SIGITE-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.
CSMR-WCRE-2014-XiaFLCW #behaviour #multi #towards: Towards more accurate multi-label software behavior learning (XX, YF, DL, ZC, XW), pp. 134–143.
ICPC-2014-KaulgudAMT #comprehension: Comprehension support during knowledge transitions: learning from field (VSK, KMA, JM, GT), pp. 205–206.
ICSME-2014-BinkleyL #information retrieval #rank: Learning to Rank Improves IR in SE (DB, DJL), pp. 441–445.
ICSME-2014-XuanM #fault #locality #metric #multi #ranking: Learning to Combine Multiple Ranking Metrics for Fault Localization (JX, MM), pp. 191–200.
ICALP-v1-2014-Volkovich #bound #on the: On Learning, Lower Bounds and (un)Keeping Promises (IV), pp. 1027–1038.
ICALP-v2-2014-DamsHK #network: Jamming-Resistant Learning in Wireless Networks (JD, MH, TK), pp. 447–458.
LATA-2014-LaurenceLNST #transducer: Learning Sequential Tree-to-Word Transducers (GL, AL, JN, SS, MT), pp. 490–502.
FM-2014-LinH #composition #concurrent #model checking #synthesis: Compositional Synthesis of Concurrent Systems through Causal Model Checking and Learning (SWL, PAH), pp. 416–431.
SEFM-2014-CasselHJS #finite #state machine: Learning Extended Finite State Machines (SC, FH, BJ, BS), pp. 250–264.
AIIDE-2014-RoweML #approach #composition #experience #interactive #optimisation: Optimizing Player Experience in Interactive Narrative Planning: A Modular Reinforcement Learning Approach (JPR, BWM, JCL).
AIIDE-2014-YoungH #game studies: Learning Micro-Management Skills in RTS Games by Imitating Experts (JY, NH).
CHI-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-PLAY-2014-GeurtsAKI #visual notation: Playfully learning visual perspective taking skills with sifteo cubes (LG, VVA, KVK, RI), pp. 107–113.
CHI-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-PLAY-2014-Melonio #co-evolution #design: Gamified co-design with cooperative learning at school (AM), pp. 295–298.
CIG-2014-BallingerL #robust: Learning robust build-orders from previous opponents with coevolution (CAB, SJL), pp. 1–8.
CIG-2014-IvanovicZLR: Reinforcement learning to control a commander for capture the flag (JI, FZ, XL0, JRV), pp. 1–8.
CIG-2014-KimK #game studies #realtime #recommendation: Learning to recommend game contents for real-time strategy gamers (HTK, KJK), pp. 1–8.
CIG-2014-OhCK #game studies: Imitation learning for combat system in RTS games with application to starcraft (ISO, HCC, KJK), pp. 1–2.
CIG-2014-ParkK #game studies #using: Learning to play fighting game using massive play data (HSP, KJK), pp. 1–2.
CIG-2014-SzubertJ #difference #game studies #network: Temporal difference learning of N-tuple networks for the game 2048 (MGS, WJ), pp. 1–8.
CIG-2014-ThillBKK #difference #game studies: Temporal difference learning with eligibility traces for the game connect four (MT, SB, PK, WK), pp. 1–8.
DiGRA-2014-Marklund #comprehension #game studies: Out of Context - Understanding the Practicalities of Learning Games (BM).
FDG-2014-BroeckhovenT #aspect-oriented #game studies #specification #using: Specifying the pedagogical aspects of narrative-based digital learning games using annotations (FVB, ODT).
FDG-2014-RoweLML #design #education #game studies: Play in the museum: Designing game-based learning environments for informal education settings (JPR, EVL, BWM, JCL).
FDG-2014-TomaiF #adaptation #behaviour #using: Adapting in-game agent behavior by observation of players using learning behavior trees (ET, RF).
FDG-2014-ZookFR #automation #game studies #parametricity: Automatic playtesting for game parameter tuning via active learning (AZ, EF, MOR).
VS-Games-2014-NinausKFNW #using: The Potential Use of Neurophysiological Signals for Learning Analytics (MN, SEK, EVCF, CN, GW), pp. 1–5.
VS-Games-2014-Schmidt: Evaluating Digital Applications for Language Learning: Outcomes and Insights (IS), p. 1.
VS-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.
CHI-2014-DontchevaMBG #crowdsourcing #performance: Combining crowdsourcing and learning to improve engagement and performance (MD, RRM, JRB, EMG), pp. 3379–3388.
CHI-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.
CHI-2014-GreenbergG #online: Learning to fail: experiencing public failure online through crowdfunding (MDG, EG), pp. 581–590.
CHI-2014-KovacsM: Smart subtitles for vocabulary learning (GK, RCM), pp. 853–862.
CHI-2014-MentisCS: Learning to see the body: supporting instructional practices in laparoscopic surgical procedures (HMM, AC, SDS), pp. 2113–2122.
CHI-2014-MonserratLZC #interactive: L.IVE: an integrated interactive video-based learning environment (TJKPM, YL, SZ, XC), pp. 3399–3402.
CHI-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.
CSCW-2014-MillerZGG #collaboration #people #research: Pair research: matching people for collaboration, learning, and productivity (RCM, HZ, EG, EG), pp. 1043–1048.
CSCW-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.
CSCW-2014-ZhuDKK #assessment #performance: Reviewing versus doing: learning and performance in crowd assessment (HZ, SPD, REK, AK), pp. 1445–1455.
DUXU-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.
DUXU-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.
DUXU-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.
DUXU-ELAS-2014-Martins #industrial #prototype: Prototyping in a Learning Environment — Digital Publishing Projects from the Escola Superior de Desenho Industrial (MAFM), pp. 195–206.
DUXU-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.
DUXU-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.
HCI-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.
HCI-AIMT-2014-MikamiM #3d #effectiveness: Effectiveness of Virtual Hands in 3D Learning Material (TM, SM), pp. 93–101.
HCI-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.
HCI-AS-2014-SchwallerKAL #feedback #gesture #visual notation: Improving In-game Gesture Learning with Visual Feedback (MS, JK, LA, DL), pp. 643–653.
HCI-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.
HCI-TMT-2014-MorDHF #education #human-computer #online: Teaching and Learning HCI Online (EM, MGD, EH, NF), pp. 230–241.
HCI-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.
HCI-TMT-2014-YajimaTS #collaboration: Proposal of Collaborative Learning Support Method in Risk Communications (HY, NT, RS), pp. 457–465.
HIMI-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.
HIMI-AS-2014-HirashimaYH #problem #word: Triplet Structure Model of Arithmetical Word Problems for Learning by Problem-Posing (TH, SY, YH), pp. 42–50.
HIMI-AS-2014-HirokawaFSY #mindmap: Learning Winespeak from Mind Map of Wine Blogs (SH, BF, TS, CY), pp. 383–393.
HIMI-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.
HIMI-AS-2014-MikamiT #music #performance: A Music Search System for Expressive Music Performance Learning (TM, KT), pp. 80–89.
HIMI-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.
HIMI-AS-2014-YamaguchiTT #process #visualisation: Visualizing Mental Learning Processes with Invisible Mazes for Continuous Learning (TY, KT, KT), pp. 137–148.
HIMI-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.
LCT-NLE-2014-KaprosP: Empowering L&D Managers through Customisation of Inline Learning Analytics (EK, NP), pp. 282–291.
LCT-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.
LCT-NLE-2014-Milde #editing #html #online: An HTML5-Based Online Editor for Creating Annotated Learning Videos (JTM), pp. 172–179.
LCT-NLE-2014-MorGHH #assessment #design #tool support: Designing Learning Tools: The Case of a Competence Assessment Tool (EM, AEGR, EH, MAH), pp. 83–94.
LCT-NLE-2014-MoriT #development: Development of a Fieldwork Support System for Group Work in Project-Based Learning (MM, AT), pp. 429–440.
LCT-NLE-2014-Piki #collaboration #process #question: Learner Engagement in Computer-Supported Collaborative Learning Activities: Natural or Nurtured? (AP), pp. 107–118.
LCT-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.
LCT-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.
LCT-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.
LCT-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.
LCT-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.
LCT-TRE-2014-Bharali #online #process: Enhancing Online Learning Activities for Groups in Flipped Classrooms (RB), pp. 269–276.
LCT-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.
LCT-TRE-2014-Castro #case study #collaboration #named: Mosca — A Case Study on Collaborative Work — Combining Dimensions while Learning (SC), pp. 388–396.
LCT-TRE-2014-EradzeL #design #interactive: Interrelation between Pedagogical Design and Learning Interaction Patterns in different Virtual Learning Environments (ME, ML), pp. 23–32.
LCT-TRE-2014-Hayes14a #approach #development #game studies #simulation: An Approach to Holistic Development of Serious Games and Learning Simulations (ATH), pp. 42–49.
LCT-TRE-2014-HiramatsuIFS #development #using: Development of the Learning System for Outdoor Study Using Zeigarnik Effect (YH, AI, MF, FS), pp. 127–137.
LCT-TRE-2014-IkedaS: Dream Drill: A Bedtime Learning Application (AI, IS), pp. 138–145.
LCT-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.
LCT-TRE-2014-MartinezMLLC #3d #interactive: Supporting Learning with 3D Interactive Applications in Early Years (ACM, MJMS, MLS, DCPL, MC), pp. 11–22.
LCT-TRE-2014-MartinWH #interactive #mobile: Sensor Based Interaction Mechanisms in Mobile Learning (KUM, MW, WH), pp. 165–172.
LCT-TRE-2014-OliveiraM #network #research: Digital Identity of Researchers and Their Personal Learning Network (NRO, LM), pp. 467–477.
LCT-TRE-2014-ShahoumianSZPH #education #simulation: Blended Simulation Based Medical Education: A Complex Learning/Training Opportunity (AS, MS, MZ, GP, JH), pp. 478–485.
LCT-TRE-2014-ShimizuO #effectiveness #question: Which Is More Effective for Learning German and Japanese Language, Paper or Digital? (RS, KO), pp. 309–318.
LCT-TRE-2014-SzklannyW #prototype: Prototyping M-Learning Course on the Basis of Puzzle Learning Methodology (KS, MW), pp. 215–226.
LCT-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.
ICEIS-v2-2014-MahmoudBAG #approach: A New Approach Based on Learning Services to Generate Appropriate Learning Paths (CBM, FB, MHA, FG), pp. 643–646.
ICEIS-v2-2014-OtonBGGB #metadata #using: Description of Accessible Learning Resources by Using Metadata (SO, CB, EG, AGC, RB), pp. 620–626.
ICEIS-v2-2014-ZhengJL #hybrid #taxonomy #using: Cross-Sensor Iris Matching using Patch-based Hybrid Dictionary Learning (BRZ, DYJ, YHL), pp. 169–174.
ICEIS-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.
CIKM-2014-DeBBGC #linear: Learning a Linear Influence Model from Transient Opinion Dynamics (AD, SB, PB, NG, SC), pp. 401–410.
CIKM-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.
CIKM-2014-GoncalvesDCSZB #multi: Multi-task Sparse Structure Learning (ARG, PD, SC, VS, FJVZ, AB), pp. 451–460.
CIKM-2014-JinZXDLH #multi: Multi-task Multi-view Learning for Heterogeneous Tasks (XJ, FZ, HX, CD, PL, QH), pp. 441–450.
CIKM-2014-MaoWHO #classification #linear #multi: Nonlinear Classification via Linear SVMs and Multi-Task Learning (XM, OW, WH, PO), pp. 1955–1958.
CIKM-2014-PfeifferNB #network #probability #using: Active Exploration in Networks: Using Probabilistic Relationships for Learning and Inference (JJPI, JN, PNB), pp. 639–648.
CIKM-2014-PimplikarGBP: Learning to Propagate Rare Labels (RP, DG, DB, GRP), pp. 201–210.
CIKM-2014-ShiKBLH #named #recommendation: CARS2: Learning Context-aware Representations for Context-aware Recommendations (YS, AK, LB, ML, AH), pp. 291–300.
CIKM-2014-VinzamuriLR: Active Learning based Survival Regression for Censored Data (BV, YL, CKR), pp. 241–250.
CIKM-2014-WangMC #parametricity: Structure Learning via Parameter Learning (WYW, KM, WWC), pp. 1199–1208.
CIKM-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.
CIKM-2014-XiePLW #framework #image #multi: A Cross-modal Multi-task Learning Framework for Image Annotation (LX, PP, YL, SW), pp. 431–440.
CIKM-2014-YangTZ #streaming: Active Learning for Streaming Networked Data (ZY, JT, YZ), pp. 1129–1138.
CIKM-2014-YuX #interactive #network #predict #scalability #social: Learning Interactions for Social Prediction in Large-scale Networks (XY, JX), pp. 161–170.
CIKM-2014-ZhongPXYM #adaptation #collaboration #recommendation: Adaptive Pairwise Preference Learning for Collaborative Recommendation with Implicit Feedbacks (HZ, WP, CX, ZY, ZM), pp. 1999–2002.
CIKM-2014-ZhuSY #information retrieval #taxonomy: Cross-Modality Submodular Dictionary Learning for Information Retrieval (FZ, LS, MY), pp. 1479–1488.
ECIR-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.
ECIR-2014-BreussT #interactive #recommendation #social #social media: Learning from User Interactions for Recommending Content in Social Media (MB, MT), pp. 598–604.
ECIR-2014-FiliceCCB #effectiveness #kernel #online: Effective Kernelized Online Learning in Language Processing Tasks (SF, GC, DC, RB), pp. 347–358.
ECIR-2014-NainiA #feature model #rank: Exploiting Result Diversification Methods for Feature Selection in Learning to Rank (KDN, ISA), pp. 455–461.
ECIR-2014-QiDCW #information management: Deep Learning for Character-Based Information Extraction (YQ, SGD, RC, JW), pp. 668–674.
ICML-c1-2014-AroraBGM #bound: Provable Bounds for Learning Some Deep Representations (SA, AB, RG, TM), pp. 584–592.
ICML-c1-2014-DenisGH #bound #matrix: Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning (FD, MG, AH), pp. 449–457.
ICML-c1-2014-DickGS #markov #online #process #sequence: Online Learning in Markov Decision Processes with Changing Cost Sequences (TD, AG, CS), pp. 512–520.
ICML-c1-2014-JainT #bound #independence: (Near) Dimension Independent Risk Bounds for Differentially Private Learning (PJ, AGT), pp. 476–484.
ICML-c1-2014-LacosteMLL: Agnostic Bayesian Learning of Ensembles (AL, MM, FL, HL), pp. 611–619.
ICML-c1-2014-LajugieBA #clustering #metric #problem: Large-Margin Metric Learning for Constrained Partitioning Problems (RL, FRB, SA), pp. 297–305.
ICML-c1-2014-LuoS #online #towards: Towards Minimax Online Learning with Unknown Time Horizon (HL, RES), pp. 226–234.
ICML-c1-2014-MohriM #algorithm #optimisation: Learning Theory and Algorithms for revenue optimization in second price auctions with reserve (MM, AMM), pp. 262–270.
ICML-c1-2014-RooshenasL #interactive #network: Learning Sum-Product Networks with Direct and Indirect Variable Interactions (AR, DL), pp. 710–718.
ICML-c1-2014-ShalitC #coordination #matrix #orthogonal: Coordinate-descent for learning orthogonal matrices through Givens rotations (US, GC), pp. 548–556.
ICML-c1-2014-ShiZ #online: Online Bayesian Passive-Aggressive Learning (TS, JZ), pp. 378–386.
ICML-c1-2014-SolomonRGB: Wasserstein Propagation for Semi-Supervised Learning (JS, RMR, LJG, AB), pp. 306–314.
ICML-c1-2014-TandonR #graph: Learning Graphs with a Few Hubs (RT, PDR), pp. 602–610.
ICML-c1-2014-Yu0KD #multi #scalability: Large-scale Multi-label Learning with Missing Labels (HFY, PJ, PK, ISD), pp. 593–601.
ICML-c2-2014-AffandiFAT #kernel #parametricity #process: Learning the Parameters of Determinantal Point Process Kernels (RHA, EBF, RPA, BT), pp. 1224–1232.
ICML-c2-2014-AminHK: Learning from Contagion (Without Timestamps) (KA, HH, MK), pp. 1845–1853.
ICML-c2-2014-AndoniPV0 #network: Learning Polynomials with Neural Networks (AA, RP, GV, LZ), pp. 1908–1916.
ICML-c2-2014-AziziAG #composition #network: Learning Modular Structures from Network Data and Node Variables (EA, EA, JEG), pp. 1440–1448.
ICML-c2-2014-BalleHP #comparison #empirical #probability: Methods of Moments for Learning Stochastic Languages: Unified Presentation and Empirical Comparison (BB, WLH, JP), pp. 1386–1394.
ICML-c2-2014-Bou-AmmarERT #multi #online #policy: Online Multi-Task Learning for Policy Gradient Methods (HBA, EE, PR, MET), pp. 1206–1214.
ICML-c2-2014-BrunskillL: PAC-inspired Option Discovery in Lifelong Reinforcement Learning (EB, LL), pp. 316–324.
ICML-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.
ICML-c2-2014-CohenW #commutative: Learning the Irreducible Representations of Commutative Lie Groups (TC, MW), pp. 1755–1763.
ICML-c2-2014-DuLBS #information management #network: Influence Function Learning in Information Diffusion Networks (ND, YL, MFB, LS), pp. 2016–2024.
ICML-c2-2014-FangCL #graph: Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness Probabilistically (YF, KCCC, HWL), pp. 406–414.
ICML-c2-2014-GrandeWH #performance #process: Sample Efficient Reinforcement Learning with Gaussian Processes (RCG, TJW, JPH), pp. 1332–1340.
ICML-c2-2014-HoangLJK #process: Nonmyopic ϵ-Bayes-Optimal Active Learning of Gaussian Processes (TNH, BKHL, PJ, MSK), pp. 739–747.
ICML-c2-2014-HoulsbyHG #matrix #robust: Cold-start Active Learning with Robust Ordinal Matrix Factorization (NH, JMHL, ZG), pp. 766–774.
ICML-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.
ICML-c2-2014-KricheneDB #convergence #on the: On the convergence of no-regret learning in selfish routing (WK, BD, AMB), pp. 163–171.
ICML-c2-2014-LevineK #network #optimisation #policy: Learning Complex Neural Network Policies with Trajectory Optimization (SL, VK), pp. 829–837.
ICML-c2-2014-LiG #classification #representation #semantics: Latent Semantic Representation Learning for Scene Classification (XL, YG), pp. 532–540.
ICML-c2-2014-LimL #metric #performance #ranking: Efficient Learning of Mahalanobis Metrics for Ranking (DL, GRGL), pp. 1980–1988.
ICML-c2-2014-LinK #constraints #performance #representation: Stable and Efficient Representation Learning with Nonnegativity Constraints (THL, HTK), pp. 1323–1331.
ICML-c2-2014-LinYHY #distance: Geodesic Distance Function Learning via Heat Flow on Vector Fields (BL, JY, XH, JY), pp. 145–153.
ICML-c2-2014-LiuD #problem #set: Learnability of the Superset Label Learning Problem (LPL, TGD), pp. 1629–1637.
ICML-c2-2014-LiZ #higher-order #problem: High Order Regularization for Semi-Supervised Learning of Structured Output Problems (YL, RSZ), pp. 1368–1376.
ICML-c2-2014-LiZ0 #multi: Bayesian Max-margin Multi-Task Learning with Data Augmentation (CL, JZ, JC), pp. 415–423.
ICML-c2-2014-MengEH #modelling #visual notation: Learning Latent Variable Gaussian Graphical Models (ZM, BE, AOHI), pp. 1269–1277.
ICML-c2-2014-MizrahiDF #linear #markov #parallel #random: Linear and Parallel Learning of Markov Random Fields (YDM, MD, NdF), pp. 199–207.
ICML-c2-2014-MnihG #network: Neural Variational Inference and Learning in Belief Networks (AM, KG), pp. 1791–1799.
ICML-c2-2014-NiuDPS #approximate #multi: Transductive Learning with Multi-class Volume Approximation (GN, BD, MCdP, MS), pp. 1377–1385.
ICML-c2-2014-PandeyD #network: Learning by Stretching Deep Networks (GP, AD), pp. 1719–1727.
ICML-c2-2014-PentinaL #bound: A PAC-Bayesian bound for Lifelong Learning (AP, CHL), pp. 991–999.
ICML-c2-2014-QinLJ #optimisation: Sparse Reinforcement Learning via Convex Optimization (ZQ, WL, FJ), pp. 424–432.
ICML-c2-2014-ReedSZL #interactive: Learning to Disentangle Factors of Variation with Manifold Interaction (SR, KS, YZ, HL), pp. 1431–1439.
ICML-c2-2014-RippelGA #order: Learning Ordered Representations with Nested Dropout (OR, MAG, RPA), pp. 1746–1754.
ICML-c2-2014-RodriguesPR #classification #multi #process: Gaussian Process Classification and Active Learning with Multiple Annotators (FR, FCP, BR), pp. 433–441.
ICML-c2-2014-SantosZ: Learning Character-level Representations for Part-of-Speech Tagging (CNdS, BZ), pp. 1818–1826.
ICML-c2-2014-SilvaKB: Active Learning of Parameterized Skills (BCdS, GK, AGB), pp. 1737–1745.
ICML-c2-2014-SongGJMHD #locality #on the: On learning to localize objects with minimal supervision (HOS, RBG, SJ, JM, ZH, TD), pp. 1611–1619.
ICML-c2-2014-SunIM #classification #linear: Learning Mixtures of Linear Classifiers (YS, SI, AM), pp. 721–729.
ICML-c2-2014-SunM #geometry #statistics: An Information Geometry of Statistical Manifold Learning (KS, SMM), pp. 1–9.
ICML-c2-2014-TrigeorgisBZS: A Deep Semi-NMF Model for Learning Hidden Representations (GT, KB, SZ, BWS), pp. 1692–1700.
ICML-c2-2014-WangHS: Active Transfer Learning under Model Shift (XW, TKH, JS), pp. 1305–1313.
ICML-c2-2014-WangNH #distance #metric #robust: Robust Distance Metric Learning via Simultaneous L1-Norm Minimization and Maximization (HW, FN, HH), pp. 1836–1844.
ICML-c2-2014-WangSSMK #metric: Two-Stage Metric Learning (JW, KS, FS, SMM, AK), pp. 370–378.
ICML-c2-2014-WenYG #nondeterminism #robust: Robust Learning under Uncertain Test Distributions: Relating Covariate Shift to Model Misspecification (JW, CNY, RG), pp. 631–639.
ICML-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.
ICPR-2014-AkinM #detection #online: Online Learning and Detection with Part-Based, Circulant Structure (OA, KM), pp. 4229–4233.
ICPR-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.
ICPR-2014-Alvarez-MezaMC #adaptation #video: Correntropy-Based Adaptive Learning to Support Video Surveillance Systems (AMÁM, SMG, GCD), pp. 2590–2595.
ICPR-2014-ArvanitopoulosBT #analysis: Laplacian Support Vector Analysis for Subspace Discriminative Learning (NA, DB, AT), pp. 1609–1614.
ICPR-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.
ICPR-2014-BertonL #graph: Graph Construction Based on Labeled Instances for Semi-supervised Learning (LB, AdAL), pp. 2477–2482.
ICPR-2014-BouillonA #classification #evolution #gesture #online: Supervision Strategies for the Online Learning of an Evolving Classifier for Gesture Commands (MB, ÉA), pp. 2029–2034.
ICPR-2014-CaiTF #recognition #taxonomy: Learning Pose Dictionary for Human Action Recognition (JxC, XT, GCF), pp. 381–386.
ICPR-2014-CaoHS #approach #classification #kernel #multi: Optimization-Based Extreme Learning Machine with Multi-kernel Learning Approach for Classification (LlC, WbH, FS), pp. 3564–3569.
ICPR-2014-ChengZHT #recognition: Semi-supervised Learning for RGB-D Object Recognition (YC, XZ, KH, TT), pp. 2377–2382.
ICPR-2014-ChenK14a: Learning to Count with Back-propagated Information (KC, JKK), pp. 4672–4677.
ICPR-2014-ChenZW #identification #metric: Relevance Metric Learning for Person Re-identification by Exploiting Global Similarities (JC, ZZ, YW), pp. 1657–1662.
ICPR-2014-CheplyginaSTPLB #classification #multi: Classification of COPD with Multiple Instance Learning (VC, LS, DMJT, JJHP, ML, MdB), pp. 1508–1513.
ICPR-2014-DengZS #recognition #speech: Linked Source and Target Domain Subspace Feature Transfer Learning — Exemplified by Speech Emotion Recognition (JD, ZZ, BWS), pp. 761–766.
ICPR-2014-DuZCW #flexibility #linear #random: Learning Flexible Binary Code for Linear Projection Based Hashing with Random Forest (SD, WZ, SC, YW), pp. 2685–2690.
ICPR-2014-FangZ #classification: Cross Domain Shared Subspace Learning for Unsupervised Transfer Classification (ZF, ZZ), pp. 3927–3932.
ICPR-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.
ICPR-2014-FiratCV #detection #representation: Representation Learning for Contextual Object and Region Detection in Remote Sensing (OF, GC, FTYV), pp. 3708–3713.
ICPR-2014-FornoniC #naive bayes #recognition: Scene Recognition with Naive Bayes Non-linear Learning (MF, BC), pp. 3404–3409.
ICPR-2014-GanSZ: An Extended Isomap for Manifold Topology Learning with SOINN Landmarks (QG, FS, JZ), pp. 1579–1584.
ICPR-2014-GeDGC: Background Subtraction with Dynamic Noise Sampling and Complementary Learning (WG, YD, ZG, YC), pp. 2341–2346.
ICPR-2014-GengWX #adaptation #estimation: Facial Age Estimation by Adaptive Label Distribution Learning (XG, QW, YX), pp. 4465–4470.
ICPR-2014-GienTCL #fuzzy #multi #predict: Dual Fuzzy Hypergraph Regularized Multi-label Learning for Protein Subcellular Location Prediction (JG, YYT, CLPC, YL), pp. 512–516.
ICPR-2014-GuoZLCZ #clustering #kernel #multi: Multiple Kernel Learning Based Multi-view Spectral Clustering (DG, JZ, XL, YC, CZ), pp. 3774–3779.
ICPR-2014-HooKPC #comprehension #image #random: Enhanced Random Forest with Image/Patch-Level Learning for Image Understanding (WLH, TKK, YP, CSC), pp. 3434–3439.
ICPR-2014-HouYW #adaptation #recognition #self: Domain Adaptive Self-Taught Learning for Heterogeneous Face Recognition (CAH, MCY, YCFW), pp. 3068–3073.
ICPR-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.
ICPR-2014-JhuoL #detection #multi #video: Video Event Detection via Multi-modality Deep Learning (IHJ, DTL), pp. 666–671.
ICPR-2014-KhoshrouCT #multi #video: Active Learning from Video Streams in a Multi-camera Scenario (SK, JSC, LFT), pp. 1248–1253.
ICPR-2014-KrauseGDLF #fine-grained #recognition: Learning Features and Parts for Fine-Grained Recognition (JK, TG, JD, LJL, FFL), pp. 26–33.
ICPR-2014-KumarG #documentation #keyword: Bayesian Active Learning for Keyword Spotting in Handwritten Documents (GK, VG), pp. 2041–2046.
ICPR-2014-LeiSLCXP #metric #similarity: Humanoid Robot Imitation with Pose Similarity Metric Learning (JL, MS, ZNL, CC, XX, SP), pp. 4240–4245.
ICPR-2014-LiuL0L #classification #image: Regularized Hierarchical Feature Learning with Non-negative Sparsity and Selectivity for Image Classification (BL, JL, XB, HL), pp. 4293–4298.
ICPR-2014-LiuWCL #automation #category theory #image: Automatic Image Attribute Selection for Zero-Shot Learning of Object Categories (LL, AW, SC, BCL), pp. 2619–2624.
ICPR-2014-LiuYHTH #recognition #visual notation: Semi-supervised Learning for Cross-Device Visual Location Recognition (PL, PY, KH, TT, HWH), pp. 2873–2878.
ICPR-2014-LiuZC #identification #metric #multi #parametricity: Parametric Local Multi-modal Metric Learning for Person Re-identification (KL, ZCZ, AC), pp. 2578–2583.
ICPR-2014-LuoJ #encoding #image #retrieval #semantics: Learning Semantic Binary Codes by Encoding Attributes for Image Retrieval (JL, ZJ), pp. 279–284.
ICPR-2014-ManfrediGC #energy #graph #image #segmentation: Learning Graph Cut Energy Functions for Image Segmentation (MM, CG, RC), pp. 960–965.
ICPR-2014-MarcaciniDHR #approach #clustering #documentation #metric: Privileged Information for Hierarchical Document Clustering: A Metric Learning Approach (RMM, MAD, ERH, SOR), pp. 3636–3641.
ICPR-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.
ICPR-2014-NieJ #linear #using: Feature Learning Using Bayesian Linear Regression Model (SN, QJ), pp. 1502–1507.
ICPR-2014-NieKZ #recognition #using: Periocular Recognition Using Unsupervised Convolutional RBM Feature Learning (LN, AK, SZ), pp. 399–404.
ICPR-2014-NilufarP #detection #programming: Learning to Detect Contours with Dynamic Programming Snakes (SN, TJP), pp. 984–989.
ICPR-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.
ICPR-2014-PatriciaTC #adaptation #multi #performance: Multi-source Adaptive Learning for Fast Control of Prosthetics Hand (NP, TT, BC), pp. 2769–2774.
ICPR-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.
ICPR-2014-PhamKC #graph #image: Semi-supervised Learning on Bi-relational Graph for Image Annotation (HDP, KHK, SC), pp. 2465–2470.
ICPR-2014-PillaiFR #classification #multi: Learning of Multilabel Classifiers (IP, GF, FR), pp. 3452–3456.
ICPR-2014-RenYZH #classification #image #nearest neighbour: Learning Convolutional Nonlinear Features for K Nearest Neighbor Image Classification (WR, YY, JZ, KH), pp. 4358–4363.
ICPR-2014-RiabchenkoKC #generative #modelling: Learning Generative Models of Object Parts from a Few Positive Examples (ER, JKK, KC), pp. 2287–2292.
ICPR-2014-RozzaMP #graph #kernel #novel: A Novel Graph-Based Fisher Kernel Method for Semi-supervised Learning (AR, MM, AP), pp. 3786–3791.
ICPR-2014-SaitoAFRSGC #using: Active Semi-supervised Learning Using Optimum-Path Forest (PTMS, WPA, AXF, PJdR, CTNS, JFG, MHdC), pp. 3798–3803.
ICPR-2014-SatoKSK #classification #multi: Learning Multiple Complex Features Based on Classification Results (YS, KK, YS, MK), pp. 3369–3373.
ICPR-2014-SavakisRP #difference #gesture #using: Gesture Control Using Active Difference Signatures and Sparse Learning (AES, RR, RWP), pp. 3969–3974.
ICPR-2014-ShenHSGM #framework #interactive: Interactive Framework for Insect Tracking with Active Learning (MS, WH, PS, CGG, DM), pp. 2733–2738.
ICPR-2014-StraehleKKH #multi #random: Multiple Instance Learning with Response-Optimized Random Forests (CNS, MK, UK, FAH), pp. 3768–3773.
ICPR-2014-UmakanthanDFS #multi #process #representation #taxonomy: Multiple Instance Dictionary Learning for Activity Representation (SU, SD, CF, SS), pp. 1377–1382.
ICPR-2014-VellankiDVP #parametricity: Nonparametric Discovery of Learning Patterns and Autism Subgroups from Therapeutic Data (PV, TVD, SV, DQP), pp. 1828–1833.
ICPR-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.
ICPR-2014-WangGJ #using: Learning with Hidden Information Using a Max-Margin Latent Variable Model (ZW, TG, QJ), pp. 1389–1394.
ICPR-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.
ICPR-2014-WangWJ: Learning with Hidden Information (ZW, XW, QJ), pp. 238–243.
ICPR-2014-WangZWB #modelling: Learning Room Occupancy Patterns from Sparsely Recovered Light Transport Models (QW, XZ, MW, KLB), pp. 1987–1992.
ICPR-2014-WanHA #image #recognition: Indoor Scene Recognition from RGB-D Images by Learning Scene Bases (SW, CH, JKA), pp. 3416–3421.
ICPR-2014-WatanabeW #analysis #component #distance #metric #performance: Logistic Component Analysis for Fast Distance Metric Learning (KW, TW), pp. 1278–1282.
ICPR-2014-WuJ #detection: Learning the Deep Features for Eye Detection in Uncontrolled Conditions (YW, QJ), pp. 455–459.
ICPR-2014-WuLWHJ #multi: Multi-label Learning with Missing Labels (BW, ZL, SW, BGH, QJ), pp. 1964–1968.
ICPR-2014-WuS #multi #recognition: Regularized Multi-view Multi-metric Learning for Action Recognition (XW, SKS), pp. 471–476.
ICPR-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.
ICPR-2014-XieUKG #incremental: Incremental Learning with Support Vector Data Description (WX, SU, SK, MG), pp. 3904–3909.
ICPR-2014-XuS #network #using: Bayesian Network Structure Learning Using Causality (ZX, SNS), pp. 3546–3551.
ICPR-2014-YangN #integration #multi: Semi-supervised Learning of Geospatial Objects through Multi-modal Data Integration (YY, SN), pp. 4062–4067.
ICPR-2014-YangXWL #realtime: Real-Time Tracking via Deformable Structure Regression Learning (XY, QX, SW, PL), pp. 2179–2184.
ICPR-2014-YangYH: Diversity-Based Ensemble with Sample Weight Learning (CY, XCY, HWH), pp. 1236–1241.
ICPR-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.
ICPR-2014-YiLLL #identification #metric: Deep Metric Learning for Person Re-identification (DY, ZL, SL, SZL), pp. 34–39.
ICPR-2014-YinYPH #case study #classification: Shallow Classification or Deep Learning: An Experimental Study (XCY, CY, WYP, HWH), pp. 1904–1909.
ICPR-2014-YooJKC #optimisation: Transfer Learning of Motion Patterns in Traffic Scene via Convex Optimization (YJY, HJ, SWK, JYC), pp. 4158–4163.
ICPR-2014-ZenRS #distance #matrix #metric: Simultaneous Ground Metric Learning and Matrix Factorization with Earth Mover’s Distance (GZ, ER, NS), pp. 3690–3695.
ICPR-2014-ZhangM14a #detection #multi: Simultaneous Detection of Multiple Facial Action Units via Hierarchical Task Structure Learning (XZ, MHM), pp. 1863–1868.
ICPR-2014-ZhangQWL #classification #online: Object Classification in Traffic Scene Surveillance Based on Online Semi-supervised Active Learning (ZZ, JQ, YW, ML), pp. 3086–3091.
ICPR-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.
ICPR-2014-ZhuS #recognition #taxonomy: Correspondence-Free Dictionary Learning for Cross-View Action Recognition (FZ, LS), pp. 4525–4530.
ICPR-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.
KDD-2014-Bengio #scalability: Scaling up deep learning (YB), p. 1966.
KDD-2014-BensonRS #multi #network #scalability: Learning multifractal structure in large networks (ARB, CR, SS), pp. 1326–1335.
KDD-2014-DalessandroCRPWP #online #scalability: Scalable hands-free transfer learning for online advertising (BD, DC, TR, CP, MHW, FJP), pp. 1573–1582.
KDD-2014-GaddeAO #graph #using: Active semi-supervised learning using sampling theory for graph signals (AG, AA, AO), pp. 492–501.
KDD-2014-GohR: Box drawings for learning with imbalanced data (STG, CR), pp. 333–342.
KDD-2014-GongZFY #multi #performance: Efficient multi-task feature learning with calibration (PG, JZ, WF, JY), pp. 761–770.
KDD-2014-GrabockaSWS: Learning time-series shapelets (JG, NS, MW, LST), pp. 392–401.
KDD-2014-Kushnir #adaptation #kernel: Active-transductive learning with label-adapted kernels (DK), pp. 462–471.
KDD-2014-LanSB #analysis: Time-varying learning and content analytics via sparse factor analysis (ASL, CS, RGB), pp. 452–461.
KDD-2014-LiangRR #personalisation: Personalized search result diversification via structured learning (SL, ZR, MdR), pp. 751–760.
KDD-2014-PerozziAS #named #online #social: DeepWalk: online learning of social representations (BP, RAR, SS), pp. 701–710.
KDD-2014-PrabhuV #classification #multi #named #performance: FastXML: a fast, accurate and stable tree-classifier for extreme multi-label learning (YP, MV), pp. 263–272.
KDD-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.
KDD-2014-QianHJPZ #approach #distance #metric #using: Distance metric learning using dropout: a structured regularization approach (QQ, JH, RJ, JP, SZ), pp. 323–332.
KDD-2014-Salakhutdinov: Deep learning (RS), p. 1973.
KDD-2014-ShaoAK #concept #data type #prototype: Prototype-based learning on concept-drifting data streams (JS, ZA, SK), pp. 412–421.
KDD-2014-TayebiEGB #embedded #predict #using: Spatially embedded co-offence prediction using supervised learning (MAT, ME, UG, PLB), pp. 1789–1798.
KDD-2014-VasishtDVK #classification #multi: Active learning for sparse bayesian multilabel classification (DV, ACD, MV, AK), pp. 472–481.
KDD-2014-WangNH #adaptation #induction #scalability: Large-scale adaptive semi-supervised learning via unified inductive and transductive model (DW, FN, HH), pp. 482–491.
KDD-2014-WangSE #collaboration #permutation: Active collaborative permutation learning (JW, NS, JE), pp. 502–511.
KDD-2014-WangSW #modelling: Unsupervised learning of disease progression models (XW, DS, FW), pp. 85–94.
KDD-2014-XuL #behaviour #problem: Product selection problem: improve market share by learning consumer behavior (SX, JCSL), pp. 851–860.
KDD-2014-YangH #parametricity: Learning with dual heterogeneity: a nonparametric bayes model (HY, JH), pp. 582–590.
KDD-2014-ZhangTMF #network: Supervised deep learning with auxiliary networks (JZ, GT, YM, WF), pp. 353–361.
KDD-2014-ZhouC #adaptation #documentation #rank: Unifying learning to rank and domain adaptation: enabling cross-task document scoring (MZ, KCCC), pp. 781–790.
KDIR-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.
KDIR-2014-SuciuICDP #word: Learning Good Opinions from Just Two Words Is Not Bad (DAS, VVI, ACC, MD, RP), pp. 233–241.
KEOD-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.
KMIS-2014-AtrashAM #collaboration: Supporting Organizational Learning with Collaborative Annotation (AA, MHA, CM), pp. 237–244.
KMIS-2014-BartuskovaK #information management: Knowledge Management and Sharing in E-Learning — Hierarchical System for Managing Learning Resources (AB, OK), pp. 179–185.
KMIS-2014-HisakaneS #visualisation: A Visualization System of Discussion Structure in Case Method Learning (DH, MS), pp. 126–132.
KR-2014-KonevLOW #lightweight #logic #ontology: Exact Learning of Lightweight Description Logic Ontologies (BK, CL, AO, FW).
KR-2014-Michael #predict: Simultaneous Learning and Prediction (LM).
MLDM-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.
MLDM-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.
MLDM-2014-KuleshovB #data mining #mining: Manifold Learning in Data Mining Tasks (APK, AVB), pp. 119–133.
MLDM-2014-NeumannHRL #case study #experience: A Robot Waiter Learning from Experiences (BN, LH, PR, JL), pp. 285–299.
MLDM-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.
RecSys-2014-BhagatWIT #matrix #recommendation #using: Recommending with an agenda: active learning of private attributes using matrix factorization (SB, UW, SI, NT), pp. 65–72.
RecSys-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.
RecSys-2014-SaveskiM #recommendation: Item cold-start recommendations: learning local collective embeddings (MS, AM), pp. 89–96.
SEKE-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.
SEKE-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.
SEKE-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.
SIGIR-2014-CostaCS #modelling #ranking: Learning temporal-dependent ranking models (MC, FMC, MJS), pp. 757–766.
SIGIR-2014-EfronWS #query: Learning sufficient queries for entity filtering (ME, CW, GS), pp. 1091–1094.
SIGIR-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.
SIGIR-2014-JiangKCC #behaviour #query: Learning user reformulation behavior for query auto-completion (JYJ, YYK, PYC, PJC), pp. 445–454.
SIGIR-2014-LengCL #image #random #retrieval #scalability: Random subspace for binary codes learning in large scale image retrieval (CL, JC, HL), pp. 1031–1034.
SIGIR-2014-LiuL #probability #segmentation #word: Probabilistic ensemble learning for vietnamese word segmentation (WL, LL), pp. 931–934.
SIGIR-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.
SIGIR-2014-PanYMLNR #image: Click-through-based cross-view learning for image search (YP, TY, TM, HL, CWN, YR), pp. 717–726.
SIGIR-2014-QiuCYLL #personalisation #ranking: Item group based pairwise preference learning for personalized ranking (SQ, JC, TY, CL, HL), pp. 1219–1222.
SIGIR-2014-SokolovHR #query: Learning to translate queries for CLIR (AS, FH, SR), pp. 1179–1182.
SIGIR-2014-SpinaGA #detection #monitoring #online #similarity #topic: Learning similarity functions for topic detection in online reputation monitoring (DS, JG, EA), pp. 527–536.
SIGIR-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.
SIGIR-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.
SIGIR-2014-WuMHR #image #personalisation: Learning to personalize trending image search suggestion (CCW, TM, WHH, YR), pp. 727–736.
SIGIR-2014-YuWZTSZ #rank: Hashing with List-Wise learning to rank (ZY, FW, YZ, ST, JS, YZ), pp. 999–1002.
SIGIR-2014-ZhuLGCN: Learning for search result diversification (YZ, YL, JG, XC, SN), pp. 293–302.
SIGIR-2014-ZhuNG #adaptation #random #social: An adaptive teleportation random walk model for learning social tag relevance (XZ, WN, MG), pp. 223–232.
MoDELS-2014-BakiSCMF #model transformation: Learning Implicit and Explicit Control in Model Transformations by Example (IB, HAS, QC, PM, MF), pp. 636–652.
ASE-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.
FSE-2014-AllamanisBBS: Learning natural coding conventions (MA, ETB, CB, CAS), pp. 281–293.
FSE-2014-YeBL #debugging #rank #using: Learning to rank relevant files for bug reports using domain knowledge (XY, RCB, CL), pp. 689–699.
ICSE-2014-HeWYZ #reasoning: Symbolic assume-guarantee reasoning through BDD learning (FH, BYW, LY, LZ), pp. 1071–1082.
ICSE-2014-JingYZWL #fault #predict #taxonomy: Dictionary learning based software defect prediction (XYJ, SY, ZWZ, SSW, JL), pp. 414–423.
SAC-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.
SAC-2014-DhanjalC #network: Learning reputation in an authorship network (CD, SC), pp. 1724–1726.
SAC-2014-LiWL #mobile #online #recognition: Online learning with mobile sensor data for user recognition (HGL, XW, ZL), pp. 64–70.
SAC-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.
CASE-2014-HabibDBHP #android: Learning human-like facial expressions for Android Phillip K. Dick (AH, SKD, ICB, DH, DOP), pp. 1159–1165.
CASE-2014-HwangLW #adaptation: Adaptive reinforcement learning in box-pushing robots (KSH, JLL, WHW), pp. 1182–1187.
CASE-2014-MaDLZ #modelling #simulation: Modeling and simulation of product diffusion considering learning effect (KPM, XD, CFL, JZ), pp. 665–670.
CASE-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.
CASE-2014-MinakaisMW: Groundhog Day: Iterative learning for building temperature control (MM, SM, JTW), pp. 948–953.
CASE-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.
DATE-2014-HanKNV: A deep learning methodology to proliferate golden signoff timing (SSH, ABK, SN, ASV), pp. 1–6.
HPCA-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.
OSDI-2014-ChilimbiSAK #performance #scalability: Project Adam: Building an Efficient and Scalable Deep Learning Training System (TMC, YS, JA, KK), pp. 571–582.
PDP-2014-FarahnakianLP #energy #using #virtual machine: Energy-Efficient Virtual Machines Consolidation in Cloud Data Centers Using Reinforcement Learning (FF, PL, JP), pp. 500–507.
STOC-2014-AwasthiBL #linear #locality #power of: The power of localization for efficiently learning linear separators with noise (PA, MFB, PML), pp. 449–458.
STOC-2014-Christiano #online #programming: Online local learning via semidefinite programming (PC), pp. 468–474.
STOC-2014-DanielyLS #complexity: From average case complexity to improper learning complexity (AD, NL, SSS), pp. 441–448.
TACAS-2014-MalerM #regular expression #scalability: Learning Regular Languages over Large Alphabets (OM, IEM), pp. 485–499.
CAV-2014-0001LMN #framework #invariant #named #robust: ICE: A Robust Framework for Learning Invariants (PG, CL, PM, DN), pp. 69–87.
CAV-2014-HeizmannHP #analysis #source code #termination: Termination Analysis by Learning Terminating Programs (MH, JH, AP), pp. 797–813.
SMT-2014-KorovinKS #towards: Towards Conflict-Driven Learning for Virtual Substitution (KK, MK, TS), p. 71.
ICDAR-2013-AgarwalGC: Greedy Search for Active Learning of OCR (AA, RG, SC), pp. 837–841.
ICDAR-2013-BougueliaBB #approach #classification #documentation: A Stream-Based Semi-supervised Active Learning Approach for Document Classification (MRB, YB, AB), pp. 611–615.
ICDAR-2013-BouillonLAR #gesture #using: Using Confusion Reject to Improve (User and) System (Cross) Learning of Gesture Commands (MB, PL, ÉA, GR), pp. 1017–1021.
ICDAR-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.
ICDAR-2013-NguyenCBO #image #interactive: Interactive Knowledge Learning for Ancient Images (NVN, MC, AB, JMO), pp. 300–304.
ICDAR-2013-PuriST #network: Bayesian Network Structure Learning and Inference Methods for Handwriting (MP, SNS, YT), pp. 1320–1324.
ICDAR-2013-SchambachR #network #sequence: Stabilize Sequence Learning with Recurrent Neural Networks by Forced Alignment (MPS, SFR), pp. 1270–1274.
ICDAR-2013-SuL #recognition: Discriminative Weighting and Subspace Learning for Ensemble Symbol Recognition (FS, TL), pp. 1088–1092.
ICDAR-2013-SuTLDT #classification #documentation #image #representation: Self Learning Classification for Degraded Document Images by Sparse Representation (BS, ST, SL, TAD, CLT), pp. 155–159.
ICDAR-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.
ICDAR-2013-Zhu0N #recognition: Sub-structure Learning Based Handwritten Chinese Text Recognition (YZ, JS, SN), pp. 295–299.
JCDL-2013-NockNB #geometry #library: Non-linear book manifolds: learning from associations the dynamic geometry of digital libraries (RN, FN, EB), pp. 313–322.
JCDL-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.
PODS-2013-AbouziedAPHS #quantifier #query #verification: Learning and verifying quantified boolean queries by example (AA, DA, CHP, JMH, AS), pp. 49–60.
TPDL-2013-MajidiC #dependence #parsing: Committee-Based Active Learning for Dependency Parsing (SM, GRC), pp. 442–445.
VLDB-2013-BrunatoB #optimisation: Learning and Intelligent Optimization (LION): One Ring to Rule Them All (MB, RB), pp. 1176–1177.
VLDB-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.
VLDB-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.
CSEET-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.
CSEET-2013-Georgas #composition #design #education #towards: Toward infusing modular and reflective design learning throughout the curriculum (JCG), pp. 274–278.
CSEET-2013-RibaudS #cost analysis #information management #problem: The cost of problem-based learning: An example in information systems engineering (VR, PS), pp. 259–263.
CSEET-2013-StejskalS #testing: Test-driven learning in high school computer science (RS, HPS), pp. 289–293.
ITiCSE-2013-Alshaigy #development #education #interactive #programming language #python: Development of an interactive learning tool to teach python programming language (BA), p. 344.
ITiCSE-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.
ITiCSE-2013-FernandesCB: A pilot project on non-conventional learning (SF, AC, LSB), p. 346.
ITiCSE-2013-German: Jump-starting team-based learning in the computer science classroom (DAG), p. 323.
ITiCSE-2013-GorlatovaSKKZ #research #scalability: Project-based learning within a large-scale interdisciplinary research effort (MG, JS, PRK, IK, GZ), pp. 207–212.
ITiCSE-2013-HawthorneC #source code: ACM core IT learning outcomes for associate-degree programs (EKH, RDC), p. 357.
ITiCSE-2013-JalilPWL #design #interactive #taxonomy: Design eye: an interactive learning environment based on the solo taxonomy (SAJ, BP, IW, ALR), pp. 22–27.
ITiCSE-2013-JohnsonCH #contest #development #game studies: Learning elsewhere: tales from an extracurricular game development competition (CJ, AC, SH), pp. 70–75.
ITiCSE-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.
ITiCSE-2013-MellodgeR #arduino #case study #experience #framework #platform #student #using: Using the arduino platform to enhance student learning experiences (PM, IR), p. 338.
ITiCSE-2013-Paule-RuizGPG #evaluation #framework #interactive: Voice interactive learning: a framework and evaluation (MPPR, VMÁG, JRPP, MRG), pp. 34–39.
ITiCSE-2013-QianYGBT #authentication #mobile #network #security: Mobile device based authentic learning for computer network and security (KQ, MY, MG, PB, LT), p. 335.
ITiCSE-2013-ReedZ #framework: A hierarchical framework for mapping and quantitatively assessing program and learning outcomes (JR, HZ), pp. 52–57.
ITiCSE-2013-RowanD #mobile #overview #using: A systematic literature review on using mobile computing as a learning intervention (MR, JD), p. 339.
ITiCSE-2013-Sanchez-Nielsen #multi #student: Producing multimedia pills to stimulate student learning and engagement (ESN), pp. 165–170.
ITiCSE-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.
ITiCSE-2013-VihavainenVLP #student #using: Scaffolding students’ learning using test my code (AV, TV, ML, MP), pp. 117–122.
ITiCSE-2013-Wildsmith #named: Kinetic: a learning environment within business (CW), p. 3.
SIGITE-2013-BrannockLN #authentication #case study #development #experience: Integrating authentic learning into a software development course: an experience report (EB, RL, NPN), pp. 195–200.
SIGITE-2013-HeinonenHLV #agile #re-engineering #using: Learning agile software engineering practices using coding dojo (KH, KH, ML, AV), pp. 97–102.
SIGITE-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.
SIGITE-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.
SIGITE-2013-ZhangZ: Supporting adult learning: enablers, barriers, and services (CZ, GZ), pp. 151–152.
CSMR-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.
DLT-2013-BolligHLM #approach #automaton: A Fresh Approach to Learning Register Automata (BB, PH, ML, BM), pp. 118–130.
ICALP-v2-2013-FuscoPP #performance: Learning a Ring Cheaply and Fast (EGF, AP, RP), pp. 557–568.
LATA-2013-BjorklundFK #automaton: MAT Learning of Universal Automata (JB, HF, AK), pp. 141–152.
AIIDE-2013-ChenKS #detection #interactive: Learning Interrogation Strategies while Considering Deceptions in Detective Interactive Stories (GYC, ECCK, VWS).
AIIDE-2013-LeeceJ #game studies #reasoning: Reinforcement Learning for Spatial Reasoning in Strategy Games (MAL, AJ).
CIG-2013-BishopM #evaluation #online: Evolutionary reature evaluation for online Reinforcement Learning (JB, RM), pp. 1–8.
CIG-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.
CIG-2013-PerezSL #monte carlo #multi #online: Online and offline learning in multi-objective Monte Carlo Tree Search (DPL, SS, SML), pp. 1–8.
CIG-2013-Schaul #game studies #interactive #modelling #video: A video game description language for model-based or interactive learning (TS), pp. 1–8.
CIG-2013-SifaB #analysis #behaviour #game studies: Archetypical motion: Supervised game behavior learning with Archetypal Analysis (RS, CB), pp. 1–8.
CIG-2013-WiensDP #behaviour #game studies #scalability: Creating large numbers of game AIs by learning behavior for cooperating units (SW, JD, SP), pp. 1–8.
DiGRA-2013-Wechselberger #game studies #question: Learning and Enjoyment in Serious Gaming - Contradiction or Complement? (UW).
FDG-2013-BarendregtF #design #game studies #interactive #student: Course on interaction games and learning for interaction design students (WB, MvF), pp. 261–268.
FDG-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.
FDG-2013-Marklund #development #game studies #on the: On the development of learning games (BBM), pp. 474–476.
VS-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.
VS-Games-2013-FeigenbaumF #case study #collaboration: Gameful Pedagogy and Collaborative Learning a Case Study of the Netsx Project (AF, AF), pp. 1–7.
VS-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.
VS-Games-2013-Wilkinson #education #game studies: Affective Educational Games: Utilizing Emotions in Game-Based Learning (PW), pp. 1–8.
GT-VMT-2013-AlshanqitiHK #graph transformation: Learning Minimal and Maximal Rules from Observations of Graph Transformations (AMA, RH, TAK).
CHI-2013-AndersonB #gesture #performance: Learning and performance with gesture guides (FA, WFB), pp. 1109–1118.
CHI-2013-EdgeCW #named: SpatialEase: learning language through body motion (DE, KYC, MW), pp. 469–472.
CHI-2013-HarpsteadMA #data analysis #education #game studies: In search of learning: facilitating data analysis in educational games (EH, BAM, VA), pp. 79–88.
CHI-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.
CHI-2013-SzafirM #adaptation #named #overview: ARTFul: adaptive review technology for flipped learning (DS, BM), pp. 1001–1010.
CSCW-2013-KowY #community: Media technologies and learning in the starcraft esport community (YMK, TY), pp. 387–398.
CSCW-2013-LinF #network: Opportunities via extended networks for teens’ informal learning (PL, SDF), pp. 1341–1352.
DHM-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.
DUXU-CXC-2013-ChoensawatSKH #education: Desirability of a Teaching and Learning Tool for Thai Dance Body Motion (WC, KS, CK, KH), pp. 171–179.
DUXU-CXC-2013-MarchettiB #game studies: Setting Conditions for Learning: Mediated Play and Socio-material Dialogue (EM, EPB), pp. 238–246.
DUXU-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.
DUXU-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.
DUXU-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.
DUXU-WM-2013-WilkinsonLC #experience #interactive: Exploring Prior Experience and the Effects of Age on Product Interaction and Learning (CRW, PL, PJC), pp. 457–466.
HCI-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.
HCI-AS-2013-AndujarEGM: Evaluating Engagement Physiologically and Knowledge Retention Subjectively through Two Different Learning Techniques (MA, JIE, JEG, PM), pp. 335–342.
HCI-AS-2013-EskildsenR #challenge #integration: Challenges for Contextualizing Language Learning — Supporting Cultural Integration (SE, MR), pp. 361–369.
HCI-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.
HCI-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.
HCI-AS-2013-HarunBON #using: Refining Rules Learning Using Evolutionary PD (AFH, SB, CO, NLMN), pp. 376–385.
HCI-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.
HCI-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.
HCI-AS-2013-LimaRSBSO #using: Innovation in Learning — The Use of Avatar for Sign Language (TL, MSR, TAS, AB, ES, HSdO), pp. 428–433.
HCI-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.
HCI-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.
HCI-AS-2013-MbathaM #experience #named: E-learning: The Power Source of Transforming the Learning Experience in an ODL Landscape (BM, MM), pp. 454–463.
HCI-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.
HCI-AS-2013-TakanoS: Nature Sound Ensemble Learning in Narrative-Episode Creation with Pictures (KT, SS), pp. 493–502.
HCI-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.
HCI-UC-2013-StarySF #interactive: Agility Based on Stakeholder Interaction — Blending Organizational Learning with Interactive BPM (CS, WS, AF), pp. 456–465.
HIMI-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.
HIMI-HSM-2013-HiyamaOMESH #artificial reality: Augmented Reality System for Measuring and Learning Tacit Artisan Skills (AH, HO, MM, EE, MS, MH), pp. 85–91.
HIMI-HSM-2013-SaitohI #detection #using #visualisation: Visualization of Anomaly Data Using Peculiarity Detection on Learning Vector Quantization (FS, SI), pp. 181–188.
HIMI-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.
HIMI-LCCB-2013-HallLS #assessment #evaluation #tool support: Psychophysiological Assessment Tools for Evaluation of Learning Technologies (RHH, NSL, HS), pp. 33–42.
HIMI-LCCB-2013-HayashiON #collaboration #interactive: An Experimental Environment for Analyzing Collaborative Learning Interaction (YH, YO, YIN), pp. 43–52.
HIMI-LCCB-2013-KanamoriTA #development #programming: Development of a Computer Programming Learning Support System Based on Reading Computer Program (HK, TT, TA), pp. 63–69.
HIMI-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.
HIMI-LCCB-2013-YamamotoKYMH #online #problem: Learning by Problem-Posing with Online Connected Media Tablets (SY, TK, YY, KM, TH), pp. 165–174.
OCSC-2013-Eustace #network: Building and Sustaining a Lifelong Adult Learning Network (KE), pp. 260–268.
OCSC-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.
EDOC-2013-Swenson #design: Designing for an Innovative Learning Organization (KDS), pp. 209–213.
ICEIS-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.
ICEIS-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.
ICEIS-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.
ICEIS-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.
CIKM-2013-BaragliaMNS #named #predict: LearNext: learning to predict tourists movements (RB, CIM, FMN, FS), pp. 751–756.
CIKM-2013-CeccarelliLOPT #metric: Learning relatedness measures for entity linking (DC, CL, SO, RP, ST), pp. 139–148.
CIKM-2013-ChengCLWAC #data type #multi: Feedback-driven multiclass active learning for data streams (YC, ZC, LL, JW, AA, ANC), pp. 1311–1320.
CIKM-2013-ChenW #classification #scalability: Cost-sensitive learning for large-scale hierarchical classification (JC, DW), pp. 1351–1360.
CIKM-2013-FangZ #feature model #multi: Discriminative feature selection for multi-view cross-domain learning (ZF, Z(Z), pp. 1321–1330.
CIKM-2013-HashemiNB #approach #network #retrieval #topic: Expertise retrieval in bibliographic network: a topic dominance learning approach (SHH, MN, HB), pp. 1117–1126.
CIKM-2013-KamathC #predict #what: Spatio-temporal meme prediction: learning what hashtags will be popular where (KYK, JC), pp. 1341–1350.
ECIR-2013-DangBC #information retrieval #rank: Two-Stage Learning to Rank for Information Retrieval (VD, MB, WBC), pp. 423–434.
ECIR-2013-JuMJ #classification #rank: Learning to Rank from Structures in Hierarchical Text Classification (QJ, AM, RJ), pp. 183–194.
ECIR-2013-NguyenTT #classification #rank #using: Folktale Classification Using Learning to Rank (DN, DT, MT), pp. 195–206.
ICML-c1-2013-0005LSL #feature model #modelling #online: Online Feature Selection for Model-based Reinforcement Learning (TTN, ZL, TS, TYL), pp. 498–506.
ICML-c1-2013-AbernethyAKD #problem #scalability: Large-Scale Bandit Problems and KWIK Learning (JA, KA, MK, MD), pp. 588–596.
ICML-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.
ICML-c1-2013-AnandkumarHJK #linear #network: Learning Linear Bayesian Networks with Latent Variables (AA, DH, AJ, SK), pp. 249–257.
ICML-c1-2013-BalcanBEL #performance: Efficient Semi-supervised and Active Learning of Disjunctions (NB, CB, SE, YL), pp. 633–641.
ICML-c1-2013-BootsG #approach: A Spectral Learning Approach to Range-Only SLAM (BB, GJG), pp. 19–26.
ICML-c1-2013-ChenK #adaptation #optimisation: Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization (YC, AK), pp. 160–168.
ICML-c1-2013-CotterSS: Learning Optimally Sparse Support Vector Machines (AC, SSS, NS), pp. 266–274.
ICML-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.
ICML-c1-2013-GolubCY: Learning an Internal Dynamics Model from Control Demonstration (MG, SC, BY), pp. 606–614.
ICML-c1-2013-GonenSS #approach #performance: Efficient Active Learning of Halfspaces: an Aggressive Approach (AG, SS, SSS), pp. 480–488.
ICML-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.
ICML-c1-2013-KadriGP #approach #kernel: A Generalized Kernel Approach to Structured Output Learning (HK, MG, PP), pp. 471–479.
ICML-c1-2013-KarbasiSS: Iterative Learning and Denoising in Convolutional Neural Associative Memories (AK, AHS, AS), pp. 445–453.
ICML-c1-2013-KumarB #bound #graph: Convex Relaxations for Learning Bounded-Treewidth Decomposable Graphs (KSSK, FRB), pp. 525–533.
ICML-c1-2013-LiLSHD #generative #using: Learning Hash Functions Using Column Generation (XL, GL, CS, AvdH, ARD), pp. 142–150.
ICML-c1-2013-LimLM #metric #robust: Robust Structural Metric Learning (DL, GRGL, BM), pp. 615–623.
ICML-c1-2013-MaatenCTW: Learning with Marginalized Corrupted Features (LvdM, MC, ST, KQW), pp. 410–418.
ICML-c1-2013-MaillardNOR #bound #representation: Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning (OAM, PN, RO, DR), pp. 543–551.
ICML-c1-2013-RuvoloE #algorithm #named #performance: ELLA: An Efficient Lifelong Learning Algorithm (PR, EE), pp. 507–515.
ICML-c1-2013-ZuluagaSKP #multi #optimisation: Active Learning for Multi-Objective Optimization (MZ, GS, AK, MP), pp. 462–470.
ICML-c2-2013-GaneshapillaiGL: Learning Connections in Financial Time Series (GG, JVG, AL), pp. 109–117.
ICML-c2-2013-GolovinSMY #ram #scalability: Large-Scale Learning with Less RAM via Randomization (DG, DS, HBM, MY), pp. 325–333.
ICML-c2-2013-KrummenacherOB #multi: Ellipsoidal Multiple Instance Learning (GK, CSO, JMB), pp. 73–81.
ICML-c2-2013-MaurerPR #multi: Sparse coding for multitask and transfer learning (AM, MP, BRP), pp. 343–351.
ICML-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.
ICML-c2-2013-MinhBM #framework #multi: A unifying framework for vector-valued manifold regularization and multi-view learning (HQM, LB, VM), pp. 100–108.
ICML-c2-2013-RanganathWBX #adaptation #probability: An Adaptive Learning Rate for Stochastic Variational Inference (RR, CW, DMB, EPX), pp. 298–306.
ICML-c2-2013-SohnZLL: Learning and Selecting Features Jointly with Point-wise Gated Boltzmann Machines (KS, GZ, CL, HL), pp. 217–225.
ICML-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.
ICML-c2-2013-TranPV #multi: Thurstonian Boltzmann Machines: Learning from Multiple Inequalities (TT, DQP, SV), pp. 46–54.
ICML-c2-2013-YangH #classification: Activized Learning with Uniform Classification Noise (LY, SH), pp. 370–378.
ICML-c3-2013-0002T #kernel: Differentially Private Learning with Kernels (PJ, AT), pp. 118–126.
ICML-c3-2013-AlmingolML #behaviour #multi: Learning Multiple Behaviors from Unlabeled Demonstrations in a Latent Controller Space (JA, LM, ML), pp. 136–144.
ICML-c3-2013-BalasubramanianYL: Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations (KB, KY, GL), pp. 289–297.
ICML-c3-2013-BalcanBM #ontology: Exploiting Ontology Structures and Unlabeled Data for Learning (NB, AB, YM), pp. 1112–1120.
ICML-c3-2013-BellemareVB #recursion: Bayesian Learning of Recursively Factored Environments (MGB, JV, MB), pp. 1211–1219.
ICML-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.
ICML-c3-2013-ChattopadhyayFDPY: Joint Transfer and Batch-mode Active Learning (RC, WF, ID, SP, JY), pp. 253–261.
ICML-c3-2013-Cheng #similarity: Riemannian Similarity Learning (LC), pp. 540–548.
ICML-c3-2013-CoatesHWWCN #off the shelf: Deep learning with COTS HPC systems (AC, BH, TW, DJW, BCC, AYN), pp. 1337–1345.
ICML-c3-2013-DalalyanHMS #modelling #programming: Learning Heteroscedastic Models by Convex Programming under Group Sparsity (ASD, MH, KM, JS), pp. 379–387.
ICML-c3-2013-DimitrakakisT: ABC Reinforcement Learning (CD, NT), pp. 684–692.
ICML-c3-2013-GensD #network: Learning the Structure of Sum-Product Networks (RG, PMD), pp. 873–880.
ICML-c3-2013-GuptaPV #approach #multi #parametricity: Factorial Multi-Task Learning : A Bayesian Nonparametric Approach (SKG, DQP, SV), pp. 657–665.
ICML-c3-2013-HockingRVB #detection #using: Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression (TH, GR, JPV, FRB), pp. 172–180.
ICML-c3-2013-HoXV #on the #taxonomy: On A Nonlinear Generalization of Sparse Coding and Dictionary Learning (JH, YX, BCV), pp. 1480–1488.
ICML-c3-2013-HuangS #markov #modelling: Spectral Learning of Hidden Markov Models from Dynamic and Static Data (TKH, JGS), pp. 630–638.
ICML-c3-2013-JancsaryNR #predict: Learning Convex QP Relaxations for Structured Prediction (JJ, SN, CR), pp. 915–923.
ICML-c3-2013-JoseGAV #kernel #performance #predict: Local Deep Kernel Learning for Efficient Non-linear SVM Prediction (CJ, PG, PA, MV), pp. 486–494.
ICML-c3-2013-JoulaniGS #feedback #online: Online Learning under Delayed Feedback (PJ, AG, CS), pp. 1453–1461.
ICML-c3-2013-JunZSR: Learning from Human-Generated Lists (KSJ, X(Z, BS, TTR), pp. 181–189.
ICML-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.
ICML-c3-2013-KontorovichNW #on the: On learning parametric-output HMMs (AK, BN, RW), pp. 702–710.
ICML-c3-2013-KoppulaS #detection #process: Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and Anticipation (HSK, AS), pp. 792–800.
ICML-c3-2013-KraehenbuehlK #convergence #parametricity #random: Parameter Learning and Convergent Inference for Dense Random Fields (PK, VK), pp. 513–521.
ICML-c3-2013-KuzborskijO: Stability and Hypothesis Transfer Learning (IK, FO), pp. 942–950.
ICML-c3-2013-LattimoreHS: The Sample-Complexity of General Reinforcement Learning (TL, MH, PS), pp. 28–36.
ICML-c3-2013-MalioutovV: Exact Rule Learning via Boolean Compressed Sensing (DMM, KRV), pp. 765–773.
ICML-c3-2013-MemisevicE #invariant #problem: Learning invariant features by harnessing the aperture problem (RM, GE), pp. 100–108.
ICML-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.
ICML-c3-2013-RamanJSS: Stable Coactive Learning via Perturbation (KR, TJ, PS, TS), pp. 837–845.
ICML-c3-2013-Romera-ParedesABP #multi: Multilinear Multitask Learning (BRP, HA, NBB, MP), pp. 1444–1452.
ICML-c3-2013-RossZYDB #policy #predict: Learning Policies for Contextual Submodular Prediction (SR, JZ, YY, DD, DB), pp. 1364–1372.
ICML-c3-2013-SchaulZL: No more pesky learning rates (TS, SZ, YL), pp. 343–351.
ICML-c3-2013-SilverNBWM #concurrent #interactive: Concurrent Reinforcement Learning from Customer Interactions (DS, LN, DB, SW, JM), pp. 924–932.
ICML-c3-2013-SimsekliCY #matrix #modelling: Learning the β-Divergence in Tweedie Compound Poisson Matrix Factorization Models (US, ATC, YKY), pp. 1409–1417.
ICML-c3-2013-SodomkaHLG #game studies #named #probability: Coco-Q: Learning in Stochastic Games with Side Payments (ES, EH, MLL, AG), pp. 1471–1479.
ICML-c3-2013-SutskeverMDH #on the: On the importance of initialization and momentum in deep learning (IS, JM, GED, GEH), pp. 1139–1147.
ICML-c3-2013-TarlowSCSZ #probability: Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning (DT, KS, LC, IS, RSZ), pp. 199–207.
ICML-c3-2013-WangNH #robust #self: Robust and Discriminative Self-Taught Learning (HW, FN, HH), pp. 298–306.
ICML-c3-2013-WangNH13a #clustering #multi: Multi-View Clustering and Feature Learning via Structured Sparsity (HW, FN, HH), pp. 352–360.
ICML-c3-2013-WangWBLT #multi #taxonomy: Max-Margin Multiple-Instance Dictionary Learning (XW, BW, XB, WL, ZT), pp. 846–854.
ICML-c3-2013-XuKHW #representation: Anytime Representation Learning (ZEX, MJK, GH, KQW), pp. 1076–1084.
ICML-c3-2013-YangLZ #matrix #multi: Multi-Task Learning with Gaussian Matrix Generalized Inverse Gaussian Model (MY, YL, ZZ), pp. 423–431.
ICML-c3-2013-YuLKJC: ∝SVM for Learning with Label Proportions (FXY, DL, SK, TJ, SFC), pp. 504–512.
ICML-c3-2013-ZemelWSPD: Learning Fair Representations (RSZ, YW, KS, TP, CD), pp. 325–333.
ICML-c3-2013-ZhangHSL #multi #named: MILEAGE: Multiple Instance LEArning with Global Embedding (DZ, JH, LS, RDL), pp. 82–90.
ICML-c3-2013-ZhangYJLH #bound #kernel #online: Online Kernel Learning with a Near Optimal Sparsity Bound (LZ, JY, RJ, ML, XH), pp. 621–629.
ICML-c3-2013-ZhouZS #kernel #multi #process: Learning Triggering Kernels for Multi-dimensional Hawkes Processes (KZ, HZ, LS), pp. 1301–1309.
ICML-c3-2013-ZweigW: Hierarchical Regularization Cascade for Joint Learning (AZ, DW), pp. 37–45.
KDD-2013-BahadoriLX #performance #probability #process: Fast structure learning in generalized stochastic processes with latent factors (MTB, YL, EPX), pp. 284–292.
KDD-2013-ChakrabartiH #scalability #social: Speeding up large-scale learning with a social prior (DC, RH), pp. 650–658.
KDD-2013-ChenHKB #named: DTW-D: time series semi-supervised learning from a single example (YC, BH, EJK, GEAPAB), pp. 383–391.
KDD-2013-DasMGW: Learning to question: leveraging user preferences for shopping advice (MD, GDFM, AG, IW), pp. 203–211.
KDD-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.
KDD-2013-GeGLZ #estimation #multi: Multi-source deep learning for information trustworthiness estimation (LG, JG, XL, AZ), pp. 766–774.
KDD-2013-GilpinED #algorithm #framework: Guided learning for role discovery (GLRD): framework, algorithms, and applications (SG, TER, IND), pp. 113–121.
KDD-2013-HaoCZ0RK #towards: Towards never-ending learning from time series streams (YH, YC, JZ, BH, TR, EJK), pp. 874–882.
KDD-2013-Howard: The business impact of deep learning (JH), p. 1135.
KDD-2013-KongY #automation #classification #distance: Discriminant malware distance learning on structural information for automated malware classification (DK, GY), pp. 1357–1365.
KDD-2013-KutzkovBBG #named: STRIP: stream learning of influence probabilities (KK, AB, FB, AG), pp. 275–283.
KDD-2013-LinWHY #information management #modelling #social: Extracting social events for learning better information diffusion models (SL, FW, QH, PSY), pp. 365–373.
KDD-2013-LiuFYX #recommendation: Learning geographical preferences for point-of-interest recommendation (BL, YF, ZY, HX), pp. 1043–1051.
KDD-2013-MorenoNK #graph #modelling: Learning mixed kronecker product graph models with simulated method of moments (SM, JN, SK), pp. 1052–1060.
KDD-2013-SutherlandPS #matrix #rank: Active learning and search on low-rank matrices (DJS, BP, JGS), pp. 212–220.
KDD-2013-TanXGW #metric #modelling #optimisation #rank #ranking: Direct optimization of ranking measures for learning to rank models (MT, TX, LG, SW), pp. 856–864.
KDD-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.
KDD-2013-WangY #query: Querying discriminative and representative samples for batch mode active learning (ZW, JY), pp. 158–166.
KDD-2013-Wright #data analysis #optimisation: Optimization in learning and data analysis (SJW), p. 3.
KDD-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.
KDD-2013-ZhangHL #multi #named: MI2LS: multi-instance learning from multiple informationsources (DZ, JH, RDL), pp. 149–157.
KDD-2013-ZhaoH #detection #online: Cost-sensitive online active learning with application to malicious URL detection (PZ, SCHH), pp. 919–927.
KDD-2013-ZhaoYNG #framework #twitter: A transfer learning based framework of crowd-selection on twitter (ZZ, DY, WN, SG), pp. 1514–1517.
KDIR-KMIS-2013-AtrashAM #enterprise #semantics: A Semantic Model for Small and Medium-sized Enterprises to Support Organizational Learning (AA, MHA, CM), pp. 476–483.
KDIR-KMIS-2013-BerkaniN #collaboration #recommendation #semantics: Semantic Collaborative Filtering for Learning Objects Recommendation (LB, ON), pp. 52–63.
KDIR-KMIS-2013-Dessne: Learning in an Organisation — Exploring the Nature of Relationships (KD), pp. 496–501.
KDIR-KMIS-2013-Eardley #information management: Negotiated Work-based Learning and Organisational Learning — The Relationship between Individual and Organisational Knowledge Management (AE), pp. 1–5.
KDIR-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.
KDIR-KMIS-2013-SaxenaBW #composition: A Cognitive Reference based Model for Learning Compositional Hierarchies with Whole-composite Tags (ABS, AB, AW), pp. 119–127.
KEOD-2013-WohlgenanntBS #automation #evolution #ontology #prototype: A Prototype for Automating Ontology Learning and Ontology Evolution (GW, SB, MS), pp. 407–412.
MLDM-2013-BouillonAA #evolution #fuzzy #gesture #recognition: Decremental Learning of Evolving Fuzzy Inference Systems: Application to Handwritten Gesture Recognition (MB, ÉA, AA), pp. 115–129.
MLDM-2013-ElGibreenA #multi #product line: Multi Model Transfer Learning with RULES Family (HE, MSA), pp. 42–56.
MLDM-2013-KoharaS #self: Typhoon Damage Scale Forecasting with Self-Organizing Maps Trained by Selective Presentation Learning (KK, IS), pp. 16–26.
MLDM-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.
RecSys-2013-HuY #process #recommendation: Interview process learning for top-n recommendation (FH, YY), pp. 331–334.
RecSys-2013-KaratzoglouBS #rank #recommendation: Learning to rank for recommender systems (AK, LB, YS), pp. 493–494.
RecSys-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.
RecSys-2013-SharmaY #community #recommendation: Pairwise learning in recommendation: experiments with community recommendation on linkedin (AS, BY), pp. 193–200.
RecSys-2013-WestonYW #rank #recommendation #statistics: Learning to rank recommendations with the k-order statistic loss (JW, HY, RJW), pp. 245–248.
SEKE-2013-BarbosaFNM #architecture #towards: Towards the Establishment of a Reference Architecture for Developing Learning Environments (EFB, MLF, EYN, JCM), pp. 350–355.
SIGIR-2013-LimsopathamMO: Learning to combine representations for medical records search (NL, CM, IO), pp. 833–836.
SIGIR-2013-Moschitti #kernel #rank #semantics: Kernel-based learning to rank with syntactic and semantic structures (AM), p. 1128.
SIGIR-2013-Shokouhi #personalisation #query: Learning to personalize query auto-completion (MS), pp. 103–112.
SIGIR-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.
SIGIR-2013-ZhangWYW #network #predict: Learning latent friendship propagation networks with interest awareness for link prediction (JZ, CW, PSY, JW), pp. 63–72.
OOPSLA-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.
POPL-2013-BotincanB #specification: Sigma*: symbolic learning of input-output specifications (MB, DB), pp. 443–456.
POPL-2013-DSilvaHK: Abstract conflict driven learning (VD, LH, DK), pp. 143–154.
SAS-2013-0001GHAN #concept #geometry #verification: Verification as Learning Geometric Concepts (RS, SG, BH, AA, AVN), pp. 388–411.
RE-2013-ShiWL #evolution #predict: Learning from evolution history to predict future requirement changes (LS, QW, ML), pp. 135–144.
RE-2013-SultanovH #requirements: Application of reinforcement learning to requirements engineering: requirements tracing (HS, JHH), pp. 52–61.
ASE-2013-DietrichCS #effectiveness #query #requirements #retrieval: Learning effective query transformations for enhanced requirements trace retrieval (TD, JCH, YS), pp. 586–591.
ASE-2013-GuoCASW #approach #performance #predict #statistics #variability: Variability-aware performance prediction: A statistical learning approach (JG, KC, SA, NS, AW), pp. 301–311.
ASE-2013-Xiao0LLS #named #type system: TzuYu: Learning stateful typestates (HX, JS, YL, SWL, CS), pp. 432–442.
ICSE-2013-MengKM #named: LASE: locating and applying systematic edits by learning from examples (NM, MK, KSM), pp. 502–511.
ICSE-2013-NamPK #fault: Transfer defect learning (JN, SJP, SK), pp. 382–391.
ICSE-2013-SykesCMKRI #adaptation #modelling: Learning revised models for planning in adaptive systems (DS, DC, JM, JK, AR, KI), pp. 63–71.
ICSE-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.
SAC-2013-BlondelSU #classification #constraints #using: Learning non-linear classifiers with a sparsity constraint using L1 regularization (MB, KS, KU), pp. 167–169.
SAC-2013-FilhoB #mobile #requirements: A requirements catalog for mobile learning environments (NFDF, EFB), pp. 1266–1271.
SAC-2013-LinCLG #approach #data-driven #distributed #predict: Distributed dynamic data driven prediction based on reinforcement learning approach (SYL, KMC, CCL, NG), pp. 779–784.
SAC-2013-LommatzschKA #hybrid #modelling #recommendation #semantics: Learning hybrid recommender models for heterogeneous semantic data (AL, BK, SA), pp. 275–276.
SAC-2013-SeelandKP #graph #kernel: Model selection based product kernel learning for regression on graphs (MS, SK, BP), pp. 136–143.
CASE-2013-LiX #adaptation: Off-line learning based adaptive dispatching rule for semiconductor wafer fabrication facility (LL, HX), pp. 1028–1033.
CASE-2013-OFlahertyE #bound #sequence: Learning to locomote: Action sequences and switching boundaries (RO, ME), pp. 7–12.
STOC-2013-BrakerskiLPRS #fault: Classical hardness of learning with errors (ZB, AL, CP, OR, DS), pp. 575–584.
TACAS-2013-ChenW #algorithm #library #named: BULL: A Library for Learning Algorithms of Boolean Functions (YFC, BYW), pp. 537–542.
TACAS-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.
CAV-2013-0001LMN #data type #invariant #linear #quantifier: Learning Universally Quantified Invariants of Linear Data Structures (PG, CL, PM, DN), pp. 813–829.
CAV-2013-ChagantyLNR #relational #smt #using: Combining Relational Learning with SMT Solvers Using CEGAR (ATC, AL, AVN, SKR), pp. 447–462.
ISSTA-2013-HowarGR #analysis #generative #hybrid #interface: Hybrid learning: interface generation through static, dynamic, and symbolic analysis (FH, DG, ZR), pp. 268–279.
ISSTA-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.
SAT-2013-Johannsen #exponential #proving: Exponential Separations in a Hierarchy of Clause Learning Proof Systems (JJ), pp. 40–51.
SAT-2013-LonsingEG #performance #pseudo #quantifier: Efficient Clause Learning for Quantified Boolean Formulas via QBF Pseudo Unit Propagation (FL, UE, AVG), pp. 100–115.
CBSE-2012-AbateCTZ #component #future of #repository: Learning from the future of component repositories (PA, RDC, RT, SZ), pp. 51–60.
DocEng-2012-MoulderM #how #layout: Learning how to trade off aesthetic criteria in layout (PM, KM), pp. 33–36.
HT-2012-SchofeggerKSG #behaviour #social: Learning user characteristics from social tagging behavior (KS, CK, PS, MG), pp. 207–212.
JCDL-2012-NewmanNHB #topic: Learning topics and related passages in books (DN, YN, KH, AB), pp. 195–198.
SIGMOD-2012-AbiteboulAMS #xml: Auto-completion learning for XML (SA, YA, TM, PS), pp. 669–672.
VLDB-2012-IseleB #programming #search-based #using: Learning Expressive Linkage Rules using Genetic Programming (RI, CB), pp. 1638–1649.
VLDB-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.
VLDB-2012-SinghG #semantics #string: Learning Semantic String Transformations from Examples (RS, SG), pp. 740–751.
CSEET-2012-AroraG #collaboration #programming #source code: Learning to Write Programs with Others: Collaborative Quadruple Programming (RA, SG), pp. 32–41.
CSEET-2012-TillmannHXB #education #game studies #named #social: Pex4Fun: Teaching and Learning Computer Science via Social Gaming (NT, JdH, TX, JB), pp. 90–91.
ITiCSE-2012-AsadB #aspect-oriented #concept #image: Are children capable of learning image processing concepts?: cognitive and affective aspects (KA, MB), pp. 227–231.
ITiCSE-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.
ITiCSE-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.
ITiCSE-2012-CamaraPV #collaboration #evaluation #framework #programming: Evaluation of a collaborative instructional framework for programming learning (LMSC, MPV, JÁVI), pp. 162–167.
ITiCSE-2012-ChristensenC: Lectures abandoned: active learning by active seminars (HBC, AVC), pp. 16–21.
ITiCSE-2012-GomesSM #behaviour #case study #student #towards: A study on students’ behaviours and attitudes towards learning to program (AJG, ÁNS, AJM), pp. 132–137.
ITiCSE-2012-GovenderG #object-oriented #programming #student: Are students learning object oriented programming in an object oriented programming course?: student voices (DWG, IG), p. 395.
ITiCSE-2012-HamadaN: A learning tool for MP3 audio compression (MH, HN), p. 397.
ITiCSE-2012-HiltonJ #array #education #on the #testing: On teaching arrays with test-driven learning in WebIDE (MH, DSJ), pp. 93–98.
ITiCSE-2012-KrausePR: Formal learning groups in an introductory CS course: a qualitative exploration (JK, IP, CR), pp. 315–320.
ITiCSE-2012-Luxton-ReillyDPS #how #process #student: Activities, affordances and attitude: how student-generated questions assist learning (ALR, PD, BP, RS), pp. 4–9.
ITiCSE-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.
ITiCSE-2012-MehtaKP #algorithm #network: Forming project groups while learning about matching and network flows in algorithms (DPM, TMK, IP), pp. 40–45.
ITiCSE-2012-MussaiL #animation #concept #object-oriented: An animation as an illustrate tool for learning concepts in oop (YM, NL), p. 386.
ITiCSE-2012-MyllymakiH #case study: Choosing a study mode in blended learning (MM, IH), pp. 291–296.
ITiCSE-2012-Sudol-DeLyserSC #comprehension #problem: Code comprehension problems as learning events (LASD, MS, SC), pp. 81–86.
ITiCSE-2012-Velazquez-Iturbide #algorithm #approach #refinement: Refinement of an experimental approach tocomputer-based, active learning of greedy algorithms (JÁVI), pp. 46–51.
SIGITE-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.
SIGITE-2012-ElnagarA #delivery #effectiveness #programming: A modified team-based learning methodology for effective delivery of an introductory programming course (AE, MA), pp. 177–182.
SIGITE-2012-Farag #online #programming: Comparing achievement of intended learning outcomes in online programming classes with blended offerings (WF), pp. 25–30.
SIGITE-2012-Kawash #problem #student: Engaging students by intertwining puzzle-based and problem-based learning (JK), pp. 227–232.
SIGITE-2012-SabinSR #challenge #interactive #online: Interactive learning online: challenges and opportunities (MS, AS, BR), pp. 201–202.
SIGITE-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.
SIGITE-2012-Settle #education #student: Turning the tables: learning from students about teaching CS1 (AS), pp. 133–138.
SIGITE-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.
DLT-2012-BoiretLN: Learning Rational Functions (AB, AL, JN), pp. 273–283.
LATA-2012-GeilkeZ #algorithm #pattern matching #polynomial: Polynomial-Time Algorithms for Learning Typed Pattern Languages (MG, SZ), pp. 277–288.
LATA-2012-Yoshinaka #context-free grammar #integration: Integration of the Dual Approaches in the Distributional Learning of Context-Free Grammars (RY), pp. 538–550.
FM-2012-AartsHKOV #abstraction #automaton #refinement: Automata Learning through Counterexample Guided Abstraction Refinement (FA, FH, HK, PO, FWV), pp. 10–27.
AIIDE-2012-YoungH #game studies #realtime: Evolutionary Learning of Goal Priorities in a Real-Time Strategy Game (JY, NH).
CIG-2012-GemineSFE #game studies #realtime: Imitative learning for real-time strategy games (QG, FS, RF, DE), pp. 424–429.
CIG-2012-PenaOPL #evolution #game studies: Learning and evolving combat game controllers (LP, SO, JMPS, SML), pp. 195–202.
CIG-2012-RunarssonL #difference #game studies: Imitating play from game trajectories: Temporal difference learning versus preference learning (TPR, SML), pp. 79–82.
CIG-2012-SwansonEJ #composition #corpus #visual notation: Learning visual composition preferences from an annotated corpus generated through gameplay (RS, DE, AJ), pp. 363–370.
CIG-2012-TastanCS #game studies: Learning to intercept opponents in first person shooter games (BT, YC, GS), pp. 100–107.
CIG-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.
CIG-2012-WittkampBHW #realtime: Noise tolerance for real-time evolutionary learning of cooperative predator-prey strategies (MW, LB, PH, RLW), pp. 25–32.
FDG-2012-PettitH #policy #simulation: Evolutionary learning of policies for MCTS simulations (JP, DPH), pp. 212–219.
FDG-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.
VS-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.
VS-Games-2012-CuratelliM #design #education #tool support: Design Criteria for Educational Tools to Overcome Mathematics Learning Difficulties (FC, CM), pp. 92–102.
VS-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.
VS-Games-2012-FreitasKNOPRS #game studies #named: GEL: Exploring Game Enhanced Learning (SdF, KK, MN, MO, MP, MR, IAS), pp. 289–292.
VS-Games-2012-HulusicP #framework #quote: “LeFCA”: Learning Framework for Children with Autism (VH, NP), pp. 4–16.
VS-Games-2012-Lombardi #game studies: Not-so-Serious Games for Language Learning. Now with 99, 9% More Humour on Top (IL), pp. 148–158.
VS-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.
VS-Games-2012-ObikweluR #framework #game studies: The Serious Game Constructivist Framework for Children's Learning (CO, JCR), pp. 32–37.
VS-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.
VS-Games-2012-PerezA12a #game studies #social: Learning with your Friend's Data: Game Entity Social Mapping in Serious Games (AMP, OA), pp. 299–300.
VS-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.
CHI-2012-ChinF #difference #health: Age differences in exploratory learning from a health information website (JC, WTF), pp. 3031–3040.
CHI-2012-DongDJKNA #game studies: Discovery-based games for learning software (TD, MD, DJ, KK, MWN, MSA), pp. 2083–2086.
CHI-2012-JainB #performance: User learning and performance with bezel menus (MJ, RB), pp. 2221–2230.
CHI-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.
CHI-2012-ParkC12a #adaptation #deployment #design: Adaptation as design: learning from an EMR deployment study (SYP, YC), pp. 2097–2106.
CHI-2012-VitakIDEG: Gaze-augmented think-aloud as an aid to learning (SAV, JEI, ATD, SE, AKG), pp. 2991–3000.
CHI-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.
CSCW-2012-HemphillO #adaptation #community #gender #overview: Learning the lingo?: gender, prestige and linguistic adaptation in review communities (LH, JO), pp. 305–314.
CSCW-2012-RzeszotarskiK #predict #wiki #word: Learning from history: predicting reverted work at the word level in wikipedia (JMR, AK), pp. 437–440.
CSCW-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.
ICEIS-J-2012-RibeiroFBKE #algorithm #approach #markov #process: Combining Learning Algorithms: An Approach to Markov Decision Processes (RR, FF, MACB, ALK, FE), pp. 172–188.
ICEIS-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.
CIKM-2012-AgarwalRSMLGF #rank #robust: Learning to rank for robust question answering (AA, HR, KS, PM, RDL, DG, JF), pp. 833–842.
CIKM-2012-AnHS #ontology #web: Learning to discover complex mappings from web forms to ontologies (YA, XH, IYS), pp. 1253–1262.
CIKM-2012-CaiZ #injection #rank: Variance maximization via noise injection for active sampling in learning to rank (WC, YZ), pp. 1809–1813.
CIKM-2012-ChaliHI #performance: Improving the performance of the reinforcement learning model for answering complex questions (YC, SAH, KI), pp. 2499–2502.
CIKM-2012-ChengZXAC #classification #on the: On active learning in hierarchical classification (YC, KZ, YX, AA, ANC), pp. 2467–2470.
CIKM-2012-Cohen #metric #random #similarity: Learning similarity measures based on random walks (WWC), p. 3.
CIKM-2012-FangS #approach #feedback #recommendation: A latent pairwise preference learning approach for recommendation from implicit feedback (YF, LS), pp. 2567–2570.
CIKM-2012-GuoMCJ #recommendation #social: Learning to recommend with social relation ensemble (LG, JM, ZC, HJ), pp. 2599–2602.
CIKM-2012-KanhabuaN #query #rank: Learning to rank search results for time-sensitive queries (NK, KN), pp. 2463–2466.
CIKM-2012-LiBCH #clustering #relational: Relational co-clustering via manifold ensemble learning (PL, JB, CC, ZH), pp. 1687–1691.
CIKM-2012-LuZZX #image #scalability #semantics #set: Semantic context learning with large-scale weakly-labeled image set (YL, WZ, KZ, XX), pp. 1859–1863.
CIKM-2012-MacdonaldSO #on the #query #rank: On the usefulness of query features for learning to rank (CM, RLTS, IO), pp. 2559–2562.
CIKM-2012-MetzgerSHS #interactive: LUKe and MIKe: learning from user knowledge and managing interactive knowledge extraction (SM, MS, KH, RS), pp. 2671–2673.
CIKM-2012-MorenoSRS #multi #named: TALMUD: transfer learning for multiple domains (OM, BS, LR, GS), pp. 425–434.
CIKM-2012-NegahbanRG #multi #performance #scalability #statistics #using: Scaling multiple-source entity resolution using statistically efficient transfer learning (SN, BIPR, JG), pp. 2224–2228.
CIKM-2012-QuanzH #generative #multi #named: CoNet: feature generation for multi-view semi-supervised learning with partially observed views (BQ, JH), pp. 1273–1282.
CIKM-2012-RamanSGB #algorithm #towards: Learning from mistakes: towards a correctable learning algorithm (KR, KMS, RGB, CJCB), pp. 1930–1934.
CIKM-2012-RenCJ #topic: Topic based pose relevance learning in dance archives (RR, JPC, JMJ), pp. 2571–2574.
CIKM-2012-ShangJLW: Learning spectral embedding via iterative eigenvalue thresholding (FS, LCJ, YL, FW), pp. 1507–1511.
CIKM-2012-SunG: Active learning for relation type extension with local and global data views (AS, RG), pp. 1105–1112.
CIKM-2012-SunSL #multi #performance #query: Fast multi-task learning for query spelling correction (XS, AS, PL), pp. 285–294.
CIKM-2012-SunWGM #hybrid #rank #recommendation: Learning to rank for hybrid recommendation (JS, SW, BJG, JM), pp. 2239–2242.
CIKM-2012-VolkovsLZ #rank: Learning to rank by aggregating expert preferences (MV, HL, RSZ), pp. 843–851.
CIKM-2012-WangC #predict #word: Learning to predict the cost-per-click for your ad words (CJW, HHC), pp. 2291–2294.
CIKM-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.
CIKM-2012-WangWYHDC #framework #modelling #novel: A novel local patch framework for fixing supervised learning models (YW, BW, JY, YH, ZHD, ZC), pp. 1233–1242.
CIKM-2012-WangXY: Importance weighted passive learning (SW, XX, YY), pp. 2243–2246.
CIKM-2012-YangTKZLDLW #mining #network: Mining competitive relationships by learning across heterogeneous networks (YY, JT, JK, YZ, JL, YD, TL, LW), pp. 1432–1441.
CIKM-2012-YaoS #relational #ubiquitous: Exploiting latent relevance for relational learning of ubiquitous things (LY, QZS), pp. 1547–1551.
CIKM-2012-ZhangHLL #rank #realtime #twitter: Query-biased learning to rank for real-time twitter search (XZ, BH, TL, BL), pp. 1915–1919.
CIKM-2012-ZhangWW #framework #interactive #ontology: An interaction framework of service-oriented ontology learning (JZ, YW, HW), pp. 2303–2306.
CIKM-2012-ZhouLZ #community #quality: Joint relevance and answer quality learning for question routing in community QA (GZ, KL, JZ), pp. 1492–1496.
CIKM-2012-ZhouZ #debugging #rank: Learning to rank duplicate bug reports (JZ, HZ), pp. 852–861.
ECIR-2012-Lubell-DoughtieH #feedback #rank: Learning to Rank from Relevance Feedback for e-Discovery (PLD, KH), pp. 535–539.
ECIR-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.
ICML-2012-AzarMK #complexity #generative #on the: On the Sample Complexity of Reinforcement Learning with a Generative Model (MGA, RM, BK), p. 222.
ICML-2012-AzimiFFBH #coordination: Batch Active Learning via Coordinated Matching (JA, AF, XZF, GB, BH), p. 44.
ICML-2012-BalleQC #modelling #optimisation: Local Loss Optimization in Operator Models: A New Insight into Spectral Learning (BB, AQ, XC), p. 236.
ICML-2012-BelletHS #classification #linear #similarity: Similarity Learning for Provably Accurate Sparse Linear Classification (AB, AH, MS), p. 193.
ICML-2012-BonillaR #probability #prototype: Discriminative Probabilistic Prototype Learning (EVB, ARK), p. 155.
ICML-2012-BronsteinSS #modelling #performance: Learning Efficient Structured Sparse Models (AMB, PS, GS), p. 33.
ICML-2012-ChambersJ: Learning the Central Events and Participants in Unlabeled Text (NC, DJ), p. 3.
ICML-2012-CharlinZB #problem: Active Learning for Matching Problems (LC, RSZ, CB), p. 23.
ICML-2012-DekelTA #adaptation #online #policy: Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret (OD, AT, RA), p. 227.
ICML-2012-DuanXT #adaptation: Learning with Augmented Features for Heterogeneous Domain Adaptation (LD, DX, IWT), p. 89.
ICML-2012-DundarAQR #modelling #online: Bayesian Nonexhaustive Learning for Online Discovery and Modeling of Emerging Classes (MD, FA, AQ, BR), p. 18.
ICML-2012-EbanBSG #online #predict #sequence: Learning the Experts for Online Sequence Prediction (EE, AB, SSS, AG), p. 38.
ICML-2012-FarabetCNL #multi #parsing: Scene parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers (CF, CC, LN, YL), p. 241.
ICML-2012-GeistSLG #approach #difference: A Dantzig Selector Approach to Temporal Difference Learning (MG, BS, AL, MG), p. 49.
ICML-2012-Gonen #kernel #multi #performance: Bayesian Efficient Multiple Kernel Learning (MG), p. 17.
ICML-2012-GongZM #multi #robust: Robust Multiple Manifold Structure Learning (DG, XZ, GGM), p. 7.
ICML-2012-GoodfellowCB #scalability: Large-Scale Feature Learning With Spike-and-Slab Sparse Coding (IJG, ACC, YB), p. 180.
ICML-2012-GuoX #classification #multi: Cross Language Text Classification via Subspace Co-regularized Multi-view Learning (YG, MX), p. 120.
ICML-2012-HanLC #modelling #multi: Cross-Domain Multitask Learning with Latent Probit Models (SH, XL, LC), p. 51.
ICML-2012-HazanK #online: Projection-free Online Learning (EH, SK), p. 239.
ICML-2012-HoiWZ: Exact Soft Confidence-Weighted Learning (SCHH, JW, PZ), p. 19.
ICML-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.
ICML-2012-Honorio #convergence #modelling #optimisation #probability: Convergence Rates of Biased Stochastic Optimization for Learning Sparse Ising Models (JH), p. 144.
ICML-2012-JalaliS #dependence #graph: Learning the Dependence Graph of Time Series with Latent Factors (AJ, SS), p. 83.
ICML-2012-JawanpuriaN: A Convex Feature Learning Formulation for Latent Task Structure Discovery (PJ, JSN), p. 199.
ICML-2012-JiangLS #3d #using: Learning Object Arrangements in 3D Scenes using Human Context (YJ, ML, AS), p. 119.
ICML-2012-JiYLJH #algorithm #bound #fault: A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound (MJ, TY, BL, RJ, JH), p. 110.
ICML-2012-KalakrishnanRPS #policy: Learning Force Control Policies for Compliant Robotic Manipulation (MK, LR, PP, SS), p. 10.
ICML-2012-KarbasiIM #rank: Comparison-Based Learning with Rank Nets (AK, SI, LM), p. 161.
ICML-2012-KumarD #multi: Learning Task Grouping and Overlap in Multi-task Learning (AK, HDI), p. 224.
ICML-2012-KumarNKD #classification #framework #kernel #multi: A Binary Classification Framework for Two-Stage Multiple Kernel Learning (AK, ANM, KK, HDI), p. 173.
ICML-2012-KumarPK #modelling #nondeterminism: Modeling Latent Variable Uncertainty for Loss-based Learning (MPK, BP, DK), p. 29.
ICML-2012-LanctotGBB #game studies: No-Regret Learning in Extensive-Form Games with Imperfect Recall (ML, RGG, NB, MB), p. 135.
ICML-2012-LeRMDCCDN #scalability #using: Building high-level features using large scale unsupervised learning (QVL, MR, RM, MD, GC, KC, JD, AYN), p. 69.
ICML-2012-LinXWZ: Total Variation and Euler’s Elastica for Supervised Learning (TL, HX, LW, HZ), p. 82.
ICML-2012-LouH: Structured Learning from Partial Annotations (XL, FAH), p. 52.
ICML-2012-MakinoT #parametricity: Apprenticeship Learning for Model Parameters of Partially Observable Environments (TM, JT), p. 117.
ICML-2012-MatuszekFZBF: A Joint Model of Language and Perception for Grounded Attribute Learning (CM, NF, LSZ, LB, DF), p. 186.
ICML-2012-Memisevic #multi #on the: On multi-view feature learning (RM), p. 140.
ICML-2012-MnihH #image #semistructured data: Learning to Label Aerial Images from Noisy Data (VM, GEH), p. 31.
ICML-2012-MohamedHG: Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning (SM, KAH, ZG), p. 91.
ICML-2012-NiuDYS #metric: Information-theoretic Semi-supervised Metric Learning via Entropy Regularization (GN, BD, MY, MS), p. 136.
ICML-2012-Painter-WakefieldP #algorithm: Greedy Algorithms for Sparse Reinforcement Learning (CPW, RP), p. 114.
ICML-2012-PassosRWD #flexibility #modelling #multi: Flexible Modeling of Latent Task Structures in Multitask Learning (AP, PR, JW, HDI), p. 167.
ICML-2012-PeharzP #network: Exact Maximum Margin Structure Learning of Bayesian Networks (RP, FP), p. 102.
ICML-2012-PiresS #estimation #linear #statistics: Statistical linear estimation with penalized estimators: an application to reinforcement learning (BAP, CS), p. 228.
ICML-2012-PlessisS: Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching (MCdP, MS), p. 159.
ICML-2012-PrasseSLS #email #identification #regular expression: Learning to Identify Regular Expressions that Describe Email Campaigns (PP, CS, NL, TS), p. 146.
ICML-2012-RossB #identification #modelling: Agnostic System Identification for Model-Based Reinforcement Learning (SR, DB), p. 247.
ICML-2012-SamdaniR #performance #predict: Efficient Decomposed Learning for Structured Prediction (RS, DR), p. 200.
ICML-2012-ScholkopfJPSZM #on the: On causal and anticausal learning (BS, DJ, JP, ES, KZ, JMM), p. 63.
ICML-2012-ShiS #adaptation #clustering: Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation (YS, FS), p. 166.
ICML-2012-ShivaswamyJ #online #predict: Online Structured Prediction via Coactive Learning (PS, TJ), p. 12.
ICML-2012-SilvaKB: Learning Parameterized Skills (BCdS, GK, AGB), p. 187.
ICML-2012-SohnL #invariant: Learning Invariant Representations with Local Transformations (KS, HL), p. 174.
ICML-2012-WangWHL #monte carlo: Monte Carlo Bayesian Reinforcement Learning (YW, KSW, DH, WSL), p. 105.
ICML-2012-XieHS #approach #automation #generative: Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting (NX, HH, MS), p. 139.
ICML-2012-XuWC: The Greedy Miser: Learning under Test-time Budgets (ZEX, KQW, OC), p. 169.
ICML-2012-YackleyL: Smoothness and Structure Learning by Proxy (BY, TL), p. 57.
ICML-2012-YangMJZZ #kernel #multi #probability #programming: Multiple Kernel Learning from Noisy Labels by Stochastic Programming (TY, MM, RJ, LZ, YZ), p. 21.
ICML-2012-ZhongK #clustering #flexibility #multi: Convex Multitask Learning with Flexible Task Clusters (WZ, JTYK), p. 66.
ICPR-2012-AbeOD #image #rank: Recognizing surface qualities from natural images based on learning to rank (TA, TO, KD), pp. 3712–3715.
ICPR-2012-AntoniukFH #markov #network: Learning Markov Networks by Analytic Center Cutting Plane Method (KA, VF, VH), pp. 2250–2253.
ICPR-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.
ICPR-2012-BaillyMPB #cost analysis: Learning global cost function for face alignment (KB, MM, PP, EB), pp. 1112–1115.
ICPR-2012-BanerjeeN #kernel #multi #process #recognition #using: Pose based activity recognition using Multiple Kernel learning (PB, RN), pp. 445–448.
ICPR-2012-CermanH #problem: Tracking with context as a semi-supervised learning and labeling problem (LC, VH), pp. 2124–2127.
ICPR-2012-ChernoffLN #fault #metric: Metric learning by directly minimizing the k-NN training error (KC, ML, MN), pp. 1265–1268.
ICPR-2012-DahmaneLDB #estimation #symmetry: Learning symmetrical model for head pose estimation (AD, SL, CD, IMB), pp. 3614–3617.
ICPR-2012-DAmbrosioIS #re-engineering: A One-per-Class reconstruction rule for class imbalance learning (RD, GI, PS), pp. 1310–1313.
ICPR-2012-DuanWLDC #named: K-CPD: Learning of overcomplete dictionaries for tensor sparse coding (GD, HW, ZL, JD, YWC), pp. 493–496.
ICPR-2012-FangZ: I don’t know the label: Active learning with blind knowledge (MF, XZ), pp. 2238–2241.
ICPR-2012-FiaschiKNH: Learning to count with regression forest and structured labels (LF, UK, RN, FAH), pp. 2685–2688.
ICPR-2012-GhanemKFZ #automation #recognition: Context-aware learning for automatic sports highlight recognition (BG, MK, MF, TZ), pp. 1977–1980.
ICPR-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.
ICPR-2012-GranaCBC #image #segmentation: Learning non-target items for interesting clothes segmentation in fashion images (CG, SC, DB, RC), pp. 3317–3320.
ICPR-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.
ICPR-2012-GutmannH #architecture #feature model #image: Learning a selectivity-invariance-selectivity feature extraction architecture for images (MG, AH), pp. 918–921.
ICPR-2012-HidoK #graph #similarity: Hash-based structural similarity for semi-supervised Learning on attribute graphs (SH, HK), pp. 3009–3012.
ICPR-2012-HinoO #kernel #multi: An improved entropy-based multiple kernel learning (HH, TO), pp. 1189–1192.
ICPR-2012-HiradeY #predict: Ensemble learning for change-point prediction (RH, TY), pp. 1860–1863.
ICPR-2012-HuangLT #invariant #recognition: Learning modality-invariant features for heterogeneous face recognition (LH, JL, YPT), pp. 1683–1686.
ICPR-2012-JinGYZ #algorithm #multi: Multi-label learning vector quantization algorithm (XBJ, GG, JY, DZ), pp. 2140–2143.
ICPR-2012-JiS12a #3d #estimation #robust: Robust 3D human pose estimation via dual dictionaries learning (HJ, FS), pp. 3370–3373.
ICPR-2012-KhanT #taxonomy: Stable discriminative dictionary learning via discriminative deviation (NK, MFT), pp. 3224–3227.
ICPR-2012-KongW #clustering #multi: A multi-task learning strategy for unsupervised clustering via explicitly separating the commonality (SK, DW), pp. 771–774.
ICPR-2012-KumarRS #predict: Learning to predict super resolution wavelet coefficients (NK, NKR, AS), pp. 3468–3471.
ICPR-2012-KumarYD #classification #documentation #retrieval: Learning document structure for retrieval and classification (JK, PY, DSD), pp. 1558–1561.
ICPR-2012-LeeKD #induction: Learning action symbols for hierarchical grammar induction (KL, TKK, YD), pp. 3778–3782.
ICPR-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.
ICPR-2012-LiHL #adaptation #multi #online #people: Online adaptive learning for multi-camera people counting (JL, LH, CL), pp. 3415–3418.
ICPR-2012-LiLLL #distance #estimation #metric: Learning distance metric regression for facial age estimation (CL, QL, JL, HL), pp. 2327–2330.
ICPR-2012-LinLZ #representation #taxonomy: Incoherent dictionary learning for sparse representation (TL, SL, HZ), pp. 1237–1240.
ICPR-2012-LiPMH #classification #email #incremental #using: Business email classification using incremental subspace learning (ML, YP, RM, HYH), pp. 625–628.
ICPR-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.
ICPR-2012-LiuL #analysis #detection #multi: Unsupervised multi-target trajectory detection, learning and analysis in complicated environments (HL, JL), pp. 3716–3720.
ICPR-2012-LiuLWZ #linear: Locally linear embedding based example learning for pan-sharpening (QL, LL, YW, ZZ), pp. 1928–1931.
ICPR-2012-LiuLYZ #composition #visual notation: Learning to describe color composition of visual objects (YL, YL, ZY, NZ), pp. 3337–3340.
ICPR-2012-LiuML #multi: Training data recycling for multi-level learning (JL, SM, YL), pp. 2314–2318.
ICPR-2012-LiuSW #recognition #taxonomy: Facial expression recognition based on discriminative dictionary learning (WL, CS, YW), pp. 1839–1842.
ICPR-2012-LiVBB #clustering #using: Feature learning using Generalized Extreme Value distribution based K-means clustering (ZL, OV, HB, RB), pp. 1538–1541.
ICPR-2012-LuLY #adaptation #classification #kernel: Adaptive kernel learning based on centered alignment for hierarchical classification (YL, JL, JY), pp. 569–572.
ICPR-2012-MarcaciniCR #approach #clustering: An active learning approach to frequent itemset-based text clustering (RMM, GNC, SOR), pp. 3529–3532.
ICPR-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.
ICPR-2012-MoZW #classification: Enhancing cross-view object classification by feature-based transfer learning (YM, ZZ, YW), pp. 2218–2221.
ICPR-2012-Nagy #web: Learning the characteristics of critical cells from web tables (GN), pp. 1554–1557.
ICPR-2012-NamA #image: Learning human preferences to sharpen images (MN, NA), pp. 2173–2176.
ICPR-2012-NayefAB: Learning feature weights of symbols, with application to symbol spotting (NN, MZA, TMB), pp. 2371–2374.
ICPR-2012-Noh #analysis #classification #metric #nearest neighbour: χ2 Metric learning for nearest neighbor classification and its analysis (SN), pp. 991–995.
ICPR-2012-PangHYQW #analysis #classification: Theoretical analysis of learning local anchors for classification (JP, QH, BY, LQ, DW), pp. 1803–1806.
ICPR-2012-PanLS #kernel: Learning kernels from labels with ideal regularization (BP, JHL, LS), pp. 505–508.
ICPR-2012-PourdamghaniRZ #estimation #graph #metric: Metric learning for graph based semi-supervised human pose estimation (NP, HRR, MZ), pp. 3386–3389.
ICPR-2012-QinZCW #online: Matting-driven online learning of Hough forests for object tracking (TQ, BZ, TJC, HW), pp. 2488–2491.
ICPR-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.
ICPR-2012-SchauerteS #image #modelling #robust #web: Learning robust color name models from web images (BS, RS), pp. 3598–3601.
ICPR-2012-SharmaHN #classification #detection #incremental #performance: Efficient incremental learning of boosted classifiers for object detection (PS, CH, RN), pp. 3248–3251.
ICPR-2012-ShenMZ #analysis #graph #online: Unsupervised online learning trajectory analysis based on weighted directed graph (YS, ZM, JZ), pp. 1306–1309.
ICPR-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.
ICPR-2012-SunBM: Unsupervised skeleton learning for manifold denoising (KS, EB, SMM), pp. 2719–2722.
ICPR-2012-TabernikKBL #low level #statistics #visual notation: Learning statistically relevant edge structure improves low-level visual descriptors (DT, MK, MB, AL), pp. 1471–1474.
ICPR-2012-TangS #independence #network #performance #testing #using: Efficient and accurate learning of Bayesian networks using chi-squared independence tests (YT, SNS), pp. 2723–2726.
ICPR-2012-TiribuziPVR #detection #framework #kernel #multi: A Multiple Kernel Learning framework for detecting altered fingerprints (MT, MP, PV, ER), pp. 3402–3405.
ICPR-2012-TuS #adaptation #classification: Dynamical ensemble learning with model-friendly classifiers for domain adaptation (WT, SS), pp. 1181–1184.
ICPR-2012-VillamizarGSM #online #random #using: Online human-assisted learning using Random Ferns (MV, AG, AS, FMN), pp. 2821–2824.
ICPR-2012-WangJ12b #network #process #recognition: Learning dynamic Bayesian network discriminatively for human activity recognition (XW, QJ), pp. 3553–3556.
ICPR-2012-WangL12b #recognition #string: String-level learning of confidence transformation for Chinese handwritten text recognition (DHW, CLL), pp. 3208–3211.
ICPR-2012-WeberBLS #segmentation: Unsupervised motion pattern learning for motion segmentation (MW, GB, ML, DS), pp. 202–205.
ICPR-2012-XiaTWLL #categorisation: Object categorization based on hierarchical learning (TX, YYT, YW, HL, LL), pp. 1419–1422.
ICPR-2012-YangLZC #image #multi #retrieval: Multi-view learning with batch mode active selection for image retrieval (WY, GL, LZ, EC), pp. 979–982.
ICPR-2012-YanKMW #automation #game studies: Automatic annotation of court games with structured output learning (FY, JK, KM, DW), pp. 3577–3580.
ICPR-2012-YanRLS #classification #multi: Active transfer learning for multi-view head-pose classification (YY, SR, OL, NS), pp. 1168–1171.
ICPR-2012-YeD #predict: Learning features for predicting OCR accuracy (PY, DSD), pp. 3204–3207.
ICPR-2012-ZhangHR #classification #gender: Hypergraph based semi-supervised learning for gender classification (ZZ, ERH, PR), pp. 1747–1750.
ICPR-2012-ZhangZNH #multi #recognition: Joint dynamic sparse learning and its application to multi-view face recognition (HZ, YZ, NMN, TSH), pp. 1671–1674.
ICPR-2012-ZhaoSS #predict: Importance-weighted label prediction for active learning with noisy annotations (LZ, GS, RS), pp. 3476–3479.
ICPR-2012-ZhaoXY #network #speech: Unsupervised Tibetan speech features Learning based on Dynamic Bayesian Networks (YZ, XX, GY), pp. 2319–2322.
ICPR-2012-ZhaoYXJ: A near-optimal non-myopic active learning method (YZ, GY, XX, QJ), pp. 1715–1718.
ICPR-2012-ZhouWXZM #recognition: Learning weighted features for human action recognition (WZ, CW, BX, ZZ, LM), pp. 1160–1163.
ICPR-2012-ZhuoCQYX #algorithm #classification #image #using: Image classification using HTM cortical learning algorithms (WZ, ZC, YQ, ZY, YX), pp. 2452–2455.
KDD-2012-GongYZ #multi #robust: Robust multi-task feature learning (PG, JY, CZ), pp. 895–903.
KDD-2012-HalawiDGK #constraints #scalability #word: Large-scale learning of word relatedness with constraints (GH, GD, EG, YK), pp. 1406–1414.
KDD-2012-HoensC: Learning in non-stationary environments with class imbalance (TRH, NVC), pp. 168–176.
KDD-2012-JainVV #kernel #multi #named: SPF-GMKL: generalized multiple kernel learning with a million kernels (AJ, SVNV, MV), pp. 750–758.
KDD-2012-LiJPS #classification #multi: Multi-domain active learning for text classification (LL, XJ, SJP, JTS), pp. 1086–1094.
KDD-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.
KDD-2012-RamanSJ #feedback #online: Online learning to diversify from implicit feedback (KR, PS, TJ), pp. 705–713.
KDD-2012-SeelandKK #clustering #graph #kernel: A structural cluster kernel for learning on graphs (MS, AK, SK), pp. 516–524.
KDD-2012-ShangJW: Semi-supervised learning with mixed knowledge information (FS, LCJ, FW), pp. 732–740.
KDD-2012-ShenJ #recommendation #social: Learning personal + social latent factor model for social recommendation (YS, RJ), pp. 1303–1311.
KDD-2012-SilvaC #matrix #online: Active learning for online bayesian matrix factorization (JGS, LC), pp. 325–333.
KDD-2012-SindhwaniG #distributed #scalability #taxonomy: Large-scale distributed non-negative sparse coding and sparse dictionary learning (VS, AG), pp. 489–497.
KDD-2012-TianZ: Learning from crowds in the presence of schools of thought (YT, JZ), pp. 226–234.
KDD-2012-XiongJXC #dependence #metric #random: Random forests for metric learning with implicit pairwise position dependence (CX, DMJ, RX, JJC), pp. 958–966.
KDD-2012-YuanWTNY #analysis #multi: Multi-source learning for joint analysis of incomplete multi-modality neuroimaging data (LY, YW, PMT, VAN, JY), pp. 1149–1157.
KDD-2012-ZhangH #induction #multi: Inductive multi-task learning with multiple view data (JZ, JH), pp. 543–551.
KDD-2012-ZhenY #multimodal #probability: A probabilistic model for multimodal hash function learning (YZ, DYY), pp. 940–948.
KDD-2012-ZhouZ #collaboration: Learning binary codes for collaborative filtering (KZ, HZ), pp. 498–506.
KDIR-2012-AbdullinN #clustering #data type #framework: A Semi-supervised Learning Framework to Cluster Mixed Data Types (AA, ON), pp. 45–54.
KDIR-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.
KDIR-2012-IkebeKT #predict #smarttech #using: Friendship Prediction using Semi-supervised Learning of Latent Features in Smartphone Usage Data (YI, MK, HT), pp. 199–205.
KDIR-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.
KEOD-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.
KEOD-2012-WohlgenanntWSS #ontology #web: Confidence Management for Learning Ontologies from Dynamic Web Sources (GW, AW, AS, MS), pp. 172–177.
KMIS-2012-AkiyoshiSK #problem #towards: A Project Manager Skill-up Simulator Towards Problem Solving-based Learning (MA, MS, NK), pp. 190–195.
KMIS-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.
KMIS-2012-HackerMHHM #collaboration: Management of Collaboration — Impacts of Virtualization to Learning & Knowledge (GH, MM, PH, GH, MM), pp. 235–239.
KMIS-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.
KMIS-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.
KR-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).
MLDM-2012-BouhamedMLR #heuristic #network: A New Learning Structure Heuristic of Bayesian Networks from Data (HB, AM, TL, AR), pp. 183–197.
MLDM-2012-HoaD: A New Learning Strategy of General BAMs (NTH, TDB), pp. 213–221.
MLDM-2012-PitelisT: Discriminant Subspace Learning Based on Support Vectors Machines (NP, AT), pp. 198–212.
MLDM-2012-ToussaintB #comparison #empirical: Proximity-Graph Instance-Based Learning, Support Vector Machines, and High Dimensionality: An Empirical Comparison (GTT, CB), pp. 222–236.
MLDM-2012-XuCG #concept #multi #using: Constructing Target Concept in Multiple Instance Learning Using Maximum Partial Entropy (TX, DKYC, IG), pp. 169–182.
RecSys-2012-DeDGM #difference #using: Local learning of item dissimilarity using content and link structure (AD, MSD, NG, PM), pp. 221–224.
RecSys-2012-Herbrich #distributed #online #realtime: Distributed, real-time bayesian learning in online services (RH), pp. 203–204.
RecSys-2012-KarimiFNS #matrix #recommendation: Exploiting the characteristics of matrix factorization for active learning in recommender systems (RK, CF, AN, LST), pp. 317–320.
RecSys-2012-SalimansPG #collaboration #ranking: Collaborative learning of preference rankings (TS, UP, TG), pp. 261–264.
RecSys-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.
SEKE-2012-AlawawdehAL #adaptation #collaboration #named: CLAT: Collaborative Learning Adaptive Tutor (AMHA, CA, LL), pp. 747–752.
SEKE-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.
SEKE-2012-XavierOC #fuzzy #logic: Evolutionary Learning and Fuzzy Logic Applied to a Load Balancer (FCX, MGdO, CLdC), pp. 256–260.
SEKE-2012-Zhang #bias #named: i2Learning: Perpetual Learning through Bias Shifting (DZ), pp. 249–255.
SIGIR-2012-BilgicB #query: Active query selection for learning rankers (MB, PNB), pp. 1033–1034.
SIGIR-2012-GaoWL #graph #information retrieval #mining #scalability: Large-scale graph mining and learning for information retrieval (BG, TW, TYL), pp. 1194–1195.
SIGIR-2012-HongBAD #rank #social: Learning to rank social update streams (LH, RB, JA, BDD), pp. 651–660.
SIGIR-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.
SIGIR-2012-KanhabuaBN #retrieval: Learning to select a time-aware retrieval model (NK, KB, KN), pp. 1099–1100.
SIGIR-2012-KovesiGA #categorisation #multi #online #performance: Fast on-line learning for multilingual categorization (MK, CG, MRA), pp. 1071–1072.
SIGIR-2012-MacdonaldTO #online #predict #query #scheduling: Learning to predict response times for online query scheduling (CM, NT, IO), pp. 621–630.
SIGIR-2012-MacdonaldTO12a #effectiveness #rank #safety: Effect of dynamic pruning safety on learning to rank effectiveness (CM, NT, IO), pp. 1051–1052.
SIGIR-2012-NiuGLC #evaluation #rank #ranking: Top-k learning to rank: labeling, ranking and evaluation (SN, JG, YL, XC), pp. 751–760.
SIGIR-2012-SeverynM #ranking #scalability: Structural relationships for large-scale learning of answer re-ranking (AS, AM), pp. 741–750.
SIGIR-2012-ZhangWDH #detection #performance #reuse: Learning hash codes for efficient content reuse detection (QZ, YW, ZD, XH), pp. 405–414.
TOOLS-EUROPE-2012-Sureka #component #debugging: Learning to Classify Bug Reports into Components (AS), pp. 288–303.
SAS-2012-GiannakopoulouRR #component #interface: Symbolic Learning of Component Interfaces (DG, ZR, VR), pp. 248–264.
REFSQ-2012-KnaussS #documentation #heuristic #requirements: Supporting Learning Organisations in Writing Better Requirements Documents Based on Heuristic Critiques (EK, KS), pp. 165–171.
ASE-2012-LuCC #fault #predict #reduction #using: Software defect prediction using semi-supervised learning with dimension reduction (HL, BC, MC), pp. 314–317.
ICSE-2012-DagenaisR #api #traceability: Recovering traceability links between an API and its learning resources (BD, MPR), pp. 47–57.
ICSE-2012-FengC #behaviour #multi: Multi-label software behavior learning (YF, ZC), pp. 1305–1308.
ICSE-2012-GrechanikFX #automation #performance #problem #testing: Automatically finding performance problems with feedback-directed learning software testing (MG, CF, QX), pp. 156–166.
SAC-2012-MinervinidF #concept #logic #probability: Learning probabilistic Description logic concepts: under different Assumptions on missing knowledge (PM, Cd, NF), pp. 378–383.
SAC-2012-NunesCM #network #similarity #social: Resolving user identities over social networks through supervised learning and rich similarity features (AN, PC, BM), pp. 728–729.
SAC-2012-OongI #classification #fuzzy #multi #performance #testing: Multilayer Fuzzy ARTMAP: fast learning and fast testing for pattern classification (THO, NAMI), pp. 27–32.
CASE-2012-AnKP #modelling #process: Grasp motion learning with Gaussian Process Dynamic Models (BA, HK, FCP), pp. 1114–1119.
CASE-2012-YamamotoD #interface: Robot interface learning user-defined voice instructions (DY, MD), pp. 926–929.
DAC-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.
DATE-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.
FASE-2012-AlrajehKRU #satisfiability #specification: Learning from Vacuously Satisfiable Scenario-Based Specifications (DA, JK, AR, SU), pp. 377–393.
STOC-2012-DaskalakisDS: Learning poisson binomial distributions (CD, ID, RAS), pp. 709–728.
TACAS-2012-DSilvaHKT #analysis #bound: Numeric Bounds Analysis with Conflict-Driven Learning (VD, LH, DK, MT), pp. 48–63.
TACAS-2012-MertenHSCJ #automaton: Demonstrating Learning of Register Automata (MM, FH, BS, SC, BJ), pp. 466–471.
CAV-2012-ChenW #incremental: Learning Boolean Functions Incrementally (YFC, BYW), pp. 55–70.
CAV-2012-LeeWY #algorithm #analysis #termination: Termination Analysis with Algorithmic Learning (WL, BYW, KY), pp. 88–104.
CSL-2012-Berardid: Knowledge Spaces and the Completeness of Learning Strategies (SB, Ud), pp. 77–91.
ICST-2012-SunSPR #cost analysis #named #reliability: CARIAL: Cost-Aware Software Reliability Improvement with Active Learning (BS, GS, AP, SR), pp. 360–369.
ICTSS-2012-Vaandrager #finite #state machine: Active Learning of Extended Finite State Machines (FWV), pp. 5–7.
LICS-2012-KomuravelliPC #probability: Learning Probabilistic Systems from Tree Samples (AK, CSP, EMC), pp. 441–450.
SAT-2012-BonetB: An Improved Separation of Regular Resolution from Pool Resolution and Clause Learning (MLB, SRB), pp. 44–57.
SAT-2012-LaitinenJN: Conflict-Driven XOR-Clause Learning (TL, TAJ, IN), pp. 383–396.
CBSE-2011-AletiM #component #deployment #optimisation: Component deployment optimisation with bayesian learning (AA, IM), pp. 11–20.
DocEng-2011-ChidlovskiiB #metric #network #recommendation #social: Local metric learning for tag recommendation in social networks (BC, AB), pp. 205–208.
ICDAR-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.
ICDAR-2011-KumarPD #classification #documentation #image #multi #using: Document Image Classification and Labeling Using Multiple Instance Learning (JK, JP, DSD), pp. 1059–1063.
ICDAR-2011-ShaoWXZZ11a #multi: Multiple Instance Learning Based Method for Similar Handwritten Chinese Characters Discrimination (YS, CW, BX, RZ, YZ), pp. 1002–1006.
ICDAR-2011-SuLZ #polynomial: Perceptron Learning of Modified Quadratic Discriminant Function (THS, CLL, XYZ), pp. 1007–1011.
ICDAR-2011-TaoLJG #locality #recognition #using: Similar Handwritten Chinese Character Recognition Using Discriminative Locality Alignment Manifold Learning (DT, LL, LJ, YG), pp. 1012–1016.
ICDAR-2011-VajdaJF #approach: A Semi-supervised Ensemble Learning Approach for Character Labeling with Minimal Human Effort (SV, AJ, GAF), pp. 259–263.
ICDAR-2011-WangDL #recognition: MQDF Discriminative Learning Based Offline Handwritten Chinese Character Recognition (YW, XD, CL), pp. 1100–1104.
SIGMOD-2011-GetoorM #modelling #relational #statistics: Learning statistical models from relational data (LG, LM), pp. 1195–1198.
CSEET-2011-ChimalakondaN #education #question #re-engineering: Can we make software engineering education better by applying learning theories? (SC, KVN), p. 561.
CSEET-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.
CSEET-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.
CSEET-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.
CSEET-2011-HattoriBLL #game studies: Erase and rewind — Learning by replaying examples (LH, AB, ML, ML), p. 558.
CSEET-2011-HoskingSKJ #re-engineering #student: Learning at the elbows of experts: Technology roadmapping with Software Engineering students (JGH, PS, EK, NJ), pp. 139–148.
CSEET-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.
CSEET-2011-TillmannHX #education #game studies #named #social: Pex4Fun: Teaching and learning computer science via social gaming (NT, JdH, TX), pp. 546–548.
CSEET-2011-TuTOBHKY: Turning real-world systems into verification-driven learning cases (ST, ST, SO, BB, BH, AK, ZY), pp. 129–138.
CSEET-2011-Virseda #education #re-engineering #semantics: A learning methodology based on semantic tableaux for software engineering education (RdVV), pp. 401–405.
ITiCSE-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.
ITiCSE-2011-BowerM #comparison: Continual and explicit comparison to promote proactive facilitation during second computer language learning (MB, AM), pp. 218–222.
ITiCSE-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.
ITiCSE-2011-CamachoM #programming: Facilitating learning dynamic programming through a previous introduction of exhaustive search (AC, AM), p. 355.
ITiCSE-2011-ChanK #education #multi #question: Do educational software systems provide satisfactory learning opportunities for “multi-sensory learning” methodology? (PC, GK), p. 358.
ITiCSE-2011-EllisH #named #student: Courseware: student learning via FOSS field trips (HJCE, GWH), p. 329.
ITiCSE-2011-GarciaMGH #interface #unification: A system for usable unification of interfaces of learning objects in m-learning (EG, LdM, AGC, JRH), p. 347.
ITiCSE-2011-Goldweber #process #turing machine: Two kinesthetic learning activities: turing machines and basic computer organization (MG), p. 335.
ITiCSE-2011-Goldweber11a #social: Computing for the social good: a service learning project (MG), p. 379.
ITiCSE-2011-HarrachA #collaboration #optimisation #process #recommendation #using: Optimizing collaborative learning processes by using recommendation systems (SH, MA), p. 389.
ITiCSE-2011-Hijon-NeiraV11a #design: A first step mapping IMS learning design and Merlin-Mo (RHN, JÁVI), p. 365.
ITiCSE-2011-HoverHR #collaboration: A collaborative linked learning space (KMH, MH, GR), p. 380.
ITiCSE-2011-HoverHRM #collaboration #how #student: Evaluating how students would use a collaborative linked learning space (KMH, MH, GR, MM), pp. 88–92.
ITiCSE-2011-KonertRGSB #ad hoc #community: Supporting peer learning with ad-hoc communities (JK, KR, SG, RS, RB), p. 393.
ITiCSE-2011-LasserreS: Effects of team-based learning on a CS1 course (PL, CS), pp. 133–137.
ITiCSE-2011-MothVB #named #syntax: SyntaxTrain: relieving the pain of learning syntax (ALAM, JV, MBA), p. 387.
ITiCSE-2011-OliveiraMR #problem #programming: From concrete to abstract?: problem domain in the learning of introductory programming (OLO, AMM, NTR), pp. 173–177.
ITiCSE-2011-PollockH #multi: Combining multiple pedagogies to boost learning and enthusiasm (LLP, TH), pp. 258–262.
ITiCSE-2011-RussellMD #approach #student: A contextualized project-based approach for improving student engagement and learning in AI courses (IR, ZM, JD), p. 368.
ITiCSE-2011-Sanchez-TorrubiaTT #algorithm #assessment #automation: GLMP for automatic assessment of DFS algorithm learning (MGST, CTB, GT), p. 351.
ITiCSE-2011-ShuhidanHD #comprehension: Understanding novice programmer difficulties via guided learning (SMS, MH, DJD), pp. 213–217.
ITiCSE-2011-VanoM #quote: “Computer science and nursery rhymes”: a learning path for the middle school (DDV, CM), pp. 238–242.
ITiCSE-2011-WolzMS #process: Kinesthetic learning of computing via “off-beat” activities (UW, MM, MS), pp. 68–72.
SIGITE-2011-Cosgrove #low cost #network: Bringing together a low-cost networking learning environment (SRC), pp. 101–106.
SIGITE-2011-DavisJ #community #linux: Learning in the GNU/Linux community (DD, IJ), pp. 21–26.
SIGITE-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.
SIGITE-2011-Mustafa #operating system #simulation #visualisation: Visualizing the modern operating system: simulation experiments supporting enhanced learning (BM), pp. 209–214.
SIGITE-2011-RenwickF #student: Learning styles of information technology students (JSR, CBF), pp. 313–314.
ICPC-J-2009-Sanz-RodriguezDA11 #evaluation #reuse #usability: Metrics-based evaluation of learning object reusability (JSR, JMD, SSA), pp. 121–140.
DLT-2011-Yoshinaka #concept #context-free grammar #towards: Towards Dual Approaches for Learning Context-Free Grammars Based on Syntactic Concept Lattices (RY), pp. 429–440.
ICALP-v1-2011-AroraG #algorithm #fault: New Algorithms for Learning in Presence of Errors (SA, RG), pp. 403–415.
ICALP-v1-2011-HarkinsH #algorithm #bound #game studies: Exact Learning Algorithms, Betting Games, and Circuit Lower Bounds (RCH, JMH), pp. 416–423.
LATA-2011-CaseJLOSS #automation #pattern matching #subclass: Automatic Learning of Subclasses of Pattern Languages (JC, SJ, TDL, YSO, PS, FS), pp. 192–203.
SFM-2011-Jonsson #automaton #modelling: Learning of Automata Models Extended with Data (BJ), pp. 327–349.
SFM-2011-Moschitti #automation #kernel #modelling: Kernel-Based Machines for Abstract and Easy Modeling of Automatic Learning (AM), pp. 458–503.
SFM-2011-SteffenHM #automaton #perspective: Introduction to Active Automata Learning from a Practical Perspective (BS, FH, MM), pp. 256–296.
AIIDE-2011-ChangMLR #behaviour #game studies: Learning and Evaluating Human-Like NPC Behaviors in Dynamic Games (YHC, RTM, TL, VR).
AIIDE-2011-DereszynskiHFDHU #behaviour #game studies #modelling #probability #realtime: Learning Probabilistic Behavior Models in Real-Time Strategy Games (EWD, JH, AF, TGD, TTH, MU).
AIIDE-2011-LinW #modelling: All the World's a Stage: Learning Character Models from Film (GIL, MAW).
AIIDE-2011-MohanL #approach #game studies #object-oriented: An Object-Oriented Approach to Reinforcement Learning in an Action Game (SM, JEL).
AIIDE-2011-TastanS #game studies #policy #using: Learning Policies for First Person Shooter Games Using Inverse Reinforcement Learning (BT, GRS).
CIG-2011-AbdullahiL #difference: Temporal difference learning with interpolated n-tuples: Initial results from a simulated car racing environment (AAA, SML), pp. 321–328.
CIG-2011-AgapitosOBT #modelling #programming #search-based #using: Learning environment models in car racing using stateful Genetic Programming (AA, MO0, AB, TT), pp. 219–226.
CIG-2011-CarvalhoO: Reinforcement learning for the soccer dribbling task (AC, RO), pp. 95–101.
CIG-2011-RoblesRL #game studies #monte carlo: Learning non-random moves for playing Othello: Improving Monte Carlo Tree Search (DR, PR, SML), pp. 305–312.
DiGRA-2011-Frank #design #education #game studies #question: Unexpected game calculations in educational wargaming: Design flaw or beneficial to learning? (AF).
DiGRA-2011-IacovidesASW #how #question: Making sense of game-play: How can we examine learning and involvement? (II, JA, ES, WW).
DiGRA-2011-MitgutschW #design #game studies #recursion: Subversive Game Design for Recursive Learning (KM, MW).
FDG-2011-FowlerC #concept #game studies #motivation #programming: Kodu game lab: improving the motivation for learning programming concepts (AF, BC), pp. 238–240.
FDG-2011-GamesK #design #game studies: Exploring adolescent's STEM learning through scaffolded game design (AIG, LK), pp. 1–8.
FDG-2011-MarshXNOKH #power of: Fun and learning: the power of narrative (TM, CX, LZN, SO, EK, JH), pp. 23–29.
VS-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.
VS-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.
VS-Games-2011-JaligamaL #education #online: An Online Virtual Learning Environment for Higher Education (VJ, FL), pp. 207–214.
VS-Games-2011-MathieuPP #approach #multi #named: Format-Store: A Multi-agent Based Approach to Experiential Learning (PM, DP, SP), pp. 120–127.
VS-Games-2011-VosinakisKZ #case study #problem: An Exploratory Study of Problem-Based Learning in Virtual Worlds (SV, PK, PZ), pp. 112–119.
VS-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.
CHI-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.
CHI-2011-EdgeSCZL #mobile #named: MicroMandarin: mobile language learning in context (DE, ES, KC, JZ, JAL), pp. 3169–3178.
CHI-2011-FiebrinkCT #evaluation #interactive: Human model evaluation in interactive supervised learning (RF, PRC, DT), pp. 147–156.
CHI-2011-HowisonTRA #concept #interactive: The mathematical imagery trainer: from embodied interaction to conceptual learning (MH, DT, DR, DA), pp. 1989–1998.
CHI-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.
CHI-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.
CHI-2011-ShaerSVFLW #interactive: Enhancing genomic learning through tabletop interaction (OS, MS, CV, TF, ML, HW), pp. 2817–2826.
CHI-2011-ToupsKHS #coordination #simulation: Zero-fidelity simulation of fire emergency response: improving team coordination learning (ZOT, AK, WAH, NS), pp. 1959–1968.
CHI-2011-TrustyT #web: Augmenting the web for second language vocabulary learning (AT, KNT), pp. 3179–3188.
CSCW-2011-NawahdahI #automation #education: Automatic adjustment of a virtual teacher’s model in a learning support system (MN, TI), pp. 693–696.
DHM-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.
DHM-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.
DUXU-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.
DUXU-v1-2011-GeorgeADMW #collaboration #multi: Multitouch Tables for Collaborative Object-Based Learning (JG, EdA, DD, DSM, GW), pp. 237–246.
DUXU-v1-2011-LeeR #architecture #collaboration #concept #mobile: Suggested Collaborative Learning Conceptual Architecture and Applications for Mobile Devices (KL, AR), pp. 611–620.
DUXU-v1-2011-Schmid #analysis #development #feedback: Development of an Augmented Feedback Application to Support Motor Learning after Stroke: Requirement Analysis (SS), pp. 305–314.
DUXU-v2-2011-ArditoLRSYAC #design #game studies #pervasive: Designing Pervasive Games for Learning (CA, RL, DR, CS, NY, NMA, MFC), pp. 99–108.
HCD-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.
HCI-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.
HCI-UA-2011-AdamsS: A Web-Based Learning Environment to Support Chemistry (CA, CS), pp. 3–11.
HCI-UA-2011-EverardJM #question #student #what: Are MIS Students Learning What They Need to Land a Job? (AE, BMJ, SM), pp. 235–236.
HCI-UA-2011-GeorgeS #collaboration #game studies: Introducing Mobility in Serious Games: Enhancing Situated and Collaborative Learning (SG, AS), pp. 12–20.
HCI-UA-2011-HayakawaNOFN #framework #visualisation: Visualization Framework for Computer System Learning (EH, YN, HO, MF, YN), pp. 21–26.
HCI-UA-2011-Huseyinov #adaptation #fuzzy #modelling #multi: Fuzzy Linguistic Modelling Cognitive / Learning Styles for Adaptation through Multi-level Granulation (IH), pp. 39–47.
HCI-UA-2011-Klenner-Moore #process: Creating a New Context for Activity in Blended Learning Environments: Engaging the Twitchy Fingers (JKM), pp. 61–67.
HCI-UA-2011-LiJN #user interface #visual notation: Haptically Enhanced User Interface to Support Science Learning of Visually Impaired (YL, SLJ, CSN), pp. 68–76.
HCI-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.
HCI-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.
HCI-UA-2011-YajimaT #collaboration: Proposal of Collaborative Learning Support Method in Risk Communications (HY, NT), pp. 113–120.
HCI-UA-2011-YamaguchiMT #evaluation #online: Evaluation of Online Handwritten Characters for Penmanship Learning Support System (TY, NM, MT), pp. 121–130.
HCI-UA-2011-YangCS #analysis #recognition: Facial Expression Recognition for Learning Status Analysis (MTY, YJC, YCS), pp. 131–138.
HIMI-v2-2011-PohlML #hybrid #standard: Transforming a Standard Lecture into a Hybrid Learning Scenario (HMP, JTM, JL), pp. 55–61.
OCSC-2011-AhmadL: Promoting Reflective Learning: The Role of Blogs in the Classroom (RA, WGL), pp. 3–11.
OCSC-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.
ICEIS-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.
ICEIS-v2-2011-NganBL #framework #monitoring #multi #query: A Service Framework for Learning, Querying and Monitoring Multivariate Time Series (CKN, AB, JL), pp. 92–101.
ICEIS-v4-2011-Marks #collaboration #student: Students’ Acceptance of E-Group Collaboration Learning (AM), pp. 269–274.
CIKM-2011-ArguelloDC #web: Learning to aggregate vertical results into web search results (JA, FD, JC), pp. 201–210.
CIKM-2011-CoffmanW #keyword #rank #relational: Learning to rank results in relational keyword search (JC, ACW), pp. 1689–1698.
CIKM-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.
CIKM-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.
CIKM-2011-FuLZZ #query: Do they belong to the same class: active learning by querying pairwise label homogeneity (YF, BL, XZ, CZ), pp. 2161–2164.
CIKM-2011-GiannopoulosBDS #rank: Learning to rank user intent (GG, UB, TD, TKS), pp. 195–200.
CIKM-2011-KasiviswanathanMBS #detection #taxonomy #topic #using: Emerging topic detection using dictionary learning (SPK, PM, AB, VS), pp. 745–754.
CIKM-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.
CIKM-2011-LiCHLJ #collaboration #online: Collaborative online learning of user generated content (GL, KC, SCHH, WL, RJ), pp. 285–290.
CIKM-2011-LinC #data fusion #query: Query sampling for learning data fusion (TCL, PJC), pp. 141–146.
CIKM-2011-LinLWX #rank: Learning to rank with cross entropy (YL, HL, JW, KX), pp. 2057–2060.
CIKM-2011-LiuCZH #random: Learning conditional random fields with latent sparse features for acronym expansion finding (JL, JC, YZ, YH), pp. 867–872.
CIKM-2011-LiuLH #bound #fault #kernel: Learning kernels with upper bounds of leave-one-out error (YL, SL, YH), pp. 2205–2208.
CIKM-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.
CIKM-2011-OroR #approach #named: SILA: a spatial instance learning approach for deep webpages (EO, MR), pp. 2329–2332.
CIKM-2011-PandeyABHCRZ #behaviour #what: Learning to target: what works for behavioral targeting (SP, MA, AB, AOH, PC, AR, MZ), pp. 1805–1814.
CIKM-2011-RamanJS #ranking: Structured learning of two-level dynamic rankings (KR, TJ, PS), pp. 291–296.
CIKM-2011-SellamanickamGS #approach #ranking: A pairwise ranking based approach to learning with positive and unlabeled examples (SS, PG, SKS), pp. 663–672.
CIKM-2011-SzummerY #rank: Semi-supervised learning to rank with preference regularization (MS, EY), pp. 269–278.
CIKM-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.
CIKM-2011-UllegaddiV #category theory #query #rank #web: Learning to rank categories for web queries (PU, VV), pp. 2065–2068.
CIKM-2011-WangCWLWO #similarity: Coupled nominal similarity in unsupervised learning (CW, LC, MW, JL, WW, YO), pp. 973–978.
CIKM-2011-WangHJT #categorisation #image #metric #multi #performance: Efficient lp-norm multiple feature metric learning for image categorization (SW, QH, SJ, QT), pp. 2077–2080.
CIKM-2011-WangHLCH #recommendation: Learning to recommend questions based on public interest (JW, XH, ZL, WHC, BH), pp. 2029–2032.
CIKM-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.
CIKM-2011-YanGC #higher-order #query #recommendation: Context-aware query recommendation by learning high-order relation in query logs (XY, JG, XC), pp. 2073–2076.
CIKM-2011-YangZKL #how #question #why: Can irrelevant data help semi-supervised learning, why and how? (HY, SZ, IK, MRL), pp. 937–946.
CIKM-2011-YanTLSL #predict: Citation count prediction: learning to estimate future citations for literature (RY, JT, XL, DS, XL), pp. 1247–1252.
CIKM-2011-ZhaoYX #independence #information management #web: Max margin learning on domain-independent web information extraction (BZ, XY, EPX), pp. 1305–1310.
CIKM-2011-ZhuZYGX: Transfer active learning (ZZ, XZ, YY, YFG, XX), pp. 2169–2172.
ECIR-2011-HofmannWR #online #rank: Balancing Exploration and Exploitation in Learning to Rank Online (KH, SW, MdR), pp. 251–263.
ECIR-2011-MacdonaldO #modelling #ranking: Learning Models for Ranking Aggregates (CM, IO), pp. 517–529.
ECIR-2011-ZhouH #comprehension #natural language #random: Learning Conditional Random Fields from Unaligned Data for Natural Language Understanding (DZ, YH), pp. 283–288.
ICML-2011-BabenkoVDB #multi: Multiple Instance Learning with Manifold Bags (BB, NV, PD, SB), pp. 81–88.
ICML-2011-BabesMLS #multi: Apprenticeship Learning About Multiple Intentions (MB, VNM, KS, MLL), pp. 897–904.
ICML-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.
ICML-2011-BuffoniCGU #standard: Learning Scoring Functions with Order-Preserving Losses and Standardized Supervision (DB, CC, PG, NU), pp. 825–832.
ICML-2011-Bylander #linear #multi #polynomial: Learning Linear Functions with Quadratic and Linear Multiplicative Updates (TB), pp. 505–512.
ICML-2011-ChakrabortyS: Structure Learning in Ergodic Factored MDPs without Knowledge of the Transition Function’s In-Degree (DC, PS), pp. 737–744.
ICML-2011-ChenPSDC #analysis #process: The Hierarchical Beta Process for Convolutional Factor Analysis and Deep Learning (BC, GP, GS, DBD, LC), pp. 361–368.
ICML-2011-ChoRI #adaptation #strict: Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines (KC, TR, AI), pp. 105–112.
ICML-2011-DauphinGB #re-engineering #scalability: Large-Scale Learning of Embeddings with Reconstruction Sampling (YD, XG, YB), pp. 945–952.
ICML-2011-DinuzzoOGP #coordination #kernel: Learning Output Kernels with Block Coordinate Descent (FD, CSO, PVG, GP), pp. 49–56.
ICML-2011-DudikLL #evaluation #policy #robust: Doubly Robust Policy Evaluation and Learning (MD, JL, LL), pp. 1097–1104.
ICML-2011-GlorotBB #adaptation #approach #classification #scalability #sentiment: Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach (XG, AB, YB), pp. 513–520.
ICML-2011-Gould #linear #markov #random: Max-margin Learning for Lower Linear Envelope Potentials in Binary Markov Random Fields (SG), pp. 193–200.
ICML-2011-GuilloryB: Simultaneous Learning and Covering with Adversarial Noise (AG, JAB), pp. 369–376.
ICML-2011-HarelM #multi: Learning from Multiple Outlooks (MH, SM), pp. 401–408.
ICML-2011-HeL #framework #multi: A Graphbased Framework for Multi-Task Multi-View Learning (JH, RL), pp. 25–32.
ICML-2011-HuWC #coordination #kernel #named #parametricity #scalability #using: BCDNPKL: Scalable Non-Parametric Kernel Learning Using Block Coordinate Descent (EH, BW, SC), pp. 209–216.
ICML-2011-JawanpuriaNR #kernel #performance #using: Efficient Rule Ensemble Learning using Hierarchical Kernels (PJ, JSN, GR), pp. 161–168.
ICML-2011-KangGS #multi: Learning with Whom to Share in Multi-task Feature Learning (ZK, KG, FS), pp. 521–528.
ICML-2011-KuwadekarN #classification #modelling #relational: Relational Active Learning for Joint Collective Classification Models (AK, JN), pp. 385–392.
ICML-2011-LeeW #identification #online #probability: Manifold Identification of Dual Averaging Methods for Regularized Stochastic Online Learning (SL, SJW), pp. 1121–1128.
ICML-2011-LeNCLPN #on the #optimisation: On optimization methods for deep learning (QVL, JN, AC, AL, BP, AYN), pp. 265–272.
ICML-2011-LiZSC #integration #modelling #on the #taxonomy #topic: On the Integration of Topic Modeling and Dictionary Learning (LL, MZ, GS, LC), pp. 625–632.
ICML-2011-LuB #modelling: Learning Mallows Models with Pairwise Preferences (TL, CB), pp. 145–152.
ICML-2011-Maaten #kernel: Learning Discriminative Fisher Kernels (LvdM), pp. 217–224.
ICML-2011-MachartPARG #kernel #probability #rank: Stochastic Low-Rank Kernel Learning for Regression (PM, TP, SA, LR, HG), pp. 969–976.
ICML-2011-MartensS #network #optimisation: Learning Recurrent Neural Networks with Hessian-Free Optimization (JM, IS), pp. 1033–1040.
ICML-2011-NgiamCKN #energy #modelling: Learning Deep Energy Models (JN, ZC, PWK, AYN), pp. 1105–1112.
ICML-2011-NgiamKKNLN #multimodal: Multimodal Deep Learning (JN, AK, MK, JN, HL, AYN), pp. 689–696.
ICML-2011-NickelTK #multi: A Three-Way Model for Collective Learning on Multi-Relational Data (MN, VT, HPK), pp. 809–816.
ICML-2011-OrabonaL #algorithm #kernel #multi #optimisation: Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning (FO, JL), pp. 249–256.
ICML-2011-QuadriantoL #multi: Learning Multi-View Neighborhood Preserving Projections (NQ, CHL), pp. 425–432.
ICML-2011-RobbianoC #plugin #ranking: Minimax Learning Rates for Bipartite Ranking and Plug-in Rules (SR, SC), pp. 441–448.
ICML-2011-SaxeKCBSN #on the #random: On Random Weights and Unsupervised Feature Learning (AMS, PWK, ZC, MB, BS, AYN), pp. 1089–1096.
ICML-2011-SmallWBT: The Constrained Weight Space SVM: Learning with Ranked Features (KS, BCW, CEB, TAT), pp. 865–872.
ICML-2011-Sohl-DicksteinBD #probability: Minimum Probability Flow Learning (JSD, PB, MRD), pp. 905–912.
ICML-2011-TamuzLBSK #adaptation #kernel: Adaptively Learning the Crowd Kernel (OT, CL, SB, OS, AK), pp. 673–680.
ICML-2011-WellingT #probability: Bayesian Learning via Stochastic Gradient Langevin Dynamics (MW, YWT), pp. 681–688.
ICML-2011-YangR #on the #using #visual notation: On the Use of Variational Inference for Learning Discrete Graphical Model (EY, PDR), pp. 1009–1016.
ICML-2011-YanRFD: Active Learning from Crowds (YY, RR, GF, JGD), pp. 1161–1168.
KDD-2011-AttenbergP #online: Online active inference and learning (JA, FJP), pp. 186–194.
KDD-2011-ChakiCG: Supervised learning for provenance-similarity of binaries (SC, CC, AG), pp. 15–23.
KDD-2011-ChenRT #adaptation #detection #incremental: Detecting bots via incremental LS-SVM learning with dynamic feature adaptation (FC, SR, PNT), pp. 386–394.
KDD-2011-ChenZY #multi #rank #robust: Integrating low-rank and group-sparse structures for robust multi-task learning (JC, JZ, JY), pp. 42–50.
KDD-2011-ChuZLTT #data type #online: Unbiased online active learning in data streams (WC, MZ, LL, AT, BLT), pp. 195–203.
KDD-2011-Cormode #privacy: Personal privacy vs population privacy: learning to attack anonymization (GC), pp. 1253–1261.
KDD-2011-GhaniK #detection #fault #interactive: Interactive learning for efficiently detecting errors in insurance claims (RG, MK), pp. 325–333.
KDD-2011-JiangBSZL #adaptation #concept #ontology: Ontology enhancement and concept granularity learning: keeping yourself current and adaptive (SJ, LB, BS, YZ, WL), pp. 1244–1252.
KDD-2011-MesterharmP #algorithm #online #using: Active learning using on-line algorithms (CM, MJP), pp. 850–858.
KDD-2011-MooreYZRL #classification #network: Active learning for node classification in assortative and disassortative networks (CM, XY, YZ, JBR, TL), pp. 841–849.
KDD-2011-RashidiC #induction #query: Ask me better questions: active learning queries based on rule induction (PR, DJC), pp. 904–912.
KDD-2011-ValizadeganJW #multi #predict: Learning to trade off between exploration and exploitation in multiclass bandit prediction (HV, RJ, SW), pp. 204–212.
KDD-2011-ZhangHLSL #approach #multi #scalability: Multi-view transfer learning with a large margin approach (DZ, JH, YL, LS, RDL), pp. 1208–1216.
KDD-2011-ZhangLS: Serendipitous learning: learning beyond the predefined label space (DZ, YL, LS), pp. 1343–1351.
KDD-2011-ZhouYLY #multi #predict: A multi-task learning formulation for predicting disease progression (JZ, LY, JL, JY), pp. 814–822.
KDIR-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.
KDIR-2011-FilhoRM #named #rank: XHITS: Learning to Rank in a Hyperlinked Structure (FBF, RPR, RLM), pp. 385–389.
KDIR-2011-GriffithOS #collaboration #parametricity: Learning Neighbourhood-based Collaborative Filtering Parameters (JG, CO, HS), pp. 452–455.
KDIR-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.
KDIR-2011-ReuterC #identification #similarity #using: Learning Similarity Functions for Event Identification using Support Vector Machines (TR, PC), pp. 208–215.
KEOD-2011-AbbesZN #ontology #semantics: Evaluating Semantic Classes Used for Ontology Building and Learning from Texts (SBA, HZ, AN), pp. 445–448.
KEOD-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.
KEOD-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.
KMIS-2011-Silva #approach #concept: Learning Organization — Concept and Proposal of a New Approach (AFdS), pp. 384–389.
MLDM-2011-CelibertoM: Investigation in Transfer Learning: Better Way to Apply Transfer Learning between Agents (LACJ, JPM), pp. 210–223.
MLDM-2011-LahbibBL #multi: Informative Variables Selection for Multi-relational Supervised Learning (DL, MB, DL), pp. 75–87.
MLDM-2011-XuGC #adaptation #kernel #multi: Adaptive Kernel Diverse Density Estimate for Multiple Instance Learning (TX, IG, DKYC), pp. 185–198.
MLDM-2011-XuM #taxonomy: Dictionary Learning Based on Laplacian Score in Sparse Coding (JX, HM), pp. 253–264.
RecSys-2011-Makrehchi #recommendation #social #topic: Social link recommendation by learning hidden topics (MM), pp. 189–196.
RecSys-2011-WuCMW #detection #named: Semi-SAD: applying semi-supervised learning to shilling attack detection (ZW, JC, BM, YW), pp. 289–292.
SEKE-2011-GaoZHL #modelling: Learning action models with indeterminate effects (JG, HHZ, DjH, LL), pp. 159–162.
SEKE-2011-SantosGSF #agile #empirical #implementation #towards: A view towards Organizational Learning: An empirical study on Scrum implementation (VAS, AG, ACMS, ALF), pp. 583–589.
SEKE-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.
SEKE-2011-ThiryZS #education #empirical #game studies #testing: Empirical study upon software testing learning with support from educational game (MT, AZ, ACdS), pp. 481–484.
SIGIR-2011-AminiU #automation #detection #multi #summary: Transductive learning over automatically detected themes for multi-document summarization (MRA, NU), pp. 1193–1194.
SIGIR-2011-AsadiMEL #pseudo #ranking #web: Pseudo test collections for learning web search ranking functions (NA, DM, TE, JJL), pp. 1073–1082.
SIGIR-2011-DaiSD #rank: Learning to rank for freshness and relevance (ND, MS, BDD), pp. 95–104.
SIGIR-2011-DaiSD11a #multi #optimisation #rank: Multi-objective optimization in learning to rank (ND, MS, BDD), pp. 1241–1242.
SIGIR-2011-GaoZLLW #feedback: Learning features through feedback for blog distillation (DG, RZ, WL, RYKL, KFW), pp. 1085–1086.
SIGIR-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.
SIGIR-2011-KumarL #rank: Learning to rank from a noisy crowd (AK, ML), pp. 1221–1222.
SIGIR-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.
SIGIR-2011-Li #graph: Learning for graphs with annotated edges (FL), pp. 1259–1260.
SIGIR-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.
SIGIR-2011-PolitzS #constraints #rank: Learning to rank under tight budget constraints (CP, RS), pp. 1173–1174.
SIGIR-2011-TianL #information retrieval #interactive: Active learning to maximize accuracy vs. effort in interactive information retrieval (AT, ML), pp. 145–154.
SIGIR-2011-WangGWL #information retrieval #parallel #rank: Parallel learning to rank for information retrieval (SW, BJG, KW, HWL), pp. 1083–1084.
SIGIR-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.
SIGIR-2011-WangWZH #online #random: Learning online discussion structures by conditional random fields (HW, CW, CZ, JH), pp. 435–444.
SIGIR-2011-WuYLLYX #rank #using: Learning to rank using query-level regression (JW, ZY, YL, HL, ZY, KX), pp. 1091–1092.
SIGIR-2011-YangLSZZ #collaboration #recommendation #using: Collaborative competitive filtering: learning recommender using context of user choice (SHY, BL, AJS, HZ, ZZ), pp. 295–304.
ECMFA-2011-DolquesDFHNP #automation #model transformation: Easing Model Transformation Learning with Automatically Aligned Examples (XD, AD, JRF, MH, CN, FP), pp. 189–204.
PADL-2011-Mooney: Learning Language from Its Perceptual Context (RJM), pp. 2–4.
POPL-2011-LiangTN #abstraction: Learning minimal abstractions (PL, OT, MN), pp. 31–42.
ICSE-2011-BorgesGLN #adaptation #evolution #requirements #specification: Learning to adapt requirements specifications of evolving systems (RVB, ASdG, LCL, BN), pp. 856–859.
SAC-2011-BhaskaranNFG #behaviour #detection #online: Deceit detection via online behavioral learning (NB, IN, MGF, VG), pp. 29–30.
SAC-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.
SAC-2011-GomesRS #concept #data type: Learning recurring concepts from data streams with a context-aware ensemble (JBG, EMR, PACS), pp. 994–999.
SAC-2011-LiuLTL #framework #game studies #interactive #platform: A cognition-based interactive game platform for learning Chinese characters (CLL, CYL, JLT, CLL), pp. 1181–1186.
SAC-2011-NawahdahI #education #physics: Positioning a virtual teacher in an MR physical task learning support system (MN, TI), pp. 1169–1174.
SAC-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.
SAC-2011-ZhangZZZX #detection #web: Harmonic functions based semi-supervised learning for web spam detection (WZ, DZ, YZ, GZ, BX), pp. 74–75.
DAC-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.
DAC-2011-KatzRZS #architecture #behaviour #generative #quality: Learning microarchitectural behaviors to improve stimuli generation quality (YK, MR, AZ, GS), pp. 848–853.
DAC-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.
DATE-2011-ArslanO #adaptation #effectiveness #optimisation #realtime: Adaptive test optimization through real time learning of test effectiveness (BA, AO), pp. 1430–1435.
FASE-2011-FengKP #automation #composition #probability #reasoning: Automated Learning of Probabilistic Assumptions for Compositional Reasoning (LF, MZK, DP), pp. 2–17.
STOC-2011-BalcanH: Learning submodular functions (MFB, NJAH), pp. 793–802.
ICLP-J-2011-CorapiRVPS #design #induction #using: Normative design using inductive learning (DC, AR, MDV, JAP, KS), pp. 783–799.
SAT-2011-SilverthornM #satisfiability: Learning Polarity from Structure in SAT (BS, RM), pp. 377–378.
VMCAI-2011-HowarSM #abstraction #automation #automaton #refinement: Automata Learning with Automated Alphabet Abstraction Refinement (FH, BS, MM), pp. 263–277.
ECSA-2010-MarcoGII #adaptation #lifecycle #paradigm #self: Learning from the Cell Life-Cycle: A Self-adaptive Paradigm (ADM, FG, PI, RI), pp. 485–488.
DRR-2010-LiuZ #detection #documentation #image: Semi-supervised learning for detecting text-lines in noisy document images (ZL, HZ), pp. 1–10.
DRR-2010-Obafemi-AjayiAF #documentation: Learning shape features for document enhancement (TOA, GA, OF), pp. 1–10.
DRR-2010-ZhangZLT #recognition: A stacked sequential learning method for investigator name recognition from web-based medical articles (XZ, JZ, DXL, GRT), pp. 1–10.
ECDL-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.
HT-2010-PaekHS #hypermedia: Spatial contiguity and implicit learning in hypertext (SP, DH, AS), pp. 291–292.
HT-2010-PrataGC #personalisation: Crossmedia personalized learning contexts (AP, NG, TC), pp. 305–306.
HT-2010-TielletPRLC #design #evaluation: Design and evaluation of a hypervideo environment to support veterinary surgery learning (CABT, AGP, EBR, JVdL, TC), pp. 213–222.
HT-2010-TielletPRLC10a #named: HVet: a hypervideo environment to support veterinary surgery learning (CABT, AGP, EBR, JVdL, TC), pp. 313–314.
PODS-2010-LemayMN #algorithm #top-down #xml: A learning algorithm for top-down XML transformations (AL, SM, JN), pp. 285–296.
SIGMOD-2010-ArasuGK #on the: On active learning of record matching packages (AA, MG, RK), pp. 783–794.
SIGMOD-2010-CortezSGM #information management #named #on-demand: ONDUX: on-demand unsupervised learning for information extraction (EC, ASdS, MAG, ESdM), pp. 807–818.
EDM-2010-Bian #clustering #process #student: Clustering Student Learning Activity Data (HB), pp. 277–278.
EDM-2010-BousbiaLBR #behaviour #using #web: Analyzing Learning Styles using Behavioral Indicators in Web based Learning Environments (NB, JML, AB, IR), pp. 279–280.
EDM-2010-ChampaignC10a #approach: A Distillation Approach to Refining Learning Objects (JC, RC), pp. 283–284.
EDM-2010-DMelloG #experience #mining: Mining Bodily Patterns of Affective Experience during Learning (SKD, ACG), pp. 31–40.
EDM-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.
EDM-2010-ForsythBGH #correlation: Higher Contributions Correlate with Higher Learning Gains (CF, HB, ACG, DFH), pp. 287–288.
EDM-2010-GoldsteinBH: Pinpointing Learning Moments; A finer grain P(J) model (ABG, RSJdB, NTH), pp. 289–290.
EDM-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.
EDM-2010-KimC #analysis #case study #experience #sentiment #student: Sentiment Analysis in Student Experiences of Learning (SMK, RAC), pp. 111–120.
EDM-2010-LehmanCO #topic: Off Topic Conversation in Expert Tutoring: Waste of Time or Learning Opportunity (BL, WLC, AO), pp. 101–110.
EDM-2010-Pavlik #comprehension #reduction: Data Reduction Methods Applied to Understanding Complex Learning Hypotheses (PIPJ), pp. 311–312.
EDM-2010-Rajibussalim #interactive #mining #student: Mining Students’ Interaction Data from a System that Support Learning by Reflection (R), pp. 249–256.
EDM-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.
EDM-2010-SoundranayagamY #order #predict #question: Can Order of Access to Learning Resources Predict Success? (HS, KY), pp. 323–324.
EDM-2010-XuR #analysis #network #online #social: Peer Production of Online Learning Resources: A Social Network Analysis (BX, MR), pp. 315–316.
ITiCSE-2010-AydinolG10a #spreadsheet #video: The effect of video tutorials on learning spreadsheets (ABA, ÖG), p. 323.
ITiCSE-2010-CoconF #education #named #online: LOMOLEHEA: learning object model for online learning based on the european higher education area (FC, EF), pp. 78–82.
ITiCSE-2010-Cross: Promoting active learning through assignments (GWC), p. 306.
ITiCSE-2010-Denny #collaboration #online: Motivating online collaborative learning (PD), p. 300.
ITiCSE-2010-EganJ: Service learning in introductory computer science (MALE, MJ), pp. 8–12.
ITiCSE-2010-HamadaS: Lego NXT as a learning tool (MH, SS), p. 321.
ITiCSE-2010-HowardJN #behaviour #design #online #using: Reflecting on online learning designs using observed behavior (LH, JJ, CN), pp. 179–183.
ITiCSE-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.
ITiCSE-2010-LeeR #algorithm #category theory #design #visualisation: Integrating categories of algorithm learning objective into algorithm visualization design: a proposal (MHL, GR), pp. 289–293.
ITiCSE-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.
ITiCSE-2010-Mirolo #analysis #multi #recursion #student: Learning (through) recursion: a multidimensional analysis of the competences achieved by CS1 students (CM), pp. 160–164.
ITiCSE-2010-QianLYL #programming: Inquiry-based active learning in introductory programming courses (KQ, CTDL, LY, JL), p. 312.
ITiCSE-2010-TuOKKT: Developing verification-driven learning cases (ST, SJO, RK, AK, ST), pp. 58–62.
SIGITE-2010-Al-khalifa #gender: Overcoming gender segregation in service learning projects: a case from Saudi Arabia (HSAK), pp. 121–124.
SIGITE-2010-GiannakosV: Comparing a well designed webcast with traditional learning (MNG, PV), pp. 65–68.
SIGITE-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.
SIGITE-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.
SIGITE-2010-Zhang #framework #student: Technology acceptance in learning settings from a student perspective: a theoretical framework (CZ), pp. 37–42.
ICSM-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.
PASTE-2010-FengG #fault #locality #modelling #probability: Learning universal probabilistic models for fault localization (MF, RG), pp. 81–88.
SCAM-2010-Zeller #in the large #mining #modelling: Learning from 6,000 Projects: Mining Models in the Large (AZ), pp. 3–6.
LATA-2010-KasprzikK #string #using: String Extension Learning Using Lattices (AK, TK), pp. 380–391.
AIIDE-2010-SharifiZS #behaviour #game studies #using: Learning Companion Behaviors Using Reinforcement Learning in Games (AS, RZ, DS).
AIIDE-2010-Torrey #multi #simulation: Crowd Simulation Via Multi-Agent Reinforcement Learning (LT).
CIG-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.
CIG-2010-Lucas #evolution #problem: Estimating learning rates in evolution and TDL: Results on a simple grid-world problem (SML), pp. 372–379.
CIG-2010-MartinezHY #modelling: Extending neuro-evolutionary preference learning through player modeling (HPM, KH, GNY), pp. 313–320.
CIG-2010-QuadfliegPKR: Learning the track and planning ahead in a car racing controller (JQ, MP, OK, GR), pp. 395–402.
FDG-2010-ArenaS #exclamation #game studies #statistics #video: Stats invaders!: learning about statistics by playing a classic video game (DA, DLS), pp. 248–249.
FDG-2010-BoyceB #game studies #motivation #using: BeadLoom Game: using game elements to increase motivation and learning (AKB, TB), pp. 25–31.
FDG-2010-EsteyLGG #design #game studies: Investigating studio-based learning in a course on game design (AE, JL, BG, AAG), pp. 64–71.
FDG-2010-NickelB #collaboration #education #game studies #multi: Games for CS education: computer-supported collaborative learning and multiplayer games (AN, TB), pp. 274–276.
FDG-2010-RoweSML #difference #perspective: Individual differences in gameplay and learning: a narrative-centered learning perspective (JPR, LRS, BWM, JCL), pp. 171–178.
FDG-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.
FDG-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.
VS-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.
CHI-2010-AmershiFKT #concept #interactive #modelling #multi: Examining multiple potential models in end-user interactive concept learning (SA, JF, AK, DST), pp. 1357–1360.
CHI-2010-CapraMVM #collaboration #multi: Tools-at-hand and learning in multi-session, collaborative search (RGC, GM, JVM, KM), pp. 951–960.
CHI-2010-DornG #design #programming #web: Learning on the job: characterizing the programming knowledge and learning strategies of web designers (BD, MG), pp. 703–712.
CHI-2010-HuangSDWKAL #mobile #music: Mobile music touch: mobile tactile stimulation for passive learning (KH, TS, EYLD, GW, DK, CA, RL), pp. 791–800.
CHI-2010-IsbisterFH #design #game studies: Designing games for learning: insights from conversations with designers (KI, MF, CH), pp. 2041–2044.
CHI-2010-KumarTSCKC #case study #mobile: An exploratory study of unsupervised mobile learning in rural India (AK, AT, GS, DC, MK, JC), pp. 743–752.
CHI-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.
CHI-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.
ICEIS-AIDSS-2010-AhdabG #network #performance: Efficient Learning of Dynamic Bayesian Networks from Timed Data (AA, MLG), pp. 226–231.
ICEIS-AIDSS-2010-MasvoulaKM #overview: A Review of Learning Methods Enhanced in Strategies of Negotiating Agents (MM, PK, DM), pp. 212–219.
ICEIS-AIDSS-2010-MoriyasuYN #self #using: Supervised Learning for Agent Positioning by using Self-organizing Map (KM, TY, HN), pp. 368–372.
ICEIS-HCI-2010-DiosERR #collaboration: Virtual and Collaborative Environment for Learning Maths (AQD, AHE, IVR, ÁMdR), pp. 86–90.
CIKM-2010-BethardJ #behaviour #modelling: Who should I cite: learning literature search models from citation behavior (SB, DJ), pp. 609–618.
CIKM-2010-BilottiECN #constraints #rank #semantics: Rank learning for factoid question answering with linguistic and semantic constraints (MWB, JLE, JGC, EN), pp. 459–468.
CIKM-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.
CIKM-2010-CebronB #parallel: Active learning in parallel universes (NC, MRB), pp. 1621–1624.
CIKM-2010-ComarTJ #multi #network: Multi task learning on multiple related networks (PMC, PNT, AKJ), pp. 1737–1740.
CIKM-2010-DuNL #adaptation: Adapting cost-sensitive learning for reject option (JD, EAN, CXL), pp. 1865–1868.
CIKM-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.
CIKM-2010-FangSS #clustering #multi: Multilevel manifold learning with application to spectral clustering (HrF, SS, YS), pp. 419–428.
CIKM-2010-FujinoUN #classification #robust: A robust semi-supervised classification method for transfer learning (AF, NU, MN), pp. 379–388.
CIKM-2010-He #classification #sentiment: Learning sentiment classification model from labeled features (YH), pp. 1685–1688.
CIKM-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.
CIKM-2010-KouCZZ #ranking: Learning to blend rankings: a monotonic transformation to blend rankings from heterogeneous domains (ZK, YC, ZZ, HZ), pp. 1921–1924.
CIKM-2010-LadY #documentation #feedback #novel #rank: Learning to rank relevant and novel documents through user feedback (AL, YY), pp. 469–478.
CIKM-2010-LinLYJS #rank: Learning to rank with groups (YL, HL, ZY, SJ, XS), pp. 1589–1592.
CIKM-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.
CIKM-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.
CIKM-2010-ShiZT: Combining link and content for collective active learning (LS, YZ, JT), pp. 1829–1832.
CIKM-2010-SonPS #classification #estimation #naive bayes: Learning naïve bayes transfer classifier throughclass-wise test distribution estimation (JWS, SBP, HJS), pp. 1729–1732.
CIKM-2010-TakamuraO #summary: Learning to generate summary as structured output (HT, MO), pp. 1437–1440.
CIKM-2010-YangKL #feature model #multi #online: Online learning for multi-task feature selection (HY, IK, MRL), pp. 1693–1696.
CIKM-2010-ZhangWWCZHZ #modelling: Learning click models via probit bayesian inference (YZ, DW, GW, WC, ZZ, BH, LZ), pp. 439–448.
CIKM-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.
CIKM-2010-ZhuZGX #classification #incremental: Transfer incremental learning for pattern classification (ZZ, XZ, YFG, XX), pp. 1709–1712.
ECIR-2010-MendozaMFP #query #web: Learning to Distribute Queries into Web Search Nodes (MM, MM, FF, BP), pp. 281–292.
ECIR-2010-PengMO #ranking: Learning to Select a Ranking Function (JP, CM, IO), pp. 114–126.
ICML-2010-BilgicMG: Active Learning for Networked Data (MB, LM, LG), pp. 79–86.
ICML-2010-BordesUW #ambiguity #ranking #semantics: Label Ranking under Ambiguous Supervision for Learning Semantic Correspondences (AB, NU, JW), pp. 103–110.
ICML-2010-BouzyM #game studies #matrix #multi: Multi-agent Learning Experiments on Repeated Matrix Games (BB, MM), pp. 119–126.
ICML-2010-BradleyG #random: Learning Tree Conditional Random Fields (JKB, CG), pp. 127–134.
ICML-2010-CaniniSG #categorisation #modelling #process: Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process (KRC, MMS, TLG), pp. 151–158.
ICML-2010-CaoLY #multi #predict: Transfer Learning for Collective Link Prediction in Multiple Heterogenous Domains (BC, NNL, QY), pp. 159–166.
ICML-2010-Cesa-BianchiSS #performance: Efficient Learning with Partially Observed Attributes (NCB, SSS, OS), pp. 183–190.
ICML-2010-ChakrabortyS #convergence #multi #safety: Convergence, Targeted Optimality, and Safety in Multiagent Learning (DC, PS), pp. 191–198.
ICML-2010-ChangSGR: Structured Output Learning with Indirect Supervision (MWC, VS, DG, DR), pp. 199–206.
ICML-2010-CortesMR #algorithm #kernel: Two-Stage Learning Kernel Algorithms (CC, MM, AR), pp. 239–246.
ICML-2010-CortesMR10a #bound #kernel: Generalization Bounds for Learning Kernels (CC, MM, AR), pp. 247–254.
ICML-2010-DavisD #bottom-up #markov #network: Bottom-Up Learning of Markov Network Structure (JD, PMD), pp. 271–278.
ICML-2010-DeselaersF #multi #random: A Conditional Random Field for Multiple-Instance Learning (TD, VF), pp. 287–294.
ICML-2010-DillonBL #analysis #generative: Asymptotic Analysis of Generative Semi-Supervised Learning (JVD, KB, GL), pp. 295–302.
ICML-2010-DruckM #generative #modelling #using: High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models (GD, AM), pp. 319–326.
ICML-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.
ICML-2010-GomesK #data type #parametricity: Budgeted Nonparametric Learning from Data Streams (RG, AK), pp. 391–398.
ICML-2010-GregorL #approximate #performance: Learning Fast Approximations of Sparse Coding (KG, YL), pp. 399–406.
ICML-2010-GrubbB #composition #network: Boosted Backpropagation Learning for Training Deep Modular Networks (AG, JAB), pp. 407–414.
ICML-2010-HarpaleY #adaptation #multi: Active Learning for Multi-Task Adaptive Filtering (AH, YY), pp. 431–438.
ICML-2010-HonorioS #modelling #multi #visual notation: Multi-Task Learning of Gaussian Graphical Models (JH, DS), pp. 447–454.
ICML-2010-HuangG #independence #ranking: Learning Hierarchical Riffle Independent Groupings from Rankings (JH, CG), pp. 455–462.
ICML-2010-HueV #kernel #on the: On learning with kernels for unordered pairs (MH, JPV), pp. 463–470.
ICML-2010-JenattonMOB #taxonomy: Proximal Methods for Sparse Hierarchical Dictionary Learning (RJ, JM, GO, FRB), pp. 487–494.
ICML-2010-KimT10a #multi #process: Gaussian Processes Multiple Instance Learning (MK, FDlT), pp. 535–542.
ICML-2010-KokD #logic #markov #network #using: Learning Markov Logic Networks Using Structural Motifs (SK, PMD), pp. 551–558.
ICML-2010-KulisB #online: Implicit Online Learning (BK, PLB), pp. 575–582.
ICML-2010-LazaricG #multi: Bayesian Multi-Task Reinforcement Learning (AL, MG), pp. 599–606.
ICML-2010-LiangJK #approach #source code: Learning Programs: A Hierarchical Bayesian Approach (PL, MIJ, DK), pp. 639–646.
ICML-2010-LiangS #interactive #multi #on the: On the Interaction between Norm and Dimensionality: Multiple Regimes in Learning (PL, NS), pp. 647–654.
ICML-2010-LiPSG #parametricity: Budgeted Distribution Learning of Belief Net Parameters (LL, BP, CS, RG), pp. 879–886.
ICML-2010-LiuHC #graph #scalability: Large Graph Construction for Scalable Semi-Supervised Learning (WL, JH, SFC), pp. 679–686.
ICML-2010-LiuNLL #analysis #graph #relational: Learning Temporal Causal Graphs for Relational Time-Series Analysis (YL, ANM, ACL, YL), pp. 687–694.
ICML-2010-LizotteBM #analysis #multi #performance #random: Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis (DJL, MHB, SAM), pp. 695–702.
ICML-2010-MaeiSBS #approximate #towards: Toward Off-Policy Learning Control with Function Approximation (HRM, CS, SB, RSS), pp. 719–726.
ICML-2010-Mahmud: Constructing States for Reinforcement Learning (MMHM), pp. 727–734.
ICML-2010-Martens #optimisation: Deep learning via Hessian-free optimization (JM), pp. 735–742.
ICML-2010-Martens10a #linear: Learning the Linear Dynamical System with ASOS (JM), pp. 743–750.
ICML-2010-McFeeL #metric #rank: Metric Learning to Rank (BM, GRGL), pp. 775–782.
ICML-2010-MeshiSJG #approximate: Learning Efficiently with Approximate Inference via Dual Losses (OM, DS, TSJ, AG), pp. 783–790.
ICML-2010-MorimuraSKHT #approximate #parametricity: Nonparametric Return Distribution Approximation for Reinforcement Learning (TM, MS, HK, HH, TT), pp. 799–806.
ICML-2010-OntanonP #approach #induction #multi: Multiagent Inductive Learning: an Argumentation-based Approach (SO, EP), pp. 839–846.
ICML-2010-Salakhutdinov #adaptation #using: Learning Deep Boltzmann Machines using Adaptive MCMC (RS), pp. 943–950.
ICML-2010-SlivkinsRG #documentation #ranking #scalability: Learning optimally diverse rankings over large document collections (AS, FR, SG), pp. 983–990.
ICML-2010-SnyderB #multi: Climbing the Tower of Babel: Unsupervised Multilingual Learning (BS, RB), pp. 29–36.
ICML-2010-SzitaS #bound #complexity #modelling: Model-based reinforcement learning with nearly tight exploration complexity bounds (IS, CS), pp. 1031–1038.
ICML-2010-TanWT #dataset #feature model: Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets (MT, LW, IWT), pp. 1047–1054.
ICML-2010-TomiokaSSK #algorithm #matrix #performance #rank: A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices (RT, TS, MS, HK), pp. 1087–1094.
ICML-2010-WalshSLD: Generalizing Apprenticeship Learning across Hypothesis Classes (TJW, KS, MLL, CD), pp. 1119–1126.
ICML-2010-WangKC: Sequential Projection Learning for Hashing with Compact Codes (JW, SK, SFC), pp. 1127–1134.
ICML-2010-XuJYKL #kernel #multi #performance: Simple and Efficient Multiple Kernel Learning by Group Lasso (ZX, RJ, HY, IK, MRL), pp. 1175–1182.
ICML-2010-YangJJ: Learning from Noisy Side Information by Generalized Maximum Entropy Model (TY, RJ, AKJ), pp. 1199–1206.
ICML-2010-YangXKL #online: Online Learning for Group Lasso (HY, ZX, IK, MRL), pp. 1191–1198.
ICML-2010-ZhaoH #framework #named #online: OTL: A Framework of Online Transfer Learning (PZ, SCHH), pp. 1231–1238.
ICML-2010-ZhuGJRHK #modelling: Cognitive Models of Test-Item Effects in Human Category Learning (XZ, BRG, KSJ, TTR, JH, CK), pp. 1247–1254.
ICPR-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.
ICPR-2010-AmateR #modelling #probability: Learning Probabilistic Models of Contours (LA, MJR), pp. 645–648.
ICPR-2010-AroraS #algorithm #performance: An Efficient and Stable Algorithm for Learning Rotations (RA, WAS), pp. 2993–2996.
ICPR-2010-AtmosukartoSH #3d #programming #search-based #using: The Use of Genetic Programming for Learning 3D Craniofacial Shape Quantifications (IA, LGS, CH), pp. 2444–2447.
ICPR-2010-BaghshahS #constraints #kernel #performance: Efficient Kernel Learning from Constraints and Unlabeled Data (MSB, SBS), pp. 3364–3367.
ICPR-2010-BalujaC #performance #retrieval: Beyond “Near Duplicates”: Learning Hash Codes for Efficient Similar-Image Retrieval (SB, MC), pp. 543–547.
ICPR-2010-BanderaMM #incremental #mobile #visual notation: Incremental Learning of Visual Landmarks for Mobile Robotics (AB, RM, RVM), pp. 4255–4258.
ICPR-2010-BlondelSU #online #recognition: Unsupervised Learning of Stroke Tagger for Online Kanji Handwriting Recognition (MB, KS, KU), pp. 1973–1976.
ICPR-2010-BoltonG #framework #multi #optimisation #random #set: Cross Entropy Optimization of the Random Set Framework for Multiple Instance Learning (JB, PDG), pp. 3907–3910.
ICPR-2010-BuyssensR #verification: Learning Sparse Face Features: Application to Face Verification (PB, MR), pp. 670–673.
ICPR-2010-CarneiroN #architecture: The Fusion of Deep Learning Architectures and Particle Filtering Applied to Lip Tracking (GC, JCN), pp. 2065–2068.
ICPR-2010-Cevikalp #distance #metric #polynomial #programming: Semi-supervised Distance Metric Learning by Quadratic Programming (HC), pp. 3352–3355.
ICPR-2010-ChenF #graph: Semi-supervised Graph Learning: Near Strangers or Distant Relatives (WC, GF), pp. 3368–3371.
ICPR-2010-CohenP #performance #robust: Reinforcement Learning for Robust and Efficient Real-World Tracking (AC, VP), pp. 2989–2992.
ICPR-2010-DagAKS #categorisation: Learning Affordances for Categorizing Objects and Their Properties (ND, IA, SK, ES), pp. 3089–3092.
ICPR-2010-DitzlerPC #algorithm #incremental: An Incremental Learning Algorithm for Non-stationary Environments and Class Imbalance (GD, RP, NVC), pp. 2997–3000.
ICPR-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.
ICPR-2010-ErdoganS #classification #framework #linear: A Unifying Framework for Learning the Linear Combiners for Classifier Ensembles (HE, MUS), pp. 2985–2988.
ICPR-2010-FanHM #classification #metric: Learning Metrics for Shape Classification and Discrimination (YF, DH, WM), pp. 2652–2655.
ICPR-2010-FausserS #approximate: Learning a Strategy with Neural Approximated Temporal-Difference Methods in English Draughts (SF, FS), pp. 2925–2928.
ICPR-2010-FengZH #detection #online #self: Online Learning with Self-Organizing Maps for Anomaly Detection in Crowd Scenes (JF, CZ, PH), pp. 3599–3602.
ICPR-2010-FuLTZ #classification #music #naive bayes #retrieval: Learning Naive Bayes Classifiers for Music Classification and Retrieval (ZF, GL, KMT, DZ), pp. 4589–4592.
ICPR-2010-GuoBC #approach #using: Support Vectors Selection for Supervised Learning Using an Ensemble Approach (LG, SB, NC), pp. 37–40.
ICPR-2010-GuoZCZG #documentation: Unsupervised Learning from Linked Documents (ZG, SZ, YC, ZZ, YG), pp. 730–733.
ICPR-2010-HanCR10a #concept #interactive #recognition #semantics: Semi-supervised and Interactive Semantic Concept Learning for Scene Recognition (XHH, YWC, XR), pp. 3045–3048.
ICPR-2010-HanFD #prototype #recognition #set: Discriminative Prototype Learning in Open Set Face Recognition (ZH, CF, XD), pp. 2696–2699.
ICPR-2010-HuangY #recognition: Learning Virtual HD Model for Bi-model Emotional Speaker Recognition (TH, YY), pp. 1614–1617.
ICPR-2010-HurWL #estimation #invariant: View Invariant Body Pose Estimation Based on Biased Manifold Learning (DH, CW, SWL), pp. 3866–3869.
ICPR-2010-JhuoL #kernel #multi #recognition: Boosted Multiple Kernel Learning for Scene Category Recognition (IHJ, DTL), pp. 3504–3507.
ICPR-2010-JiaCLW #image #performance: Efficient Learning to Label Images (KJ, LC, NL, LW), pp. 942–945.
ICPR-2010-JokoKY #linear #modelling: Learning Non-linear Dynamical Systems by Alignment of Local Linear Models (MJ, YK, TY), pp. 1084–1087.
ICPR-2010-JoshiP #adaptation #detection #incremental: Scene-Adaptive Human Detection with Incremental Active Learning (AJJ, FP), pp. 2760–2763.
ICPR-2010-KamarainenI #canonical #detection: Learning and Detection of Object Landmarks in Canonical Object Space (JKK, JI), pp. 1409–1412.
ICPR-2010-KappSM #adaptation #incremental: Adaptive Incremental Learning with an Ensemble of Support Vector Machines (MNK, RS, PM), pp. 4048–4051.
ICPR-2010-KimuraKSNMSI #canonical #correlation #named #performance: SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations (AK, HK, MS, TN, EM, HS, KI), pp. 2933–2936.
ICPR-2010-LiLD #using: Learning GMM Using Elliptically Contoured Distributions (BL, WL, LD), pp. 511–514.
ICPR-2010-LiuA #semantics #using: Learning Scene Semantics Using Fiedler Embedding (JL, SA), pp. 3627–3630.
ICPR-2010-LiuLH #multi #representation #using: Semi-supervised Trajectory Learning Using a Multi-Scale Key Point Based Trajectory Representation (YL, XL, WH), pp. 3525–3528.
ICPR-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.
ICPR-2010-NiSRM #multi #online: Particle Filter Tracking with Online Multiple Instance Learning (ZN, SS, AR, BSM), pp. 2616–2619.
ICPR-2010-OhH #process #using #video: Unsupervised Learning of Activities in Video Using Scene Context (SO, AH), pp. 3579–3582.
ICPR-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.
ICPR-2010-PhilippotBB #algorithm #classification #network #online: Bayesian Networks Learning Algorithms for Online Form Classification (EP, YB, AB), pp. 1981–1984.
ICPR-2010-PuS #probability #verification: Probabilistic Measure for Signature Verification Based on Bayesian Learning (DP, SNS), pp. 1188–1191.
ICPR-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.
ICPR-2010-RicciTZ #kernel: Learning Pedestrian Trajectories with Kernels (ER, FT, GZ), pp. 149–152.
ICPR-2010-SangWW #modelling #top-down #visual notation: A Biologically-Inspired Top-Down Learning Model Based on Visual Attention (NS, LW, YW), pp. 3736–3739.
ICPR-2010-Sarkar #classification #documentation #image: Learning Image Anchor Templates for Document Classification and Data Extraction (PS), pp. 3428–3431.
ICPR-2010-Sato #classification #design #kernel: A New Learning Formulation for Kernel Classifier Design (AS), pp. 2897–2900.
ICPR-2010-ShenYS: Learning Discriminative Features Based on Distribution (JS, WY, CS), pp. 1401–1404.
ICPR-2010-SodaI #composition #dataset #integration: Decomposition Methods and Learning Approaches for Imbalanced Dataset: An Experimental Integration (PS, GI), pp. 3117–3120.
ICPR-2010-SternigRB #classification #multi: Inverse Multiple Instance Learning for Classifier Grids (SS, PMR, HB), pp. 770–773.
ICPR-2010-SuLT10a #documentation #framework #self: A Self-Training Learning Document Binarization Framework (BS, SL, CLT), pp. 3187–3190.
ICPR-2010-SunSHE #locality #metric: Localized Supervised Metric Learning on Temporal Physiological Data (JS, DMS, JH, SE), pp. 4149–4152.
ICPR-2010-TaxHVP #clustering #concept #detection #multi #using: The Detection of Concept Frames Using Clustering Multi-instance Learning (DMJT, EH, MFV, MP), pp. 2917–2920.
ICPR-2010-TorkiEL #multi #representation #set: Learning a Joint Manifold Representation from Multiple Data Sets (MT, AME, CSL), pp. 1068–1071.
ICPR-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.
ICPR-2010-WangJHT #higher-order #kernel #multi: Multiple Kernel Learning with High Order Kernels (SW, SJ, QH, QT), pp. 2138–2141.
ICPR-2010-WangM #order #process #using: Gaussian Process Learning from Order Relationships Using Expectation Propagation (RW, SJM), pp. 605–608.
ICPR-2010-WidhalmB: Learning Major Pedestrian Flows in Crowded Scenes (PW, NB), pp. 4064–4067.
ICPR-2010-WuLW #image #retrieval #using: Enhancing SVM Active Learning for Image Retrieval Using Semi-supervised Bias-Ensemble (JW, ML, CLW), pp. 3175–3178.
ICPR-2010-XingAL #detection #multi: Multiple Human Tracking Based on Multi-view Upper-Body Detection and Discriminative Learning (JX, HA, SL), pp. 1698–1701.
ICPR-2010-YaegashiY #kernel #multi #recognition #using: Geotagged Photo Recognition Using Corresponding Aerial Photos with Multiple Kernel Learning (KY, KY), pp. 3272–3275.
ICPR-2010-ZhangLD #approach #kernel #multi #named #novel: AdaMKL: A Novel Biconvex Multiple Kernel Learning Approach (ZZ, ZNL, MSD), pp. 2126–2129.
ICPR-2010-ZhangWL #categorisation #kernel: Learning the Kernel Combination for Object Categorization (DZ, XW, BL), pp. 2929–2932.
ICPR-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.
ICPR-2010-ZhouLLT #canonical #image #visual notation: Canonical Image Selection by Visual Context Learning (WZ, YL, HL, QT), pp. 834–837.
ICPR-2010-ZhuHYL #behaviour #metric #prototype #recognition #using: Prototype Learning Using Metric Learning Based Behavior Recognition (PZ, WH, CY, LL), pp. 2604–2607.
ICPR-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.
KDD-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.
KDD-2010-AgarwalCE #online #performance #recommendation: Fast online learning through offline initialization for time-sensitive recommendation (DA, BCC, PE), pp. 703–712.
KDD-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.
KDD-2010-BozorgiSSV #heuristic #predict: Beyond heuristics: learning to classify vulnerabilities and predict exploits (MB, LKS, SS, GMV), pp. 105–114.
KDD-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.
KDD-2010-ChenLY #multi #rank: Learning incoherent sparse and low-rank patterns from multiple tasks (JC, JL, JY), pp. 1179–1188.
KDD-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.
KDD-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.
KDD-2010-HoTL #metric #reduction #sequence #similarity: Tropical cyclone event sequence similarity search via dimensionality reduction and metric learning (SSH, WT, WTL), pp. 135–144.
KDD-2010-HuhF #modelling #topic: Discriminative topic modeling based on manifold learning (SH, SEF), pp. 653–662.
KDD-2010-Lee #classification: Learning to combine discriminative classifiers: confidence based (CHL), pp. 743–752.
KDD-2010-LiuMTLL #metric #optimisation #using: Semi-supervised sparse metric learning using alternating linearization optimization (WL, SM, DT, JL, PL), pp. 1139–1148.
KDD-2010-LiuZ: Learning with cost intervals (XYL, ZHZ), pp. 403–412.
KDD-2010-SomaiyaJR #modelling: Mixture models for learning low-dimensional roles in high-dimensional data (MS, CMJ, SR), pp. 909–918.
KDD-2010-WallaceSBT: Active learning for biomedical citation screening (BCW, KS, CEB, TAT), pp. 173–182.
KDD-2010-ZhangY #metric: Transfer metric learning by learning task relationships (YZ, DYY), pp. 1199–1208.
KDD-2010-ZhangZ #dependence #multi: Multi-label learning by exploiting label dependency (MLZ, KZ), pp. 999–1008.
KDD-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.
KDIR-2010-Cebron #representation #towards: Towards Learning with Objects in a Hierarchical Representation (NC), pp. 326–329.
KDIR-2010-LourencoF #clustering #multi: Selectively Learning Clusters in Multi-EAC (AL, ALNF), pp. 491–499.
KDIR-2010-ParviainenRML #approximate #infinity #network: Interpreting Extreme Learning Machine as an Approximation to an Infinite Neural Network (EP, JR, YM, AL), pp. 65–73.
KEOD-2010-ArdilaAL #kernel #multi #ontology: Multiple Kernel Learning for Ontology Instance Matching (DA, JA, FL), pp. 311–318.
KEOD-2010-Braham #assessment #metric: A Knowledge Metric with Applications to Learning Assessment (RB), pp. 5–9.
KEOD-2010-GilCM #case study #evaluation #ontology: A Systemic Methodology for Ontology Learning — An Academic Case Study and Evaluation (RG, LC, MJMB), pp. 206–212.
KEOD-2010-Girardi #ontology: Guiding Ontology Learning and Population by Knowledge System Goals (RG), pp. 480–484.
KMIS-2010-JuvonenO: Studying IT Team Entrepreneurship as a Learning Organization (PJ, PO), pp. 332–337.
RecSys-2010-LipczakM #performance #recommendation: Learning in efficient tag recommendation (ML, EEM), pp. 167–174.
RecSys-2010-MelloAZ #impact analysis #rating: Active learning driven by rating impact analysis (CERdM, MAA, GZ), pp. 341–344.
RecSys-2010-ShiLH #collaboration #matrix #rank: List-wise learning to rank with matrix factorization for collaborative filtering (YS, ML, AH), pp. 269–272.
SEKE-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.
SEKE-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.
SIGIR-2010-BalasubramanianA: Learning to select rankers (NB, JA), pp. 855–856.
SIGIR-2010-DangBC #query #rank: Learning to rank query reformulations (VD, MB, WBC), pp. 807–808.
SIGIR-2010-DaveV: Learning the click-through rate for rare/new ads from similar ads (KSD, VV), pp. 897–898.
SIGIR-2010-GaoCWZ #rank #using: Learning to rank only using training data from related domain (WG, PC, KFW, AZ), pp. 162–169.
SIGIR-2010-HajishirziYK #adaptation #detection #similarity: Adaptive near-duplicate detection via similarity learning (HH, WtY, AK), pp. 419–426.
SIGIR-2010-Liu #information retrieval #rank: Learning to rank for information retrieval (TYL), p. 904.
SIGIR-2010-LiuW #email #multi: Multi-field learning for email spam filtering (WL, TW), pp. 745–746.
SIGIR-2010-LiuYSCCL #behaviour #rank: Learning to rank audience for behavioral targeting (NL, JY, DS, DC, ZC, YL), pp. 719–720.
SIGIR-2010-LongCZCZT #optimisation #ranking: Active learning for ranking through expected loss optimization (BL, OC, YZ, YC, ZZ, BLT), pp. 267–274.
SIGIR-2010-MojdehC #consistency #using: Semi-supervised spam filtering using aggressive consistency learning (MM, GVC), pp. 751–752.
SIGIR-2010-Wang #modelling #retrieval: Learning hidden variable models for blog retrieval (MW), p. 922.
SIGIR-2010-WangLM #rank: Learning to efficiently rank (LW, JJL, DM), pp. 138–145.
SIGIR-2010-WangWVL #clustering #documentation #metric: Text document clustering with metric learning (JW, SW, HQV, GL), pp. 783–784.
SIGIR-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.
SIGIR-2010-YueGCZJ #evaluation #retrieval #statistics: Learning more powerful test statistics for click-based retrieval evaluation (YY, YG, OC, YZ, TJ), pp. 507–514.
SAC-2010-AppiceCM: Transductive learning for spatial regression with co-training (AA, MC, DM), pp. 1065–1070.
SAC-2010-AyyappanWN #algorithm #constraints #named #network #scalability: MICHO: a scalable constraint-based algorithm for learning Bayesian networks (MA, YKW, WKN), pp. 985–989.
SAC-2010-CostaFGMO #mining #modelling: Mining models of exceptional objects through rule learning (GC, FF, MG, GM, RO), pp. 1078–1082.
CASE-2010-DoroodgarN #architecture: A hierarchical reinforcement learning based control architecture for semi-autonomous rescue robots in cluttered environments (BD, GN), pp. 948–953.
CASE-2010-LiYG: Learning compliance control of robot manipulators in contact with the unknown environment (YL, CY, SSG), pp. 644–649.
DAC-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.
DAC-2010-LaiJW #abstraction #named: BooM: a decision procedure for boolean matching with abstraction and dynamic learning (CFL, JHRJ, KHW), pp. 499–504.
STOC-2010-KalaiMV: Efficiently learning mixtures of two Gaussians (ATK, AM, GV), pp. 553–562.
CAV-2010-BolligKKLNP #automaton #framework #named: libalf: The Automata Learning Framework (BB, JPK, CK, ML, DN, DRP), pp. 360–364.
CAV-2010-ChenCFTTW #automation #reasoning: Automated Assume-Guarantee Reasoning through Implicit Learning (YFC, EMC, AF, MHT, YKT, BYW), pp. 511–526.
CAV-2010-SinghGP #abstraction #component #interface: Learning Component Interfaces with May and Must Abstractions (RS, DG, CSP), pp. 527–542.
ICLP-2010-Balduccini10 #heuristic #set: Learning Domain-Specific Heuristics for Answer Set Solvers (MB), pp. 14–23.
ICLP-2010-Pahlavi10 #higher-order #logic: Higher-order Logic Learning and λ-Progol (NP), pp. 281–285.
ICLP-J-2010-SneyersMVKS #logic #probability: CHR(PRISM)-based probabilistic logic learning (JS, WM, JV, YK, TS), pp. 433–447.
ISSTA-2010-GruskaWZ #detection #lightweight: Learning from 6, 000 projects: lightweight cross-project anomaly detection (NG, AW, AZ), pp. 119–130.
SAT-2010-Ben-SassonJ #bound #strict: Lower Bounds for Width-Restricted Clause Learning on Small Width Formulas (EBS, JJ), pp. 16–29.
SAT-2010-KlieberSGC: A Non-prenex, Non-clausal QBF Solver with Game-State Learning (WK, SS, SG, EMC), pp. 128–142.
VMCAI-2010-JungKWY #abstraction #algorithm #invariant: Deriving Invariants by Algorithmic Learning, Decision Procedures, and Predicate Abstraction (YJ, SK, BYW, KY), pp. 180–196.
DRR-2009-ZhangZLT: A semi-supervised learning method to classify grant-support zone in web-based medical articles (XZ, JZ, DXL, GRT), pp. 1–10.
ECDL-2009-Orde #library: Digital Libraries — New Landscapes for Lifelong Learning? The “InfoLitGlobal”-Project (HvO), pp. 477–478.
ECDL-2009-SuttonG #concept #education: Conceptual Discovery of Educational Resources through Learning Objectives (SAS, DG), pp. 380–383.
ECDL-2009-TakhirovSA #personalisation: Organizing Learning Objects for Personalized eLearning Services (NT, IS, TA), pp. 384–387.
HT-2009-AlAghaB #approach #hypermedia #towards: Towards a constructivist approach to learning from hypertext (IA, LB), pp. 51–56.
ICDAR-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.
ICDAR-2009-AlmaksourA #incremental #online #performance #recognition: Fast Incremental Learning Strategy Driven by Confusion Reject for Online Handwriting Recognition (AA, ÉA), pp. 81–85.
ICDAR-2009-BallS #recognition: Semi-supervised Learning for Handwriting Recognition (GRB, SNS), pp. 26–30.
ICDAR-2009-FrinkenB #network #recognition #word: Evaluating Retraining Rules for Semi-Supervised Learning in Neural Network Based Cursive Word Recognition (VF, HB), pp. 31–35.
ICDAR-2009-KaeL #on the fly #problem: Learning on the Fly: Font-Free Approaches to Difficult OCR Problems (AK, EGLM), pp. 571–575.
ICDAR-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.
ICDAR-2009-Silva #analysis #documentation #markov #modelling: Learning Rich Hidden Markov Models in Document Analysis: Table Location (ACeS), pp. 843–847.
ICDAR-2009-StefanoFFM #classification #evolution #network: Learning Bayesian Networks by Evolution for Classifier Combination (CDS, FF, ASdF, AM), pp. 966–970.
ICDAR-2009-TewariN #adaptation: Learning and Adaptation for Improving Handwritten Character Recognizers (NCT, AMN), pp. 86–90.
ICDAR-2009-WangLJ #modelling #segmentation #statistics #string: Statistical Modeling and Learning for Recognition-Based Handwritten Numeral String Segmentation (YW, XL, YJ), pp. 421–425.
ICDAR-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.
JCDL-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.
SIGMOD-2009-BabuGM #nondeterminism #scalability: Large-scale uncertainty management systems: learning and exploiting your data (SB, SG, KM), pp. 995–998.
VLDB-2009-ArasuCK #string: Learning String Transformations From Examples (AA, SC, RK), pp. 514–525.
VLDB-2009-PandaHBB #named #parallel #pipes and filters: PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce (BP, JH, SB, RJB), pp. 1426–1437.
CSEET-2009-Armarego #student: Displacing the Sage on the Stage: Student Control of Learning (JA), pp. 198–201.
CSEET-2009-ChaoR #agile #student: Agile Software Factory for Student Service Learning (JC, MR), pp. 34–40.
CSEET-2009-Goel #education #re-engineering: Enriching the Culture of Software Engineering Education through Theories of Knowledge and Learning (SG), p. 279.
CSEET-2009-RichardsonD #problem #re-engineering: Problem Based Learning in the Software Engineering Classroom (IR, YD), pp. 174–181.
CSEET-2009-Rosso-Llopart #education #re-engineering: An Examination of Learning Technologies That Support Software Engineering and Education (MRL), pp. 294–295.
EDM-2009-AbbasS #using: an Argument Learning Environment Using Agent-Based ITS (ALES) (SA, HS), pp. 200–209.
EDM-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.
EDM-2009-GongRBH #question #self #student: Does Self-Discipline impact students’ knowledge and learning? (YG, DR, JB, NTH), pp. 61–70.
EDM-2009-HershkovitzN #consistency #online #student: Consistency of Students’ Pace in Online Learning (AH, RN), pp. 71–80.
EDM-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.
EDM-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.
EDM-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.
EDM-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.
ITiCSE-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.
ITiCSE-2009-AndersonL #collaboration #community #student: Exploring technologies for building collaborative learning communities among diverse student populations (NA, CCL), pp. 243–247.
ITiCSE-2009-BuendiaCB #approach: An instructional approach to drive computer science courses through virtual learning environments (FB, JCC, JVB), pp. 6–10.
ITiCSE-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.
ITiCSE-2009-Draganova #mobile #using: Use of mobile phone technologies in learning (CD), p. 399.
ITiCSE-2009-Ginat #composition: Interleaved pattern composition and scaffolded learning (DG), pp. 109–113.
ITiCSE-2009-Hwang09a #education #operating system: Blended learning for teaching operating systems with Windows (SwH), p. 380.
ITiCSE-2009-Lasserre #adaptation #programming: Adaptation of team-based learning on a first term programming class (PL), pp. 186–190.
ITiCSE-2009-Martin: Cooperative learning to support the lacks of PBL (JGM), p. 343.
ITiCSE-2009-MhiriR #development #named: AARTIC: development of an intelligent environment for human learning (FM, SR), p. 359.
ITiCSE-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.
ITiCSE-2009-Palmer-BrownDL #feedback: Guided learning via diagnostic feedback to question responses (DPB, CD, SWL), p. 362.
ITiCSE-2009-Pantaleev #named #visual notation: Dzver: a visual computer science learning environment (AP), p. 387.
ITiCSE-2009-Radenski: Freedom of choice as motivational factor for active learning (AR), pp. 21–25.
ITiCSE-2009-Sondergaard #student: Learning from and with peers: the different roles of student peer reviewing (HS), pp. 31–35.
ITiCSE-2009-TsengHH #collaboration #education #framework #platform #ubiquitous: A collaborative ubiquitous learning platform for computer science education (JCRT, SYYH, GJH), p. 368.
ITiCSE-2009-Velazquez-IturbideP #algorithm #interactive: Active learning of greedy algorithms by means of interactive experimentation (JÁVI, APC), pp. 119–123.
ITiCSE-2009-VillalobosCJ #interactive #programming #using: Developing programming skills by using interactive learning objects (JV, NAC, CJ), pp. 151–155.
ITiCSE-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.
ITiCSE-2009-WhiteI #case study #education #experience #research: Relating research and teaching: learning from experiences and beliefs (SW, AI), pp. 75–79.
ITiCSE-2009-WiesnerB #concept #how #question: How do robots foster the learning of basic concepts in informatics? (BW, TB), p. 403.
ITiCSE-2009-ZanderTSMMHF: Learning styles: novices decide (CZ, LT, BS, LM, RM, BH, SF), pp. 223–227.
SIGITE-2009-Krichen #evolution #online #question: Evolving online learning: can attention to learning styles make it more personal? (JPK), pp. 8–12.
SIGITE-2009-StanleyC #simulation: Rhythm learning with electronic simulation (TDS, DC), pp. 24–28.
SIGITE-2009-StanleyC09a: Six years of sustainable IT service learning (TDS, DC), pp. 87–90.
MSR-2009-AyewahP #fault: Learning from defect removals (NA, WP), pp. 179–182.
ICALP-v1-2009-KlivansLS: Learning Halfspaces with Malicious Noise (ARK, PML, RAS), pp. 609–621.
LATA-2009-Akama #commutative: Commutative Regular Shuffle Closed Languages, Noetherian Property, and Learning Theory (YA), pp. 93–104.
LATA-2009-Gierasimczuk #logic: Learning by Erasing in Dynamic Epistemic Logic (NG), pp. 362–373.
LATA-2009-Jain: Hypothesis Spaces for Learning (SJ), pp. 43–58.
AIIDE-2009-TanC #adaptation #game studies #named: IMPLANT: An Integrated MDP and POMDP Learning AgeNT for Adaptive Games (CTT, HLC).
AIIDE-2009-ZhaoS #behaviour #game studies #modelling #using: Learning Character Behaviors Using Agent Modeling in Games (RZ, DS).
CIG-2009-BurrowL #difference #evolution #game studies: Evolution versus Temporal Difference Learning for learning to play Ms. Pac-Man (PB, SML), pp. 53–60.
CIG-2009-CardamoneLL #using: Learning drivers for TORCS through imitation using supervised methods (LC, DL, PLL), pp. 148–155.
CIG-2009-GalliLL #policy: Learning a context-aware weapon selection policy for Unreal Tournament III (LG, DL, PLL), pp. 310–316.
CIG-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.
CIG-2009-HoornTS: Hierarchical controller learning in a First-Person Shooter (NvH, JT, JS), pp. 294–301.
CIG-2009-Lucas09b #difference: Temporal difference learning with interpolated table value functions (SML), pp. 32–37.
CIG-2009-SzubertJK #difference: Coevolutionary Temporal Difference Learning for Othello (MGS, WJ, KK), pp. 104–111.
DiGRA-2009-Bonanno #collaboration #game studies: A Process-oriented pedagogy for collaborative game-based learning [Abstract] (PB).
DiGRA-2009-Duncan #design #game studies: Bridging Gaming and Designing: Two Sites of Informal Design Learning [Abstract] (SCD).
DiGRA-2009-Hung #education #game studies #order #video: The Order of Play: Seeing, Teaching, and Learning Meaning in Video Games (ACYH).
DiGRA-2009-MerkelSH #adaptation #game studies: Complexities of Gaming Cultures: Adolescent gamers adapting and transforming learning [Abstracts] (LM, KS, TH).
DiGRA-2009-Pearce #case study #collaboration #community #game studies: Collaboration, Creativity and Learning in a Play Community: A Study of The University of There (CP).
DiGRA-2009-PereiraR #design #game studies #guidelines: Design Guidelines for Learning Games: the Living Forest Game Design Case (LLP, LGR).
DiGRA-2009-RyanS #comprehension #game studies #interactive #using #video: Evaluating Interactive Entertainment using Breakdown: Understanding Embodied Learning in Video Games (WR, MAS).
FDG-2009-GibsonG #game studies #online #student: Online recruitment and engagement of students in game and simulation-based STEM learning (DCG, SG), pp. 285–290.
FDG-2009-HoldsworthL #case study: GPS-enabled mobiles for learning shortest paths: a pilot study (JJH, SML), pp. 86–90.
FDG-2009-McGill09a #education #effectiveness #game studies: Evaluating the effectiveness of hypothesis-based digital learning games in high school science curriculum (MM), pp. 344–345.
FDG-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.
VS-Games-2009-BloomfieldL #assessment #multi: Multi-Modal Learning and Assessment in Second Life with quizHUD (PRB, DL), pp. 217–218.
VS-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.
VS-Games-2009-JarvisF #evaluation: Evaluation of an Immersive Learning Programme to Support Triage Training (SJ, SdF), pp. 117–122.
CHI-2009-BrandtGLDK #programming #web: Two studies of opportunistic programming: interleaving web foraging, learning, and writing code (JB, PJG, JL, MD, SRK), pp. 1589–1598.
CHI-2009-HaradaWMBL #people: Longitudinal study of people learning to use continuous voice-based cursor control (SH, JOW, JM, JAB, JAL), pp. 347–356.
CHI-2009-KammererNPC #social: Signpost from the masses: learning effects in an exploratory social tag search browser (YK, RN, PP, EHhC), pp. 625–634.
CHI-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.
CHI-2009-RosnerB: Learning from IKEA hacking: I’m not one to decoupage a tabletop and call it a day (DR, JB), pp. 419–422.
CHI-2009-Thom-SantelliM: Learning by seeing: photo viewing in the workplace (JTS, DRM), pp. 2081–2090.
CHI-2009-TorreyCM #how #internet: Learning how: the search for craft knowledge on the internet (CT, EFC, DWM), pp. 1371–1380.
DHM-2009-FallonCP #assessment #risk management: Learning from Risk Assessment in Radiotherapy (EFF, LC, WJvdP), pp. 502–511.
DHM-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.
HCD-2009-FerranGMM #design #repository: User Centered Design of a Learning Object Repository (NF, AEGR, EM, JM), pp. 679–688.
HCI-AUII-2009-McMullenW #assessment #design: Relationship Learning Software: Design and Assessment (KAM, GHW), pp. 631–640.
HCI-AUII-2009-ZarraonandiaVDA #protocol: A Virtual Environment for Learning Aiport Emergency Management Protocols (TZ, MRRV, PD, IA), pp. 228–235.
HCI-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.
HCI-VAD-2009-ChalfounF #3d: Optimal Affective Conditions for Subconscious Learning in a 3D Intelligent Tutoring System (PC, CF), pp. 39–48.
HCI-VAD-2009-ChenGSEJ #detection: Computer-Based Learning to Improve Breast Cancer Detection Skills (YC, AGG, HJS, AE, JJ), pp. 49–57.
HCI-VAD-2009-DogusoyC #comprehension #eye tracking #process: An Innovative Way of Understanding Learning Processes: Eye Tracking (BD, KÇ), pp. 94–100.
HCI-VAD-2009-FicarraCV #evaluation: Communicability for Virtual Learning: Evaluation (FVCF, MCF, PMV), pp. 68–77.
HCI-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.
HCI-VAD-2009-Lane: Promoting Metacognition in Immersive Cultural Learning Environments (HCL), pp. 129–139.
HCI-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.
HCI-VAD-2009-MazzolaM #adaptation #student: Supporting Learners in Adaptive Learning Environments through the Enhancement of the Student Model (LM, RM), pp. 166–175.
HCI-VAD-2009-SaC #development #mobile #personalisation #tool support: Supporting End-User Development of Personalized Mobile Learning Tools (MdS, LC), pp. 217–225.
HCI-VAD-2009-SuLHC #mobile: Developing a Usable Mobile Flight Case Learning System in Air Traffic Control Miscommunications (KWS, KYL, PHH, ITC), pp. 770–777.
HCI-VAD-2009-TesorieroFGLP #interactive: Interactive Learning Panels (RT, HF, JAG, MDL, VMRP), pp. 236–245.
HCI-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.
HIMI-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.
HIMI-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.
HIMI-II-2009-MarusterFH #design #personalisation: Personalization for Specific Users: Designing Decision Support Systems to Support Stimulating Learning Environments (LM, NRF, RJFvH), pp. 660–668.
HIMI-II-2009-NakamuraS: Construction of Systematic Learning Support System of Business Theory and Method (YN, KS), pp. 669–678.
HIMI-II-2009-NishinoH #embedded #named #visualisation: Minato: Integrated Visualization Environment for Embedded Systems Learning (YN, EH), pp. 325–333.
HIMI-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.
HIMI-II-2009-ReichlH #education: Promoting a Central Learning Management System by Encouraging Its Use for Other Purposes Than Teaching (FR, AH), pp. 689–698.
HIMI-II-2009-Terawaki #framework: Framework for Supporting Decision Making in Learning Management System Selection (YT), pp. 699–707.
HIMI-II-2009-Wang09c #adaptation #design #development: The Design and Development of an Adaptive Web-Based Learning System (CW), pp. 716–725.
IDGD-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.
OCSC-2009-BramanVDJ: Learning Computer Science Fundamentals through Virtual Environments (JB, GV, AMAD, AJ), pp. 423–431.
OCSC-2009-ConlonP #distance #video: A Discussion of Video Capturing to Assist in Distance Learning (MC, VP), pp. 432–441.
OCSC-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.
OCSC-2009-Pozzi #community #online #social: Evaluating the Social Dimension in Online Learning Communities (FP), pp. 498–506.
OCSC-2009-PuseyM #education #heuristic #implementation #wiki: Heuristics for Implementation of Wiki Technology in Higher Education Learning (PP, GM), pp. 507–514.
ICEIS-AIDSS-2009-BombiniMBFE #framework #logic programming: A Logic Programming Framework for Learning by Imitation (GB, NDM, TMAB, SF, FE), pp. 218–223.
ICEIS-AIDSS-2009-YangLSKCGP #graph: Graph Structure Learning for Task Ordering (YY, AL, HS, BK, CMC, RG, KP), pp. 164–169.
ICEIS-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.
ICEIS-J-2009-LealQ #named #repository: CrimsonHex: A Service Oriented Repository of Specialised Learning Objects (JPL, RQ), pp. 102–113.
ICEIS-SAIC-2009-CastroFSC #programming: Fleshing Out Clues on Group Programming Learning (TC, HF, LS, ANdCJ), pp. 68–73.
CIKM-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.
CIKM-2009-CetintasSY #query: Learning from past queries for resource selection (SC, LS, HY), pp. 1867–1870.
CIKM-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.
CIKM-2009-ChenWL #kernel #novel #rank: Learning to rank with a novel kernel perceptron method (XwC, HW, XL), pp. 505–512.
CIKM-2009-GargS #classification: Active learning in partially supervised classification (PG, SS), pp. 1783–1786.
CIKM-2009-HeLL #graph: Graph-based transfer learning (JH, YL, RDL), pp. 937–946.
CIKM-2009-KuoCW #rank: Learning to rank from Bayesian decision inference (JWK, PJC, HMW), pp. 827–836.
CIKM-2009-MeloW #towards: Towards a universal wordnet by learning from combined evidence (GdM, GW), pp. 513–522.
CIKM-2009-Paranjpe #documentation #feedback: Learning document aboutness from implicit user feedback and document structure (DP), pp. 365–374.
CIKM-2009-PasternackR: Learning better transliterations (JP, DR), pp. 177–186.
CIKM-2009-QiCKKW: Combining labeled and unlabeled data with word-class distribution learning (YQ, RC, PPK, KK, JW), pp. 1737–1740.
CIKM-2009-QuanzH #scalability: Large margin transductive transfer learning (BQ, JH), pp. 1327–1336.
CIKM-2009-SunCSSWL #recommendation: Learning to recommend questions based on user ratings (KS, YC, XS, YIS, XW, CYL), pp. 751–758.
CIKM-2009-SunMG09a #graph #online #rank: Learning to rank graphs for online similar graph search (BS, PM, CLG), pp. 1871–1874.
CIKM-2009-TangL #behaviour #scalability #social: Scalable learning of collective behavior based on sparse social dimensions (LT, HL), pp. 1107–1116.
CIKM-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.
CIKM-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.
CIKM-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.
CIKM-2009-YapB: Experiments on pattern-based relation learning (WY, TB), pp. 1657–1660.
CIKM-2009-ZhangMCM #fuzzy #ontology #semantics #uml #web: Fuzzy semantic web ontology learning from fuzzy UML model (FZ, ZMM, JC, XM), pp. 1007–1016.
CIKM-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.
CIKM-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.
CIKM-2009-ZhuWZ: Label correspondence learning for part-of-speech annotation transformation (MZ, HW, JZ), pp. 1461–1464.
ECIR-2009-DonmezC #optimisation #rank: Active Sampling for Rank Learning via Optimizing the Area under the ROC Curve (PD, JGC), pp. 78–89.
ECIR-2009-EsuliS #classification #multi: Active Learning Strategies for Multi-Label Text Classification (AE, FS), pp. 102–113.
ECIR-2009-GeraniCC #retrieval: Investigating Learning Approaches for Blog Post Opinion Retrieval (SG, MJC, FC), pp. 313–324.
ECIR-2009-LeaseAC #query #rank: Regression Rank: Learning to Meet the Opportunity of Descriptive Queries (ML, JA, WBC), pp. 90–101.
ICML-2009-AdamsG #named #parametricity: Archipelago: nonparametric Bayesian semi-supervised learning (RPA, ZG), pp. 1–8.
ICML-2009-BengioLCW #education: Curriculum learning (YB, JL, RC, JW), pp. 41–48.
ICML-2009-BeygelzimerDL: Importance weighted active learning (AB, SD, JL), pp. 49–56.
ICML-2009-BurlW: Active learning for directed exploration of complex systems (MCB, EW), pp. 89–96.
ICML-2009-CamposZJ #constraints #network #using: Structure learning of Bayesian networks using constraints (CPdC, ZZ, QJ), pp. 113–120.
ICML-2009-ChengHH #ranking: Decision tree and instance-based learning for label ranking (WC, JCH, EH), pp. 161–168.
ICML-2009-ChenGR #kernel: Learning kernels from indefinite similarities (YC, MRG, BR), pp. 145–152.
ICML-2009-ChenTLY #multi: A convex formulation for learning shared structures from multiple tasks (JC, LT, JL, JY), pp. 137–144.
ICML-2009-ChoS #analysis #modelling: Learning dictionaries of stable autoregressive models for audio scene analysis (YC, LKS), pp. 169–176.
ICML-2009-Cortes #kernel #performance #question: Invited talk: Can learning kernels help performance? (CC), p. 1.
ICML-2009-DaiJXYY #framework #named: EigenTransfer: a unified framework for transfer learning (WD, OJ, GRX, QY, YY), pp. 193–200.
ICML-2009-DasguptaL #summary #tutorial: Tutorial summary: Active learning (SD, JL), p. 18.
ICML-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.
ICML-2009-DoLF #online: Proximal regularization for online and batch learning (CBD, QVL, CSF), pp. 257–264.
ICML-2009-FarhangfarGS #image: Learning to segment from a few well-selected training images (AF, RG, CS), pp. 305–312.
ICML-2009-FooDN #algorithm #multi: A majorization-minimization algorithm for (multiple) hyperparameter learning (CSF, CBD, AYN), pp. 321–328.
ICML-2009-Freund #game studies #online: Invited talk: Drifting games, boosting and online learning (YF), p. 2.
ICML-2009-GermainLLM #classification #linear: PAC-Bayesian learning of linear classifiers (PG, AL, FL, MM), pp. 353–360.
ICML-2009-HazanS #algorithm #performance: Efficient learning algorithms for changing environments (EH, CS), pp. 393–400.
ICML-2009-HuangS #linear #sequence: Learning linear dynamical systems without sequence information (TKH, JGS), pp. 425–432.
ICML-2009-HuangZM: Learning with structured sparsity (JH, TZ, DNM), pp. 417–424.
ICML-2009-JebaraWC #graph: Graph construction and b-matching for semi-supervised learning (TJ, JW, SFC), pp. 441–448.
ICML-2009-JetchevT #predict: Trajectory prediction: learning to map situations to robot trajectories (NJ, MT), pp. 449–456.
ICML-2009-KarampatziakisK #predict: Learning prediction suffix trees with Winnow (NK, DK), pp. 489–496.
ICML-2009-KokD #logic #markov #network: Learning Markov logic network structure via hypergraph lifting (SK, PMD), pp. 505–512.
ICML-2009-KolterN09a #difference #feature model: Regularization and feature selection in least-squares temporal difference learning (JZK, AYN), pp. 521–528.
ICML-2009-KotlowskiS #constraints: Rule learning with monotonicity constraints (WK, RS), pp. 537–544.
ICML-2009-KowalskiSR #kernel #multi: Multiple indefinite kernel learning with mixed norm regularization (MK, MS, LR), pp. 545–552.
ICML-2009-KunegisL #graph transformation #predict: Learning spectral graph transformations for link prediction (JK, AL), pp. 561–568.
ICML-2009-LangfordSZ #modelling: Learning nonlinear dynamic models (JL, RS, TZ), pp. 593–600.
ICML-2009-LeeGRN #network #scalability: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations (HL, RBG, RR, AYN), pp. 609–616.
ICML-2009-LiangJK #exponential #metric #product line: Learning from measurements in exponential families (PL, MIJ, DK), pp. 641–648.
ICML-2009-LiKZ #using: Semi-supervised learning using label mean (YFL, JTK, ZHZ), pp. 633–640.
ICML-2009-LiYX #collaboration #generative: Transfer learning for collaborative filtering via a rating-matrix generative model (BL, QY, XX), pp. 617–624.
ICML-2009-LuJD #geometry #metric: Geometry-aware metric learning (ZL, PJ, ISD), pp. 673–680.
ICML-2009-MairalBPS #online #taxonomy: Online dictionary learning for sparse coding (JM, FRB, JP, GS), pp. 689–696.
ICML-2009-MaSSV #identification #online #scalability: Identifying suspicious URLs: an application of large-scale online learning (JM, LKS, SS, GMV), pp. 681–688.
ICML-2009-MobahiCW #video: Deep learning from temporal coherence in video (HM, RC, JW), pp. 737–744.
ICML-2009-NeumannMP: Learning complex motions by sequencing simpler motion templates (GN, WM, JP), pp. 753–760.
ICML-2009-Niv #summary #tutorial: Tutorial summary: The neuroscience of reinforcement learning (YN), p. 16.
ICML-2009-NowozinJ #clustering #graph #linear #programming: Solution stability in linear programming relaxations: graph partitioning and unsupervised learning (SN, SJ), pp. 769–776.
ICML-2009-PazisL #policy: Binary action search for learning continuous-action control policies (JP, MGL), pp. 793–800.
ICML-2009-PoczosASGS #exclamation: Learning when to stop thinking and do something! (BP, YAY, CS, RG, NRS), pp. 825–832.
ICML-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.
ICML-2009-RainaMN #scalability #using: Large-scale deep unsupervised learning using graphics processors (RR, AM, AYN), pp. 873–880.
ICML-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.
ICML-2009-RoyLW #consistency #modelling #probability #visual notation: Learning structurally consistent undirected probabilistic graphical models (SR, TL, MWW), pp. 905–912.
ICML-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.
ICML-2009-SzitaL #polynomial: Optimistic initialization and greediness lead to polynomial time learning in factored MDPs (IS, AL), pp. 1001–1008.
ICML-2009-TaylorP #approximate #kernel: Kernelized value function approximation for reinforcement learning (GT, RP), pp. 1017–1024.
ICML-2009-Tillman #distributed #independence: Structure learning with independent non-identically distributed data (RET), pp. 1041–1048.
ICML-2009-TrespY #dependence #summary #tutorial: Tutorial summary: Learning with dependencies between several response variables (VT, KY), p. 14.
ICML-2009-VarmaB #kernel #multi #performance: More generality in efficient multiple kernel learning (MV, BRB), pp. 1065–1072.
ICML-2009-VlassisT: Model-free reinforcement learning as mixture learning (NV, MT), pp. 1081–1088.
ICML-2009-VolkovsZ #named #ranking: BoltzRank: learning to maximize expected ranking gain (MV, RSZ), pp. 1089–1096.
ICML-2009-WeinbergerDLSA #multi #scalability: Feature hashing for large scale multitask learning (KQW, AD, JL, AJS, JA), pp. 1113–1120.
ICML-2009-XuWS #predict: Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning (LX, MW, DS), pp. 1137–1144.
ICML-2009-YangJY #online: Online learning by ellipsoid method (LY, RJ, JY), pp. 1153–1160.
ICML-2009-YuanH #feature model #robust: Robust feature extraction via information theoretic learning (XY, BGH), pp. 1193–1200.
ICML-2009-YuilleZ #composition: Compositional noisy-logical learning (ALY, SZ), pp. 1209–1216.
ICML-2009-YuJ: Learning structural SVMs with latent variables (CNJY, TJ), pp. 1169–1176.
ICML-2009-ZhangKP #prototype #scalability: Prototype vector machine for large scale semi-supervised learning (KZ, JTK, BP), pp. 1233–1240.
ICML-2009-ZhangSFD: Learning non-redundant codebooks for classifying complex objects (WZ, AS, XF, TGD), pp. 1241–1248.
ICML-2009-ZhanLLZ #metric #using: Learning instance specific distances using metric propagation (DCZ, ML, YFL, ZHZ), pp. 1225–1232.
ICML-2009-ZhouSL #multi: Multi-instance learning by treating instances as non-I.I.D. samples (ZHZ, YYS, YFL), pp. 1249–1256.
ICML-2009-ZhuangTH #kernel #named #parametricity: SimpleNPKL: simple non-parametric kernel learning (JZ, IWT, SCHH), pp. 1273–1280.
KDD-2009-BeygelzimerL: The offset tree for learning with partial labels (AB, JL), pp. 129–138.
KDD-2009-ChenCBT #optimisation #random: Constrained optimization for validation-guided conditional random field learning (MC, YC, MRB, AET), pp. 189–198.
KDD-2009-DonmezCS: Efficiently learning the accuracy of labeling sources for selective sampling (PD, JGC, JGS), pp. 259–268.
KDD-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.
KDD-2009-GamaSR #algorithm #evaluation: Issues in evaluation of stream learning algorithms (JG, RS, PPR), pp. 329–338.
KDD-2009-GaoFSH: Heterogeneous source consensus learning via decision propagation and negotiation (JG, WF, YS, JH), pp. 339–348.
KDD-2009-GeXZSGW #multi: Multi-focal learning and its application to customer service support (YG, HX, WZ, RKS, XG, WW), pp. 349–358.
KDD-2009-GuptaBR: Catching the drift: learning broad matches from clickthrough data (SG, MB, MR), pp. 1165–1174.
KDD-2009-LiuKJ #graph #monitoring: Learning dynamic temporal graphs for oil-production equipment monitoring system (YL, JRK, OJ), pp. 1225–1234.
KDD-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.
KDD-2009-MaSSV #detection #web: Beyond blacklists: learning to detect malicious web sites from suspicious URLs (JM, LKS, SS, GMV), pp. 1245–1254.
KDD-2009-RendleMNS #ranking #recommendation: Learning optimal ranking with tensor factorization for tag recommendation (SR, LBM, AN, LST), pp. 727–736.
KDD-2009-TangL #relational #social: Relational learning via latent social dimensions (LT, HL), pp. 817–826.
KDD-2009-WangSAL #fault #network: Learning, indexing, and diagnosing network faults (TW, MS, DA, LL), pp. 857–866.
KDD-2009-YangSWC #classification #effectiveness #multi: Effective multi-label active learning for text classification (BY, JTS, TW, ZC), pp. 917–926.
KDD-2009-YouHC #biology #network: Learning patterns in the dynamics of biological networks (CHY, LBH, DJC), pp. 977–986.
KDIR-2009-CallejaFGA #set: A Learning Method for Imbalanced Data Sets (JdlC, OF, JG, RMAP), pp. 307–310.
KDIR-2009-ZhouZK #collaboration: The Collaborative Learning Agent (CLA) in Trident Warrior 08 Exercise (CZ, YZ, CK), pp. 323–328.
KEOD-2009-Aussenac-GillesK #documentation #ontology #xml: Ontology Learning by Analyzing XML Document Structure and Content (NAG, MK), pp. 159–165.
KEOD-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.
KMIS-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.
KMIS-2009-DochevA #semantics #towards #web: Towards Semantic Web Enhanced Learning (DD, GA), pp. 212–217.
MLDM-2009-BouthinonSV #ambiguity #concept: Concept Learning from (Very) Ambiguous Examples (DB, HS, VV), pp. 465–478.
MLDM-2009-ChanguelLB #automation #html: A General Learning Method for Automatic Title Extraction from HTML Pages (SC, NL, BBM), pp. 704–718.
MLDM-2009-LeeCWL: Learning with a Quadruped Chopstick Robot (WCL, JCC, SzW, KML), pp. 603–616.
MLDM-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.
MLDM-2009-StrumbeljRK: Learning Betting Tips from Users’ Bet Selections (ES, MRS, IK), pp. 678–688.
RecSys-2009-MaLK #recommendation #trust: Learning to recommend with trust and distrust relationships (HM, MRL, IK), pp. 189–196.
RecSys-2009-OMahonyS #recommendation: Learning to recommend helpful hotel reviews (MPO, BS), pp. 305–308.
SEKE-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.
SEKE-2009-TianCYL #approach #modelling #music #ontology: An Ontology-based Model Driven Approach for a Music Learning System (YT, FC, HY, LL), pp. 739–744.
SEKE-2009-Ye #collaboration #education #re-engineering: An Academia-Industry Collaborative Teaching and Learning Model for Software Engineering Education (HY), pp. 301–305.
SIGIR-2009-BanerjeeCR #query #rank: Learning to rank for quantity consensus queries (SB, SC, GR), pp. 243–250.
SIGIR-2009-CormackCB #rank: Reciprocal rank fusion outperforms condorcet and individual rank learning methods (GVC, CLAC, SB), pp. 758–759.
SIGIR-2009-CumminsO #framework #information retrieval #proximity: Learning in a pairwise term-term proximity framework for information retrieval (RC, CO), pp. 251–258.
SIGIR-2009-HuangH #approach #information retrieval #ranking: A bayesian learning approach to promoting diversity in ranking for biomedical information retrieval (XH, QH), pp. 307–314.
SIGIR-2009-MaKL #recommendation #social #trust: Learning to recommend with social trust ensemble (HM, IK, MRL), pp. 203–210.
SIGIR-2009-SunQTW #metric #rank #ranking #robust: Robust sparse rank learning for non-smooth ranking measures (ZS, TQ, QT, JW), pp. 259–266.
SIGIR-2009-YangWGH #query #ranking #web: Query sampling for ranking learning in web search (LY, LW, BG, XSH), pp. 754–755.
SIGIR-2009-YilmazR #rank: Deep versus shallow judgments in learning to rank (EY, SR), pp. 662–663.
RE-2009-KnaussSS #heuristic #requirements: Learning to Write Better Requirements through Heuristic Critiques (EK, KS, KS), pp. 387–388.
ESEC-FSE-2009-BruchMM #code completion: Learning from examples to improve code completion systems (MB, MM, MM), pp. 213–222.
ICSE-2009-AlrajehKRU #modelling #requirements: Learning operational requirements from goal models (DA, JK, AR, SU), pp. 265–275.
SAC-2009-LiuTS #classification #complexity #using: Assessing complexity of service-oriented computing using learning classifier systems (LL, ST, HS), pp. 2170–2171.
SAC-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.
SAC-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.
SAC-2009-RoeslerHC #case study #distance #multi: A new multimedia synchronous distance learning system: the IVA study case (VR, RH, CHC), pp. 1765–1770.
SAC-2009-SchmitzbergerRNRP #architecture: Thin client architecture in support of remote radiology learning (FFS, JER, SN, GDR, DSP), pp. 842–846.
SAC-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.
CASE-2009-BountourelisR #algorithm: Customized learning algorithms for episodic tasks with acyclic state spaces (TB, SR), pp. 627–634.
CASE-2009-SolisT #comprehension #towards: Towards enhancing the understanding of human motor learning (JS, AT), pp. 591–596.
CGO-2009-MaoS #evolution #predict #virtual machine: Cross-Input Learning and Discriminative Prediction in Evolvable Virtual Machines (FM, XS), pp. 92–101.
DAC-2009-MarrBBH: A learning digital computer (BM, AB, SB, PEH), pp. 617–618.
DATE-2009-RichterJE #framework #verification: Learning early-stage platform dimensioning from late-stage timing verification (KR, MJ, RE), pp. 851–857.
HPDC-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.
STOC-2009-KleinbergPT #game studies #multi: Multiplicative updates outperform generic no-regret learning in congestion games: extended abstract (RK, GP, ÉT), pp. 533–542.
STOC-2009-Sellie #random: Exact learning of random DNF over the uniform distribution (LS), pp. 45–54.
TACAS-2009-ChenFCTW #automaton #composition #verification: Learning Minimal Separating DFA’s for Compositional Verification (YFC, AF, EMC, YKT, BYW), pp. 31–45.
ICLP-2009-Raedt #logic #probability #tutorial: Probabilistic Logic Learning — A Tutorial Abstract (LDR), p. 39.
SAT-2009-DilkinaGS: Backdoors in the Context of Learning (BND, CPG, AS), pp. 73–79.
SAT-2009-Johannsen #bound #exponential #strict: An Exponential Lower Bound for Width-Restricted Clause Learning (JJ), pp. 128–140.
ECDL-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.
HT-2008-HeoY #empirical #information management: An empirical study of the learning effect of an ontology-driven information system (MH, MY), pp. 225–226.
HT-2008-KetterlEB #social #web: Social selected learning content out of web lectures (MK, JE, JB), pp. 231–232.
HT-2008-LawlessHW #corpus #education: Enhancing access to open corpus educational content: learning in the wild (SL, LH, VW), pp. 167–174.
JCDL-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.
VLDB-2008-NguyenNF: Learning to extract form labels (HN, THN, JF), pp. 684–694.
VLDB-2008-TalukdarJMCIPG #query: Learning to create data-integrating queries (PPT, MJ, MSM, KC, ZGI, FCNP, SG), pp. 785–796.
CSEET-2008-BarbosaSM #education #experience #testing: An Experience on Applying Learning Mechanisms for Teaching Inspection and Software Testing (EFB, SdRSdS, JCM), pp. 189–196.
CSEET-2008-RasR #information management #using: Improving Knowledge Acquisition in Capstone Projects Using Learning Spaces for Experiential Learning (ER, JR), pp. 77–84.
CSEET-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.
ITiCSE-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.
ITiCSE-2008-Bower #online: The “instructed-teacher”: a computer science online learning pedagogical pattern (MB), pp. 189–193.
ITiCSE-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.
ITiCSE-2008-CerboDS #collaboration: Extending moodle for collaborative learning (FDC, GD, GS), p. 324.
ITiCSE-2008-CharltonMD #performance #social: Evaluating the extent to which sociability and social presence affects learning performance (TC, LM, MD), p. 342.
ITiCSE-2008-ChidanandanS #question: Adopting pen-based technology to facilitate active learning in the classroom: is it right for you? (AC, SMS), p. 343.
ITiCSE-2008-Goelman #collaboration #database: Databases, non-majors and collaborative learning: a ternary relationships (DG), pp. 27–31.
ITiCSE-2008-Jackova #programming: Learning for mastery in an introductory programming course (JJ), p. 352.
ITiCSE-2008-Kolikant #education #framework: Computer-science education as a cultural encounter: a socio-cultural framework for articulating learning difficulties (YBDK), pp. 291–295.
ITiCSE-2008-Kolling #ide #named #object-oriented #programming #visual notation: Greenfoot: a highly graphical ide for learning object-oriented programming (MK), p. 327.
ITiCSE-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.
ITiCSE-2008-MurphyPK #approach #distance #education #programming: A distance learning approach to teaching eXtreme programming (CM, DBP, GEK), pp. 199–203.
ITiCSE-2008-PerezMF #operating system: Cooperative learning in operating systems laboratory (JEP, JGM, IMF), p. 323.
ITiCSE-2008-Shaban-NejadH #education #towards: Web-based dynamic learning through lexical chaining: a step forward towards knowledge-driven education (ASN, VH), p. 375.
ITiCSE-2008-SierraCF: An environment for supporting active learning in courses on language processing (JLS, AMFPC, AFV), pp. 128–132.
SIGITE-2008-MillerD: Employers’ perspectives on it learning outcomes (CSM, LD), pp. 213–218.
SIGITE-2008-Sabin #collaboration: A collaborative and experiential learning model powered by real-world projects (MS), pp. 157–164.
ICSM-2008-Hou #design #framework: Investigating the effects of framework design knowledge in example-based framework learning (DH), pp. 37–46.
CIAA-2008-GarciaPAR #automaton #finite #nondeterminism #regular expression #using: Learning Regular Languages Using Nondeterministic Finite Automata (PG, MVdP, GIA, JR), pp. 92–101.
ICALP-A-2008-Dachman-SoledLMSWW #encryption: Optimal Cryptographic Hardness of Learning Monotone Functions (DDS, HKL, TM, RAS, AW, HW), pp. 36–47.
AIIDE-2008-CutumisuS #game studies: A Demonstration of Agent Learning with Action-Dependent Learning Rates in Computer Role-Playing Games (MC, DS).
AIIDE-2008-CutumisuSBS #game studies #using: Agent Learning using Action-Dependent Learning Rates in Computer Role-Playing Games (MC, DS, MHB, RSS).
AIIDE-2008-HefnyHSA #game studies #named: Cerberus: Applying Supervised and Reinforcement Learning Techniques to Capture the Flag Games (ASH, AAH, MMS, AFA).
AIIDE-2008-KerrCC #game studies: Learning and Playing in Wubble World (WK, PRC, YHC).
AIIDE-2008-McPartlandG #game studies: Learning to be a Bot: Reinforcement Learning in Shooter Games (MM, MG).
AIIDE-2008-UlamJG #adaptation #game studies #modelling: Combining Model-Based Meta-Reasoning and Reinforcement Learning for Adapting Game-Playing Agents (PU, JJ, AKG0).
CIG-2008-Blair #evaluation #network #symmetry: Learning position evaluation for Go with Internal Symmetry Networks (AB), pp. 199–204.
CIG-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.
CIG-2008-Lucas #difference #evolution: Investigating learning rates for evolution and temporal difference learning (SML), pp. 1–7.
CIG-2008-MarivateM #game studies #social: Social Learning methods in board game agents (VNM, TM), pp. 323–328.
CIG-2008-McPartlandG #multi: Creating a multi-purpose first person shooter bot with reinforcement learning (MM, MG), pp. 143–150.
CIG-2008-MujtabaB #multi: Survival by continuous learning in a dynamic multiple task environment (HM, ARB), pp. 304–309.
CIG-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.
CIG-2008-Sahraei-ArdakaniRA #game studies: Hierarchical Nash-Q learning in continuous games (MSA, ARK, MNA), pp. 290–295.
CIG-2008-SharmaKG #game studies #generative: Learning and knowledge generation in General Games (SS, ZK, SDG), pp. 329–335.
CIG-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.
CHI-2008-CostabileALABP #challenge #exclamation #mobile: Explore! possibilities and challenges of mobile learning (MFC, ADA, RL, CA, PB, TP), pp. 145–154.
CHI-2008-FogartyTKW #concept #image #interactive #named: CueFlik: interactive concept learning in image search (JF, DST, AK, SAJW), pp. 29–38.
CHI-2008-Grammenos #game studies: Game over: learning by dying (DG), pp. 1443–1452.
CHI-2008-McQuigganRL: The effects of empathetic virtual characters on presence in narrative-centered learning environments (SWM, JPR, JCL), pp. 1511–1520.
CHI-2008-OganAJ #predict #using: Pause, predict, and ponder: use of narrative videos to improve cultural discussion and learning (AO, VA, CJ), pp. 155–162.
CHI-2008-WangM #interactive: Human-Currency Interaction: learning from virtual currency use in China (YW, SDM), pp. 25–28.
ICEIS-AIDSS-2008-MorgadoPR #evaluation #quality: An Evaluation Instrument for Learning Object Quality and Management (EMM, FJGP, ÁBR), pp. 327–332.
ICEIS-AIDSS-2008-StateCRP #algorithm #classification: A New Learning Algorithm for Classification in the Reduced Space (LS, CC, IR, PV), pp. 155–160.
ICEIS-HCI-2008-CarvalhoS #lessons learnt #usability: The Importance of Usability Criteria on Learning Management Systems: Lessons Learned (AFPdC, JCAS), pp. 154–159.
ICEIS-HCI-2008-DamaseviciusT #design #re-engineering #user interface: Learning Object Reengineering Based on Principles for Usable User Interface Design (RD, LT), pp. 124–129.
ICEIS-HCI-2008-GarciaMDS #interface #visualisation: An Interface Environment for Learning Object Search and Pre-Visualisation (LSG, ROdOM, AID, MSS), pp. 240–247.
ICEIS-HCI-2008-MileyRM: Traditional Learning Vs. e-LEARNING — Some Results from Training Call Centre Personnel (MM, JAR, CM), pp. 299–307.
ICEIS-J-2008-GullaBK08a #ontology: Association Rules and Cosine Similarities in Ontology Relationship Learning (JAG, TB, GSK), pp. 201–212.
ICEIS-SAIC-2008-CanalesP #architecture #semantics #web: Learning Technology System Architecture Based on Agents and Semantic Web (ACC, RPV), pp. 127–132.
CIKM-2008-BroderCFGJMMP: To swing or not to swing: learning when (not) to advertise (AZB, MC, MF, EG, VJ, DM, VM, VP), pp. 1003–1012.
CIKM-2008-DonmezC #multi: Proactive learning: cost-sensitive active learning with multiple imperfect oracles (PD, JGC), pp. 619–628.
CIKM-2008-DouSYW #question #ranking #web: Are click-through data adequate for learning web search rankings? (ZD, RS, XY, JRW), pp. 73–82.
CIKM-2008-HoefelE #classification #sequence: Learning a two-stage SVM/CRF sequence classifier (GH, CE), pp. 271–278.
CIKM-2008-LuoZHXH #multi: Transfer learning from multiple source domains via consensus regularization (PL, FZ, HX, YX, QH), pp. 103–112.
CIKM-2008-MaYKL #query #semantics: Learning latent semantic relations from clickthrough data for query suggestion (HM, HY, IK, MRL), pp. 709–718.
CIKM-2008-MilneW #wiki: Learning to link with wikipedia (DNM, IHW), pp. 509–518.
CIKM-2008-NiXLH #approach: Group-based learning: a boosting approach (WN, JX, HL, YH), pp. 1443–1444.
CIKM-2008-WangCZL #constraints #metric: Semi-supervised metric learning by maximizing constraint margin (FW, SC, CZ, TL), pp. 1457–1458.
ECIR-2008-AyacheQ #corpus #using #video: Video Corpus Annotation Using Active Learning (SA, GQ), pp. 187–198.
ICML-2008-BarrettN #multi #policy: Learning all optimal policies with multiple criteria (LB, SN), pp. 41–47.
ICML-2008-BickelBLS #multi: Multi-task learning for HIV therapy screening (SB, JB, TL, TS), pp. 56–63.
ICML-2008-BryanS: Actively learning level-sets of composite functions (BB, JGS), pp. 80–87.
ICML-2008-CaruanaKY #empirical #evaluation: An empirical evaluation of supervised learning in high dimensions (RC, NK, AY), pp. 96–103.
ICML-2008-ChenM: Learning to sportscast: a test of grounded language acquisition (DLC, RJM), pp. 128–135.
ICML-2008-CoatesAN #multi: Learning for control from multiple demonstrations (AC, PA, AYN), pp. 144–151.
ICML-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.
ICML-2008-DasguptaH: Hierarchical sampling for active learning (SD, DH), pp. 208–215.
ICML-2008-DekelS: Learning to classify with missing and corrupted features (OD, OS), pp. 216–223.
ICML-2008-DickHS #infinity #semistructured data: Learning from incomplete data with infinite imputations (UD, PH, TS), pp. 232–239.
ICML-2008-DiukCL #object-oriented #performance #representation: An object-oriented representation for efficient reinforcement learning (CD, AC, MLL), pp. 240–247.
ICML-2008-DonmezC #optimisation #rank #reduction: Optimizing estimated loss reduction for active sampling in rank learning (PD, JGC), pp. 248–255.
ICML-2008-DoshiPR #using: Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs (FD, JP, NR), pp. 256–263.
ICML-2008-DuchiSSC #performance: Efficient projections onto the l1-ball for learning in high dimensions (JCD, SSS, YS, TC), pp. 272–279.
ICML-2008-EpshteynVD: Active reinforcement learning (AE, AV, GD), pp. 296–303.
ICML-2008-FrankMP: Reinforcement learning in the presence of rare events (JF, SM, DP), pp. 336–343.
ICML-2008-GonenA #kernel #locality #multi: Localized multiple kernel learning (MG, EA), pp. 352–359.
ICML-2008-GordonGM #game studies: No-regret learning in convex games (GJG, AG, CM), pp. 360–367.
ICML-2008-HamL #analysis: Grassmann discriminant analysis: a unifying view on subspace-based learning (JH, DDL), pp. 376–383.
ICML-2008-HoiJ #kernel: Active kernel learning (SCHH, RJ), pp. 400–407.
ICML-2008-HuynhM #logic #markov #network #parametricity: Discriminative structure and parameter learning for Markov logic networks (TNH, RJM), pp. 416–423.
ICML-2008-KolterCNGD #programming: Space-indexed dynamic programming: learning to follow trajectories (JZK, AC, AYN, YG, CD), pp. 488–495.
ICML-2008-LanLQML #rank: Query-level stability and generalization in learning to rank (YL, TYL, TQ, ZM, HL), pp. 512–519.
ICML-2008-LazaricRB: Transfer of samples in batch reinforcement learning (AL, MR, AB), pp. 544–551.
ICML-2008-LiLW #framework #self #what: Knows what it knows: a framework for self-aware learning (LL, MLL, TJW), pp. 568–575.
ICML-2008-LoeffFR #approximate #named: ManifoldBoost: stagewise function approximation for fully-, semi- and un-supervised learning (NL, DAF, DR), pp. 600–607.
ICML-2008-MekaJCD #online #rank: Rank minimization via online learning (RM, PJ, CC, ISD), pp. 656–663.
ICML-2008-MeloMR #analysis #approximate: An analysis of reinforcement learning with function approximation (FSM, SPM, MIR), pp. 664–671.
ICML-2008-NowozinB #approach: A decoupled approach to exemplar-based unsupervised learning (SN, GHB), pp. 704–711.
ICML-2008-OuyangG #ranking: Learning dissimilarities by ranking: from SDP to QP (HO, AGG), pp. 728–735.
ICML-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.
ICML-2008-PuolamakiAK #query: Learning to learn implicit queries from gaze patterns (KP, AA, SK), pp. 760–767.
ICML-2008-RadlinskiKJ #multi #ranking: Learning diverse rankings with multi-armed bandits (FR, RK, TJ), pp. 784–791.
ICML-2008-RanzatoS #documentation #network: Semi-supervised learning of compact document representations with deep networks (MR, MS), pp. 792–799.
ICML-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.
ICML-2008-ReisingerSM #kernel #online: Online kernel selection for Bayesian reinforcement learning (JR, PS, RM), pp. 816–823.
ICML-2008-SakumaKW #privacy: Privacy-preserving reinforcement learning (JS, SK, RNW), pp. 864–871.
ICML-2008-ShiBY #modelling #using: Data spectroscopy: learning mixture models using eigenspaces of convolution operators (TS, MB, BY), pp. 936–943.
ICML-2008-SilverSM: Sample-based learning and search with permanent and transient memories (DS, RSS, MM), pp. 968–975.
ICML-2008-SindhwaniR #multi: An RKHS for multi-view learning and manifold co-regularization (VS, DSR), pp. 976–983.
ICML-2008-SokolovskaCY #modelling #probability: The asymptotics of semi-supervised learning in discriminative probabilistic models (NS, OC, FY), pp. 984–991.
ICML-2008-SuZLM #network #parametricity: Discriminative parameter learning for Bayesian networks (JS, HZ, CXL, SM), pp. 1016–1023.
ICML-2008-SyedBS #linear #programming #using: Apprenticeship learning using linear programming (US, MHB, RES), pp. 1032–1039.
ICML-2008-SzafranskiGR #kernel: Composite kernel learning (MS, YG, AR), pp. 1040–1047.
ICML-2008-WangYZ #adaptation #kernel #multi: Adaptive p-posterior mixture-model kernels for multiple instance learning (HYW, QY, HZ), pp. 1136–1143.
ICML-2008-WangZ #multi #on the: On multi-view active learning and the combination with semi-supervised learning (WW, ZHZ), pp. 1152–1159.
ICML-2008-WeinbergerS #distance #implementation #metric #performance: Fast solvers and efficient implementations for distance metric learning (KQW, LKS), pp. 1160–1167.
ICML-2008-WestonRC: Deep learning via semi-supervised embedding (JW, FR, RC), pp. 1168–1175.
ICML-2008-WingateS #exponential #predict #product line: Efficiently learning linear-linear exponential family predictive representations of state (DW, SPS), pp. 1176–1183.
ICML-2008-XiaLWZL #algorithm #approach #rank: Listwise approach to learning to rank: theory and algorithm (FX, TYL, JW, WZ, HL), pp. 1192–1199.
ICML-2008-YaoL #difference: Preconditioned temporal difference learning (HY, ZQL), pp. 1208–1215.
ICPR-2008-AlpcanB #algorithm #distributed #parallel: A discrete-time parallel update algorithm for distributed learning (TA, CB), pp. 1–4.
ICPR-2008-Arevalillo-HerraezFD #image #metric #retrieval #similarity: Learning combined similarity measures from user data for image retrieval (MAH, FJF, JD), pp. 1–4.
ICPR-2008-BasakLC #summary #video: Video summarization with supervised learning (JB, VL, SC), pp. 1–4.
ICPR-2008-CamposJ #constraints #network #parametricity #using: Improving Bayesian Network parameter learning using constraints (CPdC, QJ), pp. 1–4.
ICPR-2008-ChangLAH08a #collaboration #image #using: Using collaborative learning for image contrast enhancement (YC, DJL, JKA, YH), pp. 1–4.
ICPR-2008-DehzangiMCL #classification #fuzzy #speech #using: Fuzzy rule selection using Iterative Rule Learning for speech data classification (OD, BM, CES, HL), pp. 1–4.
ICPR-2008-DuinP #difference #matrix #on the: On refining dissimilarity matrices for an improved NN learning (RPWD, EP), pp. 1–4.
ICPR-2008-FabletLSMCB #using: Weakly supervised learning using proportion-based information: An application to fisheries acoustics (RF, RL, CS, JM, PC, JMB), pp. 1–4.
ICPR-2008-FuR #multi #performance: Fast multiple instance learning via L1, 2 logistic regression (ZF, ARK), pp. 1–4.
ICPR-2008-FuSHLT #image #kernel #multi #set: Multiple kernel learning from sets of partially matching image features (SYF, GS, ZGH, ZzL, MT), pp. 1–4.
ICPR-2008-GhanemVW #relational: Learning in imbalanced relational data (ASG, SV, GAWW), pp. 1–4.
ICPR-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.
ICPR-2008-GuiHY #consistency: An improvement on learning with local and global consistency (JG, DSH, ZY), pp. 1–4.
ICPR-2008-HuAS08a #using: Learning motion patterns in crowded scenes using motion flow field (MH, SA, MS), pp. 1–5.
ICPR-2008-HuWJHG #detection #online: Human reappearance detection based on on-line learning (LH, YW, SJ, QH, WG), pp. 1–4.
ICPR-2008-JinLH #prototype: Prototype learning with margin-based conditional log-likelihood loss (XJ, CLL, XH), pp. 1–4.
ICPR-2008-JradGB #constraints #multi #performance: Supervised learning rule selection for multiclass decision with performance constraints (NJ, EGM, PB), pp. 1–4.
ICPR-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.
ICPR-2008-LiaoJ #network #parametricity #semistructured data: Exploiting qualitative domain knowledge for learning Bayesian network parameters with incomplete data (WL, QJ), pp. 1–4.
ICPR-2008-LiaoL #kernel #novel #robust: A novel robust kernel for appearance-based learning (CTL, SHL), pp. 1–4.
ICPR-2008-LiDM #feature model #locality #using: Localized feature selection for Gaussian mixtures using variational learning (YL, MD, YM), pp. 1–4.
ICPR-2008-LiuWBM #kernel #linear: Semi-supervised learning by locally linear embedding in kernel space (RL, YW, TB, DM), pp. 1–4.
ICPR-2008-LiuZDY #detection #sequence #video: Video attention: Learning to detect a salient object sequence (TL, NZ, WD, ZY), pp. 1–4.
ICPR-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.
ICPR-2008-NaYKC: Relevant pattern selection for subspace learning (JHN, SMY, MK, JYC), pp. 1–4.
ICPR-2008-NguyenBP #approach #set: A supervised learning approach for imbalanced data sets (GHN, AB, SLP), pp. 1–4.
ICPR-2008-NingXZGH #detection #difference: Temporal difference learning to detect unsafe system states (HN, WX, YZ, YG, TSH), pp. 1–4.
ICPR-2008-PerezO #invariant #programming #search-based: Learning invariant region descriptor operators with genetic programming and the F-measure (CBP, GO), pp. 1–4.
ICPR-2008-QuQY: Learning a discriminative sparse tri-value transform (ZQ, GQ, PCY), pp. 1–4.
ICPR-2008-SudoOTKA #detection #incremental #online: Online anomal movement detection based on unsupervised incremental learning (KS, TO, HT, HK, KA), pp. 1–4.
ICPR-2008-TorselloD #generative #graph: Supervised learning of a generative model for edge-weighted graphs (AT, DLD), pp. 1–4.
ICPR-2008-WangWCW #algorithm #clustering: A clustering algorithm combine the FCM algorithm with supervised learning normal mixture model (WW, CW, XC, AW), pp. 1–4.
ICPR-2008-WangZ #collaboration #distributed: Collaborative learning by boosting in distributed environments (SW, CZ), pp. 1–4.
ICPR-2008-WuF #3d #classification #multi #using: Multiple view based 3D object classification using ensemble learning of local subspaces (JW, KF), pp. 1–4.
ICPR-2008-ZhaoGLJ #modelling: Spatio-temporal patches for night background modeling by subspace learning (YZ, HG, LL, YJ), pp. 1–4.
ICPR-2008-Zhu #documentation #image: Augment document image binarization by learning (YZ), pp. 1–4.
ICPR-2008-ZhuBQ #lazy evaluation: Bagging very weak learners with lazy local learning (XZ, CB, WQ), pp. 1–4.
KDD-2008-ChakrabartiKSB #ranking: Structured learning for non-smooth ranking losses (SC, RK, US, CB), pp. 88–96.
KDD-2008-ChengT: Semi-supervised learning with data calibration for long-term time series forecasting (HC, PNT), pp. 133–141.
KDD-2008-ChenJCLWY #classification #kernel: Learning subspace kernels for classification (JC, SJ, BC, QL, MW, JY), pp. 106–114.
KDD-2008-CuiDSAJ: Learning methods for lung tumor markerless gating in image-guided radiotherapy (YC, JGD, GCS, BMA, SBJ), pp. 902–910.
KDD-2008-DavisD #metric #problem: Structured metric learning for high dimensional problems (JVD, ISD), pp. 195–203.
KDD-2008-ElkanN #classification: Learning classifiers from only positive and unlabeled data (CE, KN), pp. 213–220.
KDD-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.
KDD-2008-LingD #query: Active learning with direct query construction (CXL, JD), pp. 480–487.
KDD-2008-LingDXYY: Spectral domain-transfer learning (XL, WD, GRX, QY, YY), pp. 488–496.
KDD-2008-MadaniH #on the: On updates that constrain the features’ connections during learning (OM, JH), pp. 515–523.
KDD-2008-SinghG #matrix #relational: Relational learning via collective matrix factorization (APS, GJG), pp. 650–658.
KDD-2008-SunJY #classification #multi: Hypergraph spectral learning for multi-label classification (LS, SJ, JY), pp. 668–676.
KDD-2008-WuLCC #symmetry: Asymmetric support vector machines: low false-positive learning under the user tolerance (SHW, KPL, CMC, MSC), pp. 749–757.
KDD-2008-WuXC #clustering #incremental #named: SAIL: summation-based incremental learning for information-theoretic clustering (JW, HX, JC), pp. 740–748.
KDD-2008-ZhangSPN #documentation #multi #topic #web: Learning from multi-topic web documents for contextual advertisement (YZ, ACS, JCP, MN), pp. 1051–1059.
RecSys-2008-DrachslerHK #navigation: Navigation support for learners in informal learning environments (HD, HGKH, RK), pp. 303–306.
SIGIR-2008-AminiTG #algorithm #ranking: A boosting algorithm for learning bipartite ranking functions with partially labeled data (MRA, TVT, CG), pp. 99–106.
SIGIR-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.
SIGIR-2008-DruckMM #using: Learning from labeled features using generalized expectation criteria (GD, GSM, AM), pp. 595–602.
SIGIR-2008-DuhK #rank: Learning to rank with partially-labeled data (KD, KK), pp. 251–258.
SIGIR-2008-GuiverS #process #rank: Learning to rank with SoftRank and Gaussian processes (JG, ES), pp. 259–266.
SIGIR-2008-HarpaleY #collaboration #personalisation: Personalized active learning for collaborative filtering (AH, YY), pp. 91–98.
SIGIR-2008-LeeKJ #algorithm #constraints: Fixed-threshold SMO for Joint Constraint Learning Algorithm of Structural SVM (CL, HK, MGJ), pp. 829–830.
SIGIR-2008-LiWA #graph #query: Learning query intent from regularized click graphs (XL, YYW, AA), pp. 339–346.
SIGIR-2008-TsaiWC #case study #information retrieval #multi: A study of learning a merge model for multilingual information retrieval (MFT, YTW, HHC), pp. 195–202.
SIGIR-2008-VelosoAGM #rank #using: Learning to rank at query-time using association rules (AV, HMdA, MAG, WMJ), pp. 267–274.
SIGIR-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.
SIGIR-2008-XuLLLM #evaluation #metric #optimisation #rank: Directly optimizing evaluation measures in learning to rank (JX, TYL, ML, HL, WYM), pp. 107–114.
SIGIR-2008-YuZXG #categorisation #design #using: trNon-greedy active learning for text categorization using convex ansductive experimental design (KY, SZ, WX, YG), pp. 635–642.
SIGIR-2008-ZhangL #multi: Learning with support vector machines for query-by-multiple-examples (DZ, WSL), pp. 835–836.
SIGIR-2008-ZhouXZY #rank: Learning to rank with ties (KZ, GRX, HZ, YY), pp. 275–282.
OOPSLA-2008-SimpkinsBIM #adaptation #programming language #towards: Towards adaptive programming: integrating reinforcement learning into a programming language (CS, SB, CLIJ, MM), pp. 603–614.
RE-2008-JonesLML #requirements: Use and Influence of Creative Ideas and Requirements for a Work-Integrated Learning System (SJ, PL, NAMM, SNL), pp. 289–294.
RE-2008-RegevGW #approach #education #requirements: Requirements Engineering Education in the 21st Century, An Experiential Learning Approach (GR, DCG, AW), pp. 85–94.
SAC-2008-CarvalhoAZ #health #process: Learning activities on health care supported by common sense knowledge (AFPdC, JCAS, SZM), pp. 1385–1389.
SAC-2008-CorreaLSM #composition #network: Neural network based systems for computer-aided musical composition: supervised x unsupervised learning (DCC, ALML, JHS, JFM), pp. 1738–1742.
SAC-2008-MartinsSBPS #information retrieval #ubiquitous: Context-aware information retrieval on a ubiquitous medical learning environment (DSM, LHZS, MB, AFdP, WLdS), pp. 2348–2349.
SAC-2008-StrapparavaM #identification: Learning to identify emotions in text (CS, RM), pp. 1556–1560.
SAC-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.
ATEM-J-2006-DubeyJA #context-free grammar #set: Learning context-free grammar rules from a set of program (AD, PJ, SKA), pp. 223–240.
ASPLOS-2008-LuPSZ #concurrent #debugging: Learning from mistakes: a comprehensive study on real world concurrency bug characteristics (SL, SP, ES, YZ), pp. 329–339.
CASE-2008-StabelliniZ #approach #network #self: Interference aware self-organization for wireless sensor networks: A reinforcement learning approach (LS, JZ), pp. 560–565.
CASE-2008-WeiP #implementation #industrial: An implementation of iterative learning control in industrial production machines (DW, RP), pp. 472–477.
DAC-2008-BastaniKWC #predict #set: Speedpath prediction based on learning from a small set of examples (PB, KK, LCW, EC), pp. 217–222.
DAC-2008-CoskunRG #multi #online #using: Temperature management in multiprocessor SoCs using online learning (AKC, TSR, KCG), pp. 890–893.
PDP-2008-GelgonN #distributed: Decentralized Learning of a Gaussian Mixture with Variational Bayes-based Aggregation (MG, AN), pp. 422–428.
STOC-2008-BlumLR #approach #database #privacy: A learning theory approach to non-interactive database privacy (AB, KL, AR), pp. 609–618.
STOC-2008-Feldman #algorithm: Evolvability from learning algorithms (VF), pp. 619–628.
STOC-2008-GopalanKK: Agnostically learning decision trees (PG, ATK, ARK), pp. 527–536.
STOC-2008-KalaiMV #on the: On agnostic boosting and parity learning (ATK, YM, EV), pp. 629–638.
STOC-2008-KhotS #on the: On hardness of learning intersection of two halfspaces (SK, RS), pp. 345–354.
ISSTA-2008-SankaranarayananCIG: Dynamic inference of likely data preconditions over predicates by tree learning (SS, SC, FI, AG), pp. 295–306.
SAT-2008-StachniakB #satisfiability: Speeding-Up Non-clausal Local Search for Propositional Satisfiability with Clause Learning (ZS, AB), pp. 257–270.
ECDL-2007-Fitzgerald #development #education #library: Applications for Digital Libraries in Language Learning and the Professional Development of Teachers (AF), pp. 579–582.
HT-2007-BrownFB: Real users, real results: examining the limitations of learning styles within AEH (EJB, TF, TJB), pp. 57–66.
HT-2007-FigueiraL #interactive #using #visualisation: Interaction visualization in web-based learning using igraphs (ÁRF, JBL), pp. 45–46.
HT-2007-GodboleJMR #concept #interactive #towards: Toward interactive learning by concept ordering (SG, SJ, SM, GR), pp. 149–150.
HT-2007-LeblancA #using: Using forum in an organizational learning context (AL, MHA), pp. 41–42.
ICDAR-2007-ChenLJ #pseudo #recognition: Learning Handwritten Digit Recognition by the Max-Min Posterior Pseudo-Probabilities Method (XC, XL, YJ), pp. 342–346.
ICDAR-2007-Dengel #classification #documentation: Learning of Pattern-Based Rules for Document Classification (AD), pp. 123–127.
ICDAR-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.
ICDAR-2007-YeVRSL: Learning to Group Text Lines and Regions in Freeform Handwritten Notes (MY, PAV, SR, HS, CL), pp. 28–32.
JCDL-2007-KeMF #classification #collaboration #distributed #documentation: Collaborative classifier agents: studying the impact of learning in distributed document classification (WK, JM, YF), pp. 428–437.
JCDL-2007-MimnoM07a #library: Organizing the OCA: learning faceted subjects from a library of digital books (DMM, AM), pp. 376–385.
JCDL-2007-ReckerGWHMP #case study #how #online: A study of how online learning resource are used (MR, SG, AEW, SH, XM, BP), pp. 179–180.
JCDL-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.
CSEET-2007-Armarego: Learning from Reflection: Practitioners as Adult Learners (JA), pp. 55–63.
CSEET-2007-KanerP #education #testing: Practice and Transfer of Learning in the Teaching of Software Testing (CK, SP), pp. 157–166.
CSEET-2007-KrogstieB #collaboration #re-engineering #student: Cross-Community Collaboration and Learning in Customer-Driven Software Engineering Student Projects (BRK, BB), pp. 336–343.
CSEET-2007-PortK #case study #experience #re-engineering: Laptop Enabled Active Learning in the Software Engineering Classroom: An Experience Report (DP, RK), pp. 262–274.
CSEET-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.
ITiCSE-2007-AlstesL #named #network #online #programming: VERKKOKE: learning routing and network programming online (AA, JL), pp. 91–95.
ITiCSE-2007-Arnold #interactive #logic: Introducing propositional logic and queueing theory with the infotraffic interactive learning environments (RA), p. 356.
ITiCSE-2007-BagleyC #collaboration #java #programming: Collaboration and the importance for novices in learning java computer programming (CAB, CCC), pp. 211–215.
ITiCSE-2007-BarnesRPCG #game studies #named: Game2Learn: building CS1 learning games for retention (TB, HR, EP, AC, AG), pp. 121–125.
ITiCSE-2007-CassenSALN #generative #interactive #visual notation: A visual learning engine for interactive generation ofinstructional materials (TC, KRS, JA, DL, AN), p. 319.
ITiCSE-2007-CukiermanT: Learning strategies sessions within the classroom in computing science university courses (DC, DMT), p. 341.
ITiCSE-2007-GalpinSC #student: Learning styles and personality types of computer science students at a South African university (VCG, IDS, PyC), pp. 201–205.
ITiCSE-2007-HonigP #experience #outsourcing #re-engineering: A classroom outsourcing experience for software engineering learning (WLH, TP), pp. 181–185.
ITiCSE-2007-KorteAPG #approach #education #novel: Learning by game-building: a novel approach to theoretical computer science education (LK, SA, HP, JG), pp. 53–57.
ITiCSE-2007-LeidlR #how #question: How will future learning work in the third dimension? (ML, GR), p. 329.
ITiCSE-2007-OliverGMA #using: Using disruptive technology for explorative learning (IO, KG, AM, CA), pp. 96–100.
ITiCSE-2007-Sanchez-TorrubiaTC #algorithm #graph #interactive #tool support: New interactive tools for graph algorithms active learning (MGST, CTB, JC), p. 337.
SIGITE-2007-ChanFL #collaboration: Facilitating cross-cultural learning through collaborative skypecasting (AC, MF, MJWL), pp. 59–66.
SIGITE-2007-Frye #education #network: Wireless sensor networks: learning and teaching (LMF), pp. 269–270.
SIGITE-2007-Krichen #education #online: Investigating learning styles in the online educational environment (JPK), pp. 127–134.
SIGITE-2007-LeungC #information management: Knowledge management system for electronic learning of IT skills (CHL, YYC), pp. 53–58.
SIGITE-2007-MiertschinW #concept #using: Using concept maps to navigate complex learning environments (SLM, CLW), pp. 175–184.
SIGITE-2007-RutherfoordR #design #how: Universal instructional design for learning how to apply in a virtual world (RHR, JKR), pp. 141–146.
SIGITE-2007-SabinH #education #online: Teaching and learning in live online classrooms (MS, BH), pp. 41–48.
SIGITE-2007-WebsterM #experience #student: Student reflections on an academic service learning experience in a computer science classroom (LDW, EJM), pp. 207–212.
ICSM-2007-CorboGP #source code: Smart Formatter: Learning Coding Style from Existing Source Code (FC, CDG, MDP), pp. 525–526.
IFM-2007-OostdijkRTVW #encryption #protocol #testing #verification: Integrating Verification, Testing, and Learning for Cryptographic Protocols (MO, VR, JT, RGdV, TACW), pp. 538–557.
AIIDE-2007-ZhangN #sequence: Learning a Table Soccer Robot a New Action Sequence by Observing and Imitating (DZ0, BN), pp. 61–67.
CIG-2007-AkatsukaS #classification #game studies #video: Reward Allotment Considered Roles for Learning Classifier System For Soccer Video Games (YA, YS), pp. 288–295.
CIG-2007-KimCC #evolution #hybrid: Hybrid of Evolution and Reinforcement Learning for Othello Players (KJK, HC, SBC), pp. 203–209.
CIG-2007-KnittelBS #concept: Concept Accessibility as Basis for Evolutionary Reinforcement Learning of Dots and Boxes (AK, TB, AS), pp. 140–145.
CIG-2007-LucasT #difference #evolution: Point-to-Point Car Racing: an Initial Study of Evolution Versus Temporal Difference Learning (SML, JT), pp. 260–267.
CIG-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.
CIG-2007-Mayer #difference: Board Representations for Neural Go Players Learning by Temporal Difference (HAM), pp. 183–188.
CIG-2007-NaveedCH #game studies #hybrid: Hybrid Evolutionary Learning Approaches for The Virus Game (MHN, PIC, MAH), pp. 196–202.
CIG-2007-QuekG #adaptation #evolution: Adaptation of Iterated Prisoner's Dilemma Strategies by Evolution and Learning (HQ, CKG), pp. 40–47.
CIG-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.
CIG-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.
CIG-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.
DiGRA-2007-KirjavainenNK #development #game studies: Team Structure in the Development of Game-based Learning Environments (AK, TN, MK).
DiGRA-2007-Magnussen #education #game studies: Teacher roles in learning games - When games become situated in schools (RM).
DiGRA-2007-PepplerK #education #game studies #what: What Videogame Making Can Teach Us About Literacy and Learning: Alternative Pathways into Participatory Culture (KAP, YBK).
DiGRA-2007-SorensenM #education #game studies #perspective: Serious Games in language learning and teaching - a theoretical perspective (BHS, BM).
CHI-2007-CockburnKAZ #interface: Hard lessons: effort-inducing interfaces benefit spatial learning (AC, POK, JA, SZ), pp. 1571–1580.
CHI-2007-GrossmanDB #online: Strategies for accelerating on-line learning of hotkeys (TG, PD, RB), pp. 1591–1600.
CHI-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.
CHI-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.
HCI-AS-2007-CarusiM #education #interactive #process: An Essay About the Relevance of Educational Interactive Systems in the Learning Process (AC, CRM), pp. 183–189.
HCI-AS-2007-ChoK #collaboration #contest: Suppressing Competition in a Computer-Supported Collaborative Learning System (KC, BK), pp. 208–214.
HCI-AS-2007-KimJCHH: The Effect of Tangible Pedagogical Agents on Children’s Interest and Learning (JhK, DhJ, HSC, JYH, KHH), pp. 270–277.
HCI-AS-2007-LiuKL #approach: Breaking the Traditional E-Learning Mould: Support for the Learning Preference Approach (FL, JK, LL), pp. 294–301.
HCI-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.
HCI-AS-2007-SaC07a #detection #ubiquitous: Detecting Learning Difficulties on Ubiquitous Scenarios (MdS, LC), pp. 235–244.
HCI-AS-2007-SanchezSS #game studies #mobile: Mobile Game-Based Methodology for Science Learning (JS, AS, MS), pp. 322–331.
HCI-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.
HCI-AS-2007-ThengW #usability: Perceived Usefulness and Usability of Weblogs for Collaborating Learning (YLT, ELYW), pp. 361–370.
HCI-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.
HCI-AS-2007-YuC #collaboration #process: Creating Computer Supported Collaborative Learning Activities with IMS LD (DY, XC), pp. 391–400.
HCI-MIE-2007-FabriEM: Emotionally Expressive Avatars for Chatting, Learning and Therapeutic Intervention (MF, SYAE, DJM), pp. 275–285.
HCI-MIE-2007-SerbanTM #behaviour #interface #predict: A Learning Interface Agent for User Behavior Prediction (GS, AT, GSM), pp. 508–517.
HIMI-IIE-2007-AlsharaI #integration #using: Business Integration Using the Interdisciplinary Project Based Learning Model (IPBL) (OKA, MI), pp. 823–833.
HIMI-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.
HIMI-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.
HIMI-IIE-2007-IbrahimA #interactive: Impact of Interactive Learning on Knowledge Retention (MI, OAS), pp. 347–355.
HIMI-IIE-2007-JeongL #interactive #ubiquitous: Context Aware Human Computer Interaction for Ubiquitous Learning (CJ, EL), pp. 364–373.
HIMI-IIE-2007-TsengLH #mobile: A Mobile Environment for Chinese Language Learning (CCT, CHL, WLH), pp. 485–489.
OCSC-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.
OCSC-2007-ChoC #collaboration #self: Self-Awareness in a Computer Supported Collaborative Learning Environment (KC, MHC), pp. 284–291.
ICEIS-AIDSS-2007-PessiotTUAG #collaboration #rank: Learning to Rank for Collaborative Filtering (JFP, TVT, NU, MRA, PG), pp. 145–151.
ICEIS-AIDSS-2007-RamabadranG #approach #flexibility: Intelligent E-Learning Systems — An Intelligent Approach to Flexible Learning Methodologies (SR, VG), pp. 107–112.
ICEIS-AIDSS-2007-YingboJJ #predict #process #using #workflow: Using Decision Tree Learning to Predict Workflow Activity Time Consumption (YL, JW, JS), pp. 69–75.
ICEIS-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.
ICEIS-J-2007-LuciaFPT07a #collaboration #distributed: A Service Oriented Collaborative Distributed Learning Object Management System (ADL, RF, IP, GT), pp. 341–354.
ICEIS-SAIC-2007-LuciaFPT #collaboration #distributed #named: CD-LOMAS: A Collaborative Distributed Learning Object Management System (ADL, RF, IP, GT), pp. 34–44.
ICEIS-SAIC-2007-MorgadoRP #evaluation: Key Issues for Learning Objects Evaluation (EMM, ÁBR, FJGP), pp. 149–154.
CIKM-2007-ErtekinHBG #classification: Learning on the border: active learning in imbalanced data classification (SE, JH, LB, CLG), pp. 127–136.
CIKM-2007-LiuTZ #network: Ensembling Bayesian network structure learning on limited data (FL, FT, QZ), pp. 927–930.
CIKM-2007-OuyangLL #summary #topic: Developing learning strategies for topic-based summarization (OY, SL, WL), pp. 79–86.
CIKM-2007-Pereira: Learning to join everything (FP0), pp. 9–10.
CIKM-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.
CIKM-2007-WangJZZ #summary #web: Learning query-biased web page summarization (CW, FJ, LZ, HJZ), pp. 555–562.
ECIR-2007-DavyL #categorisation #query: Active Learning with History-Based Query Selection for Text Categorisation (MD, SL), pp. 695–698.
ECIR-2007-Gori: Learning in Hyperlinked Environments (MG), p. 3.
ECIR-2007-Monz #query: Model Tree Learning for Query Term Weighting in Question Answering (CM), pp. 589–596.
ECIR-2007-XuAZ #feedback: Incorporating Diversity and Density in Active Learning for Relevance Feedback (ZX, RA, YZ), pp. 246–257.
ECIR-2007-YeungBCK #approach #documentation: A Bayesian Approach for Learning Document Type Relevance (PCKY, SB, CLAC, MK), pp. 753–756.
ICML-2007-AgarwalC #graph #random #rank: Learning random walks to rank nodes in graphs (AA, SC), pp. 9–16.
ICML-2007-AndoZ #generative: Two-view feature generation model for semi-supervised learning (RKA, TZ), pp. 25–32.
ICML-2007-Azran #algorithm #markov #multi #random: The rendezvous algorithm: multiclass semi-supervised learning with Markov random walks (AA), pp. 49–56.
ICML-2007-Bar-HillelW #distance #similarity: Learning distance function by coding similarity (ABH, DW), pp. 65–72.
ICML-2007-BickelBS: Discriminative learning for differing training and test distributions (SB, MB, TS), pp. 81–88.
ICML-2007-BunescuM #multi: Multiple instance learning for sparse positive bags (RCB, RJM), pp. 105–112.
ICML-2007-CaoQLTL #approach #rank: Learning to rank: from pairwise approach to listwise approach (ZC, TQ, TYL, MFT, HL), pp. 129–136.
ICML-2007-ChengV #image: Learning to compress images and videos (LC, SVNV), pp. 161–168.
ICML-2007-DaiYXY: Boosting for transfer learning (WD, QY, GRX, YY), pp. 193–200.
ICML-2007-DavisKJSD #metric: Information-theoretic metric learning (JVD, BK, PJ, SS, ISD), pp. 209–216.
ICML-2007-DollarRB #algorithm #analysis: Non-isometric manifold learning: analysis and an algorithm (PD, VR, SJB), pp. 241–248.
ICML-2007-Hanneke #bound #complexity: A bound on the label complexity of agnostic active learning (SH), pp. 353–360.
ICML-2007-HoiJL #constraints #kernel #matrix #parametricity: Learning nonparametric kernel matrices from pairwise constraints (SCHH, RJ, MRL), pp. 361–368.
ICML-2007-HulseKN: Experimental perspectives on learning from imbalanced data (JVH, TMK, AN), pp. 935–942.
ICML-2007-Jaeger #network #parametricity #relational: Parameter learning for relational Bayesian networks (MJ), pp. 369–376.
ICML-2007-KimP #recursion: A recursive method for discriminative mixture learning (MK, VP), pp. 409–416.
ICML-2007-KrauseG #approach #process: Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach (AK, CG), pp. 449–456.
ICML-2007-KropotovV #on the: On one method of non-diagonal regularization in sparse Bayesian learning (DK, DV), pp. 457–464.
ICML-2007-LeeCVK #multi: Learning a meta-level prior for feature relevance from multiple related tasks (SIL, VC, DV, DK), pp. 489–496.
ICML-2007-LiLL #scalability: Large-scale RLSC learning without agony (WL, KHL, KSL), pp. 529–536.
ICML-2007-LiYW #distance #framework #metric #reduction: A transductive framework of distance metric learning by spectral dimensionality reduction (FL, JY, JW), pp. 513–520.
ICML-2007-Mahadevan #3d #adaptation #multi #using: Adaptive mesh compression in 3D computer graphics using multiscale manifold learning (SM), pp. 585–592.
ICML-2007-MannM #robust #scalability: Simple, robust, scalable semi-supervised learning via expectation regularization (GSM, AM), pp. 593–600.
ICML-2007-MihalkovaM #bottom-up #logic #markov #network: Bottom-up learning of Markov logic network structure (LM, RJM), pp. 625–632.
ICML-2007-MoschittiZ #effectiveness #kernel #performance #relational: Fast and effective kernels for relational learning from texts (AM, FMZ), pp. 649–656.
ICML-2007-NiCD #multi #process: Multi-task learning for sequential data via iHMMs and the nested Dirichlet process (KN, LC, DBD), pp. 689–696.
ICML-2007-OsentoskiM: Learning state-action basis functions for hierarchical MDPs (SO, SM), pp. 705–712.
ICML-2007-ParkerFT #performance #query #retrieval: Learning for efficient retrieval of structured data with noisy queries (CP, AF, PT), pp. 729–736.
ICML-2007-PetersS: Reinforcement learning by reward-weighted regression for operational space control (JP, SS), pp. 745–750.
ICML-2007-PhuaF #approximate #linear: Tracking value function dynamics to improve reinforcement learning with piecewise linear function approximation (CWP, RF), pp. 751–758.
ICML-2007-RainaBLPN #self: Self-taught learning: transfer learning from unlabeled data (RR, AB, HL, BP, AYN), pp. 759–766.
ICML-2007-RakotomamonjyBCG #kernel #multi #performance: More efficiency in multiple kernel learning (AR, FRB, SC, YG), pp. 775–782.
ICML-2007-SternHG #game studies: Learning to solve game trees (DHS, RH, TG), pp. 839–846.
ICML-2007-SunJSF #algorithm #kernel: A kernel-based causal learning algorithm (XS, DJ, BS, KF), pp. 855–862.
ICML-2007-TaylorS: Cross-domain transfer for reinforcement learning (MET, PS), pp. 879–886.
ICML-2007-WachmanK #kernel #order: Learning from interpretations: a rooted kernel for ordered hypergraphs (GW, RK), pp. 943–950.
ICML-2007-WangYF #difference #on the: On learning with dissimilarity functions (LW, CY, JF), pp. 991–998.
ICML-2007-WangZQ #metric #towards: Dirichlet aggregation: unsupervised learning towards an optimal metric for proportional data (HYW, HZ, HQ), pp. 959–966.
ICML-2007-WilsonFRT #approach #multi: Multi-task reinforcement learning: a hierarchical Bayesian approach (AW, AF, SR, PT), pp. 1015–1022.
ICML-2007-WoznicaKH: Learning to combine distances for complex representations (AW, AK, MH), pp. 1031–1038.
ICML-2007-WuYYS: Local learning projections (MW, KY, SY, BS), pp. 1039–1046.
ICML-2007-XueDC #flexibility #matrix #multi #process: The matrix stick-breaking process for flexible multi-task learning (YX, DBD, LC), pp. 1063–1070.
ICML-2007-XuF #linear #on the #ranking: On learning linear ranking functions for beam search (YX, AF), pp. 1047–1054.
ICML-2007-YeCJ #kernel #parametricity #programming: Discriminant kernel and regularization parameter learning via semidefinite programming (JY, JC, SJ), pp. 1095–1102.
ICML-2007-YuTY #multi #robust: Robust multi-task learning with t-processes (SY, VT, KY), pp. 1103–1110.
ICML-2007-ZhangAV #multi #random: Conditional random fields for multi-agent reinforcement learning (XZ, DA, SVNV), pp. 1143–1150.
ICML-2007-ZhaoL #feature model: Spectral feature selection for supervised and unsupervised learning (ZZ, HL), pp. 1151–1157.
ICML-2007-ZhouB #clustering #multi: Spectral clustering and transductive learning with multiple views (DZ, CJCB), pp. 1159–1166.
ICML-2007-ZhouX #multi #on the: On the relation between multi-instance learning and semi-supervised learning (ZHZ, JMX), pp. 1167–1174.
ICML-2007-ZienO #kernel #multi: Multiclass multiple kernel learning (AZ, CSO), pp. 1191–1198.
KDD-2007-ChenZYL #adaptation #clustering #distance #metric: Nonlinear adaptive distance metric learning for clustering (JC, ZZ, JY, HL), pp. 123–132.
KDD-2007-DeodharG #clustering #framework: A framework for simultaneous co-clustering and learning from complex data (MD, JG), pp. 250–259.
KDD-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.
KDD-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.
KDD-2007-Parthasarathy #data mining #mining: Data mining at the crossroads: successes, failures and learning from them (SP), pp. 1053–1055.
KDD-2007-RadlinskiJ #ranking: Active exploration for learning rankings from clickthrough data (FR, TJ), pp. 570–579.
KDD-2007-Schickel-ZuberF #clustering #recommendation #using: Using hierarchical clustering for learning theontologies used in recommendation systems (VSZ, BF), pp. 599–608.
KDD-2007-Sculley #feedback: Practical learning from one-sided feedback (DS), pp. 609–618.
KDD-2007-ShengL: Partial example acquisition in cost-sensitive learning (VSS, CXL), pp. 638–646.
KDD-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.
MLDM-2007-CeciABM #relational: Transductive Learning from Relational Data (MC, AA, NB, DM), pp. 324–338.
MLDM-2007-EkdahlK #classification #on the: On Concentration of Discrete Distributions with Applications to Supervised Learning of Classifiers (ME, TK), pp. 2–16.
MLDM-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.
MLDM-2007-JiangI: Dynamic Distance-Based Active Learning with SVM (JJ, HHSI), pp. 296–309.
MLDM-2007-Kertesz-FarkasKP #classification #equivalence: Equivalence Learning in Protein Classification (AKF, AK, SP), pp. 824–837.
MLDM-2007-Lehmann #hybrid #ontology: Hybrid Learning of Ontology Classes (JL), pp. 883–898.
MLDM-2007-VanderlooyMS #empirical #evaluation: Off-Line Learning with Transductive Confidence Machines: An Empirical Evaluation (SV, LvdM, IGSK), pp. 310–323.
RecSys-2007-RubensS #collaboration: Influence-based collaborative active learning (NR, MS), pp. 145–148.
RecSys-2007-TaghipourKG #approach #recommendation #web: Usage-based web recommendations: a reinforcement learning approach (NT, AAK, SSG), pp. 113–120.
RecSys-2007-TiemannP #hybrid #music #recommendation #towards: Towards ensemble learning for hybrid music recommendation (MT, SP), pp. 177–178.
SEKE-2007-FarEHA #approach #concept #named #ontology #statistics: Adjudicator: A Statistical Approach for Learning Ontology Concepts from Peer Agents (BHF, AHE, NH, MA), p. 654–?.
SEKE-2007-FollecoKHS #quality: Learning from Software Quality Data with Class Imbalance and Noise (AF, TMK, JVH, CS), p. 487–?.
SIGIR-2007-EfthimiadisF #education #information retrieval #named: IR-Toolbox: an experiential learning tool for teaching IR (ENE, NGF), p. 914.
SIGIR-2007-ErtekinHG #problem: Active learning for class imbalance problem (SE, JH, CLG), pp. 823–824.
SIGIR-2007-JansenSB #online #paradigm: Viewing online searching within a learning paradigm (BJJ, BKS, DLB), pp. 859–860.
SIGIR-2007-VelipasaogluSP #constraints: Improving active learning recall via disjunctive boolean constraints (EV, HS, JOP), pp. 893–894.
SIGIR-2007-XuL07a #rank: Learning to rank collections (JX, XL), pp. 765–766.
SIGIR-2007-ZhangHRJ #query #using: Query rewriting using active learning for sponsored search (WVZ, XH, BR, RJ), pp. 853–854.
SIGIR-2007-ZhengCSZ #framework #ranking #using: A regression framework for learning ranking functions using relative relevance judgments (ZZ, KC, GS, HZ), pp. 287–294.
ICSE-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.
ICSE-2007-Zualkernan #programming #using: Using Soloman-Felder Learning Style Index to Evaluate Pedagogical Resources for Introductory Programming Classes (IAZ), pp. 723–726.
SAC-2007-BarratT #recognition: A progressive learning method for symbols recognition (SB, ST), pp. 627–631.
SAC-2007-RulloCP #categorisation: Learning rules with negation for text categorization (PR, CC, VLP), pp. 409–416.
DATE-2007-Huang: Dynamic learning based scan chain diagnosis (YH0), pp. 510–515.
PPoPP-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.
STOC-2007-GuhaM #algorithm #approximate #problem: Approximation algorithms for budgeted learning problems (SG, KM), pp. 104–113.
TACAS-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.
CAV-2007-SinhaC #composition #lazy evaluation #satisfiability #using #verification: SAT-Based Compositional Verification Using Lazy Learning (NS, EMC), pp. 39–54.
SAT-2007-ArgelichM #satisfiability: Partial Max-SAT Solvers with Clause Learning (JA, FM), pp. 28–40.
TestCom-FATES-2007-ShahbazLG #component #integration #testing: Learning and Integration of Parameterized Components Through Testing (MS, KL, RG), pp. 319–334.
VMCAI-2007-Madhusudan #algorithm #verification: Learning Algorithms and Formal Verification (PM), p. 214.
DocEng-2006-ChidlovskiiFL #documentation #interface #named: ALDAI: active learning documents annotation interface (BC, JF, LL), pp. 184–185.
DocEng-2006-LecerfC #documentation: Document annotation by active learning techniques (LL, BC), pp. 125–127.
ECDL-2006-LeeTGF #approach #design: An Exploratory Factor Analytic Approach to Understand Design Features for Academic Learning Environments (SSL, YLT, DHLG, SSBF), pp. 315–328.
ECDL-2006-WuW #library #towards: Towards a Digital Library for Language Learning (SW, IHW), pp. 341–352.
JCDL-2006-CarvalhoGLS: Learning to deduplicate (MGdC, MAG, AHFL, ASdS), pp. 41–50.
JCDL-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.
JCDL-2006-MoenMEPS #analysis #metadata: Learning from artifacts: metadata utilization analysis (WEM, SDM, AE, SP, GS), pp. 270–271.
JCDL-2006-NicholsBDT #library: Learning by building digital libraries (DMN, DB, JSD, MBT), pp. 185–186.
VLDB-2006-ShivamBC #cost analysis #modelling #optimisation: Active and Accelerated Learning of Cost Models for Optimizing Scientific Applications (PS, SB, JSC), pp. 535–546.
CSEET-2006-WangS #re-engineering: Writing as a Tool for Learning Software Engineering (AIW, CFS), pp. 35–42.
ITiCSE-2006-AmzadO #modelling: Model based project centered team learning (IA, AJO), p. 328.
ITiCSE-2006-BirdC #problem: Building a search engine to drive problem-based learning (SB, JRC), pp. 153–157.
ITiCSE-2006-Ellis06a #approach #named #self: Self-grading: an approach to supporting self-directed learning (HJCE), p. 349.
ITiCSE-2006-GriswoldS #performance #scalability #ubiquitous: Ubiquitous presenter: fast, scalable active learning for the whole classroom (WGG, BS), p. 358.
ITiCSE-2006-HielscherW #automaton #education #formal method #named: AtoCC: learning environment for teaching theory of automata and formal languages (MH, CW), p. 306.
ITiCSE-2006-HughesP #object-oriented #programming #student: ASSISTing CS1 students to learn: learning approaches and object-oriented programming (JH, DRP), pp. 275–279.
ITiCSE-2006-KeenanPCM #agile: Learning project planning the agile way (FK, SP, GC, KM), p. 324.
ITiCSE-2006-OKellyG #approach #education #problem #programming: RoboCode & problem-based learning: a non-prescriptive approach to teaching programming (JO, JPG), pp. 217–221.
ITiCSE-2006-PlimmerA #education #human-computer: Peer teaching extends HCI learning (BP, RA), pp. 53–57.
ITiCSE-2006-Quade #hybrid #re-engineering: Developing a hybrid software engineering curse that promotes project-based active learning (AMQ), p. 308.
ITiCSE-2006-Rodger #automaton #formal method: Learning automata and formal languages interactively with JFLAP (SHR), p. 360.
SIGITE-2006-Gutierrez #approach #named #security: Stingray: a hands-on approach to learning information security (FG), pp. 53–58.
AIIDE-2006-WhiteB #game studies #multi: The Self Organization of Context for Learning in MultiAgent Games (CDW, DB), pp. 92–97.
CIG-2006-BouzyC #monte carlo: Monte-Carlo Go Reinforcement Learning Experiments (BB, GC), pp. 187–194.
CIG-2006-KarpovDVSM #evaluation #game studies #integration: Integration and Evaluation of Exploration-Based Learning in Games (IK, TD, CV, KOS, RM), pp. 39–44.
CIG-2006-LucasR #co-evolution #difference #evaluation: Temporal Difference Learning Versus Co-Evolution for Acquiring Othello Position Evaluation (SML, TPR), pp. 52–59.
CIG-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.
CHI-2006-GweonRCZ #adaptation #collaboration #online: Providing support for adaptive scripting in an on-line collaborative learning environment (GG, CPR, RC, ZZ), pp. 251–260.
CHI-2006-Moher #distributed #embedded #simulation: Embedded phenomena: supporting science learning with classroom-sized distributed simulations (TM), pp. 691–700.
CHI-2006-SiekCR #how #people: Pride and prejudice: learning how chronically ill people think about food (KAS, KHC, YR), pp. 947–950.
CSCW-2006-Danis #collaboration #performance: Forms of collaboration in high performance computing: exploring implications for learning (CD), pp. 501–504.
CSCW-2006-RazaviI #behaviour #information management: A grounded theory of information sharing behavior in a personal learning space (MNR, LI), pp. 459–468.
ICEIS-HCI-2006-Patokorpi: Constructivist Instructional Principles, Learner Psychology and Technological Enablers of Learning (EP), pp. 103–109.
ICEIS-SAIC-2006-LuciaFGPT #legacy #migration #multi #video: Migrating Legacy Video Lectures to Multimedia Learning Objects (ADL, RF, MG, IP, GT), pp. 51–58.
ICEIS-SAIC-2006-MarjanovicSMRG #approach #collaboration #process: Supporting Complex Collaborative Learning Activities — The Libresource Approach (OM, HSM, PM, FAR, CG), pp. 59–65.
ICEIS-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.
CIKM-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.
ECIR-2006-VildjiounaiteK: Learning Links Between a User’s Calendar and Information Needs (EV, VK), pp. 557–560.
ICML-2006-AbbeelQN #modelling #using: Using inaccurate models in reinforcement learning (PA, MQ, AYN), pp. 1–8.
ICML-2006-AgarwalBB #graph #higher-order: Higher order learning with graphs (SA, KB, SB), pp. 17–24.
ICML-2006-AsgharbeygiSL #difference #relational: Relational temporal difference learning (NA, DJS, PL), pp. 49–56.
ICML-2006-BalcanB #formal method #on the #similarity: On a theory of learning with similarity functions (MFB, AB), pp. 73–80.
ICML-2006-BalcanBL: Agnostic active learning (MFB, AB, JL), pp. 65–72.
ICML-2006-BowlingMJNW #policy #predict #using: Learning predictive state representations using non-blind policies (MHB, PM, MJ, JN, DFW), pp. 129–136.
ICML-2006-BrefeldS: Semi-supervised learning for structured output variables (UB, TS), pp. 145–152.
ICML-2006-CaruanaN #algorithm #comparison #empirical: An empirical comparison of supervised learning algorithms (RC, ANM), pp. 161–168.
ICML-2006-CheungK #framework #multi: A regularization framework for multiple-instance learning (PMC, JTK), pp. 193–200.
ICML-2006-ConitzerG #algorithm #online #problem: Learning algorithms for online principal-agent problems (and selling goods online) (VC, NG), pp. 209–216.
ICML-2006-DegrisSW #markov #problem #process: Learning the structure of Factored Markov Decision Processes in reinforcement learning problems (TD, OS, PHW), pp. 257–264.
ICML-2006-DenisMR #classification #naive bayes #performance: Efficient learning of Naive Bayes classifiers under class-conditional classification noise (FD, CNM, LR), pp. 265–272.
ICML-2006-desJardinsEW #set: Learning user preferences for sets of objects (Md, EE, KW), pp. 273–280.
ICML-2006-EpshteynD: Qualitative reinforcement learning (AE, GD), pp. 305–312.
ICML-2006-FinkSSU #multi #online: Online multiclass learning by interclass hypothesis sharing (MF, SSS, YS, SU), pp. 313–320.
ICML-2006-GlobersonR #robust: Nightmare at test time: robust learning by feature deletion (AG, STR), pp. 353–360.
ICML-2006-Haffner #kernel #performance: Fast transpose methods for kernel learning on sparse data (PH), pp. 385–392.
ICML-2006-Hanneke #analysis #graph: An analysis of graph cut size for transductive learning (SH), pp. 393–399.
ICML-2006-HertzBW #classification #kernel: Learning a kernel function for classification with small training samples (TH, ABH, DW), pp. 401–408.
ICML-2006-HoiJZL #classification #image: Batch mode active learning and its application to medical image classification (SCHH, RJ, JZ, MRL), pp. 417–424.
ICML-2006-KellerMP #approximate #automation #programming: Automatic basis function construction for approximate dynamic programming and reinforcement learning (PWK, SM, DP), pp. 449–456.
ICML-2006-KonidarisB #information management: Autonomous shaping: knowledge transfer in reinforcement learning (GK, AGB), pp. 489–496.
ICML-2006-KulisSD #kernel #matrix #rank: Learning low-rank kernel matrices (BK, MAS, ISD), pp. 505–512.
ICML-2006-McAuleyCSF #higher-order #image: Learning high-order MRF priors of color images (JJM, TSC, AJS, MOF), pp. 617–624.
ICML-2006-NaorR: Learning to impersonate (MN, GNR), pp. 649–656.
ICML-2006-NejatiLK #network: Learning hierarchical task networks by observation (NN, PL, TK), pp. 665–672.
ICML-2006-NevmyvakaFK #execution: Reinforcement learning for optimized trade execution (YN, YF, MK), pp. 673–680.
ICML-2006-PoupartVHR: An analytic solution to discrete Bayesian reinforcement learning (PP, NAV, JH, KR), pp. 697–704.
ICML-2006-RahmaniG #multi #named: MISSL: multiple-instance semi-supervised learning (RR, SAG), pp. 705–712.
ICML-2006-RainaNK #using: Constructing informative priors using transfer learning (RR, AYN, DK), pp. 713–720.
ICML-2006-RuckertK #approach #statistics: A statistical approach to rule learning (UR, SK), pp. 785–792.
ICML-2006-SenG #markov #network: Cost-sensitive learning with conditional Markov networks (PS, LG), pp. 801–808.
ICML-2006-SilvaS #metric #modelling: Bayesian learning of measurement and structural models (RBdAeS, RS), pp. 825–832.
ICML-2006-SinghiL #bias #classification #set: Feature subset selection bias for classification learning (SKS, HL), pp. 849–856.
ICML-2006-SongE #human-computer #interface: Classifying EEG for brain-computer interfaces: learning optimal filters for dynamical system features (LS, JE), pp. 857–864.
ICML-2006-StrehlLWLL: PAC model-free reinforcement learning (ALS, LL, EW, JL, MLL), pp. 881–888.
ICML-2006-StrehlMLH #problem: Experience-efficient learning in associative bandit problems (ALS, CM, MLL, HH), pp. 889–896.
ICML-2006-XuWSS #predict: Discriminative unsupervised learning of structured predictors (LX, DFW, FS, DS), pp. 1057–1064.
ICML-2006-YuBT #design: Active learning via transductive experimental design (KY, JB, VT), pp. 1081–1088.
ICPR-v1-2006-Al-ZubiS #adaptation: Learning to Imitate Human Movement to Adapt to Environmental Changes (SAZ, GS), pp. 191–194.
ICPR-v1-2006-FredJ #clustering #similarity: Learning Pairwise Similarity for Data Clustering (ALNF, AKJ), pp. 925–928.
ICPR-v1-2006-IshidaTIMM #generative #identification: Identification of degraded traffic sign symbols by a generative learning method (HI, TT, II, YM, HM), pp. 531–534.
ICPR-v1-2006-JiangXT: Shape Alignment by Learning a Landmark-PDM Coupled Model (YJ, JX, HTT), pp. 959–962.
ICPR-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.
ICPR-v1-2006-OngB #clustering: Learning Wormholes for Sparsely Labelled Clustering (EJO, RB), pp. 916–919.
ICPR-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.
ICPR-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.
ICPR-v2-2006-AutioL #online #sequence: Online Learning of Discriminative Patterns from Unlimited Sequences of Candidates (IA, JTL), pp. 437–440.
ICPR-v2-2006-ChenJY #reduction #robust: Robust Nonlinear Dimensionality Reduction for Manifold Learning (HC, GJ, KY), pp. 447–450.
ICPR-v2-2006-GaoLL #approach #classification #optimisation: An ensemble classifier learning approach to ROC optimization (SG, CHL, JHL), pp. 679–682.
ICPR-v2-2006-GuoQ #3d: Learning and Inference of 3D Human Poses from Gaussian Mixture Modeled Silhouettes (FG, GQ), pp. 43–47.
ICPR-v2-2006-HarpazH #geometry: Exploiting the Geometry of Gene Expression Patterns for Unsupervised Learning (RH, RMH), pp. 670–674.
ICPR-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.
ICPR-v2-2006-JonssonF: Correspondence-free Associative Learning (EJ, MF), pp. 441–446.
ICPR-v2-2006-KelmPM #classification #generative #multi: Combining Generative and Discriminative Methods for Pixel Classification with Multi-Conditional Learning (BMK, CP, AM), pp. 828–832.
ICPR-v2-2006-LernerM #classification #image #network: Learning Bayesian Networks for Cytogenetic Image Classification (BL, RM), pp. 772–775.
ICPR-v2-2006-PungprasertyingCK #analysis #approach #migration #performance: Migration Analysis: An Alternative Approach for Analyzing Learning Performance (PP, RC, BK), pp. 837–840.
ICPR-v2-2006-ScalzoP: Unsupervised Learning of Dense Hierarchical Appearance Represe (FS, JHP), pp. 395–398.
ICPR-v2-2006-StefanoDMF: Improving Dynamic Learning Vector Quantization (CDS, CD, AM, ASdF), pp. 804–807.
ICPR-v2-2006-SungZL #scalability #set: Accelerating the SVM Learning for Very Large Data Sets (ES, YZ, XL), pp. 484–489.
ICPR-v2-2006-WuLZH: A Semi-supervised SVM for Manifold Learning (ZW, ChL, JZ, JH), pp. 490–493.
ICPR-v2-2006-XuWH #algorithm: A maximum margin discriminative learning algorithm for temporal signals (WX, JW, ZH), pp. 460–463.
ICPR-v2-2006-ZhangPB #classification #representation: Learning Optimal Filter Representation for Texture Classification (PZ, JP, BPB), pp. 1138–1141.
ICPR-v2-2006-ZhangR #incremental: A New Data Selection Principle for Semi-Supervised Incremental Learning (RZ, AIR), pp. 780–783.
ICPR-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.
ICPR-v2-2006-ZhengLY #kernel #problem: Weakly Supervised Learning on Pre-image Problem in Kernel Methods (WSZ, JHL, PCY), pp. 711–715.
ICPR-v2-2006-ZouL #performance #sequence: The Generalization Performance of Learning Machine Based on Phi-mixing Sequence (BZ, LL), pp. 548–551.
ICPR-v3-2006-AlahariPJ #online #recognition: Learning Mixtures of Offline and Online features for Handwritten Stroke Recognition (KA, SLP, CVJ), pp. 379–382.
ICPR-v3-2006-GunselK: Perceptual Audio Watermarking by Learning in Wavelet Domain (BG, SK), pp. 383–386.
ICPR-v3-2006-IsukapalliE #identification #policy: Learning Policies for Efficiently Identifying Objects of Many Classes (RI, AME, RG), pp. 356–361.
ICPR-v3-2006-Martinez-ArroyoS #classification #naive bayes: Learning an Optimal Naive Bayes Classifier (MMA, LES), pp. 1236–1239.
ICPR-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.
ICPR-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.
ICPR-v4-2006-Martinez-ArroyoS06a #classification #naive bayes: Learning an Optimal Naive Bayes Classifier (MMA, LES), p. 958.
ICPR-v4-2006-YangLPZZ #detection: Active Learning Based Pedestrian Detection in Real Scenes (TY, JL, QP, CZ, YZ), pp. 904–907.
ICPR-v4-2006-ZhengLL #network: Control Double Inverted Pendulum by Reinforcement Learning with Double CMAC Network (YZ, SL, ZL), pp. 639–642.
KDD-2006-AbeZL #detection: Outlier detection by active learning (NA, BZ, JL), pp. 504–509.
KDD-2006-AgarwalCA #rank: Learning to rank networked entities (AA, SC, SA), pp. 14–23.
KDD-2006-CarvalhoC #feature model #online #performance: Single-pass online learning: performance, voting schemes and online feature selection (VRC, WWC), pp. 548–553.
KDD-2006-HoiLC #classification #kernel: Learning the unified kernel machines for classification (SCHH, MRL, EYC), pp. 187–196.
KDD-2006-LongWZY #graph: Unsupervised learning on k-partite graphs (BL, XW, Z(Z, PSY), pp. 317–326.
KDD-2006-RosalesF #linear #metric #programming: Learning sparse metrics via linear programming (RR, GF), pp. 367–373.
SIGIR-2006-AgichteinBDR #interactive #modelling #predict #web: Learning user interaction models for predicting web search result preferences (EA, EB, STD, RR), pp. 3–10.
SIGIR-2006-CarteretteP #ranking: Learning a ranking from pairwise preferences (BC, DP), pp. 629–630.
SIGIR-2006-HuangZL #taxonomy: Refining hierarchical taxonomy structure via semi-supervised learning (RH, ZZ, WL), pp. 653–654.
SIGIR-2006-LacerdaCGFZR: Learning to advertise (AL, MC, MAG, WF, NZ, BARN), pp. 549–556.
SIGIR-2006-WuJ #framework #graph #multi: A graph-based framework for relation propagation and its application to multi-label learning (MW, RJ), pp. 717–718.
SIGIR-2006-ZhaZFS #difference #information retrieval #query: Incorporating query difference for learning retrieval functions in information retrieval (HZ, ZZ, HF, GS), pp. 721–722.
SAC-2006-CraigL #classification #using: Protein classification using transductive learning on phylogenetic profiles (RAC, LL), pp. 161–166.
SAC-2006-Ferrer-TroyanoAS #classification #data type #incremental: Data streams classification by incremental rule learning with parameterized generalization (FJFT, JSAR, JCRS), pp. 657–661.
SAC-2006-PechenizkiyPT #feature model #reduction: The impact of sample reduction on PCA-based feature extraction for supervised learning (MP, SP, AT), pp. 553–558.
CASE-2006-ReveliotisB #algorithm #performance: Efficient learning algorithms for episodic tasks with acyclic state spaces (SR, TB), pp. 411–418.
CASE-2006-ZhouD #game studies: An evolutionary game model on supply chains learning through imitation (MZ, FD), pp. 645–648.
DAC-2006-WangGG #deduction #difference #logic: Predicate learning and selective theory deduction for a difference logic solver (CW, AG, MKG), pp. 235–240.
PDP-2006-ClematisFQ #distributed: Interacting with Learning Objects in a Distributed Environment (AC, PF, AQ), pp. 322–329.
PDP-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.
FASE-2006-RaffeltS #automaton #library #named: LearnLib: A Library for Automata Learning and Experimentation (HR, BS), pp. 377–380.
STOC-2006-AngluinACW #injection: Learning a circuit by injecting values (DA, JA, JC, YW), pp. 584–593.
STOC-2006-Feldman #approximate #logic #query: Hardness of approximate two-level logic minimization and PAC learning with membership queries (VF), pp. 363–372.
CAV-2006-VardhanV #named #verification: LEVER: A Tool for Learning Based Verification (AV, MV), pp. 471–474.
FATES-RV-2006-VeanesRC #online #testing: Online Testing with Reinforcement Learning (MV, PR, CC), pp. 240–253.
ICLP-2006-Aguilar-Solis #approach #constraints #parsing #semantics: Learning Semantic Parsers: A Constraint Handling Rule Approach (DAS), pp. 447–448.
SAT-2006-YuM #constraints #linear #smt: Lemma Learning in SMT on Linear Constraints (YY, SM), pp. 142–155.
ECDL-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.
ECDL-2005-NajjarKVD #empirical #evaluation: Finding Appropriate Learning Objects: An Empirical Evaluation (JN, JK, RV, ED), pp. 323–335.
HT-2005-BerlangaG #adaptation #design #modelling #navigation #specification #using: Modelling adaptive navigation support techniques using the IMS learning design specification (AJB, FJG), pp. 148–150.
ICDAR-2005-BargeronVS #detection: Boosting-based Transductive Learning for Text Detection (DB, PAV, PYS), pp. 1166–1171.
ICDAR-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.
ICDAR-2005-FengHG #approach #semantics #web: A Learning Approach to Discovering Web Page Semantic Structures (JF, PH, MG), pp. 1055–1059.
ICDAR-2005-LavenLR #analysis #approach #documentation #image #statistics: A Statistical Learning Approach To Document Image Analysis (KL, SL, STR), pp. 357–361.
ICDAR-2005-RaghavendraNSRS #online #prototype #recognition: Prototype Learning Methods for Online Handwriting Recognition (BSR, CKN, GS, AGR, MS), pp. 287–291.
ICDAR-2005-Szummer #diagrams #random: Learning Diagram Parts with Hidden Random Fields (MS), pp. 1188–1193.
JCDL-2005-ChangHTLTG #education: Evaluating G-portal for geography learning and teaching (CHC, JGH, YLT, EPL, TST, DHLG), pp. 21–22.
JCDL-2005-FoxG #education #library: Introduction to (teaching/learning about) digital libraries (EAF, MAG), p. 419.
SIGMOD-2005-BragaCCR #named #query #visual notation #xml: XQBE: a visual environment for learning XML query languages (DB, AC, SC, AR), pp. 903–905.
VLDB-2005-ZhangHJLZ #cost analysis #query #statistics #xml: Statistical Learning Techniques for Costing XML Queries (NZ, PJH, VJ, GML, CZ), pp. 289–300.
CSEET-2005-BunseGOPS #education #re-engineering: xd Software Engineering Education Applying a Blended Learning Strategy for (CB, IG, MO, CP, SSN), pp. 95–102.
CSEET-2005-Ellis #online #re-engineering: Autonomous Learning in Online and Traditional Versions of a Software Engineering Course (HJCE), pp. 69–76.
CSEET-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.
ITiCSE-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.
ITiCSE-2005-ChamillardS #education: Learning styles across the curriculum (ATC, RES), pp. 241–245.
ITiCSE-2005-DavisW #convergence #education #multi: A research-led curriculum in multimedia: learning about convergence (HCD, SW), pp. 29–33.
ITiCSE-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.
ITiCSE-2005-Granger #collaboration #communication #concept: Learning technical concepts with collaboration and communication skills (MJG), p. 391.
ITiCSE-2005-HurtadoV: Learning UNIX in first year of computer engineering (MASH, CVP), p. 392.
ITiCSE-2005-LoftusR #programming #question: Extreme programming promotes extreme learning? (CWL, MR), pp. 311–315.
ITiCSE-2005-Marcelino #programming: Learning repetition structures in programming (MJM), p. 351.
ITiCSE-2005-NugentSSPL #design #development #validation: Design, development, and validation of a learning object for CS1 (GN, LKS, AS, SP, JL), p. 370.
ITiCSE-2005-Truong: The environment for learning to program (NT), p. 383.
ITiCSE-2005-TruongBR #web: Learning to program through the web (NT, PB, PR), pp. 9–13.
ITiCSE-2005-Vinha #reuse #theory and practice: Reusable learning objects: theory to practice (AV), p. 413.
SIGITE-2005-AbernethyTPR #repository: A learning object repository in support of introductory IT courses (KA, KT, GP, HR), pp. 223–227.
SIGITE-2005-Backhouse #analysis #design: Learning individual group skills for software analysis and design in Africa (JB), pp. 107–112.
SIGITE-2005-BaileyMB: Creative learning with practical applications for 802.11 wireless communications (MGB, JHM, NHB), pp. 369–370.
SIGITE-2005-FulbrightR #student: IPC incorporated: a student-run IT services company for experiential learning (RF, RLR), pp. 211–216.
SIGITE-2005-IqbalE #assessment #education: Scenario based method for teaching, learning and assessment (RI, PE), pp. 261–266.
SIGITE-2005-MarchantT #student #using: Using pre-release software to SPUR student learning (AM, BT), pp. 143–148.
SIGITE-2005-OliverP: Mixed-project-based learning methodology in computer electronic engineering (JO, MP), pp. 291–294.
SIGITE-2005-PatchaS #development #distance #internet: Development of an internet based distance learning program at Virginia Tech (AP, GS), pp. 379–380.
SIGITE-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.
SIGITE-2005-WillisM #tool support: Mind tools for enhancing thinking and learning skills (CLW, SLM), pp. 249–254.
MSR-2005-HuangL #mining #process #verification #version control: Mining version histories to verify the learning process of Legitimate Peripheral Participants (SKH, KmL), pp. 21–25.
CIAA-2005-GarciaRCA #question: Is Learning RFSAs Better Than Learning DFAs? (PG, JR, AC, GIA), pp. 343–344.
CIAA-2005-HigueraPT #automaton #finite #probability #recognition: Learning Stochastic Finite Automata for Musical Style Recognition (CdlH, FP, FT), pp. 345–346.
AIIDE-2005-GorniakB #sequence: Sequence Learning by Backward Chaining in Synthetic Characters (PG, BB), pp. 51–56.
AIIDE-2005-StanleyCM #game studies #realtime #video: Real-time Learning in the NERO Video Game (KOS, RC, RM), pp. 159–160.
CIG-2005-BradleyH #adaptation #game studies #using: Adapting Reinforcement Learning for Computer Games: Using Group Utility Functions (JB, GH).
CIG-2005-DenzingerW #behaviour: Combining Coaching and Learning to Create Cooperative Character Behavior (JD, CW).
CIG-2005-KokHBV #coordination: Utile Coordination: Learning Interdependencies Among Cooperative Agents (JRK, PJH, BB, NAV).
CIG-2005-YangG #multi #overview #towards: A Survey on Multiagent Reinforcement Learning Towards Multi-Robot Systems (EY, DG).
DiGRA-2005-Becker #education #game studies #how: How Are Games Educational? Learning Theories Embodied in Games (KB).
DiGRA-2005-BeckerJ #game studies #question #what: Games for Learning: Are Schools Ready for What's to Come? (KB, MJ).
DiGRA-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).
DiGRA-2005-Eaton #comprehension #game studies: Narrative comprehension in computer games: Implications for literacy and learning (IE).
DiGRA-2005-Engeli #design #editing #game studies: Playful Play with Games: Linking Level Editing to Learning in Art and Design (ME).
DiGRA-2005-Folmann #game studies #music: Game Music - learning from the Movies (TBF).
DiGRA-2005-Galarneau #authentication #case study #experience #game studies #simulation: Authentic Learning Experiences Through Play: Games, Simulations and the Construction of Knowledge (LG).
DiGRA-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).
DiGRA-2005-Hayes #social: Learning and Literacies in the Social World of Tony Hawk Underground 2 (ERH).
DiGRA-2005-KaoGK #game studies #multi #quote: “A Totally Different World”: Playing and Learning in Multi-User Virtual Environments (LK, CG, YBK).
DiGRA-2005-Magnussen #framework #game studies #platform: Learning Games as a Platform for Simulated Science Practice (RM).
DiGRA-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).
DiGRA-2005-ParasB #design #education #effectiveness #game studies #motivation: Game, Motivation, and Effective Learning: An Integrated Model for Educational Game Design (BSP, JB).
DiGRA-2005-Pelletier05a #design #education #game studies: Studying Games in School: learning and teaching about game design, play and culture (CP).
DiGRA-2005-SauveLPBAK #game studies #online #realtime: Playing And Learning Without Borders: A Real-time Online Play Environment (LS, VL, WP, GMB, VGSA, DK).
DiGRA-2005-SweedykL #game studies: Games, Metaphor, and Learning (ES, MdL).
DiGRA-2005-UlicsakSFWF #design #game studies: Time out? Exploring the role of reflection in the design of games for learning (MHU, SS, KF, BW).
DiGRA-2005-WilliamsonSS #game studies #peer-to-peer #social: Racing Academy: peer-to-peer learning in a social racing game (BW, SS, RS).
CHI-2005-BondarenkoJ: Dcuments at Hand: Learning from Paper to Improve Digital Technologies (OB, RJ), pp. 121–130.
CHI-2005-XieLGM #image: Learning user interest for image browsing on small-form-factor devices (XX, HL, SG, WYM), pp. 671–680.
CHI-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.
EDOC-2005-FerreiraF #lifecycle #workflow: Learning, planning, and the life cycle of workflow management (DRF, HMF), pp. 39–46.
ICEIS-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.
ICEIS-v2-2005-LokugeA #hybrid #multi: Handling Multiple Events in Hybrid BDI Agents with Reinforcement Learning: A Container Application (PL, DA), pp. 83–90.
ICEIS-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.
ICEIS-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.
ICEIS-v5-2005-Goren-Bar #evaluation #interactive #student: Student’s Evaluation of Web-Based Learning Technologies in a Humancomputer Interaction Course (DGB), pp. 206–212.
ICEIS-v5-2005-IslamARR #distance #mobile: Mobile Telephone Technology as a Distance Learning Tool (YMI, MA, ZR, MR), pp. 226–232.
ICEIS-v5-2005-LeR #named: LINC: A Web-Based Learning Tool for Mixed-Mode Learning (THL, JR), pp. 154–160.
CIKM-2005-AminiTULG #documentation #using #xml: Learning to summarise XML documents using content and structure (MRA, AT, NU, ML, PG), pp. 297–298.
CIKM-2005-RoussinovFN05a #approach #information retrieval: Discretization based learning approach to information retrieval (DR, WF, FADN), pp. 321–322.
CIKM-2005-XiongSK #database #multi #privacy: Privacy leakage in multi-relational databases via pattern based semi-supervised learning (HX, MS, VK), pp. 355–356.
ICML-2005-AbbeelN: Exploration and apprenticeship learning in reinforcement learning (PA, AYN), pp. 1–8.
ICML-2005-AndersonM #algorithm #markov #modelling: Active learning for Hidden Markov Models: objective functions and algorithms (BA, AM), pp. 9–16.
ICML-2005-BlockeelPS #multi: Multi-instance tree learning (HB, DP, AS), pp. 57–64.
ICML-2005-BurgeL #network: Learning class-discriminative dynamic Bayesian networks (JB, TL), pp. 97–104.
ICML-2005-BurgesSRLDHH #rank #using: Learning to rank using gradient descent (CJCB, TS, ER, AL, MD, NH, GNH), pp. 89–96.
ICML-2005-ChangK: Hedged learning: regret-minimization with learning experts (YHC, LPK), pp. 121–128.
ICML-2005-ChuG #process: Preference learning with Gaussian processes (WC, ZG), pp. 137–144.
ICML-2005-CortesMW: A general regression technique for learning transductions (CC, MM, JW), pp. 153–160.
ICML-2005-CrandallG #game studies: Learning to compete, compromise, and cooperate in repeated general-sum games (JWC, MAG), pp. 161–168.
ICML-2005-DaumeM #approximate #optimisation #predict #scalability: Learning as search optimization: approximate large margin methods for structured prediction (HDI, DM), pp. 169–176.
ICML-2005-DrakeV: A practical generalization of Fourier-based learning (AD, DV), pp. 185–192.
ICML-2005-DriessensD #first-order #modelling: Combining model-based and instance-based learning for first order regression (KD, SD), pp. 193–200.
ICML-2005-EngelMM #process: Reinforcement learning with Gaussian processes (YE, SM, RM), pp. 201–208.
ICML-2005-GirolamiR #kernel #modelling: Hierarchic Bayesian models for kernel learning (MG, SR), pp. 241–248.
ICML-2005-GroisW #approach #comprehension: Learning strategies for story comprehension: a reinforcement learning approach (EG, DCW), pp. 257–264.
ICML-2005-HerbsterPW #graph #online: Online learning over graphs (MH, MP, LW), pp. 305–312.
ICML-2005-IlghamiMNA #approximate: Learning approximate preconditions for methods in hierarchical plans (OI, HMA, DSN, DWA), pp. 337–344.
ICML-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.
ICML-2005-JodogneP #interactive #visual notation: Interactive learning of mappings from visual percepts to actions (SJ, JHP), pp. 393–400.
ICML-2005-KokD #logic #markov #network: Learning the structure of Markov logic networks (SK, PMD), pp. 441–448.
ICML-2005-LangfordZ #classification #performance: Relating reinforcement learning performance to classification performance (JL, BZ), pp. 473–480.
ICML-2005-Mahadevan: Proto-value functions: developmental reinforcement learning (SM), pp. 553–560.
ICML-2005-MichelsSN #using: High speed obstacle avoidance using monocular vision and reinforcement learning (JM, AS, AYN), pp. 593–600.
ICML-2005-NatarajanT #multi: Dynamic preferences in multi-criteria reinforcement learning (SN, PT), pp. 601–608.
ICML-2005-NatarajanTADFR #first-order #modelling #probability: Learning first-order probabilistic models with combining rules (SN, PT, EA, TGD, AF, ACR), pp. 609–616.
ICML-2005-Niculescu-MizilC #predict: Predicting good probabilities with supervised learning (ANM, RC), pp. 625–632.
ICML-2005-OntanonP #multi: Recycling data for multi-agent learning (SO, EP), pp. 633–640.
ICML-2005-PernkopfB #classification #generative #network #parametricity: Discriminative versus generative parameter and structure learning of Bayesian network classifiers (FP, JAB), pp. 657–664.
ICML-2005-RayC #comparison #empirical #multi: Supervised versus multiple instance learning: an empirical comparison (SR, MC), pp. 697–704.
ICML-2005-RosellHRP #why: Why skewing works: learning difficult Boolean functions with greedy tree learners (BR, LH, SR, DP), pp. 728–735.
ICML-2005-RousuSSS #classification #modelling #multi: Learning hierarchical multi-category text classification models (JR, CS, SS, JST), pp. 744–751.
ICML-2005-SiddiqiM #performance: Fast inference and learning in large-state-space HMMs (SMS, AWM), pp. 800–807.
ICML-2005-SilvaS #identification #modelling: New d-separation identification results for learning continuous latent variable models (RBdAeS, RS), pp. 808–815.
ICML-2005-SimsekWB #clustering #graph #identification: Identifying useful subgoals in reinforcement learning by local graph partitioning (ÖS, APW, AGB), pp. 816–823.
ICML-2005-SindhwaniNB: Beyond the point cloud: from transductive to semi-supervised learning (VS, PN, MB), pp. 824–831.
ICML-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.
ICML-2005-SunD #approach: Explanation-Augmented SVM: an approach to incorporating domain knowledge into SVM learning (QS, GD), pp. 864–871.
ICML-2005-TaskarCKG #approach #modelling #predict #scalability: Learning structured prediction models: a large margin approach (BT, VC, DK, CG), pp. 896–903.
ICML-2005-ToussaintV #modelling: Learning discontinuities with products-of-sigmoids for switching between local models (MT, SV), pp. 904–911.
ICML-2005-Wiewiora #predict: Learning predictive representations from a history (EW), pp. 964–971.
ICML-2005-WolfeJS #predict: Learning predictive state representations in dynamical systems without reset (BW, MRJ, SPS), pp. 980–987.
ICML-2005-XuTYYK #relational: Dirichlet enhanced relational learning (ZX, VT, KY, SY, HPK), pp. 1004–1011.
ICML-2005-YuTS #multi #process: Learning Gaussian processes from multiple tasks (KY, VT, AS), pp. 1012–1019.
ICML-2005-ZhouHS #graph: Learning from labeled and unlabeled data on a directed graph (DZ, JH, BS), pp. 1036–1043.
ICML-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.
KDD-2005-FanLH #image #mining #semantics #statistics: Mining images on semantics via statistical learning (JF, HL, MSH), pp. 22–31.
KDD-2005-LowdM: Adversarial learning (DL, CM), pp. 641–647.
KDD-2005-MeruguG #data flow #distributed #framework #semistructured data: A distributed learning framework for heterogeneous data sources (SM, JG), pp. 208–217.
KDD-2005-PhanNHH: Improving discriminative sequential learning with rare--but--important associations (XHP, MLN, TBH, SH), pp. 304–313.
KDD-2005-RadlinskiJ #feedback #query #rank: Query chains: learning to rank from implicit feedback (FR, TJ), pp. 239–248.
KDD-2005-YangL #predict: Learning to predict train wheel failures (CY, SL), pp. 516–525.
LSO-2005-Fajtak: Kick-off Workshops and Project Retrospectives: A Good Learning Software Organization Practice (FFF), pp. 112–114.
LSO-2005-Salo #agile #development #validation: Systematical Validation of Learning in Agile Software Development Environment (OS), pp. 92–96.
MLDM-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.
MLDM-2005-GhoshGYB05a #parametricity: Determining Regularization Parameters for Derivative Free Neural Learning (RG, MG, JY, AMB), pp. 71–79.
MLDM-2005-KuhnertK #feedback: Autonomous Vehicle Steering Based on Evaluative Feedback by Reinforcement Learning (KDK, MK), pp. 405–414.
MLDM-2005-ScalzoP #visual notation: Unsupervised Learning of Visual Feature Hierarchies (FS, JHP), pp. 243–252.
MLDM-2005-SilvaJNP #geometry #metric #using: Diagnosis of Lung Nodule Using Reinforcement Learning and Geometric Measures (ACS, VRdSJ, AdAN, ACdP), pp. 295–304.
SEKE-2005-GaoCMYB #modelling #object-oriented: An Object-Oriented Modeling Learning Support System With Inspection Comments (TG, KMLC, HM, ILY, FBB), pp. 211–216.
SEKE-2005-HongCC #fuzzy #performance: Learning Efficiency Improvement of Fuzzy CMAC by Aitken Acceleration Method (CMH, CMC, HYC), pp. 556–595.
SEKE-2005-KinjoH #modelling #object-oriented: An Object-Oriented Modeling Learning Support System With Inspection Comments (TK, AH), pp. 223–228.
SEKE-2005-SiciliaCR #ontology #process: Ontologies of Software Artifacts and Activities: Resource Annotation and Application to Learning Technologies (MÁS, JJC, DR), pp. 145–150.
SIGIR-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.
SIGIR-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.
SIGIR-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.
ASE-2005-VardhanV #branch #verification: Learning to verify branching time properties (AV, MV), pp. 325–328.
ESEC-FSE-2005-ChatleyT #eclipse #named: KenyaEclipse: learning to program in eclipse (RC, TT), pp. 245–248.
SAC-2005-BoninoCP #automation #concept #network: Automatic learning of text-to-concept mappings exploiting WordNet-like lexical networks (DB, FC, FP), pp. 1639–1644.
SAC-2005-Ferrer-TroyanoAS #data type #incremental: Incremental rule learning based on example nearness from numerical data streams (FJFT, JSAR, JCRS), pp. 568–572.
SAC-2005-FradkinK #classification: Methods for learning classifier combinations: no clear winner (DF, PBK), pp. 1038–1043.
SAC-2005-GamaMR #data type: Learning decision trees from dynamic data streams (JG, PM, PPR), pp. 573–577.
SAC-2005-KatayamaKN #process: Reinforcement learning agents with primary knowledge designed by analytic hierarchy process (KK, TK, HN), pp. 14–21.
SAC-2005-LunaLSHHB: Learning system to introduce GIS to civil engineers (RL, WTL, JMS, RHH, MGH, MB), pp. 1737–1738.
SAC-2005-PandeyGM #algorithm #probability #scheduling: Stochastic scheduling of active support vector learning algorithms (GP, HG, PM), pp. 38–42.
SAC-2005-TebriBC #incremental: Incremental profile learning based on a reinforcement method (HT, MB, CC), pp. 1096–1101.
SAC-2005-ZhangM #privacy: Privacy preserving learning in negotiation (SZ, FM), pp. 821–825.
DAC-2005-ParthasarathyICB: Structural search for RTL with predicate learning (GP, MKI, KTC, FB), pp. 451–456.
DATE-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.
DATE-2005-IyerPC #constraints #performance #theorem proving: Efficient Conflict-Based Learning in an RTL Circuit Constraint Solver (MKI, GP, KTC), pp. 666–671.
STOC-2005-KaplanKM: Learning with attribute costs (HK, EK, YM), pp. 356–365.
STOC-2005-MosselR #markov #modelling: Learning nonsingular phylogenies and hidden Markov models (EM, SR), pp. 366–375.
STOC-2005-Regev #encryption #fault #linear #on the #random: On lattices, learning with errors, random linear codes, and cryptography (OR), pp. 84–93.
CAV-2005-AlurMN #composition #verification: Symbolic Compositional Verification by Learning Assumptions (RA, PM, WN), pp. 548–562.
CAV-2005-LoginovRS #abstraction #induction #refinement: Abstraction Refinement via Inductive Learning (AL, TWR, SS), pp. 519–533.
SAT-2005-GentR: Local and Global Complete Solution Learning Methods for QBF (IPG, AGDR), pp. 91–106.
WICSA-2004-BardramCH #approach #architecture #design #prototype: Architectural Prototyping: An Approach for Grounding Architectural Design and Learning (JB, HBC, KMH), pp. 15–24.
DocEng-2004-ChidlovskiiF #documentation #legacy: Supervised learning for the legacy document conversion (BC, JF), pp. 220–228.
HT-2004-DavisB #case study #experience #migration: Experiences migrating microcosm learning materials (HCD, RAB), pp. 141–142.
JCDL-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.
JCDL-2004-HanGZLT #ambiguity: Two supervised learning approaches for name disambiguation in author citations (HH, CLG, HZ, CL, KT), pp. 296–305.
JCDL-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.
CSEET-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.
CSEET-2004-Milewski #human-computer: Software Engineers and HCI Practitioners Learning to Work Together: A Preliminary Look at Expectations (AEM), pp. 45–49.
ITiCSE-2004-ArgolloHMBFBLMR #collaboration #research #student: Graduate students learning strategies through research collaboration (EA, MH, DM, GB, PCF, FB, EL, JCM, DR), p. 262.
ITiCSE-2004-ChesnevarGM #automaton #formal method: Didactic strategies for promoting significant learning in formal languages and automata theory (CIC, MPG, AGM), pp. 7–11.
ITiCSE-2004-Dixon #automation #education: A single CASE environment for teaching and learning (MD), p. 271.
ITiCSE-2004-Ford04a #generative #programming: A learning object generator for programming (LF), p. 268.
ITiCSE-2004-Garner #programming #using: The use of a code restructuring tool in the learning of programming (SG), p. 277.
ITiCSE-2004-Kerren #education #generative: Generation as method for explorative learning in computer science education (AK), pp. 77–81.
ITiCSE-2004-Kumar #java #programming: Web-based tutors for learning programming in C++/Java (AK), p. 266.
ITiCSE-2004-LeskaR #concept #game studies #using: Learning O-O concepts in CS I using game projects (CL, JRR), p. 237.
ITiCSE-2004-McKennaL #concept: Constructivist or instructivist: pedagogical concepts practically applied to a computer learning environment (PM, BL), pp. 166–170.
ITiCSE-2004-MelinC #student: Project oriented student work: learning & examination (UM, SC), pp. 87–91.
ITiCSE-2004-PaciniFF #database #problem #spreadsheet #tool support: Learning problem solving with spreadsheet and database tools (GP, GF, AF), p. 267.
ITiCSE-2004-PahlBK #database #interactive #multi: Supporting active database learning and training through interactive multimedia (CP, RB, CK), pp. 27–31.
ITiCSE-2004-PowellMGFR #programming: Dyslexia and learning computer programming (NP, DJM, JG, JF, JR), p. 242.
ITiCSE-2004-RamalingamLW #modelling #self: Self-efficacy and mental models in learning to program (VR, DL, SW), pp. 171–175.
ITiCSE-2004-RatcliffeHE #collaboration #student: Enhancing student learning through collaboration (MR, JH, WE), p. 272.
ITiCSE-2004-SadiqOSL #named #online #sql: SQLator: an online SQL learning workbench (SWS, MEO, WS, JYCL), pp. 223–227.
ITiCSE-2004-Sheard #community: Electronic learning communities: strategies for establishment and management (JS), pp. 37–41.
ITiCSE-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.
ITiCSE-2004-SitthiworachartJ #assessment #effectiveness #programming: Effective peer assessment for learning computer programming (JS, MJ), pp. 122–126.
ITiCSE-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.
SIGITE-2004-AlotaibyCWWS #named: Teacher-driven: web-based learning system (FTA, JXC, EJW, HW, DS), p. 284.
SIGITE-2004-Crowley #design #security: Experiential learning and security lab design (EC), pp. 169–176.
SIGITE-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.
SIGITE-2004-DoubledayK: Shared extensible learning spaces (ND, SK), pp. 144–148.
SIGITE-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.
SIGITE-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.
IWPC-2004-HammoudaGKS #diagrams #modelling #uml: Tool-Supported Customization of UML Class Diagrams for Learning Complex System Models (IH, OG, KK, TS), pp. 24–33.
ICALP-2004-AlonA: Learning a Hidden Subgraph (NA, VA), pp. 110–121.
CSCW-2004-CubranicMSB #case study #development: Learning from project history: a case study for software development (DC, GCM, JS, KSB), pp. 82–91.
ICEIS-v2-2004-BendouM #graph #network: Learning Bayesian Networks with Largest Chain Graphs (MB, PM), pp. 184–190.
ICEIS-v2-2004-ColaceSVF #algorithm #automation #ontology: A Semi-Automatic Bayesian Algorithm for Ontology Learning (FC, MDS, MV, PF), pp. 191–196.
ICEIS-v2-2004-ColaceSVF04a #algorithm #comparison #network: Bayesian Network Structural Learning from Data: An Algorithms Comparison (FC, MDS, MV, PF), pp. 527–530.
ICEIS-v2-2004-Kabiri #approximate #comparison #network: A Comparison Between the Proportional Keen Approximator and the Neural Networks Learning Methods (PK), pp. 159–164.
ICEIS-v3-2004-Nobre #complexity #design: Organisational Learning — Foundational Roots for Design for Complexity (ÂLN), pp. 85–93.
ICEIS-v4-2004-Carneiro #challenge #network #process: Learning Processes and the Role of Technological Networks as an Innovative Challenge (AC), pp. 497–501.
ICEIS-v4-2004-FloresGVS: Amplia Learning Environment: A Proposal for Pedagogical Negotiation (CDF, JCG, RMV, LJS), pp. 279–286.
ICEIS-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.
ICEIS-v5-2004-SalcedoY #library #metadata: Supporting Course Sequencing in a Digital Library: Usage of Dynamic Metadata for Learning Objects (RMS, YY), pp. 319–324.
ICEIS-v5-2004-SantanaS #hypermedia: Accessing Hypermedia Systems Efectiveness in Learning Contexts (SS, AS), pp. 250–253.
CIKM-2004-LiO #identification #music: Semi-supervised learning for music artists style identification (TL, MO), pp. 152–153.
CIKM-2004-LiuZYYYCBM #metric #similarity: Learning similarity measures in non-orthogonal space (NL, BZ, JY, QY, SY, ZC, FB, WYM), pp. 334–341.
CIKM-2004-MaZMS #framework #query #similarity #using: A framework for refining similarity queries using learning techniques (YM, QZ, SM, DYS), pp. 158–159.
ICML-2004-AgarwalT #3d: Learning to track 3D human motion from silhouettes (AA, BT).
ICML-2004-BachLJ #algorithm #kernel #multi: Multiple kernel learning, conic duality, and the SMO algorithm (FRB, GRGL, MIJ).
ICML-2004-BahamondeBDQLCAG #case study #set: Feature subset selection for learning preferences: a case study (AB, GFB, JD, JRQ, OL, JJdC, JA, FG).
ICML-2004-BilenkoBM #clustering #constraints #metric: Integrating constraints and metric learning in semi-supervised clustering (MB, SB, RJM).
ICML-2004-BlumLRR #random #using: Semi-supervised learning using randomized mincuts (AB, JDL, MRR, RR).
ICML-2004-Bouckaert #classification: Estimating replicability of classifier learning experiments (RRB).
ICML-2004-BrefeldS: Co-EM support vector learning (UB, TS).
ICML-2004-Brinker #ranking: Active learning of label ranking functions (KB).
ICML-2004-CastilloW #case study #comparative #multi: A comparative study on methods for reducing myopia of hill-climbing search in multirelational learning (LPC, SW).
ICML-2004-ConitzerS #bound #communication #complexity #game studies: Communication complexity as a lower bound for learning in games (VC, TS).
ICML-2004-EliazarP #mobile #modelling #probability: Learning probabilistic motion models for mobile robots (AIE, RP).
ICML-2004-GaoWLC #approach #categorisation #multi #robust: A MFoM learning approach to robust multiclass multi-label text categorization (SG, WW, CHL, TSC).
ICML-2004-GoldenbergM #scalability: Tractable learning of large Bayes net structures from sparse data (AG, AWM).
ICML-2004-GrossmanD #classification #network: Learning Bayesian network classifiers by maximizing conditional likelihood (DG, PMD).
ICML-2004-HuangYKL #classification #scalability: Learning large margin classifiers locally and globally (KH, HY, IK, MRL).
ICML-2004-JamesS #predict: Learning and discovery of predictive state representations in dynamical systems with reset (MRJ, SPS).
ICML-2004-KashimaT #algorithm #graph #kernel #sequence: Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs (HK, YT).
ICML-2004-LawrenceP: Learning to learn with the informative vector machine (NDL, JCP).
ICML-2004-MannorMHK #abstraction #clustering: Dynamic abstraction in reinforcement learning via clustering (SM, IM, AH, UK).
ICML-2004-MelvilleM: Diverse ensembles for active learning (PM, RJM).
ICML-2004-MerkeS #approximate #convergence #linear: Convergence of synchronous reinforcement learning with linear function approximation (AM, RS).
ICML-2004-MoralesS #behaviour: Learning to fly by combining reinforcement learning with behavioural cloning (EFM, CS).
ICML-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).
ICML-2004-NguyenS #clustering #using: Active learning using pre-clustering (HTN, AWMS).
ICML-2004-OngMCS #kernel: Learning with non-positive kernels (CSO, XM, SC, AJS).
ICML-2004-PieterN: Apprenticeship learning via inverse reinforcement learning (PA, AYN).
ICML-2004-Potts #incremental #linear: Incremental learning of linear model trees (DP).
ICML-2004-RosalesAF #clustering #using: Learning to cluster using local neighborhood structure (RR, KA, BJF).
ICML-2004-RosencrantzGT #predict: Learning low dimensional predictive representations (MR, GJG, ST).
ICML-2004-RuckertK #bound #towards: Towards tight bounds for rule learning (UR, SK).
ICML-2004-RudarySP #adaptation #constraints #reasoning: Adaptive cognitive orthotics: combining reinforcement learning and constraint-based temporal reasoning (MRR, SPS, MEP).
ICML-2004-Ryabko #online: Online learning of conditionally I.I.D. data (DR).
ICML-2004-Shalev-ShwartzSN #online #pseudo: Online and batch learning of pseudo-metrics (SSS, YS, AYN).
ICML-2004-SimsekB #abstraction #identification #using: Using relative novelty to identify useful temporal abstractions in reinforcement learning (ÖS, AGB).
ICML-2004-TaoSVO #approximate #multi: SVM-based generalized multiple-instance learning via approximate box counting (QT, SDS, NVV, TTO).
ICML-2004-TaskarCK #markov #network: Learning associative Markov networks (BT, VC, DK).
ICML-2004-ToutanovaMN #dependence #modelling #random #word: Learning random walk models for inducing word dependency distributions (KT, CDM, AYN).
ICML-2004-WeinbergerSS #kernel #matrix #reduction: Learning a kernel matrix for nonlinear dimensionality reduction (KQW, FS, LKS).
ICML-2004-Zadrozny #bias #classification: Learning and evaluating classifiers under sample selection bias (BZ).
ICML-2004-ZhangYK #algorithm #kernel #matrix #using: Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm (ZZ, DYY, JTK).
ICPR-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.
ICPR-v1-2004-GocciaSD #classification #fuzzy #recognition: Learning Optimal Classifier Through Fuzzy Recognition Rate Maximization (MG, CS, SGD), pp. 204–207.
ICPR-v1-2004-GokcenJD #bound: Comparing Optimal Bounding Ellipsoid and Support Vector Machine Active Learning (IG, DJ, JRD), pp. 172–175.
ICPR-v1-2004-LeangB: Learning Integrated Perception-Based Speed Control (PL, BB), pp. 813–816.
ICPR-v1-2004-YiKZ #classification: Classifier Combination based on Active Learning (XY, ZK, CZ), pp. 184–187.
ICPR-v2-2004-FangQ #detection: Learning Sample Subspace with Application to Face Detection (JF, GQ), pp. 423–426.
ICPR-v2-2004-JingZLZZ #image #retrieval: Learning in Hidden Annotation-Based Image Retrieval (FJ, BZ, ML, HZ, JZ), pp. 1001–1004.
ICPR-v2-2004-KaneS #classification #image #network: Bayesian Network Structure Learning and Inference in Indoor vs. Outdoor Image Classification (MJK, AES), pp. 479–482.
ICPR-v2-2004-LindgrenH #component #image #independence #representation: Learning High-level Independent Components of Images through a Spectral Representation (JTL, AH), pp. 72–75.
ICPR-v2-2004-LiuS: Reinforcement Learning-Based Feature Learning for Object Tracking (FL, JS), pp. 748–751.
ICPR-v2-2004-SageB: Joint Spatial and Temporal Structure Learning for Task based Control (KS, HB), pp. 48–51.
ICPR-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.
ICPR-v3-2004-FanG: Hierarchical Object Indexing and Sequential Learning (XF, DG), pp. 65–68.
ICPR-v3-2004-KoKB04a #multi #problem: Improved N-Division Output Coding for Multiclass Learning Problems (JK, EK, HB), pp. 470–473.
ICPR-v3-2004-LuoKGHSRH #multi: Active Learning to Recognize Multiple Types of Plankton (TL, KK, DBG, LOH, SS, AR, TH), pp. 478–481.
ICPR-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.
ICPR-v3-2004-NeuhausB #approach #distance #edit distance #graph #probability: A Probabilistic Approach to Learning Costs for Graph Edit Distance (MN, HB), pp. 389–393.
ICPR-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.
ICPR-v3-2004-ShiNGY #classification: Critical Vector Learning to Construct RBF Classifiers (DS, GSN, JG, DSY), pp. 359–362.
ICPR-v4-2004-Cardenas #classification #multi #prototype #string: A Learning Model for Multiple-Prototype Classification of Strings (RAM), pp. 420–423.
ICPR-v4-2004-ChenC04a #bidirectional #dependence #network: Improvement of Bidirectional Recurrent Neural Network for Learning Long-Term Dependencies (JC, NSC), pp. 593–596.
ICPR-v4-2004-FabletJB #automation #estimation #image #statistics #using: Automatic Fish Age Estimation from Otolith Images using Statistical Learning (RF, NLJ, AB), pp. 503–506.
ICPR-v4-2004-McKennaN #using: Learning Spatial Context from Tracking using Penalised Likelihoods (SJM, HNC), pp. 138–141.
ICPR-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.
ICPR-v4-2004-QinandS04a #algorithm #kernel #novel #prototype: A Novel Kernel Prototype-Based Learning Algorithm (AKQ, PNS), pp. 621–624.
ICPR-v4-2004-RaytchevYS #estimation: Head Pose Estimation by Nonlinear Manifold Learning (BR, IY, KS), pp. 462–466.
ICPR-v4-2004-SamsonB #clustering #parallel #robust #video: Learning Classes for Video Interpretation with a Robust Parallel Clustering Method (VS, PB), pp. 569–572.
ICPR-v4-2004-StefanoDM #approach: A Dynamic Approach to Learning Vector Quantization (CDS, CD, AM), pp. 601–604.
ICPR-v4-2004-WuCW04a #recognition: Face Recognition Based on Discriminative Manifold Learning (YW, KLC, LW), pp. 171–174.
KDD-2004-AbeVAS: Cross channel optimized marketing by reinforcement learning (NA, NKV, CA, RS), pp. 767–772.
KDD-2004-AbeZL #multi: An iterative method for multi-class cost-sensitive learning (NA, BZ, JL), pp. 3–11.
KDD-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.
KDD-2004-EvgeniouP #multi: Regularized multi--task learning (TE, MP), pp. 109–117.
KDD-2004-KolterM #bytecode #detection: Learning to detect malicious executables in the wild (JZK, MAM), pp. 470–478.
KDD-2004-KummamuruKA #difference #metric: Learning spatially variant dissimilarity (SVaD) measures (KK, RK, RA), pp. 611–616.
KDD-2004-PopesculU #clustering #concept #relational #statistics: Cluster-based concept invention for statistical relational learning (AP, LHU), pp. 665–670.
KDD-2004-TruongLB #dataset #random #using: Learning a complex metabolomic dataset using random forests and support vector machines (YT, XL, CB), pp. 835–840.
KR-2004-PasulaZK #probability #relational: Learning Probabilistic Relational Planning Rules (HP, LSZ, LPK), pp. 683–691.
LSO-2004-ChauM #agile #tool support: Tool Support for Inter-team Learning in Agile Software Organizations (TC, FM), pp. 98–109.
LSO-2004-FalboRBT #how #risk management #using: Learning How to Manage Risks Using Organizational Knowledge (RdAF, FBR, GB, DFT), pp. 7–18.
LSO-2004-HolzM #past present future #research: Research on Learning Software Organizations — Past, Present, and Future (HH, GM), pp. 1–6.
LSO-2004-MelnikR: Impreciseness and Its Value from the Perspective of Software Organizations and Learning (GM, MMR), pp. 122–130.
LSO-2004-Roth-Berghofer: Learning from HOMER, a Case-Based Help Desk Support System (TRB), pp. 88–97.
LSO-2004-SousaAO #maintenance: Learning Software Maintenance Organizations (KDdS, NA, KMdO), pp. 67–77.
SEKE-2004-DantasBW #game studies #project management: A Simulation-Based Game for Project Management Experiential Learning (ARD, MdOB, CMLW), pp. 19–24.
SEKE-2004-MaxvilleLA #component: Learning to Select Software Components (VM, CPL, JA), pp. 421–426.
SIGIR-2004-LamHC #mining #similarity: Learning phonetic similarity for matching named entity translations and mining new translations (WL, RH, PSC), pp. 289–296.
SIGIR-2004-RoussinovR #web: Learning patterns to answer open domain questions on the web (DR, JARF), pp. 500–501.
SIGIR-2004-XiLB #effectiveness #ranking: Learning effective ranking functions for newsgroup search (WX, JL, EB), pp. 394–401.
SIGIR-2004-ZengHCMM #clustering #web: Learning to cluster web search results (HJZ, QCH, ZC, WYM, JM), pp. 210–217.
RE-2004-HaleyNST #categorisation #requirements: The Conundrum of Categorising Requirements: Managing Requirements for Learning on the Move (DTH, BN, HCS, JT), pp. 309–314.
SAC-2004-BergholzC #interface #query #web: Learning query languages of Web interfaces (AB, BC), pp. 1114–1121.
SAC-2004-DerntlM #case study #concept #evaluation #experience: Patterns for blended, Person-Centered learning: strategy, concepts, experiences, and evaluation (MD, RMP), pp. 916–923.
SAC-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.
SAC-2004-NeelyLEBNG #architecture #distributed: An architecture for supporting vicarious learning in a distributed environment (SN, HL, DME, JB, JN, XG), pp. 963–970.
SAC-2004-ZaneroS #detection: Unsupervised learning techniques for an intrusion detection system (SZ, SMS), pp. 412–419.
DAC-2004-WangMCA #on the: On path-based learning and its applications in delay test and diagnosis (LCW, TMM, KTC, MSA), pp. 492–497.
DATE-v1-2004-Wang #simulation #validation: Regression Simulation: Applying Path-Based Learning In Delay Test and Post-Silicon Validation (LCW), pp. 692–695.
PDP-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.
PDP-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.
STOC-2004-AwerbuchK #adaptation #distributed #feedback #geometry: Adaptive routing with end-to-end feedback: distributed learning and geometric approaches (BA, RDK), pp. 45–53.
SAT-2004-SangBBKP #component #effectiveness: Combining Component Caching and Clause Learning for Effective Model Counting (TS, FB, PB, HAK, TP), pp. 20–28.
ECDL-2003-QinG #education #metadata: Incorporating Educational Vocabulary in Learning Object Metadata Schemes (JQ, CJG), pp. 52–57.
ECDL-2003-SmithABFHNTU #concept: The ADEPT Concept-Based Digital Learning Environment (TRS, DA, OAB, MF, WH, RN, TT, AU), pp. 300–312.
ICDAR-2003-Legal-AyalaF #approach #image #segmentation: Image Segmentation By Learning Approach (HALA, JF), pp. 819–823.
ICDAR-2003-RyuK #recognition #word: Learning the lexicon from raw texts for open-vocabulary Korean word recognition (SR, JHK), pp. 202–206.
ICDAR-2003-ShimizuOWK #image #network: Mirror Image Learning for Autoassociative Neural Networks (SS, WO, TW, FK), pp. 804–808.
ICDAR-2003-TakahashiN #recognition: A class-modular GLVQ ensemble with outlier learning for handwritten digit recognition (KT, DN), pp. 268–272.
JCDL-2003-OldenettelMR #approach #library: Integrating Digital Libraries into Learning Environments: The LEBONED Approach (FO, MM, DR), pp. 280–290.
JCDL-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–?.
JCDL-2003-SouthwickS #library: Learning Digital Library Technology Across Borders (SBS, RS), pp. 179–181.
CSEET-2003-AlfonsoM #re-engineering: Learning Software Engineering with Group Work (MIA, FM), p. 309–?.
ITiCSE-2003-ChalkBP #design #education #programming: Designing and evaluating learning objects for introductory programming education (PC, CB, PP), p. 240.
ITiCSE-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.
ITiCSE-2003-EkateriniSP #education #problem: Teaching IT in secondary education through problem-based learning could be really beneficial (GE, BS, GP), p. 243.
ITiCSE-2003-Garvin-DoxasB #interactive: Creating learning environments that support interaction (KGD, LJB), p. 276.
ITiCSE-2003-GunawardenaA #approach #education #programming: A customized learning objects approach to teaching programming (AG, VA), p. 264.
ITiCSE-2003-KurhilaMNFT #peer-to-peer #web: Peer-to-peer learning with open-ended writable Web (JK, MM, PN, PF, HT), pp. 173–177.
ITiCSE-2003-Leska #java #user interface #using: Learning to develop GUIs in Java using closed labs (CL), p. 228.
ITiCSE-2003-LynchM #student: The winds of change: students’ comfort level in different learning environments (KL, SM), pp. 70–73.
ITiCSE-2003-MirmotahariHK #architecture: Difficulties learning computer architecture (OM, CH, JK), p. 247.
ITiCSE-2003-Nodelman #programming #theory and practice: Learning computer graphics by programming: linking theory and practice (VN), p. 261.
ITiCSE-2003-PearsPE #online: Enriching online learning resources with “explanograms” (ANP, LP, CE), p. 237.
ICSM-2003-LinosB #maintenance #re-engineering: Service Learning in Software Engineering and Maintenance (PKL, CBK), p. 336–?.
WCRE-2003-Murphy: Learning from the Past (GCM), pp. 2–3.
DLT-2003-DrewesH #education: Learning a Regular Tree Language from a Teacher (FD, JH), pp. 279–291.
Haskell-2003-HeerenLI #haskell: Helium, for learning Haskell (BH, DL, AvI), pp. 62–71.
ICEIS-v2-2003-BendouM #network #semistructured data: Learning Bayesian Networks From Noisy Data (MB, PM), pp. 26–33.
ICEIS-v2-2003-ColaceSFV #network #ontology: Ontology Learning Through Bayesian Networks (FC, MDS, PF, MV), pp. 430–433.
ICEIS-v2-2003-KeeniGS #network #on the #performance #using: On Fast Learning of Neural Networks Using Back Propagation (KK, KG, HS), pp. 266–271.
ICEIS-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.
ICEIS-v4-2003-SemeraroLDL: Learning User Profiles for Intelligent Search (GS, PL, MD, OL), pp. 426–429.
ICEIS-v4-2003-TyrvainenJS #case study #on the: On Estimating the Amount of Learning Materials a Case Study (PT, MJ, AS), pp. 127–135.
CIKM-2003-ZhangOR #using: Learning cross-document structural relationships using boosting (ZZ, JO, DRR), pp. 124–130.
ECIR-2003-TianC #collaboration #rating #recommendation #similarity: Learning User Similarity and Rating Style for Collaborative Recommendation (LFT, KWC), pp. 135–145.
ICML-2003-Bar-HillelHSW #distance #equivalence #using: Learning Distance Functions using Equivalence Relations (ABH, TH, NS, DW), pp. 11–18.
ICML-2003-BaramEL #algorithm #online: Online Choice of Active Learning Algorithms (YB, REY, KL), pp. 19–26.
ICML-2003-BerardiCEM #analysis #layout #logic programming #source code: Learning Logic Programs for Layout Analysis Correction (MB, MC, FE, DM), pp. 27–34.
ICML-2003-Bouckaert #algorithm #testing: Choosing Between Two Learning Algorithms Based on Calibrated Tests (RRB), pp. 51–58.
ICML-2003-Brinker: Incorporating Diversity in Active Learning with Support Vector Machines (KB), pp. 59–66.
ICML-2003-BrownW #ambiguity #composition #network #using: The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods (GB, JLW), pp. 67–74.
ICML-2003-CerquidesM #modelling #naive bayes: Tractable Bayesian Learning of Tree Augmented Naive Bayes Models (JC, RLdM), pp. 75–82.
ICML-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.
ICML-2003-CozmanCC #modelling: Semi-Supervised Learning of Mixture Models (FGC, IC, MCC), pp. 99–106.
ICML-2003-CumbyR #kernel #on the #relational: On Kernel Methods for Relational Learning (CMC, DR), pp. 107–114.
ICML-2003-DriessensR #relational: Relational Instance Based Regression for Relational Reinforcement Learning (KD, JR), pp. 123–130.
ICML-2003-EngelMM #approach #difference #process: Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning (YE, SM, RM), pp. 154–161.
ICML-2003-Even-DarMM: Action Elimination and Stopping Conditions for Reinforcement Learning (EED, SM, YM), pp. 162–169.
ICML-2003-GargR: Margin Distribution and Learning (AG, DR), pp. 210–217.
ICML-2003-GeibelW: Perceptron Based Learning with Example Dependent and Noisy Costs (PG, FW), pp. 218–225.
ICML-2003-IsaacS: Goal-directed Learning to Fly (AI, CS), pp. 258–265.
ICML-2003-Joachims #clustering #graph: Transductive Learning via Spectral Graph Partitioning (TJ), pp. 290–297.
ICML-2003-KennedyJ #problem: Characteristics of Long-term Learning in Soar and its Application to the Utility Problem (WGK, KADJ), pp. 337–344.
ICML-2003-KirshnerPS #permutation: Unsupervised Learning with Permuted Data (SK, SP, PS), pp. 345–352.
ICML-2003-KotnikK #self: The Significance of Temporal-Difference Learning in Self-Play Training TD-Rummy versus EVO-rummy (CK, JKK), pp. 369–375.
ICML-2003-KrawiecB #synthesis #visual notation: Visual Learning by Evolutionary Feature Synthesis (KK, BB), pp. 376–383.
ICML-2003-KwokT #kernel: Learning with Idealized Kernels (JTK, IWT), pp. 400–407.
ICML-2003-LagoudakisP #classification: Reinforcement Learning as Classification: Leveraging Modern Classifiers (MGL, RP), pp. 424–431.
ICML-2003-LaudD #analysis: The Influence of Reward on the Speed of Reinforcement Learning: An Analysis of Shaping (AL, GD), pp. 440–447.
ICML-2003-LeeL #using: Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression (WSL, BL), pp. 448–455.
ICML-2003-McGovernJ #identification #multi #predict #relational #using: Identifying Predictive Structures in Relational Data Using Multiple Instance Learning (AM, DJ), pp. 528–535.
ICML-2003-MooreW #network: Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure Learning (AWM, WKW), pp. 552–559.
ICML-2003-OntanonP #multi: Justification-based Multiagent Learning (SO, EP), pp. 576–583.
ICML-2003-RichardsonD #multi: Learning with Knowledge from Multiple Experts (MR, PMD), pp. 624–631.
ICML-2003-RuckertK #probability: Stochastic Local Search in k-Term DNF Learning (UR, SK), pp. 648–655.
ICML-2003-RussellZ: Q-Decomposition for Reinforcement Learning Agents (SJR, AZ), pp. 656–663.
ICML-2003-SinghLJPS #predict: Learning Predictive State Representations (SPS, MLL, NKJ, DP, PS), pp. 712–719.
ICML-2003-StimpsonG #approach #social: Learning To Cooperate in a Social Dilemma: A Satisficing Approach to Bargaining (JLS, MAG), pp. 728–735.
ICML-2003-TaskarWK #testing: Learning on the Test Data: Leveraging Unseen Features (BT, MFW, DK), pp. 744–751.
ICML-2003-WangD #modelling #policy: Model-based Policy Gradient Reinforcement Learning (XW, TGD), pp. 776–783.
ICML-2003-WangSPZ #modelling #principle: Learning Mixture Models with the Latent Maximum Entropy Principle (SW, DS, FP, YZ), pp. 784–791.
ICML-2003-WiewioraCE: Principled Methods for Advising Reinforcement Learning Agents (EW, GWC, CE), pp. 792–799.
ICML-2003-WinnerV #named: DISTILL: Learning Domain-Specific Planners by Example (EW, MMV), pp. 800–807.
ICML-2003-WuC #adaptation: Adaptive Feature-Space Conformal Transformation for Imbalanced-Data Learning (GW, EYC), pp. 816–823.
ICML-2003-Zhang #kernel #metric #multi #representation #scalability #towards: Learning Metrics via Discriminant Kernels and Multidimensional Scaling: Toward Expected Euclidean Representation (ZZ), pp. 872–879.
ICML-2003-ZhangH #taxonomy: Learning from Attribute Value Taxonomies and Partially Specified Instances (JZ, VH), pp. 880–887.
ICML-2003-ZhangXC #adaptation: Exploration and Exploitation in Adaptive Filtering Based on Bayesian Active Learning (YZ, WX, JPC), pp. 896–903.
ICML-2003-ZhuGL #using: Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions (XZ, ZG, JDL), pp. 912–919.
KDD-2003-Koller #relational #statistics: Statistical learning from relational data (DK), p. 4.
KDD-2003-NevilleJFH #probability #relational: Learning relational probability trees (JN, DJ, LF, MH), pp. 625–630.
KDD-2003-SarawagiCG #named #probability #topic: Cross-training: learning probabilistic mappings between topics (SS, SC, SG), pp. 177–186.
MLDM-2003-ComiteGT #multi: Learning Multi-label Alternating Decision Trees from Texts and Data (FDC, RG, MT), pp. 35–49.
MLDM-2003-Craw #reasoning: Introspective Learning to Build Case-Based Reasoning (CBR) Knowledge Containers (SC), pp. 1–6.
MLDM-2003-KrawiecB #recognition: Coevolutionary Feature Learning for Object Recognition (KK, BB), pp. 224–238.
MLDM-2003-KuhnertK #classification #image: A Learning Autonomous Driver System on the Basis of Image Classification and Evolutional Learning (KDK, MK), pp. 400–412.
SEKE-2003-ChenJ #fuzzy #induction #information management #multi #named: MFILM: a multi-dimensional fuzzy inductive learning method for knowledge acquisition (YTC, BJ), pp. 445–449.
SIGIR-2003-GaoWLC #approach #categorisation: A maximal figure-of-merit learning approach to text categorization (SG, WW, CHL, TSC), pp. 174–181.
SAC-2003-LiZLO #classification #functional #semistructured data: Gene Functional Classification by Semisupervised Learning from Heterogeneous Data (TL, SZ, QL, MO), pp. 78–82.
SAC-2003-RumetshoferW #adaptation #approach #aspect-oriented: An Approach for Adaptable Learning Systems with Respect to Psychological Aspects (HR, WW), pp. 558–563.
DAC-2003-GuptaGWYA #bound #model checking #satisfiability: Learning from BDDs in SAT-based bounded model checking (AG, MKG, CW, ZY, PA), pp. 824–829.
DATE-2003-LuWCH #correlation #satisfiability: A Circuit SAT Solver With Signal Correlation Guided Learning (FL, LCW, KTC, RCYH), pp. 10892–10897.
PDP-2003-SanchezLARVB #architecture #multi: A multi-tiered agent-based architecture for a cooperative learning environment (ESV, ML, RRA, AR, XAVS, SB), pp. 500–506.
STOC-2003-MosselOS: Learning juntas (EM, RO, RAS), pp. 206–212.
TACAS-2003-CobleighGP #composition #verification: Learning Assumptions for Compositional Verification (JMC, DG, CSP), pp. 331–346.
CAV-2003-HungarNS #automaton #optimisation: Domain-Specific Optimization in Automata Learning (HH, ON, BS), pp. 315–327.
SAT-2003-SabharwalBK #performance #problem #using: Using Problem Structure for Efficient Clause Learning (AS, PB, HAK), pp. 242–256.
JCDL-2002-McMartinT #library: Digital library services for authors of learning materials (FPM, YT), pp. 117–118.
SIGMOD-2002-MarklL: Learning table access cardinalities with LEO (VM, GML), p. 613.
VLDB-2002-SarawagiBKM #alias #interactive #named: ALIAS: An Active Learning led Interactive Deduplication System (SS, AB, AK, CM), pp. 1103–1106.
CSEET-2002-Armarego #design #problem: Advanced Software Design: A Case in Problem-Based Learning (JA), pp. 44–54.
CSEET-2002-UmphressH #education #process: Software Process as a Foundation for Teaching, Learning and Accrediting (DAU, JAHJ), pp. 160–169.
ITiCSE-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.
ITiCSE-2002-Cassel #network: Very active learning of network routing (LNC), p. 195.
ITiCSE-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.
ITiCSE-2002-FabregaMJM #network: A virtual network laboratory for learning IP networking (LF, JM, TJ, DM), pp. 161–164.
ITiCSE-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.
ITiCSE-2002-Hazzan #abstraction #concept: Reducing abstraction level when learning computability theory concepts (OH), pp. 156–160.
ITiCSE-2002-Lapidot #experience #self: Self-assessment as a powerful learning experience (TL), p. 198.
ITiCSE-2002-LastDHW #collaboration #student: Learning from students: continuous improvement in international collaboration (MZL, MD, MLH, MW), pp. 136–140.
ITiCSE-2002-Nygaard #object-oriented: COOL (comprehensive object-oriented learning) (KN), p. 218.
ITiCSE-2002-ParkinsonR #performance #question: Do cognitive styles affect learning performance in different computer media? (AP, JAR), pp. 39–43.
ITiCSE-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.
ITiCSE-2002-WaltersASBK: Increasing learning and decreasing costs in a computer fluency course (DW, CA, BS, DTB, HK), pp. 208–212.
CHI-2002-Ehret #user interface #visual notation: Learning where to look: location learning in graphical user interfaces (BDE), pp. 211–218.
CHI-2002-SnowdonG #experience: Diffusing information in organizational settings: learning from experience (DS, AG), pp. 331–338.
CHI-2002-ZhaiSA: Movement model, hits distribution and learning in virtual keyboarding (SZ, AES, JA), pp. 17–24.
ICEIS-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.
ICEIS-2002-IglesiasMCCF #database #design #education #fault: Learning to Teach Database Design by Trial and Error (AI, PM, DC, EC, FF), pp. 500–505.
ICEIS-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.
CIKM-2002-HuangCA #comparison #web: Comparison of interestingness functions for learning web usage patterns (XH, NC, AA), pp. 617–620.
ICML-2002-BianchettiRS #concept #constraints #relational: Constraint-based Learning of Long Relational Concepts (JAB, CR, MS), pp. 35–42.
ICML-2002-ChisholmT #random: Learning Decision Rules by Randomized Iterative Local Search (MC, PT), pp. 75–82.
ICML-2002-DietterichBMS #probability #refinement: Action Refinement in Reinforcement Learning by Probability Smoothing (TGD, DB, RLdM, CS), pp. 107–114.
ICML-2002-DriessensD #relational: Integrating Experimentation and Guidance in Relational Reinforcement Learning (KD, SD), pp. 115–122.
ICML-2002-FerriFH #using: Learning Decision Trees Using the Area Under the ROC Curve (CF, PAF, JHO), pp. 139–146.
ICML-2002-GhavamzadehM: Hierarchically Optimal Average Reward Reinforcement Learning (MG, SM), pp. 195–202.
ICML-2002-GonzalezHC #concept #graph #relational: Graph-Based Relational Concept Learning (JAG, LBH, DJC), pp. 219–226.
ICML-2002-GuestrinLP #coordination: Coordinated Reinforcement Learning (CG, MGL, RP), pp. 227–234.
ICML-2002-GuestrinPS #modelling: Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs (CG, RP, DS), pp. 235–242.
ICML-2002-Hengst: Discovering Hierarchy in Reinforcement Learning with HEXQ (BH), pp. 243–250.
ICML-2002-JensenN #bias #feature model #relational: Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning (DJ, JN), pp. 259–266.
ICML-2002-KakadeL #approximate: Approximately Optimal Approximate Reinforcement Learning (SK, JL), pp. 267–274.
ICML-2002-LanckrietCBGJ #kernel #matrix #programming: Learning the Kernel Matrix with Semi-Definite Programming (GRGL, NC, PLB, LEG, MIJ), pp. 323–330.
ICML-2002-LaudD #behaviour: Reinforcement Learning and Shaping: Encouraging Intended Behaviors (AL, GD), pp. 355–362.
ICML-2002-LeckieR #distributed #probability: Learning to Share Distributed Probabilistic Beliefs (CL, KR), pp. 371–378.
ICML-2002-MerkeS #approximate #convergence: A Necessary Condition of Convergence for Reinforcement Learning with Function Approximation (AM, RS), pp. 411–418.
ICML-2002-Mladenic #normalisation #using #word: Learning word normalization using word suffix and context from unlabeled data (DM), pp. 427–434.
ICML-2002-MusleaMK #multi #robust: Active + Semi-supervised Learning = Robust Multi-View Learning (IM, SM, CAK), pp. 435–442.
ICML-2002-OatesDB #context-free grammar: Learning k-Reversible Context-Free Grammars from Positive Structural Examples (TO, DD, VB), pp. 459–465.
ICML-2002-OLZ #using: Stock Trading System Using Reinforcement Learning with Cooperative Agents (JO, JWL, BTZ), pp. 451–458.
ICML-2002-PanangadanD #2d #correlation #navigation: Learning Spatial and Temporal Correlation for Navigation in a 2-Dimensional Continuous World (AP, MGD), pp. 474–481.
ICML-2002-ParkZ: A Boosted Maximum Entropy Model for Learning Text Chunking (SBP, BTZ), pp. 482–489.
ICML-2002-PeshkinS #experience: Learning from Scarce Experience (LP, CRS), pp. 498–505.
ICML-2002-PickettB #algorithm #named: PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning (MP, AGB), pp. 506–513.
ICML-2002-Ryan #automation #behaviour #modelling #using: Using Abstract Models of Behaviours to Automatically Generate Reinforcement Learning Hierarchies (MRKR), pp. 522–529.
ICML-2002-SeriT #modelling: Model-based Hierarchical Average-reward Reinforcement Learning (SS, PT), pp. 562–569.
ICML-2002-ShapiroL #using: Separating Skills from Preference: Using Learning to Program by Reward (DGS, PL), pp. 570–577.
ICML-2002-Stirling: Learning to Fly by Controlling Dynamic Instabilities (DS), pp. 586–593.
ICML-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.
ICML-2002-ZhangGYF #image #multi #retrieval #using: Content-Based Image Retrieval Using Multiple-Instance Learning (QZ, SAG, WY, JEF), pp. 682–689.
ICML-2002-ZubekD #heuristic: Pruning Improves Heuristic Search for Cost-Sensitive Learning (VBZ, TGD), pp. 19–26.
ICPR-v1-2002-HadidKP #analysis #linear #using: Unsupervised Learning Using Locally Linear Embedding: Experiments with Face Pose Analysis (AH, OK, MP), pp. 111–114.
ICPR-v1-2002-HaroE #video: Learning Video Processing by Example (AH, IAE), pp. 487–491.
ICPR-v1-2002-RobertsMR #3d #online: Online Appearance Learning or 3D Articulated Human Tracking (TJR, SJM, IWR), pp. 425–428.
ICPR-v2-2002-Al-ShaherH #modelling #online #performance: Fast On-Line learning of Point Distribution Models (AAAS, ERH), pp. 208–211.
ICPR-v2-2002-Amin #prototype #using: Prototyping Structural Description Using Decision Tree Learning Techniques (AA), pp. 76–79.
ICPR-v2-2002-ChiuLY #personalisation: Learning User Preference in a Personalized CBIR Systeml (CYC, HCL, SNY), p. 532–?.
ICPR-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.
ICPR-v2-2002-KherfiZB #feedback #image #retrieval: Learning from Negative Example in Relevance Feedback for Content-Based Image Retrieval (MLK, DZ, AB), pp. 933–936.
ICPR-v2-2002-Lashkia: Learning with Relevant Features and Examples (GVL), pp. 68–71.
ICPR-v2-2002-LiuB #concept #semantics #video #visual notation: Learning Semantic Visual Concepts from Video (JL, BB), pp. 1061–1064.
ICPR-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.
ICPR-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.
ICPR-v2-2002-ShiWOK #case study #comparative #image: Comparative Study on Mirror Image Learning (MIL) and GLVQ (MS, TW, WO, FK), p. 248–?.
ICPR-v2-2002-TohM #approach #network: A Global Transformation Approach to RBF Neural Network Learning (KAT, KZM), pp. 96–99.
ICPR-v2-2002-Torkkola02a #feature model #problem: Learning Feature Transforms Is an Easier Problem Than Feature Selection (KT), pp. 104–107.
ICPR-v2-2002-WechslerDL #process #using: Hierarchical Interpretation of Human Activities Using Competitive Learning (HW, ZD, FL), pp. 338–341.
ICPR-v3-2002-ArtacJL #incremental #online #recognition #visual notation: Incremental PCA or On-Line Visual Learning and Recognition (MA, MJ, AL), pp. 781–784.
ICPR-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.
ICPR-v3-2002-ChartierL #image #network: Learning and Extracting Edges from Images by a Modified Hopfield Neural Network (SC, RL), pp. 431–434.
ICPR-v3-2002-ChoudhuryRPP #detection #network: Boosting and Structure Learning in Dynamic Bayesian Networks for Audio-Visual Speaker Detection (TC, JMR, VP, AP), p. 789–?.
ICPR-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.
ICPR-v3-2002-LuoWH02a #approach #graph: Graph Spectral Approach for Learning View Structure (BL, RCW, ERH), pp. 785–788.
ICPR-v3-2002-Sakano #how #query #search-based: Genetic Translator: How to Apply Query Learning to Practical OCR (HS), pp. 184–187.
ICPR-v3-2002-SinghR #recognition #robust: Background Learning for Robust Face Recognition (RKS, ANR), pp. 525–528.
ICPR-v3-2002-SuW #identification #process: A Learning Process to the Identification of Feature Points on Chinese Characters (YMS, JFW), pp. 93–97.
ICPR-v4-2002-KubotaMK #fault #optimisation: A Discriminative Learning Criterion for the Overall Optimization of Error and Reject (SK, HM, YK), pp. 98–102.
ICPR-v4-2002-LiuSF #classification #polynomial: Learning Quadratic Discriminant Function for Handwritten Character Classification (CLL, HS, HF), pp. 44–47.
KDD-2002-AntalGF #clustering #network #on the: On the potential of domain literature for clustering and Bayesian network learning (PA, PG, GF), pp. 405–414.
KDD-2002-Ben-DavidGS #data flow #framework: A theoretical framework for learning from a pool of disparate data sources (SBD, JG, RS), pp. 443–449.
KDD-2002-CohenR #clustering #integration #scalability #set: Learning to match and cluster large high-dimensional data sets for data integration (WWC, JR), pp. 475–480.
KDD-2002-KruengkraiJ #algorithm #classification #parallel: A parallel learning algorithm for text classification (CK, CJ), pp. 201–206.
KDD-2002-MahoneyC #detection #modelling #network #novel: Learning nonstationary models of normal network traffic for detecting novel attacks (MVM, PKC), pp. 376–385.
KDD-2002-PednaultAZ: Sequential cost-sensitive decision making with reinforcement learning (EPDP, NA, BZ), pp. 259–268.
KDD-2002-SarawagiB #interactive #using: Interactive deduplication using active learning (SS, AB), pp. 269–278.
KDD-2002-TejadaKM #identification #independence #string: Learning domain-independent string transformation weights for high accuracy object identification (ST, CAK, SM), pp. 350–359.
KDD-2002-YuHC #classification #named #using #web: PEBL: positive example based learning for Web page classification using SVM (HY, JH, KCCC), pp. 239–248.
KR-2002-BeygelzimerR #complexity #network: Inference Complexity as a Model-Selection Criterion for Learning Bayesian Networks (AB, IR), pp. 558–567.
LSO-2002-AngkasaputraPRT #collaboration #implementation: The Collaborative Learning Methodology CORONET-Train: Implementation and Guidance (NA, DP, ER, ST), pp. 13–24.
LSO-2002-HenningerM #agile #concept #development #question: Learning Software Organizations and Agile Software Development: Complementary or Contradictory Concepts? (SH, FM), pp. 1–3.
LSO-2002-HofmannW #approach #community: Building Communities among Software Engineers: The ViSEK Approach to Intra- and Inter-Organizational Learning (BH, VW), pp. 25–33.
LSO-2002-NeuB #comprehension #process #simulation: Learning and Understanding a Software Process through Simulation of Its Underlying Model (HN, UBK), pp. 81–93.
LSO-2002-Ruhe #paradigm #re-engineering: Software Engineering Decision Support ? A New Paradigm for Learning Software Organizations (GR), pp. 104–113.
SEKE-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.
SEKE-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.
SEKE-2002-MaidantchikMS #requirements: Learning organizational knowledge: an evolutionary proposal for requirements engineering (CM, MM, GS), pp. 151–157.
SEKE-2002-TortoraSVD #multi: A multilevel learning management system (GT, MS, GV, PD), pp. 541–547.
SIGIR-2002-AminiG #summary #using: The use of unlabeled data to improve supervised learning for text summarization (MRA, PG), pp. 105–112.
SAC-2002-BoughanemT #adaptation #incremental: Incremental adaptive filtering: profile learning and threshold calibration (MB, MT), pp. 640–644.
SAC-2002-ElishRF #collaboration #network: Evaluating collaborative software in supporting organizational learning with Bayesian Networks (MOE, DCR, JEF), pp. 992–996.
SAC-2002-NevesBR #classification #game studies: Learning the risk board game with classifier systems (AN, OB, ACR), pp. 585–589.
SAC-2002-SeleznyovM #detection: Learning temporal patterns for anomaly intrusion detection (AS, OM), pp. 209–213.
HPCA-2002-CintraT #parallel #thread: Speculative Multithreading Eliminating Squashes through Learning Cross-Thread Violations in Speculative Parallelization for Multiprocessors (MHC, JT), pp. 43–54.
STOC-2002-HellersteinR #using: Exact learning of DNF formulas using DNF hypotheses (LH, VR), pp. 465–473.
ICLP-2002-MartinNSS #logic #prolog: Learning in Logic with RichProlog (EM, PMN, AS, FS), pp. 239–254.
ECDL-2001-ColemanSBM #library: Learning Spaces in Digital Libraries (AC, TRS, OAB, REM), pp. 251–262.
HT-2001-ConlanHLWA #adaptation #metadata: Extending eductional metadata schemas to describe adaptive learning resources (OC, CH, PL, VPW, DA), pp. 161–162.
ICDAR-2001-DongKS #framework #multi #pattern matching #pattern recognition #recognition: A Multi-Net Local Learning Framework for Pattern Recognition (JxD, AK, CYS), pp. 328–332.
ICDAR-2001-HoqueF #classification: An Improved Learning Scheme for the Moving Window Classifier (SH, MCF), pp. 607–611.
ICDAR-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.
ICDAR-2001-ValvenyM #using: Learning of Structural Descriptions of Graphic Symbols Using Deformable Template Matching (EV, EM), pp. 455–459.
ICDAR-2001-WakabayashiSOK #image #recognition: Accuracy Improvement of Handwritten Numeral Recognition by Mirror Image Learning (TW, MS, WO, FK), pp. 338–343.
JCDL-2001-LaleufS #component #repository: A component repository for learning objects: a progress report (JRL, AMS), pp. 33–40.
JCDL-2001-MacColl: Project ANGEL: an open virtual learning envoronment with sophisticated access management (JM), pp. 122–123.
VLDB-2001-StillgerLMK #named: LEO — DB2’s LEarning Optimizer (MS, GML, VM, MK), pp. 19–28.
CSEET-2001-ArmaregoFR #development #online #re-engineering: Constructing Software Engineering Knowledge: Development of an Online Learning Environment (JA, LF, GGR), pp. 258–267.
CSEET-2001-RatcliffeTW: A Learning Environment for First Year Software Engineers (MR, LT, JW), pp. 268–275.
ITiCSE-2001-CarboneHMG #programming: Characteristics of programming exercises that lead to poor learning tendencies: Part II (AC, JH, IM, DG), pp. 93–96.
ITiCSE-2001-Chalk: Scaffolding learning in virtual environments (PC), pp. 85–88.
ITiCSE-2001-ChoiC #design #education #interactive #multi #object-oriented #using: Using interactive multimedia for teaching and learning object oriented software design (SHC, SC), p. 176.
ITiCSE-2001-CiesielskiM #algorithm #animation #student #using: Using animation of state space algorithms to overcome student learning difficulties (VC, PM), pp. 97–100.
ITiCSE-2001-Ginat #algorithm #problem: Metacognitive awareness utilized for learning control elements in algorithmic problem solving (DG), pp. 81–84.
ITiCSE-2001-Kumar #c++ #interactive #pointer: Learning the interaction between pointers and scope in C++ (ANK), pp. 45–48.
ITiCSE-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.
ITiCSE-2001-Putnik #integration #on the: On integration of learning and technology (ZP), p. 185.
ITiCSE-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.
ITiCSE-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.
ICALP-2001-Servedio #quantum: Separating Quantum and Classical Learning (RAS), pp. 1065–1080.
FLOPS-2001-Ferri-RamirezHR #functional #incremental #logic programming #source code: Incremental Learning of Functional Logic Programs (CF, JHO, MJRQ), pp. 233–247.
FLOPS-2001-Sato #logic programming #source code: Parameterized Logic Programs where Computing Meets Learning (TS), pp. 40–60.
CHI-2001-CorbettA #feedback: Locus of feedback control in computer-based tutoring: impact on learning rate, achievement and attitudes (ATC, JRA), pp. 245–252.
CHI-2001-RossonS #education #reuse #simulation: Teachers as simulation programmers: minimalist learning and reuse (MBR, CDS), pp. 237–244.
SVIS-2001-Faltin #algorithm #constraints #interactive: Structure and Constraints in Interactive Exploratory Algorithm Learning (NF), pp. 213–226.
SVIS-2001-RossG #education #named #web: Hypertextbooks: Animated, Active Learning, Comprehensive Teaching and Learning Resources for the Web (RJR, MTG), pp. 269–284.
ICEIS-v2-2001-AudyBF #information management: Information Systems Planning: Contributions from Organizational Learning (JLNA, JLB, HF), pp. 873–879.
ICEIS-v2-2001-BressanAAG #3d #multi #web: Multiuser 3D Learning Environments in the Web (CMB, SdA, RBdA, CG), pp. 1170–1173.
CIKM-2001-NottelmannF #classification #datalog #probability: Learning Probabilistic Datalog Rules for Information Classification and Transformation (HN, NF), pp. 387–394.
ICML-2001-AmarDGZ #multi: Multiple-Instance Learning of Real-Valued Data (RAA, DRD, SAG, QZ), pp. 3–10.
ICML-2001-BlumC #graph #using: Learning from Labeled and Unlabeled Data using Graph Mincuts (AB, SC), pp. 19–26.
ICML-2001-BowlingV #convergence: Convergence of Gradient Dynamics with a Variable Learning Rate (MHB, MMV), pp. 27–34.
ICML-2001-ChajewskaKO #behaviour: Learning an Agent’s Utility Function by Observing Behavior (UC, DK, DO), pp. 35–42.
ICML-2001-ChoiR #approximate #difference #fixpoint #performance: A Generalized Kalman Filter for Fixed Point Approximation and Efficient Temporal Difference Learning (DC, BVR), pp. 43–50.
ICML-2001-EngelM #embedded #markov #process: Learning Embedded Maps of Markov Processes (YE, SM), pp. 138–145.
ICML-2001-Furnkranz: Round Robin Rule Learning (JF), pp. 146–153.
ICML-2001-Geibel #bound: Reinforcement Learning with Bounded Risk (PG), pp. 162–169.
ICML-2001-GetoorFKT #modelling #probability #relational: Learning Probabilistic Models of Relational Structure (LG, NF, DK, BT), pp. 170–177.
ICML-2001-GhavamzadehM: Continuous-Time Hierarchical Reinforcement Learning (MG, SM), pp. 186–193.
ICML-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.
ICML-2001-JafariGGE #equilibrium #game studies #nash #on the: On No-Regret Learning, Fictitious Play, and Nash Equilibrium (AJ, AG, DG, GE), pp. 226–233.
ICML-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.
ICML-2001-Krawiec #comparison: Pairwise Comparison of Hypotheses in Evolutionary Learning (KK), pp. 266–273.
ICML-2001-Lee #collaboration #recommendation: Collaborative Learning and Recommender Systems (WSL), pp. 314–321.
ICML-2001-MarchandS #set: Learning with the Set Covering Machine (MM, JST), pp. 345–352.
ICML-2001-McGovernB #automation #using: Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density (AM, AGB), pp. 361–368.
ICML-2001-PerkinsB #set: Lyapunov-Constrained Action Sets for Reinforcement Learning (TJP, AGB), pp. 409–416.
ICML-2001-PrecupSD #approximate #difference: Off-Policy Temporal Difference Learning with Function Approximation (DP, RSS, SD), pp. 417–424.
ICML-2001-RoyM #estimation #fault #reduction #towards: Toward Optimal Active Learning through Sampling Estimation of Error Reduction (NR, AM), pp. 441–448.
ICML-2001-SatoK #markov #problem: Average-Reward Reinforcement Learning for Variance Penalized Markov Decision Problems (MS, SK), pp. 473–480.
ICML-2001-SingerV #implementation #performance: Learning to Generate Fast Signal Processing Implementations (BS, MMV), pp. 529–536.
ICML-2001-StoneS #scalability #towards: Scaling Reinforcement Learning toward RoboCup Soccer (PS, RSS), pp. 537–544.
ICML-2001-Venkataraman: A procedure for unsupervised lexicon learning (AV), pp. 569–576.
ICML-2001-Wiering #using: Reinforcement Learning in Dynamic Environments using Instantiated Information (MW), pp. 585–592.
ICML-2001-Wyatt #using: Exploration Control in Reinforcement Learning using Optimistic Model Selection (JLW), pp. 593–600.
ICML-2001-ZinkevichB #markov #multi #process #symmetry: Symmetry in Markov Decision Processes and its Implications for Single Agent and Multiagent Learning (MZ, TRB), p. 632–?.
KDD-2001-KaltonLWY #clustering: Generalized clustering, supervised learning, and data assignment (AK, PL, KW, JPY), pp. 299–304.
KDD-2001-ZadroznyE: Learning and making decisions when costs and probabilities are both unknown (BZ, CE), pp. 204–213.
LSO-2001-FeldmannA #on the: On the Status of Learning Software Organizations in the Year 2001 (RLF, KDA), pp. 2–7.
LSO-2001-Henninger: Organizational Learning in Dynamic Domains (SH), pp. 8–16.
LSO-2001-PfahlADR #collaboration #named: CORONET-Train: A Methodology for Web-Based Collaborative Learning in Software Organisations (DP, NA, CD, GR), pp. 37–51.
LSO-2001-Segal #case study #process: Organisational Learning and Software Process Improvement: A Case Study (JS), pp. 68–82.
LSO-2001-StarkloffP #approach #development: Process-Integrated Learning: The ADVISOR Approach for Corporate Development (PS, KP), pp. 152–162.
MLDM-2001-BhanuD #clustering #concept #feedback #fuzzy: Concepts Learning with Fuzzy Clustering and Relevance Feedback (BB, AD), pp. 102–116.
MLDM-2001-DongKS #framework #recognition: Local Learning Framework for Recognition of Lowercase Handwritten Characters (JxD, AK, CYS), pp. 226–238.
MLDM-2001-Fernau #xml: Learning XML Grammars (HF), pp. 73–87.
MLDM-2001-KollmarH #feature model: Feature Selection for a Real-World Learning Task (DK, DHH), pp. 157–172.
MLDM-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.
MLDM-2001-Krzyzak #classification #network #using: Nonlinear Function Learning and Classification Using Optimal Radial Basis Function Networks (AK), pp. 217–225.
MLDM-2001-LinderP #how: How to Automate Neural Net Based Learning (RL, SJP), pp. 206–216.
MLDM-2001-ShiWOK #image #recognition: Mirror Image Learning for Handwritten Numeral Recognition (MS, TW, WO, FK), pp. 239–248.
SEKE-2001-NavarroH #adaptation #game studies: Adapting Game Technology to Support Individual and Organizational Learning (EON, AvdH), pp. 347–354.
SEKE-2001-PfahlR: System Dynamics as an Enabling Technology for Learning in Software Organizations (DP, GR), pp. 355–362.
SIGIR-2001-Joachims #classification #statistics: A Statistical Learning Model of Text Classification for Support Vector Machines (TJ), pp. 128–136.
SIGIR-2001-LeeS #clustering #image #retrieval #using: Intelligent Object-based Image Retrieval Using Cluster-driven Personal Preference Learning (KML, WNS), pp. 436–437.
RE-2001-Kovitz #backtracking #development: Is Backtracking so Bad? The Role of Learning in Software Development (BK), p. 272.
SAC-2001-KallesK #design #game studies #on the #using #verification: On verifying game designs and playing strategies using reinforcement learning (DK, PK), pp. 6–11.
SAC-2001-LeeGA #multi: A multi-neural-network learning for lot sizing and sequencing on a flow-shop (IL, JNDG, ADA), pp. 36–40.
SAC-2001-OkabeY #documentation #interactive #relational #retrieval: Interactive document retrieval with relational learning (MO, SY), pp. 27–31.
DAC-2001-GizdarskiF #complexity #framework: A Framework for Low Complexity Static Learning (EG, HF), pp. 546–549.
DATE-2001-NovikovG #multi #performance: An efficient learning procedure for multiple implication checks (YN, EIG), pp. 127–135.
STOC-2001-KlivansS01a: Learning DNF in time 2Õ(n1/3) (AK, RAS), pp. 258–265.
STOC-2001-SanjeevK: Learning mixtures of arbitrary gaussians (SA, RK), pp. 247–257.
SAT-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.
ECDL-2000-SemeraroEFF #interactive #library #profiling #tool support: Interaction Profiling in Digital Libraries through Learning Tools (GS, FE, NF, SF), pp. 229–238.
HT-2000-FischerS #adaptation #automation #hypermedia: Automatic creation of exercises in adaptive hypermedia learning systems (SF, RS), pp. 49–55.
HT-2000-SpalterS #distance #hypermedia #jit #reuse: Reusable hypertext structures for distance and JIT learning (AMS, RMS), pp. 29–38.
SIGMOD-2000-ChenDLT #named #query #web: Fact: A Learning Based Web Query Processing System (SC, YD, HL, ZT), p. 587.
SIGMOD-2000-WattezCBFF #benchmark #metric #query: Benchmarking Queries over Trees: Learning the Hard Truth the Hard Way (FW, SC, VB, GF, CF), pp. 510–511.
VLDB-2000-DiaoLCT #query #towards #web: Toward Learning Based Web Query Processing (YD, HL, SC, ZT), pp. 317–328.
CSEET-2000-KorneckiZE #concept #programming #realtime: Learning Real-Time Programming Concepts through VxWorks Lab Experiments (AJK, JZ, DE), p. 294–?.
ITiCSE-2000-Chalk #re-engineering #using: Apprenticeship learning of software engineering using Webworlds (PC), pp. 112–115.
ITiCSE-2000-Hobbs #assessment #email: Email groups for learning and assessment (MH), p. 183.
ITiCSE-2000-KhuriH #algorithm #image #interactive: Interactive packages for learning image compression algorithms (SK, HCH), pp. 73–76.
ITiCSE-2000-OuCLL #web: Instructional instruments for Web group learning systems: the grouping, intervention, and strategy (KLO, GDC, CCL, BJL), pp. 69–72.
ITiCSE-2000-RosbottomCF #online: A generic model for on-line learning (JR, JC, DF), pp. 108–111.
ITiCSE-2000-SpalterS #case study #education #experience #interactive: Integrating interactive computer-based learning experiences into established curricula: a case study (AMS, RMS), pp. 116–119.
CHI-2000-CorbettT #difference: Instructional interventions in computer-based tutoring: differential impact on learning time and accuracy (ATC, HJT), pp. 97–104.
CSCW-2000-CadizBSGGJ #collaboration #distance #distributed #video: Distance learning through distributed collaborative video viewing (JJC, AB, ES, AG, JG, GJ), pp. 135–144.
CSCW-2000-SingleySFFS #algebra #collaboration: Algebra jam: supporting teamwork and managing roles in a collaborative learning environment (MKS, MS, PGF, RGF, SS), pp. 145–154.
ICEIS-2000-KleinerSB #estimation: Self Organizing Maps for Value Estimation to Solve Reinforcement Learning Tasks (AK, BS, OB), pp. 149–156.
ICEIS-2000-NobreC #information management: Information Systems and Learning Organisations (ALN, MPeC), pp. 327–332.
ICEIS-2000-PetersHW #database #design #distributed: Action Learning in a Decentralized Organization-The Case of Designing a Distributed Database (SCAP, MSHH, CEW), pp. 519–520.
CIKM-2000-GhaniJ #database #multi: Learning a Monolingual Language Model from a Multilingual Text Database (RG, RJ), pp. 187–193.
CIKM-2000-LamL #documentation: Learning to Extract Hierarchical Information from Semi-structured Documents (WL, WYL), pp. 250–257.
ICML-2000-AlerBI #information management #representation: Knowledge Representation Issues in Control Knowledge Learning (RA, DB, PI), pp. 1–8.
ICML-2000-AllenG #comparison #empirical: Model Selection Criteria for Learning Belief Nets: An Empirical Comparison (TVA, RG), pp. 1047–1054.
ICML-2000-BaxterB: Reinforcement Learning in POMDP’s via Direct Gradient Ascent (JB, PLB), pp. 41–48.
ICML-2000-BoschZ #in memory #multi: Unpacking Multi-valued Symbolic Features and Classes in Memory-Based Language Learning (AvdB, JZ), pp. 1055–1062.
ICML-2000-Bowling #convergence #multi #problem: Convergence Problems of General-Sum Multiagent Reinforcement Learning (MHB), pp. 89–94.
ICML-2000-CampbellCS #classification #query #scalability: Query Learning with Large Margin Classifiers (CC, NC, AJS), pp. 111–118.
ICML-2000-ChangCM: Learning to Create Customized Authority Lists (HC, DC, AM), pp. 127–134.
ICML-2000-ChoiY #database: Learning to Select Text Databases with Neural Nets (YSC, SIY), pp. 135–142.
ICML-2000-ChownD #approach #divide and conquer #information management: A Divide and Conquer Approach to Learning from Prior Knowledge (EC, TGD), pp. 143–150.
ICML-2000-CoelhoG #approach: Learning in Non-stationary Conditions: A Control Theoretic Approach (JACJ, RAG), pp. 151–158.
ICML-2000-Cohen #automation #concept #web: Automatically Extracting Features for Concept Learning from the Web (WWC), pp. 159–166.
ICML-2000-CohnC #documentation #identification: Learning to Probabilistically Identify Authoritative Documents (DC, HC), pp. 167–174.
ICML-2000-ConradtTVS #online: On-line Learning for Humanoid Robot Systems (JC, GT, SV, SS), pp. 191–198.
ICML-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.
ICML-2000-DeJong #empirical: Hidden Strengths and Limitations: An Empirical Investigation of Reinforcement Learning (GD), pp. 215–222.
ICML-2000-DyB #identification #order #set: Feature Subset Selection and Order Identification for Unsupervised Learning (JGD, CEB), pp. 247–254.
ICML-2000-FariasR #approximate #fixpoint: Fixed Points of Approximate Value Iteration and Temporal-Difference Learning (DPdF, BVR), pp. 207–214.
ICML-2000-FernG #empirical #online: Online Ensemble Learning: An Empirical Study (AF, RG), pp. 279–286.
ICML-2000-FiechterR #scalability: Learning Subjective Functions with Large Margins (CNF, SR), pp. 287–294.
ICML-2000-ForsterW #bound: Relative Loss Bounds for Temporal-Difference Learning (JF, MKW), pp. 295–302.
ICML-2000-GiordanaSSB #framework #relational: Analyzing Relational Learning in the Phase Transition Framework (AG, LS, MS, MB), pp. 311–318.
ICML-2000-GoldbergM #modelling #multi: Learning Multiple Models for Reward Maximization (DG, MJM), pp. 319–326.
ICML-2000-GoldmanZ: Enhancing Supervised Learning with Unlabeled Data (SAG, YZ), pp. 327–334.
ICML-2000-GordonM: Learning Filaments (GJG, AM), pp. 335–342.
ICML-2000-HallH #information retrieval #multi #natural language: Learning Curved Multinomial Subfamilies for Natural Language Processing and Information Retrieval (KBH, TH), pp. 351–358.
ICML-2000-Heskes #empirical: Empirical Bayes for Learning to Learn (TH), pp. 367–374.
ICML-2000-HougenGS #approach: An Integrated Connectionist Approach to Reinforcement Learning for Robotic Control (DFH, MLG, JRS), pp. 383–390.
ICML-2000-HuangSK #constraints #declarative: Learning Declarative Control Rules for Constraint-BAsed Planning (YCH, BS, HAK), pp. 415–422.
ICML-2000-KatayamaKK #using: A Universal Generalization for Temporal-Difference Learning Using Haar Basis Functions (SK, HK, SK), pp. 447–454.
ICML-2000-Khardon: Learning Horn Expressions with LogAn-H (RK), pp. 471–478.
ICML-2000-KimN #network #set: Learning Bayesian Networks for Diverse and Varying numbers of Evidence Sets (ZWK, RN), pp. 479–486.
ICML-2000-LagoudakisL #algorithm #using: Algorithm Selection using Reinforcement Learning (MGL, MLL), pp. 511–518.
ICML-2000-LaneB #interface #reduction: Data Reduction Techniques for Instance-Based Learning from Human/Computer Interface Data (TL, CEB), pp. 519–526.
ICML-2000-LauerR #algorithm #distributed #multi: An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems (ML, MAR), pp. 535–542.
ICML-2000-Li #online: Selective Voting for Perception-like Online Learning (YL), pp. 559–566.
ICML-2000-MamitsukaA #database #mining #performance #query #scalability: Efficient Mining from Large Databases by Query Learning (HM, NA), pp. 575–582.
ICML-2000-MorimotoD #behaviour #using: Acquisition of Stand-up Behavior by a Real Robot using Hierarchical Reinforcement Learning (JM, KD), pp. 623–630.
ICML-2000-MuggletonBS #biology #product line #sequence: Learning Chomsky-like Grammars for Biological Sequence Families (SM, CHB, AS), pp. 631–638.
ICML-2000-NgR #algorithm: Algorithms for Inverse Reinforcement Learning (AYN, SJR), pp. 663–670.
ICML-2000-NikovskiN #mobile #modelling #navigation #probability: Learning Probabilistic Models for Decision-Theoretic Navigation of Mobile Robots (DN, IRN), pp. 671–678.
ICML-2000-PaccanaroH #concept #distributed #linear: Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space (AP, GEH), pp. 711–718.
ICML-2000-PennockMGH #algorithm: A Normative Examination of Ensemble Learning Algorithms (DMP, PMRI, CLG, EH), pp. 735–742.
ICML-2000-PfahringerBG #algorithm: Meta-Learning by Landmarking Various Learning Algorithms (BP, HB, CGGC), pp. 743–750.
ICML-2000-PiaterG #development #visual notation: Constructive Feature Learning and the Development of Visual Expertise (JHP, RAG), pp. 751–758.
ICML-2000-Randlov #physics #problem: Shaping in Reinforcement Learning by Changing the Physics of the Problem (JR), pp. 767–774.
ICML-2000-RandlovBR #algorithm: Combining Reinforcement Learning with a Local Control Algorithm (JR, AGB, MTR), pp. 775–782.
ICML-2000-Reynolds #adaptation #bound #clustering: Adaptive Resolution Model-Free Reinforcement Learning: Decision Boundary Partitioning (SIR), pp. 783–790.
ICML-2000-RichterS #modelling: Knowledge Propagation in Model-based Reinforcement Learning Tasks (CR, JS), pp. 791–798.
ICML-2000-RyanR: Learning to Fly: An Application of Hierarchical Reinforcement Learning (MRKR, MDR), pp. 807–814.
ICML-2000-SannerALL #performance: Achieving Efficient and Cognitively Plausible Learning in Backgammon (SS, JRA, CL, MCL), pp. 823–830.
ICML-2000-SchohnC #less is more: Less is More: Active Learning with Support Vector Machines (GS, DC), pp. 839–846.
ICML-2000-SchuurmansS #adaptation: An Adaptive Regularization Criterion for Supervised Learning (DS, FS), pp. 847–854.
ICML-2000-SegalK #incremental: Incremental Learning in SwiftFile (RS, JOK), pp. 863–870.
ICML-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.
ICML-2000-SilvaL #hybrid: Obtaining Simplified Rule Bases by Hybrid Learning (RBdAeS, TBL), pp. 879–886.
ICML-2000-SingerV #modelling #performance #predict: Learning to Predict Performance from Formula Modeling and Training Data (BS, MMV), pp. 887–894.
ICML-2000-SmartK: Practical Reinforcement Learning in Continuous Spaces (WDS, LPK), pp. 903–910.
ICML-2000-SohT #image #using: Using Learning by Discovery to Segment Remotely Sensed Images (LKS, CT), pp. 919–926.
ICML-2000-Strens #framework: A Bayesian Framework for Reinforcement Learning (MJAS), pp. 943–950.
ICML-2000-Talavera #concept #feature model #incremental #probability: Feature Selection and Incremental Learning of Probabilistic Concept Hierarchies (LT), pp. 951–958.
ICML-2000-TellerV #evolution #performance #programming: Efficient Learning Through Evolution: Neural Programming and Internal Reinforcement (AT, MMV), pp. 959–966.
ICML-2000-TongK #classification: Support Vector Machine Active Learning with Application sto Text Classification (ST, DK), pp. 999–1006.
ICML-2000-TorkkolaC: Mutual Information in Learning Feature Transformations (KT, WMC), pp. 1015–1022.
ICML-2000-TowellPM: Learning Priorities From Noisy Examples (GGT, TP, MRM), pp. 1031–1038.
ICML-2000-VaithyanathanD: Hierarchical Unsupervised Learning (SV, BD), pp. 1039–1046.
ICML-2000-Veeser #approach #automaton #finite: An Evolutionary Approach to Evidence-Based Learning of Deterministic Finite Automata (SV), pp. 1071–1078.
ICML-2000-VijayakumarS #incremental #realtime: Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space (SV, SS), pp. 1079–1086.
ICML-2000-WnagZ #approach #lazy evaluation #multi #problem: Solving the Multiple-Instance Problem: A Lazy Learning Approach (JW, JDZ), pp. 1119–1126.
ICML-2000-YangAP #effectiveness #multi #validation: Combining Multiple Learning Strategies for Effective Cross Validation (YY, TA, TP), pp. 1167–1174.
ICML-2000-Zaanen #recursion #syntax #using: Bootstrapping Syntax and Recursion using Alginment-Based Learning (MvZ), pp. 1063–1070.
ICPR-v1-2000-BhanuF #image #interactive #segmentation: Learning Based Interactive Image Segmentation (BB, SF), pp. 1299–1302.
ICPR-v1-2000-LiuW #recognition #representation: Learning the Face Space — Representation and Recognition (CL, HW), pp. 1249–1256.
ICPR-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.
ICPR-v1-2000-PalettaPP #analysis #recognition #using: Learning Temporal Context in Active Object Recognition Using Bayesian Analysis (LP, MP, AP), pp. 1695–1699.
ICPR-v1-2000-PiaterG #network #recognition: Feature Learning for Recognition with Bayesian Networks (JHP, RAG), pp. 1017–1020.
ICPR-v2-2000-BuhmannZ #clustering: Active Learning for Hierarchical Pairwise Data Clustering (JMB, TZ), pp. 2186–2189.
ICPR-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.
ICPR-v2-2000-Caelli #feature model #image #modelling #performance #predict: Learning Image Feature Extraction: Modeling, Tracking and Predicting Human Performance (TC), pp. 2215–2218.
ICPR-v2-2000-ChouS #algorithm #classification #multi: A Hierarchical Multiple Classifier Learning Algorithm (YYC, LGS), pp. 2152–2155.
ICPR-v2-2000-Figueiredo #approximate #on the: On Gaussian Radial Basis Function Approximations: Interpretation, Extensions, and Learning Strategies (MATF), pp. 2618–2621.
ICPR-v2-2000-HiraokaHHMMY #algorithm #analysis #linear: Successive Learning of Linear Discriminant Analysis: Sanger-Type Algorithm (KH, KiH, MH, HM, TM, SY), pp. 2664–2667.
ICPR-v2-2000-HongH #sequence: Learning to Extract Temporal Signal Patterns from Temporal Signal Sequence (PH, TSH), pp. 2648–2651.
ICPR-v2-2000-KavallieratouSFK #segmentation #using: Handwritten Character Segmentation Using Transformation-Based Learning (EK, ES, NF, GKK), pp. 2634–2637.
ICPR-v2-2000-KeglKN #classification #complexity #network: Radial Basis Function Networks and Complexity Regularization in Function Learning and Classification (BK, AK, HN), pp. 2081–2086.
ICPR-v2-2000-LawK #clustering #modelling #sequence: Rival Penalized Competitive Learning for Model-Based Sequence Clustering (MHCL, JTK), pp. 2195–2198.
ICPR-v2-2000-LohRW #incremental #named #network: IFOSART: A Noise Resistant Neural Network Capable of Incremental Learning (AWKL, MCR, GAWW), pp. 2985–2988.
ICPR-v2-2000-MitraMP #database #incremental #scalability: Data Condensation in Large Databases by Incremental Learning with Support Vector Machines (PM, CAM, SKP), pp. 2708–2711.
ICPR-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.
ICPR-v2-2000-NaphadeCHF #modelling #multi: Learning Sparse Multiple Cause Models (MRN, LSC, TSH, BJF), pp. 2642–2647.
ICPR-v2-2000-Sato #classification #fault: A Learning Method for Definite Canonicalization Based on Minimum Classification Error (AS), pp. 2199–2202.
ICPR-v4-2000-HeisterkampPD #image #query #retrieval: Feature Relevance Learning with Query Shifting for Content-Based Image Retrieval (DRH, JP, HKD), pp. 4250–4253.
ICPR-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.
KDD-2000-IyengarAZ #adaptation #using: Active learning using adaptive resampling (VSI, CA, TZ), pp. 91–98.
KDD-2000-KimSM #feature model #search-based: Feature selection in unsupervised learning via evolutionary search (YK, WNS, FM), pp. 365–369.
KDD-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.
KR-2000-BisoRS #constraints: Experimental Results on Learning Soft Constraints (AB, FR, AS), pp. 435–444.
KR-2000-CumbyR #relational: Relational Representations that Facilitate Learning (CMC, DR), pp. 425–434.
KR-2000-MartinG #concept #policy #using: Learning Generalized Policies in Planning Using Concept Languages (MM, HG), pp. 667–677.
SIGIR-2000-AsadovS #documentation #navigation #semantics: Semantic Explorer — navigation in documents collections, Proxima Daily — learning personal newspaper (VA, SS), p. 388.
SIGIR-2000-Hofmann #modelling #probability #web: Learning probabilistic models of the Web (TH), pp. 369–371.
SIGIR-2000-ZhaiJE #adaptation #approach #heuristic: Exploration of a heuristic approach to threshold learning in adaptive filtering (CZ, PJ, DAE), pp. 360–362.
TOOLS-EUROPE-2000-NobleW #game studies: GOF Pursuit — Learning Patterns by Playing (JN, CW), p. 462.
ICSE-2000-Ramakrishnan #interactive #internet #named #object-oriented #testing #visual notation: LIGHTVIEWS — visual interactive Internet environment for learning OO software testing (SR), pp. 692–695.
SAC-2000-BarraCPGRS #distance #education: Teach++: A Cooperative Distance Learning and Teaching Environment (MB, GC, UFP, VG, CR, VS), pp. 124–130.
SAC-2000-PereiraC #adaptation #behaviour #information retrieval: The Influence of Learning in the Behaviour of Information Retrieval Adaptive Agents (FBP, EC), pp. 452–457.
SAC-2000-RoselliCLPS: WWW-Based Cooperative Learning (TR, CC, SL, MVP, GS), pp. 1014–1020.
FASE-2000-Hernandez-OralloR #lifecycle #quality: Software as Learning: Quality Factors and Life-Cycle Revised (JHO, MJRQ), pp. 147–162.
STOC-2000-BlumKW #problem #query #statistics: Noise-tolerant learning, the parity problem, and the statistical query model (AB, AK, HW), pp. 435–440.
CL-2000-KameyaS #logic programming #performance #source code: Efficient EM Learning with Tabulation for Parameterized Logic Programs (YK, TS), pp. 269–284.
HT-1999-SeebergSRFS: Individual Tables of Contents in Web-Based Learning Systems (CS, AS, KR, SF, RS), pp. 167–168.
ICDAR-1999-HebertPG #detection #incremental #using: Cursive Character Detection using Incremental Learning (JFH, MP, NG), pp. 808–811.
ICDAR-1999-Ho #identification #keyword #performance #word: Fast Identification of Stop Words for Font Learning and Keyword Spotting (TKH), pp. 333–336.
ICDAR-1999-LebourgeoisBE #using: Structure Relation between Classes for Supervised Learning using Pretopology (FL, MB, HE), pp. 33–36.
ICDAR-1999-LiN #classification #documentation: A Document Classification and Extraction System with Learning Ability (XL, PAN), pp. 197–200.
ICDAR-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.
ICDAR-1999-MiletzkiBS: Continuous Learning Systems: Postal Address Readers with Built-In Learning Capability (UM, TB, HS), pp. 329–332.
ICDAR-1999-Walischewski #automation: Learning Regions of Interest in Postal Automation (HW), pp. 317–320.
ITiCSE-1999-Ben-AriK #concurrent #parallel #process: Thinking parallel: the process of learning concurrency (MBA, YBDK), pp. 13–16.
ITiCSE-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.
ITiCSE-1999-DavyJ #education #programming: Research-led innovation in teaching and learning programming (JD, TJ), pp. 5–8.
ITiCSE-1999-DeeR #approach #education: ACOM (“computing for all”): an integrated approach to the teaching and learning of information technology (HD, PR), p. 195.
ITiCSE-1999-Faltin #algorithm #design #game studies: Designing courseware on algorithms for active learning with virtual board games (NF), pp. 135–138.
ITiCSE-1999-HabermanG #distance #education: Distance learning model with local workshop sessions applied to in-service teacher training (BH, DG), pp. 64–67.
ITiCSE-1999-LowderH #feedback #student: Web-based student feedback to improve learning (JL, DH), pp. 151–154.
ITiCSE-1999-MiaoPW #collaboration: Combining the metaphors of an institute and of networked computers for building collaborative learning environments (YM, HRP, MW), p. 188.
ITiCSE-1999-ScherzP: An organizer for project-based learning and instruction in computer science (ZS, SP), pp. 88–90.
ITiCSE-1999-SheardH #student: A special learning environment for repeat students (JS, DH), pp. 56–59.
ITiCSE-1999-Taylor99a #education: Math link: linking curriculum, instructional strategies, and technology to enhance teaching and learning (HGT), p. 201.
ITiCSE-1999-Utting #education: Gathering and disseminating good practice at teaching and learning conferences (IU), p. 202.
ICALP-1999-Watanabe: From Computational Learning Theory to Discovery Science (OW0), pp. 134–148.
WIA-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.
AGTIVE-1999-FischerKB #fuzzy #graph: Learning and Rewriting in Fuzzy Rule Graphs (IF, MK, MRB), pp. 263–270.
CHI-1999-MoherJOG: Bridging Strategies for VR-Based Learning (TGM, AEJ, SO, MG), pp. 536–543.
CHI-1999-PlowmanKLST #design #multi: Designing Multimedia for Learning: Narrative Guidance and Narrative Construction (LP, RL, DL, MS, JT), pp. 310–317.
CHI-1999-Soto #analysis #quality #semantics: Learning and Performing by Exploration: Label Quality Measured by Latent Semantic Analysis (RS), pp. 418–425.
HCI-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.
HCI-CCAD-1999-CarroMR #adaptation #education: Teaching tasks in an adaptive learning environment (RMC, RM, EP, PR), pp. 740–744.
HCI-CCAD-1999-Chiu #algorithm #approach #search-based #using: Learning path planning using genetic algorithm approach (CC), pp. 71–75.
HCI-CCAD-1999-Danielsson #network: Learning in networks (UD), pp. 407–411.
HCI-CCAD-1999-FachB #adaptation #design: Training wheels: an “old” method for designing modern and adaptable learning environments (PWF, MB), pp. 725–729.
HCI-CCAD-1999-HartmannSMGS #tool support: Tools for computer-supported learning in organisations (EAH, DS, KM, MG, HS), pp. 377–381.
HCI-CCAD-1999-JohnsonO #multi #problem #using: Innovative mathematical learning environments — Using multimedia to solve real world problems (LFJ, POJ), pp. 677–681.
HCI-CCAD-1999-KashiharaUT #visualisation: Visualizing knowledge structure for exploratory learning in hyperspace (AK, HU, JT), pp. 667–671.
HCI-CCAD-1999-KasviKVPR: Supporting a learning operative organization (JJJK, IK, MV, AP, LR), pp. 197–201.
HCI-CCAD-1999-KutayHW #human-computer: Achieving learning outcomes in HCI for computing — an experiential testbed (CK, PH, GW), pp. 626–631.
HCI-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.
HCI-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.
HCI-CCAD-1999-NealI #case study #distance #education #experience: Asynchronous distance learning for corporate education: experiences with Lotus LearningSpace (LN, DI), pp. 750–754.
HCI-CCAD-1999-OppermannS #adaptation #mobile: Adaptive mobile museum guide for information and learning on demand (RO, MS), pp. 642–646.
HCI-CCAD-1999-PatelKR: Cognitive apprenticeship based learning environment in numeric domains (AP, K, DR), pp. 637–641.
HCI-CCAD-1999-Seufert #named #network: PLATO — “electronic cookbook” for Internet-based learning networks (SS), pp. 707–711.
HCI-CCAD-1999-Siemer-Matravers #collaboration: Collaborative learning — a cure for intelligent tutoring systems (JSM), pp. 652–656.
HCI-CCAD-1999-SinitsaM #interactive #taxonomy: Interactive dictionary in a context of learning (KMS, AM), pp. 662–666.
HCI-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.
HCI-EI-1999-AzarovM #aspect-oriented #distance: Psychological Aspects of the Organization of the Distance Learning (SSA, OVM), pp. 124–128.
HCI-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.
HCI-EI-1999-HuangWC #programming: A Flow-chart Based Learning System for Computer Programming (KHH, KW, SYC), pp. 1298–1302.
HCI-EI-1999-Nyssen #towards: Training Simulators in Anesthesia: Towards a Hierarchy of Learning Situations (ASN), pp. 890–894.
HCI-EI-1999-PentlandRW #adaptation #gesture #interface #word: Perceptual Intelligence: learning gestures and words for individualized, adaptive interfaces (AP, DR, CRW), pp. 286–290.
HCI-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.
HCI-EI-1999-TanoT #adaptation #user interface: User Adaptation of the Pen-based User Interface by Reinforcement Learning (ST, MT), pp. 233–237.
HCI-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.
ICEIS-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.
CIKM-1999-AponWD #approach #parallel: A Learning Approach to Processor Allocation in Parallel Systems (AWA, TDW, LWD), pp. 531–537.
CIKM-1999-WidyantoroIY #adaptation #algorithm: An Adaptive Algorithm for Learning Changes in User Interests (DHW, TRI, JY), pp. 405–412.
ICML-1999-AbeL #concept #linear #probability #using: Associative Reinforcement Learning using Linear Probabilistic Concepts (NA, PML), pp. 3–11.
ICML-1999-AbeN #internet: Learning to Optimally Schedule Internet Banner Advertisements (NA, AN), pp. 12–21.
ICML-1999-BontempiBB #predict: Local Learning for Iterated Time-Series Prediction (GB, MB, HB), pp. 32–38.
ICML-1999-Bosch #abstraction #in memory: Instance-Family Abstraction in Memory-Based Language Learning (AvdB), pp. 39–48.
ICML-1999-Boyan #difference: Least-Squares Temporal Difference Learning (JAB), pp. 49–56.
ICML-1999-BrodieD #induction #using: Learning to Ride a Bicycle using Iterated Phantom Induction (MB, GD), pp. 57–66.
ICML-1999-FreundM #algorithm: The Alternating Decision Tree Learning Algorithm (YF, LM), pp. 124–133.
ICML-1999-GervasioIL #adaptation #evaluation #scheduling: Learning User Evaluation Functions for Adaptive Scheduling Assistance (MTG, WI, PL), pp. 152–161.
ICML-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.
ICML-1999-Kadous #multi: Learning Comprehensible Descriptions of Multivariate Time Series (MWK), pp. 454–463.
ICML-1999-LentL #performance: Learning Hierarchical Performance Knowledge by Observation (MvL, JEL), pp. 229–238.
ICML-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.
ICML-1999-PalhangS #induction #logic programming: Learning Discriminatory and Descriptive Rules by an Inductive Logic Programming System (MP, AS), pp. 288–297.
ICML-1999-PeshkinMK #memory management #policy: Learning Policies with External Memory (LP, NM, LPK), pp. 307–314.
ICML-1999-PriceB #multi: Implicit Imitation in Multiagent Reinforcement Learning (BP, CB), pp. 325–334.
ICML-1999-RennieM #using #web: Using Reinforcement Learning to Spider the Web Efficiently (JR, AM), pp. 335–343.
ICML-1999-SakakibaraK #context-free grammar #using: GA-based Learning of Context-Free Grammars using Tabular Representations (YS, MK), pp. 354–360.
ICML-1999-ThompsonCM #information management #natural language #parsing: Active Learning for Natural Language Parsing and Information Extraction (CAT, MEC, RJM), pp. 406–414.
ICML-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.
ICML-1999-VaithyanathanD #clustering #documentation: Model Selection in Unsupervised Learning with Applications To Document Clustering (SV, BD), pp. 433–443.
ICML-1999-Zhang #approach: An Region-Based Learning Approach to Discovering Temporal Structures in Data (WZ), pp. 484–492.
ICML-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.
ICML-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–?.
KDD-1999-FanSZ #distributed #online #scalability: The Application of AdaBoost for Distributed, Scalable and On-Line Learning (WF, SJS, JZ), pp. 362–366.
KDD-1999-SyedLS99a #concept #incremental: Handling Concept Drifts in Incremental Learning with Support Vector Machines (NAS, HL, KKS), pp. 317–321.
MLDM-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.
MLDM-1999-AltamuraELM #documentation: Symbolic Learning Techniques in Paper Document Processing (OA, FE, FAL, DM), pp. 159–173.
MLDM-1999-GiacintoR #automation #classification #design #multi: Automatic Design of Multiple Classifier Systems by Unsupervised Learning (GG, FR), pp. 131–143.
MLDM-1999-Jahn #image #preprocessor: Unsupervised Learning of Local Mean Gray Values for Image Pre-processing (HJ), pp. 64–74.
MLDM-1999-KingL #clustering #information retrieval: Non-hierarchical Clustering with Rival Penalized Competitive Learning for Information Retrieval (IK, TKL), pp. 116–130.
MLDM-1999-Petrou #pattern matching #pattern recognition #recognition: Learning in Pattern Recognition (MP), pp. 1–12.
OOPSLA-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.
TOOLS-USA-1999-Ramakrishnan #community #distributed #education #testing #visualisation: Visualizing O-O Testing in Virtual Communities — Distributed Teaching and Learning (SR), p. 300–?.
SAC-1999-VenkataramanaR #automaton #framework: A Learning Automata Based Framework for Task Assignment in Heterogeneous Computing Systems (RDV, NR), pp. 541–547.
DATE-1999-Marques-SilvaG #equivalence #recursion #satisfiability #using: Combinational Equivalence Checking Using Satisfiability and Recursive Learning (JPMS, TG), pp. 145–149.
STOC-1999-Servedio #complexity: Computational Sample Complexity and Attribute-Efficient Learning (RAS), pp. 701–710.
CSL-1999-Balcazar #consistency #query: The Consistency Dimension, Compactness, and Query Learning (JLB), pp. 2–13.
ICLP-1999-SatoF #logic programming: Reactive Logic Programming by Reinforcement Learning (TS, SF), p. 617.
ECDL-1998-PaliourasPKSM #community: Learning User Communities for Improving the Services of Information Providers (GP, CP, VK, CDS, VM), pp. 367–383.
CSEET-1998-Hislop #education #network: Teaching Via Asynchronous Learning Networks (GWH), pp. 16–35.
ITiCSE-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.
ITiCSE-1998-Casey #education #modelling #web: Learning “from” or “through” the Web: models of Web based education (DC), pp. 51–54.
ITiCSE-1998-DavidovicT: Open learning environment and instruction system (OLEIS) (AD, ET), pp. 69–73.
ITiCSE-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.
ITiCSE-1998-GrayBS #java: A constructivist learning environment implemented in Java (JG, TB, CS), pp. 94–97.
ITiCSE-1998-LewisM #comparison #compilation: A comparison between novice and experienced compiler users in a learning environment (SL, GM), pp. 157–161.
ITiCSE-1998-TiwariH #collaboration #student #using: Learning groupware through using groupware-computer supported collaborative learning with face to face students (AT, CH), pp. 236–238.
ITiCSE-1998-WhitehurstPI #distance #student: Utilising the student model in distance learning (RAW, CLP, JSI), pp. 254–256.
CHI-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.
CHI-1998-JacksonKS #adaptation #design #interactive: The Design of Guided Learner-Adaptable Scaffolding in Interactive Learning Environments (SLJ, JK, ES), pp. 187–194.
CHI-1998-RoseDMBN #community #design #implementation: Building an Electronic Learning Community: From Design to Implementation (AR, WD, GM, JBJ, VN), pp. 203–210.
CHI-1998-Strommen #interface: When the Interface is a Talking Dinosaur: Learning Across Media with ActiMates Barney (ES), pp. 288–295.
CHI-1998-SumnerT #case study #design #experience: New Media, New Practices: Experiences in Open Learning Course Design (TS, JT), pp. 432–439.
CIKM-1998-DumaisPHS #algorithm #categorisation #induction: Inductive Learning Algorithms and Representations for Text Categorization (STD, JCP, DH, MS), pp. 148–155.
CIKM-1998-HongL #fuzzy: Learning Fuzzy Knowledge from Training Examples (TPH, CYL), pp. 161–166.
CIKM-1998-YuL #adaptation #algorithm #online: A New On-Line Learning Algorithm for Adaptive Text Filtering (KLY, WL), pp. 156–160.
ICML-1998-AbeM #query #using: Query Learning Strategies Using Boosting and Bagging (NA, HM), pp. 1–9.
ICML-1998-AlerBI #approach #multi #programming #search-based: Genetic Programming and Deductive-Inductive Learning: A Multi-Strategy Approach (RA, DB, PI), pp. 10–18.
ICML-1998-AnglanoGBS #concept #evaluation: An Experimental Evaluation of Coevolutive Concept Learning (CA, AG, GLB, LS), pp. 19–27.
ICML-1998-BillsusP #collaboration: Learning Collaborative Information Filters (DB, MJP), pp. 46–54.
ICML-1998-BonetG #sorting: Learning Sorting and Decision Trees with POMDPs (BB, HG), pp. 73–81.
ICML-1998-Dietterich: The MAXQ Method for Hierarchical Reinforcement Learning (TGD), pp. 118–126.
ICML-1998-DzeroskiRB #relational: Relational Reinforcement Learning (SD, LDR, HB), pp. 136–143.
ICML-1998-Freitag #information management #multi: Multistrategy Learning for Information Extraction (DF), pp. 161–169.
ICML-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.
ICML-1998-GaborKS #multi: Multi-criteria Reinforcement Learning (ZG, ZK, CS), pp. 197–205.
ICML-1998-GarciaN #algorithm #analysis: A Learning Rate Analysis of Reinforcement Learning Algorithms in Finite-Horizon (FG, SMN), pp. 215–223.
ICML-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.
ICML-1998-HuW #algorithm #framework #multi: Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm (JH, MPW), pp. 242–250.
ICML-1998-JuilleP #case study: Coevolutionary Learning: A Case Study (HJ, JBP), pp. 251–259.
ICML-1998-KearnsS: Near-Optimal Reinforcement Learning in Polynominal Time (MJK, SPS), pp. 260–268.
ICML-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.
ICML-1998-KollerF #approximate #probability #process #using: Using Learning for Approximation in Stochastic Processes (DK, RF), pp. 287–295.
ICML-1998-LittmanJK #corpus #independence #representation: Learning a Language-Independent Representation for Terms from a Partially Aligned Corpus (MLL, FJ, GAK), pp. 314–322.
ICML-1998-MargaritisT #3d #image #sequence: Learning to Locate an Object in 3D Space from a Sequence of Camera Images (DM, ST), pp. 332–340.
ICML-1998-MaronR #classification #multi: Multiple-Instance Learning for Natural Scene Classification (OM, ALR), pp. 341–349.
ICML-1998-McCallumN #classification: Employing EM and Pool-Based Active Learning for Text Classification (AM, KN), pp. 350–358.
ICML-1998-MooreSBL #named #optimisation: Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions (AWM, JGS, JAB, MSL), pp. 386–394.
ICML-1998-Ng #feature model #on the: On Feature Selection: Learning with Exponentially Many Irrelevant Features as Training Examples (AYN), pp. 404–412.
ICML-1998-PendrithM #analysis #markov: An Analysis of Direct Reinforcement Learning in Non-Markovian Domains (MDP, MM), pp. 421–429.
ICML-1998-RandlovA #using: Learning to Drive a Bicycle Using Reinforcement Learning and Shaping (JR, PA), pp. 463–471.
ICML-1998-ReddyT #first-order #source code: Learning First-Order Acyclic Horn Programs from Entailment (CR, PT), pp. 472–480.
ICML-1998-RyanP #architecture #composition #named: RL-TOPS: An Architecture for Modularity and Re-Use in Reinforcement Learning (MRKR, MDP), pp. 481–487.
ICML-1998-SamuelCV: An Investigation of Transformation-Based Learning in Discourse (KS, SC, KVS), pp. 497–505.
ICML-1998-SaundersGV #algorithm: Ridge Regression Learning Algorithm in Dual Variables (CS, AG, VV), pp. 515–521.
ICML-1998-StuartB: Learning the Grammar of Dance (JMS, EB), pp. 547–555.
ICML-1998-SuttonPS: Intra-Option Learning about Temporally Abstract Actions (RSS, DP, SPS), pp. 556–564.
ICPR-1998-BukerK #hybrid: Learning in an active hybrid vision system (UB, BK), pp. 178–181.
ICPR-1998-ConnellJ #online #prototype: Learning prototypes for online handwritten digits (SDC, AKJ), pp. 182–184.
ICPR-1998-DayP #modelling: A projection filter for use with parameterised learning models (MJSD, JSP), pp. 867–869.
ICPR-1998-DutaJ #concept #image: Learning the human face concept in black and white images (ND, AKJ), pp. 1365–1367.
ICPR-1998-HickinbothamHA: Learning feature characteristics (SJH, ERH, JA), pp. 1160–1164.
ICPR-1998-KeglKN #classification #network #parametricity: Radial basis function networks in nonparametric classification and function learning (BK, AK, HN), pp. 565–570.
ICPR-1998-KnutssonBL #multi: Learning multidimensional signal processing (HK, MB, TL), pp. 1416–1420.
ICPR-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.
ICPR-1998-Mizutani #classification #fault: Discriminative learning for minimum error and minimum reject classification (HM), pp. 136–140.
ICPR-1998-Nagy #estimation #persistent: Persistent issues in learning and estimation (GN), pp. 561–564.
ICPR-1998-PengB #recognition: Local reinforcement learning for object recognition (JP, BB), pp. 272–274.
ICPR-1998-SatoY #classification #using: A formulation of learning vector quantization using a new misclassification measure (AS, KY), pp. 322–325.
ICPR-1998-WengH #recognition #sequence: Sensorimotor action sequence learning with application to face recognition under discourse (J(W, WSH), pp. 252–254.
KDD-1998-AndersonM #performance: ADtrees for Fast Counting and for Fast Learning of Association Rules (BSA, AWM), pp. 134–138.
KDD-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.
KDD-1998-GrecuB #data mining #distributed #mining: Coactive Learning for Distributed Data Mining (DLG, LAB), pp. 209–213.
KDD-1998-HandleyLR #predict: Learning to Predict the Duration of an Automobile Trip (SH, PL, FAR), pp. 219–223.
KDD-1998-LaneB #concept #identification #online #security: Approaches to Online Learning and Concept Drift for User Identification in Computer Security (TL, CEB), pp. 259–263.
KDD-1998-MoodyS: Reinforcement Learning for Trading Systems and Portfolios (JEM, MS), pp. 279–283.
KDD-1998-WeissH #predict #sequence: Learning to Predict Rare Events in Event Sequences (GMW, HH), pp. 359–363.
SIGIR-1998-Callan: Learning While Filtering Focuments (JPC), pp. 224–231.
FSE-1998-MasudaSU #design pattern: Applying Design Patterns to Decision Tree Learning System (GM, NS, KU), pp. 111–120.
ICSE-1998-HanakawaMM #development #simulation: A Learning Curve Based Simulation Model for Software Development (NH, SM, KiM), pp. 350–359.
SAC-1998-BillardL #automaton #behaviour #distributed #simulation: Simulation of period-doubling behavior in distributed learning automata (EB, SL), pp. 690–695.
SAC-1998-ChungC #interactive #multi: A multimedia system for interactive learning of organ literature (SC, SC), pp. 117–121.
DAC-1998-El-MalehKR #performance: A Fast Sequential Learning Technique for Real Circuits with Application to Enhancing ATPG Performance (AHEM, MK, JR), pp. 625–631.
STOC-1998-Bshouty #algorithm #composition #theorem: A New Composition Theorem for Learning Algorithms (NHB), pp. 583–589.
STOC-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.
ICDAR-1997-JunkerH #classification #documentation: Evaluating OCR and Non-OCR Text Representations for Learning Document Classifiers (MJ, RH), pp. 1060–1066.
ICDAR-1997-WaizumiKSN #classification #using: High speed rough classification for handwritten characters using hierarchical learning vector quantization (YW, NK, KS, YN), pp. 23–27.
ICDAR-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.
ITiCSE-1997-Boulet #distance: Distance learning of the management of software projects (MMB), pp. 136–138.
ITiCSE-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.
ITiCSE-1997-DankelH #distance #using: The use of the WWW to support distance learning through NTU (DDDI, JH), pp. 8–10.
ITiCSE-1997-Janser #algorithm #interactive #visualisation: An interactive learning system visualizing computer graphics algorithms (AWJ), pp. 21–23.
ITiCSE-1997-Makkonen #collaboration #hypermedia #question: Does collaborative hypertext support better engagement in learning of the basics in informatics? (PM), pp. 130–132.
ITiCSE-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.
ITiCSE-1997-RoblesFPA #communication #distance #multi #using: Using multimedia communication technologies in distance learning (TR, DF, EP, SA), pp. 6–7.
ITiCSE-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.
ITiCSE-WGR-1997-Maurer #distributed #education: The emergence of sophisticated distributed teaching and learning environments (HM), pp. 112–113.
DLT-1997-DavidES #string: Learning String Adjunct and Tree Adjunct Languages (NGD, JDE, KGS), pp. 411–427.
CHI-1997-RappinGRL #interface #usability: Balancing Usability and Learning in an Interface (NR, MG, MR, PL), pp. 479–486.
CHI-1997-ScaifeRAD #design #interactive: Designing For or Designing With? Informant Design For Interactive Learning Environments (MS, YR, FA, MD), pp. 343–350.
HCI-SEC-1997-DasaiKY #collaboration #distance: A Collaborative Distance Learning System and its Experimental Results (TD, HK, KY), pp. 165–168.
HCI-SEC-1997-EnyedyVG #design #interactive: Designing Interactions for Guided Inquiry Learning Environments (NE, PV, BG), pp. 157–160.
HCI-SEC-1997-Keating: Computer Based Learning: GroupSystems[R] in the Wireless Classroom (CCK), pp. 119–122.
HCI-SEC-1997-MurphyKG #interface: Enhancing the Interface to Provide Intelligent Computer Aided Language Learning (MM, AK, AG), pp. 149–152.
HCI-SEC-1997-Neal #distance #multi #using: Using Multiple Technologies for Distance Learning (LN), pp. 111–114.
HCI-SEC-1997-PatelK #design #interactive #interface: Granular Interface Design: Decomposing Learning Tasks and Enhancing Tutoring Interaction (AP, K), pp. 161–164.
HCI-SEC-1997-WilliamsFSTE #education #named #student: PEBBLES: Providing Education by Bringing Learning Environments to Students (LAW, DIF, GS, JT, RE), pp. 115–118.
CIKM-1997-ChengBL #approach #network: Learning Belief Networks from Data: An Information Theory Based Approach (JC, DAB, WL), pp. 325–331.
ICML-1997-AtkesonS: Robot Learning From Demonstration (CGA, SS), pp. 12–20.
ICML-1997-Auer #approach #empirical #evaluation #multi #on the: On Learning From Multi-Instance Examples: Empirical Evaluation of a Theoretical Approach (PA), pp. 21–29.
ICML-1997-BottaGP #first-order #logic #named: FONN: Combining First Order Logic with Connectionist Learning (MB, AG, RP), pp. 46–56.
ICML-1997-DattaK #prototype: Learning Symbolic Prototypes (PD, DFK), pp. 75–82.
ICML-1997-Decatur #classification #induction: PAC Learning with Constant-Partition Classification Noise and Applications to Decision Tree Induction (SED), pp. 83–91.
ICML-1997-Fiechter #bound #online: Expected Mistake Bound Model for On-Line Reinforcement Learning (CNF), pp. 116–124.
ICML-1997-Friedman #network: Learning Belief Networks in the Presence of Missing Values and Hidden Variables (NF), pp. 125–133.
ICML-1997-KimuraMK #approximate: Reinforcement Learning in POMDPs with Function Approximation (HK, KM, SK), pp. 152–160.
ICML-1997-PrecupS: Exponentiated Gradient Methods for Reinforcement Learning (DP, RSS), pp. 272–277.
ICML-1997-ReddyT #using: Learning Goal-Decomposition Rules using Exercises (CR, PT), pp. 278–286.
ICML-1997-RistadY #distance #edit distance #string: Learning String Edit Distance (ESR, PNY), pp. 287–295.
ICML-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.
ICML-1997-Schapire #multi #problem #using: Using output codes to boost multiclass learning problems (RES), pp. 313–321.
ICML-1997-SuematsuHL #approach #markov: A Bayesian Approach to Model Learning in Non-Markovian Environments (NS, AH, SL), pp. 349–357.
ICML-1997-TadepalliD: Hierarchical Explanation-Based Reinforcement Learning (PT, TGD), pp. 358–366.
KDD-1997-Hekanaho #concept: GA-Based Rule Enhancement in Concept Learning (JH), pp. 183–186.
KDD-1997-PazzaniMS: Beyond Concise and Colorful: Learning Intelligible Rules (MJP, SM, WRS), pp. 235–238.
KDD-1997-RubinsteinH: Discriminative vs Informative Learning (YDR, TH), pp. 49–53.
KDD-1997-Soderland #web: Learning to Extract Text-Based Information from the World Wide Web (SS), pp. 251–254.
KDD-1997-ZighedRF #multi: Optimal Multiple Intervals Discretization of Continuous Attributes for Supervised Learning (DAZ, RR, FF), pp. 295–298.
SIGIR-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.
SIGIR-1997-SinghalMB #query: Learning Routing Queries in a Query Zone (AS, MM, CB), pp. 25–32.
SAC-1997-Goldberg: Virtual teams virtual projects = real learning (AG), p. 1.
SAC-1997-SolowayN #education #future of #lessons learnt: The future of computers in education: learning 10 lessons from the past (ES, CAN), p. 2.
STOC-1997-AuerLS #approximate #pseudo #set: Approximating Hyper-Rectangles: Learning and Pseudo-Random Sets (PA, PML, AS), pp. 314–323.
STOC-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.
CADE-1997-KolbeB #named #proving: Plagiator — A Learning Prover (TK, JB), pp. 256–259.
ITiCSE-1996-BrodlieWW #novel #visualisation: Scientific visualization — some novel approaches to learning (KB, JDW, HW), pp. 28–32.
ITiCSE-1996-CaoLLPZ #education #information management: Integrating CSCW in a cooperative learning environment to teach information systems (NVC, AL, ML, OP, CZ), pp. 125–129.
ITiCSE-1996-FinkelW: Computer supported peer learning in an introductory computer science course (DF, CEW), pp. 55–56.
ITiCSE-1996-JohansenKB #interactive: Interactive learning with gateway labs (MJ, JK, DB), p. 232.
ITiCSE-1996-LeesC #natural language #operating system: Applying natural language technology to the learning of operating systems functions (BL, JC), pp. 11–13.
ITiCSE-1996-McConnell: Active learning and its use in computer science (JJM), pp. 52–54.
ITiCSE-1996-Prey #education: Cooperative learning and closed laboratories in an undergraduate computer science curriculum (JCP), pp. 23–24.
ITiCSE-1996-Tjaden #how #student #visual notation: How visual software influences learning in college students (BJT), p. 229.
CHI-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.
CSCW-1996-HiltzT #collaboration #network #online #theory and practice: Asynchronous Learning Networks: The Theory and Practice of Collaborative Learning Online (SRH, MT), p. 5.
AKDDM-1996-HsuK #induction #optimisation #query #semantics #using: Using Inductive Learning To Generate Rules for Semantic Query Optimization (CNH, CAK), pp. 425–445.
CIKM-1996-Huffman: Learning to Extract Information From Text Based on User-Provided Examples (SBH), pp. 154–163.
ICML-1996-AbeL #modelling #using #word: Learning Word Association Norms Using Tree Cut Pair Models (NA, HL), pp. 3–11.
ICML-1996-BlanzieriK #network #online: Learning Radial Basis Function Networks On-line (EB, PK), pp. 37–45.
ICML-1996-BoyanM #evaluation #scalability: Learning Evaluation Functions for Large Acyclic Domains (JAB, AWM), pp. 63–70.
ICML-1996-Caruana #algorithm #multi: Algorithms and Applications for Multitask Learning (RC), pp. 87–95.
ICML-1996-DietterichKM #framework: Applying the Waek Learning Framework to Understand and Improve C4.5 (TGD, MJK, YM), pp. 96–104.
ICML-1996-EmdeW #relational: Relational Instance-Based Learning (WE, DW), pp. 122–130.
ICML-1996-EzawaSN #network #risk management: Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management (KJE, MS, SWN), pp. 139–147.
ICML-1996-FriedmanG #network: Discretizing Continuous Attributes While Learning Bayesian Networks (NF, MG), pp. 157–165.
ICML-1996-GeibelW #concept #relational: Learning Relational Concepts with Decision Trees (PG, FW), pp. 166–174.
ICML-1996-GoetzKM #adaptation #online: On-Line Adaptation of a Signal Predistorter through Dual Reinforcement Learning (PG, SK, RM), pp. 175–181.
ICML-1996-GordonS #parametricity #statistics: Nonparametric Statistical Methods for Experimental Evaluations of Speedup Learning (GJG, AMS), pp. 200–206.
ICML-1996-GreinerGR #classification: Learning Active Classifiers (RG, AJG, DR), pp. 207–215.
ICML-1996-Hekanaho #concept: Background Knowledge in GA-based Concept Learning (JH), pp. 234–242.
ICML-1996-JappyNG #horn clause #robust #source code: Negative Robust Learning Results from Horn Clause Programs (PJ, RN, OG), pp. 258–265.
ICML-1996-KoenigS #distance #navigation: Passive Distance Learning for Robot Navigation (SK, RGS), pp. 266–274.
ICML-1996-Mahadevan: Sensitive Discount Optimality: Unifying Discounted and Average Reward Reinforcement Learning (SM), pp. 328–336.
ICML-1996-Moore: Reinforcement Learning in Factories: The Auton Project (AWM0), p. 556.
ICML-1996-Munos #algorithm #convergence: A Convergent Reinforcement Learning Algorithm in the Continuous Case: The Finite-Element Reinforcement Learning (RM), pp. 337–345.
ICML-1996-OliverBW #using: Unsupervised Learning Using MML (JJO, RAB, CSW), pp. 364–372.
ICML-1996-PendrithR #difference: Actual Return Reinforcement Learning versus Temporal Differences: Some Theoretical and Experimental Results (MDP, MRKR), pp. 373–381.
ICML-1996-Perez #representation: Representing and Learning Quality-Improving Search Control Knowledge (MAP), pp. 382–390.
ICML-1996-PerezR #concept: Learning Despite Concept Variation by Finding Structure in Attribute-based Data (EP, LAR), pp. 391–399.
ICML-1996-ReddyTR #composition #empirical: Theory-guided Empirical Speedup Learning of Goal Decomposition Rules (CR, PT, SR), pp. 409–417.
ICML-1996-Saerens #fault: Non Mean Square Error Criteria for the Training of Learning Machines (MS), pp. 427–434.
ICML-1996-SinghP #classification #network #performance: Efficient Learning of Selective Bayesian Network Classifiers (MS, GMP), pp. 453–461.
ICML-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.
ICML-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.
ICML-1996-ThrunO #algorithm #multi: Discovering Structure in Multiple Learning Tasks: The TC Algorithm (ST, JO), pp. 489–497.
ICML-1996-TirriKM: Prababilistic Instance-Based Learning (HT, PK, PM), pp. 507–515.
ICML-1996-ZuckerG #performance #representation: Representation Changes for Efficient Learning in Structural Domains (JDZ, JGG), pp. 543–551.
ICPR-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.
ICPR-1996-AlquezarS #context-sensitive grammar #regular expression: Learning of context-sensitive languages described by augmented regular expressions (RA, AS), pp. 745–749.
ICPR-1996-BebisGLS #modelling #recognition: Learning affine transformations of the plane for model-based object recognition (GB, MG, NdVL, MS), pp. 60–64.
ICPR-1996-Bobrowski #classification #set: Piecewise-linear classifiers, formal neurons and separability of the learning sets (LB), pp. 224–228.
ICPR-1996-BurgeBM #component #polymorphism #recognition: Recognition and learning with polymorphic structural components (MB, WB, WM), pp. 19–23.
ICPR-1996-FrankH #approach: Pretopological approach for supervised learning (FL, HE), pp. 256–260.
ICPR-1996-HoogsB #modelling: Model-based learning of segmentations (AH, RB), pp. 494–499.
ICPR-1996-KositskyU: Learning class regions by the union of ellipsoids (MK, SU), pp. 750–757.
ICPR-1996-LuettinTB96a: Learning to recognise talking faces (JL, NAT, SWB), pp. 55–59.
ICPR-1996-Muraki #fault #statistics: Error correction scheme augmented with statistical and lexical learning capability, for Japanese OCR (KM), pp. 560–564.
ICPR-1996-MuraseN #approach #generative #recognition: Learning by a generation approach to appearance-based object recognition (HM, SKN), pp. 24–29.
ICPR-1996-PelilloF #network: Autoassociative learning in relaxation labeling networks (MP, AMF), pp. 105–110.
ICPR-1996-PengB #recognition: Delayed reinforcement learning for closed-loop object recognition (JP, BB), pp. 310–314.
ICPR-1996-SainzS #context-sensitive grammar #modelling #using: Learning bidimensional context-dependent models using a context-sensitive language (MS, AS), pp. 565–569.
ICPR-1996-Stoyanov #network: An improved backpropagation neural network learning (IPS), pp. 586–588.
ICPR-1996-WengC #incremental #navigation: Incremental learning for vision-based navigation (JW, SC), pp. 45–49.
ICPR-1996-Yamakawa #feature model #recognition: Matchability-oriented feature selection for recognition structure learning (HY), pp. 123–127.
ICPR-1996-ZanardiHC #interactive #mobile: Mutual learning or unsupervised interactions between mobile robots (CZ, JYH, PC), pp. 40–44.
ICPR-1996-ZhengB #adaptation #detection: Adaptive object detection based on modified Hebbian learning (YJZ, BB), pp. 164–168.
KDD-1996-Feelders #modelling #using: Learning from Biased Data Using Mixture Models (AJF), pp. 102–107.
KDD-1996-Musick #network: Rethinking the Learning of Belief Network Probabilities (RM), pp. 120–125.
KDD-1996-Sahami #classification #dependence: Learning Limited Dependence Bayesian Classifiers (MS), pp. 335–338.
KDD-1996-StolorzC #markov #monte carlo #visual notation: Harnessing Graphical Structure in Markov Chain Monte Carlo Learning (PES, PCC), pp. 134–139.
KDD-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.
KR-1996-Ghallab #on the #online #recognition #representation: On Chronicles: Representation, On-line Recognition and Learning (MG), pp. 597–606.
SIGIR-1996-CohenS #categorisation: Context-sensitive Learning Methods for Text Categorization (WWC, YS), pp. 307–315.
TRI-Ada-1996-NebeshF #ada #component #html #using: Learning to Use Ada 95 Components Using HTML Linking (BN, MBF), pp. 207–210.
TRI-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.
STOC-1996-BergadanoCV #query: Learning Sat-k-DNF Formulas from Membership Queries (FB, DC, SV), pp. 126–130.
STOC-1996-BshoutyGMST #concept #geometry: Noise-Tolerant Distribution-Free Learning of General Geometric Concepts (NHB, SAG, HDM, SS, HT), pp. 151–160.
STOC-1996-Cesa-BianchiDFS #bound: Noise-Tolerant Learning Near the Information-Theoretic Bound (NCB, ED, PF, HUS), pp. 141–150.
STOC-1996-KearnsM #algorithm #on the #top-down: On the Boosting Ability of Top-Down Decision Tree Learning Algorithms (MJK, YM), pp. 459–468.
CADE-1996-DenzingerS #proving #theorem proving: Learning Domain Knowledge to Improve Theorem Proving (JD, SS), pp. 62–76.
ICDAR-v1-1995-TakasuSK #documentation #image: A rule learning method for academic document image processing (AT, SS, EK), pp. 239–242.
ICDAR-v2-1995-MatsunagaK #case study #classification #statistics: An experimental study of learning curves for statistical pattern classifiers (TM, HK), pp. 1103–1106.
CSEE-1995-DickJ #education #industrial: Industry Involvement in Undergraduate Curricula: Reinforcing Learning by Applying the Principles (GND, SFJ), pp. 51–63.
CSEE-1995-Mahy #re-engineering: From TRAINING to LEARNING: The Reengineering of Training at DMR Group Inc. (IM), p. 433.
ICALP-1995-FortnowFGKKSS: Measure, Category and Learning Theory (LF, RF, WIG, MK, SAK, CHS, FS), pp. 558–569.
CHI-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.
CHI-1995-BauerJ #interactive #modelling: Modeling Time-Constrained Learning in a Highly Interactive Task (MIB, BEJ), pp. 19–26.
CHI-1995-JohnP #approach #case study #using: Learning and Using the Cognitive Walkthrough Method: A Case Study Approach (BEJ, HP), pp. 429–436.
CHI-1995-MitchellPB #using: Learning to Write Together Using Groupware (AM, IP, RB), pp. 288–295.
CIKM-1995-ChenM #information management: Learning Subjective Relevance to Facilitate Information Access (JRC, NM), pp. 218–225.
ICML-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.
ICML-1995-AlmuallimAK #on the: On Handling Tree-Structured Attributed in Decision Tree Learning (HA, YA, SK), pp. 12–20.
ICML-1995-Baird #algorithm #approximate: Residual Algorithms: Reinforcement Learning with Function Approximation (LCBI), pp. 30–37.
ICML-1995-Benson #induction #modelling: Inductive Learning of Reactive Action Models (SB), pp. 47–54.
ICML-1995-CichoszM #difference #performance: Fast and Efficient Reinforcement Learning with Truncated Temporal Differences (PC, JJM), pp. 99–107.
ICML-1995-Cohen95a #categorisation #relational: Text Categorization and Relational Learning (WWC), pp. 124–132.
ICML-1995-Cussens #algorithm #analysis #finite: A Bayesian Analysis of Algorithms for Learning Finite Functions (JC), pp. 142–149.
ICML-1995-DattaK #concept #prototype: Learning Prototypical Concept Descriptions (PD, DFK), pp. 158–166.
ICML-1995-DietterichF #perspective: Explanation-Based Learning and Reinforcement Learning: A Unified View (TGD, NSF), pp. 176–184.
ICML-1995-Fuchs #adaptation #heuristic #parametricity #proving: Learning Proof Heuristics by Adaptive Parameters (MF), pp. 235–243.
ICML-1995-GambardellaD #approach #named #problem: Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem (LMG, MD), pp. 252–260.
ICML-1995-Heckerman #network: Learning With Bayesian Networks (DH), p. 588.
ICML-1995-Hekanaho #concept #multimodal: Symbiosis in Multimodal Concept Learning (JH), pp. 278–285.
ICML-1995-KimuraYK #probability: Reinforcement Learning by Stochastic Hill Climbing on Discounted Reward (HK, MY, SK), pp. 295–303.
ICML-1995-KrishnanLV: Learning to Make Rent-to-Buy Decisions with Systems Applications (PK, PML, JSV), pp. 233–330.
ICML-1995-Lang #named: NewsWeeder: Learning to Filter Netnews (KL), pp. 331–339.
ICML-1995-Littlestone #algorithm: Comparing Several Linear-threshold Learning Algorithms on Tasks Involving Superfluous Attributes (NL), pp. 353–361.
ICML-1995-LittmanCK #policy #scalability: Learning Policies for Partially Observable Environments: Scaling Up (MLL, ARC, LPK), pp. 362–370.
ICML-1995-MaassW #performance: Efficient Learning with Virtual Threshold Gates (WM, MKW), pp. 378–386.
ICML-1995-McCallum: Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State (AM), pp. 387–395.
ICML-1995-MoriartyM #evolution #performance: Efficient Learning from Delayed Rewards through Symbiotic Evolution (DEM, RM), pp. 396–404.
ICML-1995-Niyogi #complexity: Free to Choose: Investigating the Sample Complexity of Active Learning of Real Valued Functions (PN), pp. 405–412.
ICML-1995-NockG #on the: On Learning Decision Committees (RN, OG), pp. 413–420.
ICML-1995-Pomerleau: Learning for Automotive Collision Avoidance and Autonomous Control (DP), p. 589.
ICML-1995-SalganicoffU #multi #using: Active Exploration and Learning in real-Valued Spaces using Multi-Armed Bandit Allocation Indices (MS, LHU), pp. 480–487.
ICML-1995-StreetMW #approach #induction #predict: An Inductive Learning Approach to Prognostic Prediction (WNS, OLM, WHW), pp. 522–530.
ICML-1995-TowellVGJ #information retrieval: Learning Collection FUsion Strategies for Information Retrieval (GGT, EMV, NKG, BJL), pp. 540–548.
ICML-1995-Wang #approach #incremental: Learning by Observation and Practice: An Incremental Approach for Planning Operator Acquisition (XW), pp. 549–557.
ICML-1995-Weiss: Learning with Rare Cases and Small Disjuncts (GMW), pp. 558–565.
ICML-1995-YamazakiPM #ambiguity #natural language: Learning Hierarchies from Ambiguous Natural Language Data (TY, MJP, CJM), pp. 575–583.
KDD-1995-AugierVK #algorithm #first-order #logic #search-based: Learning First Order Logic Rules with a Genetic Algorithm (SA, GV, YK), pp. 21–26.
KDD-1995-CortesJC #quality: Limits on Learning Machine Accuracy Imposed by Data Quality (CC, LDJ, WPC), pp. 57–62.
KDD-1995-HuC #database #set #similarity: Rough Sets Similarity-Based Learning from Databases (XH, NC), pp. 162–167.
KDD-1995-SpirtesM #network: Learning Bayesian Networks with Discrete Variables from Data (PS, CM), pp. 294–299.
SEKE-1995-LiangT #domain model #modelling: Apprenticeship Learning of Domain Models (YL, GT), pp. 54–62.
SIGIR-1995-VoorheesGJ: Learning Collection Fusion Strategies (EMV, NKG, BJL), pp. 172–179.
ICSE-1995-HenningerLR #analysis #approach: An Organizational Learning Approach to Domain Analysis (SH, KL, AR), pp. 95–104.
SAC-1995-GuzdialRC #collaboration #education #interactive #multi: Collaborative and multimedia interactive learning environment for engineering education (MG, NR, DC), pp. 5–9.
SAC-1995-Tschichold-Gurman #classification #fuzzy #generative #incremental #using: Generation and improvement of fuzzy classifiers with incremental learning using fuzzy RuleNet (NNTG), pp. 466–470.
DAC-1995-JainMF #verification: Advanced Verification Techniques Based on Learning (JJ, RM, MF), pp. 420–426.
ICLP-1995-Sato #logic programming #semantics #source code #statistics: A Statistical Learning Method for Logic Programs with Distribution Semantics (TS), pp. 715–729.
CSEE-1994-MooreP #experience #re-engineering: Learning by Doing: Goals & Experience of Two Software Engineering Project Courses (MMM, CP), pp. 151–164.
CHI-1994-KurtenbachB94a #performance: User learning and performance with marking menus (GK, WB), pp. 258–264.
CSCW-1994-WanJ #approach #collaboration #using: Computer Supported Collaborative Learning Using CLARE: The Approach and Experimental Findings (DW, PMJ), pp. 187–198.
CIKM-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.
ICML-1994-AhaLLM #recursion #set: Learning Recursive Relations with Randomly Selected Small Training Sets (DWA, SL, CXL, SM), pp. 12–18.
ICML-1994-Elomaa: In Defense of C4.5: Notes Learning One-Level Decision Trees (TE), pp. 62–69.
ICML-1994-GervasioD #approach #incremental: An Incremental Learning Approach for Completable Planning (MTG, GD), pp. 78–86.
ICML-1994-Gil #incremental #refinement: Learning by Experimentation: Incremental Refinement of Incomplete Planning Domains (YG), pp. 87–95.
ICML-1994-GiordanaSZ #algorithm #concept #search-based: Learning Disjunctive Concepts by Means of Genetic Algorithms (AG, LS, FZ), pp. 96–104.
ICML-1994-Heger: Consideration of Risk in Reinformance Learning (MH), pp. 105–111.
ICML-1994-LewisC #nondeterminism: Heterogenous Uncertainty Sampling for Supervised Learning (DDL, JC), pp. 148–156.
ICML-1994-Littman #framework #game studies #markov #multi: Markov Games as a Framework for Multi-Agent Reinforcement Learning (MLL), pp. 157–163.
ICML-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.
ICML-1994-Mataric: Reward Functions for Accelerated Learning (MJM), pp. 181–189.
ICML-1994-SchapireW #algorithm #analysis #on the #worst-case: On the Worst-Case Analysis of Temporal-Difference Learning Algorithms (RES, MKW), pp. 266–274.
ICML-1994-SinghJJ #markov #process: Learning Without State-Estimation in Partially Observable Markovian Decision Processes (SPS, TSJ, MIJ), pp. 284–292.
ICML-1994-TchoumatchenkoG #framework: A Baysian Framework to Integrate Symbolic and Neural Learning (IT, JGG), pp. 302–308.
ICML-1994-ZuckerG #concept: Selective Reformulation of Examples in Concept Learning (JDZ, JGG), pp. 352–360.
KDD-1994-Furnkranz #comparison #concept #relational: A Comparison of Pruning Methods for Relational Concept Learning (JF), pp. 371–382.
KDD-1994-HeckermanGC #network #statistics: Learning Bayesian Networks: The Combination of Knowledge and Statistical Data (DH, DG, DMC), pp. 85–96.
KDD-1994-HuCX #database: Learning Data Trend Regularities From Databases in a Dynamic Environment (XH, NC, JX), pp. 323–334.
KDD-1994-Kaufman #development #multi #tool support #using: Comparing International Development Patterns Using Multi-Operator Learning and Discovery Tools (KAK), pp. 431–440.
KDD-1994-ShenMOZ #database #deduction #induction #using: Using Metagueries to Integrate Inductive Learning and Deductive Database Technology (WMS, BGM, KO, CZ), pp. 335–346.
KR-1994-Carbonell #information management #representation: Knowledge Representation Issues in Integrated Planning and Learning Systems (JGC), p. 633.
KR-1994-CohenH #logic: Learning the Classic Description Logic: Theoretical and Experimental Results (WWC, HH), pp. 121–133.
SEKE-1994-AbranDMMS #analysis #hypermedia #using: Structured hypertext for using and learning function point analysis (AA, JMD, DM, MM, DSP), pp. 164–171.
SEKE-1994-ReynoldsZ #algorithm #using: Learning to understand software from examples using cultural algorithms (RGR, EZ), pp. 188–192.
SIGIR-1994-Allen #information retrieval #performance: Perceptual Speed, Learning and Information Retrieval Performance (BA), pp. 71–80.
SIGIR-1994-ApteDW #automation #categorisation #independence #modelling #towards: Towards Language Independent Automated Learning of Text Categorisation Models (CA, FD, SMW), pp. 23–30.
SIGIR-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.
OOPSLA-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.
LOPSTR-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.
SAC-1994-Chen: Application of Boolean expression minimization to learning via hierarchical generalization (JC), pp. 303–307.
SAC-1994-HughesWK: Virtual space learning: creating text-based learning environments (BH, JW, BK), pp. 578–582.
SAC-1994-Janikow #algorithm #fuzzy #search-based: A genetic algorithm for learning fuzzy controllers (CZJ), pp. 232–236.
SAC-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.
SAC-1994-WongM #specification #verification: Specification and verification of learning (KWW, RAM), pp. 6–9.
STOC-1994-ApsitisFS #approach: Choosing a learning team: a topological approach (KA, RF, CHS), pp. 283–289.
STOC-1994-AuerL #simulation: Simulating access to hidden information while learning (PA, PML), pp. 263–272.
STOC-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.
STOC-1994-Sitharam #algorithm #generative #pseudo: Pseudorandom generators and learning algorithms for AC (MS), pp. 478–486.
ICDAR-1993-Dengel #documentation: Initial learning of document structure (AD), pp. 86–90.
ICDAR-1993-Ho #independence #recognition: Recognition of handwritten digits by combining independent learning vector quantizations (TKH), pp. 818–821.
ICDAR-1993-Kawatani #polynomial #recognition: Handprinted numeral recognition with the learning quadratic discriminant function (TK), pp. 14–17.
ICDAR-1993-KuritaK #database #image #visual notation: Learning of personal visual impression for image database systems (TK, TK), pp. 547–552.
ICDAR-1993-LebourgeoisH: A contextual processing for an OCR system, based on pattern learning (FL, JLH), pp. 862–865.
ICDAR-1993-SatohMS #comprehension #image: Drawing image understanding system with capability of rule learning (SS, HM, MS), pp. 119–124.
HCI-SHI-1993-HutchingsHC #hypermedia: A Model of Learning with Hypermedia Systems (GH, WH, CJC), pp. 494–499.
HCI-SHI-1993-LeclercM #natural language: Natural Language as Object and Medium in Computer-Based Learning (SL, SdM), pp. 373–378.
HCI-SHI-1993-NogamiYYM #development: Development of a Simulation-Based Intelligent Tutoring System for Assisting PID Control Learning (TN, YY, IY, SM), pp. 814–818.
HCI-SHI-1993-RizzoPCB: Control of Complex System by Situated Knowledge: The Role of Implicit Learning (AR, OP, CC, SB), pp. 855–860.
HCI-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.
INTERCHI-1993-NilsenJOBRM #performance: The growth of software skill: a longitudinal look at learning & performance (EN, HSJ, JSO, KB, HHR, SM), pp. 149–156.
INTERCHI-1993-StaskoBL #algorithm #analysis #animation #empirical: Do algorithm animations assist learning?: an empirical study and analysis (JTS, ANB, CL), pp. 61–66.
CIKM-1993-ChanS #multi: Experiments on Multi-Strategy Learning by Meta-Learning (PKC, SJS), pp. 314–323.
CIKM-1993-EickJ #algorithm #classification #search-based: Learning Bayesian Classification Rules through Genetic Algorithms (CFE, DJ), pp. 305–313.
ICML-1993-BrezellecS #bottom-up #named: ÉLÉNA: A Bottom-Up Learning Method (PB, HS), pp. 9–16.
ICML-1993-Cardie #using: Using Decision Trees to Improve Case-Based Learning (CC), pp. 25–32.
ICML-1993-Caruana #bias #induction #knowledge-based #multi: Multitask Learning: A Knowledge-Based Source of Inductive Bias (RC), pp. 41–48.
ICML-1993-ClarkM #induction #modelling #using: Using Qualitative Models to Guide Inductive Learning (PC, SM), pp. 49–56.
ICML-1993-CravenS #network #using: Learning Symbolic Rules Using Artificial Neural Networks (MC, JWS), pp. 73–80.
ICML-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.
ICML-1993-DattaK #concept #multi: Concept Sharing: A Means to Improve Multi-Concept Learning (PD, DFK), pp. 89–96.
ICML-1993-FrazierP: Learning From Entailment: An Application to Propositional Horn Sentences (MF, LP), pp. 120–127.
ICML-1993-GratchCD #network #scheduling: Learning Search Control Knowledge for Deep Space Network Scheduling (JG, SAC, GD), pp. 135–142.
ICML-1993-HuffmanL #interactive #natural language: Learning Procedures from Interactive Natural Language Instructions (SBH, JEL), pp. 143–150.
ICML-1993-JordanJ #approach #divide and conquer #statistics: Supervised Learning and Divide-and-Conquer: A Statistical Approach (MIJ, RAJ), pp. 159–166.
ICML-1993-Kaelbling #probability: Hierarchical Learning in Stochastic Domains: Preliminary Results (LPK), pp. 167–173.
ICML-1993-KimR: Constraining Learning with Search Control (JK, PSR), pp. 174–181.
ICML-1993-Lin #scalability: Scaling Up Reinforcement Learning for Robot Control (LJL), pp. 182–189.
ICML-1993-MitchellT #comparison #network: Explanation Based Learning: A Comparison of Symbolic and Neural Network Approaches (TMM, ST), pp. 197–204.
ICML-1993-Mladenic #combinator #concept #induction #optimisation: Combinatorial Optimization in Inductive Concept Learning (DM), pp. 205–211.
ICML-1993-NortonH #probability: Learning DNF Via Probabilistic Evidence Combination (SWN, HH), pp. 220–227.
ICML-1993-Quinlan #modelling: Combining Instance-Based and Model-Based Learning (JRQ), pp. 236–243.
ICML-1993-RagavanR #concept #lookahead: Lookahead Feature Construction for Learning Hard Concepts (HR, LAR), pp. 252–259.
ICML-1993-Salganicoff #adaptation: Density-Adaptive Learning and Forgetting (MS), pp. 276–283.
ICML-1993-Schwartz: A Reinforcement Learning Method for Maximizing Undiscounted Rewards (AS), pp. 298–305.
ICML-1993-SuttonW #online #random: Online Learning with Random Representations (RSS, SDW), pp. 314–321.
ICML-1993-Tadepalli #bias #query: Learning from Queries and Examples with Tree-structured Bias (PT), pp. 322–329.
ICML-1993-Tan #independence #multi: Multi-Agent Reinforcement Learning: Independent versus Cooperative Agents (MT), pp. 330–337.
FSE-1993-Bergadano #generative #testing: Test Case Generation by Means of Learning Techniques (FB), pp. 149–162.
SAC-1993-GallionSCB #algorithm: Dynamic ID3: A Symbolic Learning Algorithm for Many-Valued Attribute Domains (RG, CLS, DCSC, WEB), pp. 14–20.
SAC-1993-KountanisS #concept #graph: Graphs as a Language to Describe Learning System Concepts (DIK, ES), pp. 469–475.
SAC-1993-VaidyanathanL #analysis #bound: Analysis of Upper Bound in Valiant’s Model for Learning Bounded CNF Expressions (SV, SL), pp. 754–761.
DAC-1993-PomeranzR #generative #incremental #named #testing: INCREDYBLE-TG: INCREmental DYnamic test generation based on LEarning (IP, SMR), pp. 80–85.
HPDC-1993-FletcherO #distributed #network #parallel: Parallel and Distributed Systems for Constructive Neural Network Learning (JF, ZO), pp. 174–178.
STOC-1993-FreundKRRSS #automaton #finite #performance #random: Efficient learning of typical finite automata from random walks (YF, MJK, DR, RR, RES, LS), pp. 315–324.
STOC-1993-Kearns #performance #query #statistics: Efficient noise-tolerant learning from statistical queries (MJK), pp. 392–401.
STOC-1993-Kharitonov #encryption: Cryptographic hardness of distribution-specific learning (MK), pp. 372–381.
STOC-1993-Maass #bound #complexity: Bounds for the computational power and learning complexity of analog neural nets (WM), pp. 335–344.
HT-ECHT-1992-Eco #education #hypermedia #multi: Hypermedia for Teaching and Learning: A Multimedia Guide to the History of European Civilization (MuG) (UE), p. 288.
PODS-1992-Greiner #performance #query: Learning Efficient Query Processing Strategies (RG), pp. 33–46.
CHI-1992-Clancey #overview #research: Overview of the Institute for Research on Learning (WJC), pp. 571–572.
CHI-1992-Spohrer #case study #experience #prototype: Simulation-based learning systems: prototypes and experiences (AJ, JCS), pp. 523–524.
CSCW-1992-BerlinJ #collaboration #problem: Consultants and Apprentices: Observations about Learning and Collaborative Problem Solving (LMB, RJ), pp. 130–137.
CSCW-1992-Orlikowski #implementation: Learning from Notes: Organizational Issues in Groupware Implementation (WJO), pp. 362–369.
KR-1992-GreinerS #approximate: Learning Useful Horn Approximations (RG, DS), pp. 383–392.
ML-1992-AlmuallimD #concept #on the: On Learning More Concepts (HA, TGD), pp. 11–19.
ML-1992-Bhatnagar: Learning by Incomplete Explanation-Based Learning (NB), pp. 37–42.
ML-1992-Chen: Improving Path Planning with Learning (PCC), pp. 55–61.
ML-1992-Christiansen #nondeterminism #predict: Learning to Predict in Uncertain Continuous Tasks (ADC), pp. 72–81.
ML-1992-ClouseU #education: A Teaching Method for Reinforcement Learning (JAC, PEU), pp. 92–110.
ML-1992-ConverseH: Learning to Satisfy Conjunctive Goals (TMC, KJH), pp. 117–122.
ML-1992-CoxR #multi: Multistrategy Learning with Introspective Meta-Explanations (MTC, AR), pp. 123–128.
ML-1992-Etzioni #analysis: An Asymptotic Analysis of Speedup Learning (OE), pp. 129–136.
ML-1992-GiordanaS #algorithm #concept #search-based #using: Learning Structured Concepts Using Genetic Algorithms (AG, CS), pp. 169–178.
ML-1992-GratchD #analysis #problem: An Analysis of Learning to Plan as a Search Problem (JG, GD), pp. 179–188.
ML-1992-GrefenstetteR #approach: An Approach to Anytime Learning (JJG, CLR), pp. 189–195.
ML-1992-HirschbergP #analysis #concept: Average Case Analysis of Learning κ-CNF Concepts (DSH, MJP), pp. 206–211.
ML-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.
ML-1992-Janikow #contest #induction: Combining Competition and Cooperation in Supervised Inductive Learning (CZJ), pp. 241–248.
ML-1992-KononenkoK #generative #multi #optimisation #probability: Learning as Optimization: Stochastic Generation of Multiple Knowledge (IK, MK), pp. 257–262.
ML-1992-Mahadevan #modelling #probability: Enhancing Transfer in Reinforcement Learning by Building Stochastic Models of Robot Actions (SM), pp. 290–299.
ML-1992-Mao #named: THOUGHT: An Integrated Learning System for Acquiring Knowledge Structure (CM), pp. 300–309.
ML-1992-Markov #approach #concept: An Approach to Concept Learning Based on Term Generalization (ZM), pp. 310–315.
ML-1992-McCallum #performance #proximity #using: Using Transitional Proximity for Faster Reinforcement Learning (AM), pp. 316–321.
ML-1992-RubyK #optimisation: Learning Episodes for Optimization (DR, DFK), pp. 379–384.
ML-1992-SammutHKM: Learning to Fly (CS, SH, DK, DM), pp. 385–393.
ML-1992-Singh #algorithm #modelling #scalability: Scaling Reinforcement Learning Algorithms by Learning Variable Temporal Resolution Models (SPS), pp. 406–415.
ML-1992-Tesauro #difference: Temporal Difference Learning of Backgammon Strategy (GT), pp. 451–457.
ML-1992-Zhang: Selecting Typical Instances in Instance-Based Learning (JZ), pp. 470–479.
OOPSLA-1992-LiuGG #object-oriented #question #what: What Contributes to Successful Object-Oriented Learning? (CL, SG, BG), pp. 77–86.
STOC-1992-Angluin #overview: Computational Learning Theory: Survey and Selected Bibliography (DA), pp. 351–369.
STOC-1992-BlumR #performance #query: Fast Learning of k-Term DNF Formulas with Queries (AB, SR), pp. 382–389.
STOC-1992-BshoutyHH: Learning Arithmetic Read-Once Formulas (NHB, TRH, LH), pp. 370–381.
CHI-1991-PalmiterE #evaluation: An evaluation of animated demonstrations of learning computer-based tasks (SP, JE), pp. 257–263.
KDD-1991-BergadanoGSBM: Integrated Learning in a Real Domain (FB, AG, LS, FB, DDM), pp. 277–288.
KDD-1991-UthurusamyFS: Learning Useful Rules from Inconclusive Data (RU, UMF, WSS), pp. 141–158.
ML-1991-Bain: Experiments in Non-Monotonic Learning (MB), pp. 380–384.
ML-1991-Berenji #approximate #refinement: Refinement of Approximate Reasoning-based Controllers by Reinforcement Learning (HRB), pp. 475–479.
ML-1991-BottaRSS #abduction #using: Improving Learning Using Causality and Abduction (MB, SR, LS, SBS), pp. 480–484.
ML-1991-Brand: Decision-Theoretic Learning in an Action System (MB), pp. 283–287.
ML-1991-BratkoMV #modelling: Learning Qualitative Models of Dynamic Systems (IB, SM, AV), pp. 385–388.
ML-1991-BrunkP #algorithm #concept #relational: An Investigation of Noise-Tolerant Relational Concept Learning Algorithms (CB, MJP), pp. 389–393.
ML-1991-ChienGD #on the: On Becoming Decreasingly Reactive: Learning to Deliberate Minimally (SAC, MTG, GD), pp. 288–292.
ML-1991-CobbG #persistent: Learning the Persistence of Actions in Reactive Control Rules (HGC, JJG), pp. 292–297.
ML-1991-Day #csp #heuristic #problem: Learning Variable Descriptors for Applying Heuristics Across CSP Problems (DSD), pp. 127–131.
ML-1991-desJardins #bias #probability: Probabilistic Evaluating of Bias for Learning Systems (Md), pp. 495–499.
ML-1991-DzeroskiL #comparison #empirical: Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL (SD, NL), pp. 399–402.
ML-1991-Goel #formal method #incremental: Model Revision: A Theory of Incremental Model Learning (AKG), pp. 605–609.
ML-1991-GokerM #incremental #information retrieval: Incremental Learning in a Probalistic Information Retrieval System (AG, TLM), pp. 255–259.
ML-1991-HastingsLL #word: Learning Words From Context (PMH, SLL, RKL), pp. 55–59.
ML-1991-Herrmann: Learning Analytical Knowledge About VLSI-Design from Observation (JH), pp. 610–614.
ML-1991-HirakiGYA #image: Learning Spatial Relations from Images (KH, JHG, YY, YA), pp. 407–411.
ML-1991-HsuS #evaluation: Learning Football Evaluation for a Walking Robot (GTH, RGS), pp. 303–307.
ML-1991-JordanR #modelling: Internal World Models and Supervised Learning (MIJ, DER), pp. 70–74.
ML-1991-Kadie #induction: Quantifying the Value of Constructive Induction, Knowledge, and Noise Filtering on Inductive Learning (CMK), pp. 153–157.
ML-1991-Kadie91a #concept #set: Continous Conceptual Set Covering: Learning Robot Operators From Examples (CMK), pp. 615–619.
ML-1991-KijsirikulNS #logic programming #performance #source code: Efficient Learning of Logic Programs with Non-determinant, Non-discriminating Literals (BK, MN, MS), pp. 417–421.
ML-1991-KokarR: Learning to Select a Model in a Changing World (MMK, SAR), pp. 313–317.
ML-1991-Krulwich: Learning from Deliberated Reactivity (BK), pp. 318–322.
ML-1991-Kwok #adaptation #architecture #query #using: Query Learning Using an ANN with Adaptive Architecture (KLK), pp. 260–264.
ML-1991-LeckieZ #approach #induction: Learning Search Control Rules for Planning: An Inductive Approach (CL, IZ), pp. 422–426.
ML-1991-Lewis #information retrieval: Learning in Intelligent Information Retrieval (DDL), pp. 235–239.
ML-1991-Lin #education #self: Self-improvement Based on Reinforcement Learning, Planning and Teaching (LJL), pp. 323–327.
ML-1991-MahadevanC #architecture #scalability: Scaling Reinforcement Learning to Robotics by Exploiting the Subsumption Architecture (SM, JC), pp. 328–332.
ML-1991-MartinB #bias #variability: Variability Bias and Category Learning (JDM, DB), pp. 90–94.
ML-1991-Maza #concept #prototype: A Prototype Based Symbolic Concept Learning System (MdlM), pp. 41–45.
ML-1991-MillanT: Learning to Avoid Obstacles Through Reinforcement (JdRM, CT), pp. 298–302.
ML-1991-OliveiraS #concept #network: Learning Concepts by Synthesizing Minimal Threshold Gate Networks (ALO, ALSV), pp. 193–197.
ML-1991-PageF: Learning Constrained Atoms (CDPJ, AMF), pp. 427–431.
ML-1991-PazzaniBS #approach #concept #relational: A Knowledge-intensive Approach to Learning Relational Concepts (MJP, CB, GS), pp. 432–436.
ML-1991-Pierce #set: Learning a Set of Primitive Actions with an Uninterpreted Sensorimotor Apparatus (DRP), pp. 338–342.
ML-1991-RagavanR #empirical: Relations, Knowledge and Empirical Learning (HR, LAR), pp. 188–192.
ML-1991-Reich #design: Design Integrated Learning Systems for Engineering Design (YR), pp. 635–639.
ML-1991-Schlimmer #consistency #database #induction: Database Consistency via Inductive Learning (JCS), pp. 640–644.
ML-1991-SilversteinP #induction #relational: Relational Clichés: Constraining Induction During Relational Learning (GS, MJP), pp. 203–207.
ML-1991-Singh #composition: Transfer of Learning Across Compositions of Sequentail Tasks (SPS), pp. 348–352.
ML-1991-SuttonM #polynomial: Learning Polynomial Functions by Feature Construction (RSS, CJM), pp. 208–212.
ML-1991-Tadepalli: Learning with Incrutable Theories (PT), pp. 544–548.
ML-1991-Tan #representation: Learning a Cost-Sensitive Internal Representation for Reinforcement Learning (MT), pp. 358–362.
ML-1991-TecuciM #adaptation #multi: A Method for Multistrategy Task-Adaptive Learning Based on Plausible Justifications (GT, RSM), pp. 549–553.
ML-1991-VanLehnJ #correctness #physics: Learning Physics Via Explanation-Based Learning of Correctness and Analogical Search Control (KV, RMJ), pp. 110–114.
ML-1991-WhitehallL #case study #how #knowledge-based: A Study of How Domain Knowledge Improves Knowledge-Based Learning Systems (BLW, SCYL), pp. 559–563.
ML-1991-Wixson #composition #scalability: Scaling Reinforcement Learning Techniques via Modularity (LEW), pp. 3368–372.
ML-1991-YamanishiK #probability #search-based #sequence: Learning Stochastic Motifs from Genetic Sequences (KY, AK), pp. 467–471.
ECOOP-1991-BergsteinL #incremental #optimisation #taxonomy: Incremental Class Dictionary Learning and Optimization (PLB, KJL), pp. 377–396.
LOPSTR-1991-Eusterbrock #abstraction #logic programming #source code: Speed-up Transformations of Logic Programs by Abstraction and Learning (JE), pp. 167–182.
WSA-1991-Breuer #analysis #synthesis: An Analysis/Synthesis Language with Learning Strategies (PTB), pp. 202–209.
STOC-1991-KushilevitzM #fourier #using: Learning Decision Trees Using the Fourier Sprectrum (EK, YM), pp. 455–464.
STOC-1991-LittlestoneLW #linear #online: On-Line Learning of Linear Functions (NL, PML, MKW), pp. 465–475.
ICALP-1990-JainS: Language Learning by a “Team” (SJ, AS), pp. 153–166.
ICALP-1990-Watanabe #formal method #query: A Formal Study of Learning via Queries (OW0), pp. 139–152.
CHI-1990-CarrollSBA #smalltalk: A view matcher for learning Smalltalk (JMC, JAS, RKEB, SRA), pp. 431–437.
CHI-1990-HowesP #analysis #semantics: Semantic analysis during exploratory learning (AH, SJP), pp. 399–406.
CSCW-1990-BullenB #experience #user interface: Learning from User Experience with Groupware (CVB, JLB), pp. 291–302.
ML-1990-ArunkumarY #information management #representation #using: Knowledge Acquisition from Examples using Maximal Representation Learning (SA, SY), pp. 2–8.
ML-1990-BergadanoGSMB: Integrated Learning in a real Domain (FB, AG, LS, DDM, FB), pp. 322–329.
ML-1990-ChanW #analysis #induction #performance #probability: Performance Analysis of a Probabilistic Inductive Learning System (KCCC, AKCW), pp. 16–23.
ML-1990-Cohen #analysis #concept #representation: An Analysis of Representation Shift in Concept Learning (WWC), pp. 104–112.
ML-1990-Cohen90a #approximate: Learning Approximate Control Rules of High Utility (WWC), pp. 268–276.
ML-1990-Epstein: Learning Plans for Competitive Domains (SLE), pp. 190–197.
ML-1990-GenestMP #approach: Explanation-Based Learning with Incomplete Theories: A Three-step Approach (JG, SM, BP), pp. 286–294.
ML-1990-Hammond #process: Learning and Enforcement: Stabilizing Environments to Facilitate Activity (KJH), pp. 204–210.
ML-1990-Hirsh #bound #consistency #nondeterminism: Learning from Data with Bounded Inconsistency (HH), pp. 32–39.
ML-1990-Hume #induction: Learning Procedures by Environment-Driven Constructive Induction (DVH), pp. 113–121.
ML-1990-Kaelbling: Learning Functions in k-DNF from Reinforcement (LPK), pp. 162–169.
ML-1990-KoMT #string: Learning String Patterns and Tree Patterns from Examples (KIK, AM, WGT), pp. 384–391.
ML-1990-Lehman: A General Method for Learning Idiosyncratic Grammars (JFL), pp. 368–376.
ML-1990-LytinenM #comparison: A Comparison of Learning Techniques in Second Language Learning (SLL, CEM), pp. 377–383.
ML-1990-ObradovicP #multi: Learning with Discrete Multi-Valued Neurons (ZO, IP), pp. 392–399.
ML-1990-PazzaniS #algorithm #analysis: Average Case Analysis of Conjunctive Learning Algorithms (MJP, WS), pp. 339–347.
ML-1990-Ram #incremental: Incremental Learning of Explanation Patterns and Their Indices (AR), pp. 313–320.
ML-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.
ML-1990-SammutC #performance #question: Is Learning Rate a Good Performance Criterion for Learning? (CS, JC), pp. 170–178.
ML-1990-SchoenauerS #incremental: Incremental Learning of Rules and Meta-rules (MS, MS), pp. 49–57.
ML-1990-Segen #clustering #graph: Graph Clustering and Model Learning by Data Compression (JS), pp. 93–101.
ML-1990-SilverFIVB #framework #multi: A Framework for Multi-Paradigmatic Learning (BS, WJF, GAI, JV, KB), pp. 348–356.
ML-1990-Sutton #approximate #architecture #programming: Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming (RSS), pp. 216–224.
ML-1990-WhiteheadB: Active Perception and Reinforcement Learning (SDW, DHB), pp. 179–188.
SEKE-1990-EstevaR #induction #reuse: Learning to Recognize Reusable Software by Induction (JCE, RGR), pp. 19–24.
SEKE-1990-Mazurov #parallel #process: Parallel Processes of Decision Making and Multivalued Interpretation of Contradictory Data by Learning Neuron Machines (VDM), p. 165.
STOC-1990-Blum #infinity: Learning Boolean Functions in an Infinite Atribute Space (AB), pp. 64–72.
CHI-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.
CHI-1989-LeePB #metric: Learning and transfer of measurement tasks (AYL, PGP, WAB), pp. 115–120.
ML-1989-Aha #concept #incremental #independence: Incremental, Instance-Based Learning of Independent and Graded Concept Descriptions (DWA), pp. 387–391.
ML-1989-Anderson #network: Tower of Hanoi with Connectionist Networks: Learning New Features (CWA), pp. 345–349.
ML-1989-BarlettaK #empirical: Improving Explanation-Based Indexing with Empirical Learning (RB, RK), pp. 84–86.
ML-1989-BergadanoGP #deduction #induction #top-down: Deduction in Top-Down Inductive Learning (FB, AG, SP), pp. 23–25.
ML-1989-Buntine #classification #using: Learning Classification Rules Using Bayes (WLB), pp. 94–98.
ML-1989-Chan #induction: Inductive Learning with BCT (PKC), pp. 104–108.
ML-1989-Chien: Learning by Analyzing Fortuitous Occurrences (SAC), pp. 249–251.
ML-1989-ClearwaterCHB #incremental: Incremental Batch Learning (SHC, TPC, HH, BGB), pp. 366–370.
ML-1989-ConverseHM: Learning from Opportunity (TMC, KJH, MM), pp. 246–248.
ML-1989-Cornuejols #incremental: An Exploration Into Incremental Learning: the INFLUENCE System (AC), pp. 383–386.
ML-1989-Diederich: “Learning by Instruction” in connectionist Systems (JD), pp. 66–68.
ML-1989-Dietterich #induction: Limitations on Inductive Learning (TGD), pp. 124–128.
ML-1989-Fawcett: Learning from Plausible Explanations (TF), pp. 37–39.
ML-1989-FisherMMST: Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems (DHF, KBM, RJM, JWS, GGT), pp. 169–173.
ML-1989-Flann #abstraction #problem: Learning Appropriate Abstractions for Planning in Formation Problems (NSF), pp. 235–239.
ML-1989-Fogarty #algorithm #incremental #realtime #search-based: An Incremental Genetic Algorithm for Real-Time Learning (TCF), pp. 416–419.
ML-1989-FriedrichN #algorithm #induction #using: Using Domain Knowledge to Improve Inductive Learning Algorithms for Diagnosis (GF, WN), pp. 75–77.
ML-1989-GamsK #empirical: New Empirical Learning Mechanisms Perform Significantly Better in Real Life Domains (MG, AK), pp. 99–103.
ML-1989-GervasioD: Explanation-Based Learning of Reactive Operations (MTG, GD), pp. 252–254.
ML-1989-Grefenstette #algorithm #incremental #search-based: Incremental Learning of Control Strategies with Genetic algorithms (JJG), pp. 340–344.
ML-1989-Haines: Explanation Based Learning as Constrained Search (DH), pp. 43–45.
ML-1989-HilliardLRP #approach #classification #hybrid #problem #scheduling: Learning Decision Rules for scheduling Problems: A Classifier Hybrid Approach (MRH, GEL, GR, MRP), pp. 188–190.
ML-1989-Hirsh #empirical: Combining Empirical and Analytical Learning with Version Spaces (HH), pp. 29–33.
ML-1989-Jones #problem: Learning to Retrieve Useful Information for Problem Solving (RMJ), pp. 212–214.
ML-1989-Kaelbling #embedded #framework: A Formal Framework for Learning in Embedded Systems (LPK), pp. 350–353.
ML-1989-Katz #network: Integrating Learning in a Neural Network (BFK), pp. 69–71.
ML-1989-Keller #compilation #performance: Compiling Learning Vocabulary from a Performance System Description (RMK), pp. 482–495.
ML-1989-Knoblock #abstraction: Learning Hierarchies of Abstraction Spaces (CAK), pp. 241–245.
ML-1989-LambertTL #algorithm #concept #hybrid #recursion: Generalized Recursive Splitting Algorithms for Learning Hybrid Concepts (BLL, DKT, SCYL), pp. 496–498.
ML-1989-Langley #empirical: Unifying Themes in Empirical and Explanation-Based Learning (PL), pp. 2–4.
ML-1989-LeviPS: Learning Tactical Plans for Pilot Aiding (KRL, DLP, VLS), pp. 191–193.
ML-1989-Marie #bias #dependence: Building A Learning Bias from Perceived Dependencies (CdSM), pp. 501–502.
ML-1989-Martin: Reducing Redundant Learning (JDM), pp. 396–399.
ML-1989-MasonCM: Experiments in Robot Learning (MTM, ADC, TMM), pp. 141–145.
ML-1989-MatwinM: Learning Procedural Knowledge in the EBG Context (SM, JM), pp. 197–199.
ML-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.
ML-1989-Morris: Reducing Search and Learning Goal Preferences (SM), pp. 46–48.
ML-1989-NumaoS #similarity: Explanation-Based Acceleration of Similarity-Based Learning (MN, MS), pp. 58–60.
ML-1989-ORorkeCO: Learning to Recognize Plans Involving Affect (PO, TC, AO), pp. 209–211.
ML-1989-Paredis #behaviour: Learning the Behavior of Dynamical Systems form Examples (JP), pp. 137–140.
ML-1989-Pazzani: Explanation-Based Learning with Week Domain Theories (MJP), pp. 72–74.
ML-1989-Puget #invariant: Learning Invariants from Explanations (JFP), pp. 200–204.
ML-1989-RasZ #concept: Imprecise Concept Learning within a Growing Language (ZWR, MZ), pp. 314–319.
ML-1989-Redmond #reasoning: Combining Case-Based Reasoning, Explanation-Based Learning, and Learning form Instruction (MR), pp. 20–22.
ML-1989-RudyK: Learning to Plan in Complex Domains (DR, DFK), pp. 180–182.
ML-1989-SarrettP #algorithm #empirical: One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning (WS, MJP), pp. 26–28.
ML-1989-ScottM #case study #experience #nondeterminism: Uncertainty Based Selection of Learning Experiences (PDS, SM), pp. 358–361.
ML-1989-Selfridge #adaptation #case study #contest: Atoms of Learning II: Adaptive Strategies A Study of Two-Person Zero-Sum Competition (OGS), pp. 412–415.
ML-1989-Shavlik #analysis #empirical: An Empirical Analysis of EBL Approaches for Learning Plan Schemata (JWS), pp. 183–187.
ML-1989-ShavlikT #network: Combining Explanation-Based Learning and Artificial Neural Networks (JWS, GGT), pp. 90–93.
ML-1989-SobekL #using: Using Learning to Recover Side-Effects of Operators in Robotics (RPS, JPL), pp. 205–208.
ML-1989-Spackman #detection #induction #tool support: Signal Detection Theory: Valuable Tools for Evaluating Inductive Learning (KAS), pp. 160–163.
ML-1989-TanS #approach #concept #recognition: Cost-Sensitive Concept Learning of Sensor Use in Approach ad Recognition (MT, JCS), pp. 392–395.
ML-1989-TecuciK #multi: Multi-Strategy Learning in Nonhomongeneous Domain Theories (GT, YK), pp. 14–16.
ML-1989-Utgoff #incremental: Improved Training Via Incremental Learning (PEU), pp. 362–365.
ML-1989-WefaldR #adaptation: Adaptive Learning of Decision-Theoretic Search Control Knowledge (EW, SJR), pp. 408–411.
ML-1989-Widmer #deduction #integration: A Tight Integration of Deductive Learning (GW), pp. 11–13.
ML-1989-Wollowski: A Schema for an Integrated Learning System (MW), pp. 87–89.
ML-1989-YagerF: Participatory Learning: A Constructivist Model (RRY, KMF), pp. 420–425.
ML-1989-ZhangM: A Description of Preference Criterion in Constructive Learning: A Discussion of Basis Issues (JZ, RSM), pp. 17–19.
STOC-1989-KearnsV #automaton #encryption #finite: Cryptographic Limitations on Learning Boolean Formulae and Finite Automata (MJK, LGV), pp. 433–444.
SEI-1988-Stevens: SEI Demonstration: Advanced Learning Technologies Project (SS), p. 120.
CSCW-1988-Hiltz #collaboration: Collaborative Learning in a Virtual Classroom: Highlights of Findings (SRH), pp. 282–290.
ML-1988-Amsterdam: Extending the Valiant Learning Model (JA), pp. 381–394.
ML-1988-Carpineto #approach #generative: An Approach Based on Integrated Learning to Generating Stories (CC), pp. 298–304.
ML-1988-Cohen #multi: Generalizing Number and Learning from Multiple Examples in Explanation Based Learning (WWC), pp. 256–269.
ML-1988-Etzioni #approach #reliability: Hypothesis Filtering: A Practical Approach to Reliable Learning (OE), pp. 416–429.
ML-1988-Gross #concept #incremental #multi #using: Incremental Multiple Concept Learning Using Experiments (KPG), pp. 65–72.
ML-1988-Helft #first-order: Learning Systems of First-Order Rules (NH), pp. 395–401.
ML-1988-Hirsh #reasoning: Reasoning about Operationality for Explanation-Based Learning (HH), pp. 214–220.
ML-1988-IbaWL #concept #incremental: Trading Off Simplicity and Coverage in Incremental concept Learning (WI, JW, PL), pp. 73–79.
ML-1988-JongS #game studies #using: Using Experience-Based Learning in Game Playing (KADJ, ACS), pp. 284–290.
ML-1988-Kadie #named: Diffy-S: Learning Robot Operator Schemata from Examples (CMK), pp. 430–436.
ML-1988-Lynne: Competitive Reinforcement Learning (KJL), pp. 188–199.
ML-1988-MahadevanT #on the: On the Tractability of Learning from Incomplete Theories (SM, PT), pp. 235–241.
ML-1988-MarkovitchS: The Role of Forgetting in Learning (SM, PDS), pp. 459–465.
ML-1988-NatarajanT #framework: Two New Frameworks for Learning (BKN, PT), pp. 402–415.
ML-1988-Pazzani: Integrated Learning with Incorrect and Incomplete Theories (MJP), pp. 291–297.
ML-1988-Segen #graph #modelling: Learning Graph Models of Shape (JS), pp. 29–35.
ML-1988-Spackman #category theory: Learning Categorical Decision Criteria in Biomedical Domains (KAS), pp. 36–46.
ML-1988-Tesauro: Connectionist Learning of Expert Backgammon Evaluations (GT), pp. 200–206.
ML-1988-Williams: Learning to Program by Examining and Modifying Cases (RSW), pp. 318–324.
ML-1988-WisniewskiA #induction: Some Interesting Properties of a Connectionist Inductive Learning System (EJW, JAA), pp. 181–187.
SIGIR-1988-YuM #information retrieval: Two Learning Schemes in Information Retrieval (CTY, HM), pp. 201–218.
PPEALS-1988-TambeKGFMN #named #parallel: Soar/PSM-E: Investigating Match Parallelism in a Learning Production System (MT, DK, AG, CF, BM, AN), pp. 146–160.
STOC-1988-KearnsL #fault: Learning in the Presence of Malicious Errors (MJK, ML), pp. 267–280.
CADE-1988-DonatW #higher-order #using: Learning and Applying Generalised Solutions using Higher Order Resolution (MRD, LAW), pp. 41–60.
ICALP-1987-PittS #probability: Probability and Plurality for Aggregations of Learning Machines (LP, CHS), pp. 1–10.
ICALP-1987-Valiant #formal method: Recent Developments in the Theory of Learning (LGV), p. 563.
HCI-CE-1987-Bosser #evaluation: The Evaluation of Learning Requirement of IT Systems (TB), pp. 45–52.
SIGIR-1987-OommenM #automaton #clustering #performance #probability #using: Fast Object Partitioning Using Stochastic Learning Automata (BJO, DCYM), pp. 111–122.
STOC-1987-Natarajan #on the: On Learning Boolean Functions (BKN), pp. 296–304.
CSL-1987-RinnS #fault: Learning by Teams from Examples with Errors (RR, BS), pp. 223–234.
SIGIR-1986-DeogunR #clustering #documentation #framework #information retrieval: User-Oriented Document Clustering: A Framework for Learning in Information Retrieval (JSD, VVR), pp. 157–163.
VLDB-1985-BorgidaW #database #exception: Accommodating Exceptions in Databases, and Refining the Schema by Learning from them (AB, KEW), pp. 72–81.
SIGIR-1985-Gordon #algorithm #documentation: A Learning Algorithm Applied to Document Description (MG), pp. 179–186.
SIGIR-1984-Allan #information retrieval: Computerised Information Retrieval Systems for Open Learning (BA), pp. 325–341.