BibSLEIGH — learn stem

Used together with:

base (503)
use (452)
model (328)
machin (311)
system (286)

Stem learn$ (all stems)

4419 papers:

ECSA-2015-KiwelekarW #architecture #learning: Learning Objectives for a Course on Software Architecture (AWK, HSW), pp. 169–180.
CASE-2015-ChenXZCL #effectiveness #learning #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-FarhanPWL #algorithm #machine learning #predict #using: Predicting individual thermal comfort using machine learning algorithms (AAF, KRP, BW, PBL), pp. 708–713.
CASE-2015-LiX #energy #learning #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 #learning #markov #smarttech #using: Using Hidden Semi-Markov Model for learning behavior in smarthomes (AP, SA, NC, AEA, NR), pp. 752–757.
CASE-2015-SrinivasanBSSR #automation #machine learning #modelling #network #using: Modelling time-varying delays in networked automation systems with heterogeneous networks using machine learning techniques (SS, FB, GS, BS, SR), pp. 362–368.
CASE-2015-SundarkumarRNG #api #detection #machine learning #modelling #topic: Malware detection via API calls, topic models and machine learning (GGS, VR, IN, VG), pp. 1212–1217.
CASE-2015-SustoM #approach #machine learning #multi #predict: Slow release drug dissolution profile prediction in pharmaceutical manufacturing: A multivariate and machine learning approach (GAS, SFM), pp. 1218–1223.
CASE-2015-SuWCRT #adaptation #fuzzy #learning: 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-WatteyneAV #lessons learnt #scalability: Lessons learned from large-scale dense IEEE802.15.4 connectivity traces (TW, CA, XV), pp. 145–150.
CASE-2015-ZhangWZZ #automaton #learning #optimisation #performance: Incorporation of ordinal optimization into learning automata for high learning efficiency (JZ, CW, DZ, MZ), pp. 1206–1211.
DAC-2015-SztipanovitsBNK #cyber-physical #design #lessons learnt: Design tool chain for cyber-physical systems: lessons learned (JS, TB, SN, XDK, EKJ), p. 6.
DAC-2015-VenkataramaniRL #classification #energy #machine learning: Scalable-effort classifiers for energy-efficient machine learning (SV, AR, JL, MS), p. 6.
DATE-2015-ChenKXMLYVSCY #algorithm #array #learning: 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 #learning #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 #learning #online #scheduling #streaming: Big-data streaming applications scheduling with online learning and concept drift detection (KK, MvdS), pp. 1547–1550.
DATE-2015-RenTB #detection #learning #statistics: Detection of illegitimate access to JTAG via statistical learning in chip (XR, VGT, RD(B), pp. 109–114.
DATE-2015-ZhuM #linear #machine learning #optimisation #programming #using: Optimizing dynamic trace signal selection using machine learning and linear programming (CSZ, SM), pp. 1289–1292.
DocEng-2015-Paoli #documentation #what: Documents as Data, Data as Documents: What we learned about Semi-Structured Information for our Open World of Cloud & Devices (JP), p. 1.
DocEng-2015-SilvaFLCOSR #automation #documentation #machine learning #summary: Automatic Text Document Summarization Based on Machine Learning (GPeS, RF, RDL, LdSC, HO, SJS, MR), pp. 191–194.
DRR-2015-FuLLQT #diagrams #learning #multi #retrieval: A diagram retrieval method with multi-label learning (SF, XL, LL, JQ, ZT).
HT-2015-KirchnerR #collaboration #in the cloud #learning: Collaborative Learning in the Cloud: A Cross-Cultural Perspective of Collaboration (KK, LR), pp. 333–336.
HT-2015-MishraDBS #analysis #incremental #learning #sentiment: Sentiment Analysis with Incremental Human-in-the-Loop Learning and Lexical Resource Customization (SM, JD, JB, ES), pp. 323–325.
SIGMOD-2015-HuangBTRTR #machine learning #scalability: Resource Elasticity for Large-Scale Machine Learning (BH, MB, YT, BR, ST, FRR), pp. 137–152.
SIGMOD-2015-KumarNP #learning #linear #modelling #normalisation: Learning Generalized Linear Models Over Normalized Data (AK, JFN, JMP), pp. 1969–1984.
SIGMOD-2015-ReABCJKR #database #machine learning #question: Machine Learning and Databases: The Sound of Things to Come or a Cacophony of Hype? (CR, DA, MB, MIC, MIJ, TK, RR), pp. 283–284.
VLDB-2015-KumarJYNP #machine learning #normalisation #optimisation: Demonstration of Santoku: Optimizing Machine Learning over Normalized Data (AK, MJ, BY, JFN, JMP), pp. 1864–1875.
VLDB-2015-QianGJ #adaptation #comparison #learning: Learning User Preferences By Adaptive Pairwise Comparison (LQ, JG, HVJ), pp. 1322–1333.
ITiCSE-2015-AlshammariAH #adaptation #education #learning #security: The Impact of Learning Style Adaptivity in Teaching Computer Security (MA, RA, RJH), pp. 135–140.
ITiCSE-2015-AndersonNM #programming: Facilitating Programming Success in Data Science Courses through Gamified Scaffolding and Learn2Mine (PEA, TN, RAM), pp. 99–104.
ITiCSE-2015-Annamaa #ide #learning #programming #python: Thonny, : a Python IDE for Learning Programming (AA), p. 343.
ITiCSE-2015-Cukierman #learning #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: Learning and Teaching Computing Sustainability (MH), p. 338.
ITiCSE-2015-Harms #community #learning #source code: Department Programs to Encourage and Support Service Learning and Community Engagement (DEH), p. 330.
ITiCSE-2015-MartinezGB #comparison #concept #framework #learning #multi #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-ParreiraPC #c #named #student: PCRS-C: Helping Students Learn C (DMP, AP, MC), p. 347.
ITiCSE-2015-QuinsonO #education #learning #programming: A Teaching System to Learn Programming: the Programmer’s Learning Machine (MQ, GO), pp. 260–265.
ITiCSE-2015-SantosSFN #agile #development #framework #learning #mobile: Combining Challenge-Based Learning and Scrum Framework for Mobile Application Development (ARS, AS, PF, MN), pp. 189–194.
ITiCSE-2015-SettleLS #community #learning: A Computer Science Linked-courses Learning Community (AS, JL, TS), pp. 123–128.
ITiCSE-2015-TarmazdiVSFF #learning #using #visualisation: Using Learning Analytics to Visualise Computer Science Teamwork (HT, RV, CS, KEF, NJGF), pp. 165–170.
ITiCSE-2015-Tudor #learning #optimisation #query #xml: Virtual Learning Laboratory about Query Optimization against XML Data (LNT), p. 348.
ICSME-2015-CorleyDK #feature model #learning: Exploring the use of deep learning for feature location (CSC, KD, NAK), pp. 556–560.
ICSME-2015-PiorkowskiFSBKH #bias #debugging #developer #how #information management: To fix or to learn? How production bias affects developers’ information foraging during debugging (DP, SDF, CS, MMB, IK, AZH, JM, CH, AH), pp. 11–20.
MSR-2015-BirdCG #bibliography #code review #framework #lessons learnt: Lessons Learned from Building and Deploying a Code Review Analytics Platform (CB, TC, MG), pp. 191–201.
MSR-2015-WhiteVVP #learning #repository #towards: Toward Deep Learning Software Repositories (MW, CV, MLV, DP), pp. 334–345.
STOC-2015-BarakKS #composition #learning #taxonomy: Dictionary Learning and Tensor Decomposition via the Sum-of-Squares Method (BB, JAK, DS), pp. 143–151.
STOC-2015-Bresler #graph #learning #modelling: Efficiently Learning Ising Models on Arbitrary Graphs (GB), pp. 771–782.
STOC-2015-GeHK #learning: Learning Mixtures of Gaussians in High Dimensions (RG, QH, SMK), pp. 761–770.
STOC-2015-HardtP #bound #learning: Tight Bounds for Learning a Mixture of Two Gaussians (MH, EP), pp. 753–760.
STOC-2015-LiRSS #learning #statistics: Learning Arbitrary Statistical Mixtures of Discrete Distributions (JL, YR, LJS, CS), pp. 743–752.
LATA-2015-Yoshinaka #boolean grammar #grammar inference #learning: Learning Conjunctive Grammars and Contextual Binary Feature Grammars (RY), pp. 623–635.
FM-2015-Damm #analysis #automation #lessons learnt #named #verification: AVACS: Automatic Verification and Analysis of Complex Systems Highlights and Lessons Learned (WD), pp. 18–19.
SEFM-2015-Muhlberg0DLP #learning #source code #verification: Learning Assertions to Verify Linked-List Programs (JTM, DHW, MD, GL, FP), pp. 37–52.
ICFP-2015-ZhuNJ #learning #refinement: Learning refinement types (HZ, AVN, SJ), pp. 400–411.
CHI-2015-AmershiCDLSS #analysis #machine learning #named #performance #tool support: ModelTracker: Redesigning Performance Analysis Tools for Machine Learning (SA, MC, SMD, BL, PYS, JS), pp. 337–346.
CHI-2015-BerardR #assessment #human-computer #learning #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-CaiGGM #education #named: Wait-Learning: Leveraging Wait Time for Second Language Education (CJC, PJG, JRG, RCM), pp. 3701–3710.
CHI-2015-DavisK #learning #student: Investigating High School Students’ Perceptions of Digital Badges in Afterschool Learning (KD, EK), pp. 4043–4046.
CHI-2015-KardanC #adaptation #evaluation #interactive #learning #simulation: Providing Adaptive Support in an Interactive Simulation for Learning: An Experimental Evaluation (SK, CC), pp. 3671–3680.
CHI-2015-KatanGF #development #interactive #interface #machine learning #people #using: Using Interactive Machine Learning to Support Interface Development Through Workshops with Disabled People (SK, MG, RF), pp. 251–254.
CHI-2015-Noble #learning #self: Resilience Ex Machina: Learning a Complex Medical Device for Haemodialysis Self-Treatment (PJN), pp. 4147–4150.
CHI-2015-NoroozMJMF #approach #learning #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 #learning: Twist and Learn: Interface Learning in 3DOF Exploration of 3D Scatterplots (MMS, JLB, AS, KCSB), pp. 313–316.
CHI-2015-StrohmayerCB #learning #people: Exploring Learning Ecologies among People Experiencing Homelessness (AS, RC, MB), pp. 2275–2284.
CHI-2015-Walther-FranksS #design #game studies #learning: 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 #learning #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-Anya #design #exclamation #question #what: Bridge the Gap!: What Can Work Design in Crowdwork Learn from Work Design Theories? (OA), pp. 612–627.
CSCW-2015-ChengB #classification #hybrid #machine learning #named: Flock: Hybrid Crowd-Machine Learning Classifiers (JC, MSB), pp. 600–611.
CSCW-2015-CoetzeeLFHH #interactive #learning #scalability: Structuring Interactions for Large-Scale Synchronous Peer Learning (DC, SL, AF, BH, MAH), pp. 1139–1152.
CSCW-2015-DornSS #collaboration #learning: Piloting TrACE: Exploring Spatiotemporal Anchored Collaboration in Asynchronous Learning (BD, LBS, AS), pp. 393–403.
CSCW-2015-JiaWXRC #behaviour #learning #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-EH-2015-NakamuraKKMK #development #self #student #using: Development of a Self-learning System for Chest Auscultation Skills Using an RFID Reader for Nursing Students (MN, KK, YK, JM, MKP), pp. 474–481.
DHM-HM-2015-NishimuraK #case study #learning: A Study on Learning Effects of Marking with Highlighter Pen (HN, NK), pp. 357–367.
DUXU-DD-2015-Fabri #design #education #lessons learnt #student: Thinking with a New Purpose: Lessons Learned from Teaching Design Thinking Skills to Creative Technology Students (MF), pp. 32–43.
DUXU-DD-2015-KremerL #design #experience #learning #research #user interface: Learning from Experience Oriented Disciplines for User Experience Design — A Research Agenda (SK, UL), pp. 306–314.
DUXU-DD-2015-Schneidermeier #lessons learnt #usability: Lessons Learned in Usability Consulting (TS), pp. 247–255.
DUXU-IXD-2015-BorgesonFKTR #energy #learning #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 #learning #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 #learning #modelling #risk management #user interface: Application of Dashboards and Scorecards for Learning Models IT Risk Management: A User Experience (EC), pp. 153–165.
DUXU-IXD-2015-Ovesleova #adaptation #concept #motivation #online #student #user interface: E-Learning Platforms and Lacking Motivation in Students: Concept of Adaptable UI for Online Courses (HO), pp. 218–227.
DUXU-UI-2015-BeltranUPSSSPCA #design #game studies #learning: 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 #learning #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 #learning: 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 #learning: 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 #learning #ubiquitous: A Ubiquitous Lecture Archive Learning Platform with Note-Centered Approach (SH, JD), pp. 294–303.
HIMI-IKC-2015-HayashiH #analysis #concept #learning #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 #learning: 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: Learning State Model for Value Co-Creative Education Services (KK, KM, SM, YN, TI, YS), pp. 341–349.
HIMI-IKC-2015-TogawaK #collaboration #framework #using: Disaster Recovery Framework for e-Learning Environment Using Private Cloud Collaboration and Emergency Alerts (ST, KK), pp. 588–596.
HIMI-IKC-2015-WatanabeTA #abstraction #development #learning #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 #learning #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 #learning #student #using: Using Augmented Reality Technology in Assisting English Learning for Primary School Students (SB, CK, PB), pp. 24–32.
LCT-2015-DalipiYK #analysis #performance #semantics #using: Enhancing the Learner’s Performance Analysis Using SMEUS Semantic E-learning System and Business Intelligence Technologies (FD, SYY, ZK), pp. 208–217.
LCT-2015-DirinN #design #development #framework: Assessments of User Centered Design Framework for M-learning Application Development (AD, MN), pp. 62–74.
LCT-2015-DuA #artificial reality #design #evaluation #learning: Design and Evaluation of a Learning Assistant System with Optical Head-Mounted Display (OHMD) (XD, AA), pp. 75–86.
LCT-2015-FardounAC #education #evaluation: Creation of Meaningful-Learning and Continuous Evaluation Education System (HMF, AA, APC), pp. 218–226.
LCT-2015-FardounAC15a #self #student: Construction of Educative Micro-Worlds to Build Students’ Creativity in Terms of Their Own Self-Learning (HMF, AAMAG, APC), pp. 349–360.
LCT-2015-FonsecaRVG #3d #education #learning: From Formal to Informal 3D Learning. Assesment of Users in the Education (DF, ER, FV, ODG), pp. 460–469.
LCT-2015-GoelMTPSYD #collaboration #learning #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 #learning #student #tool support: Exploring Student Interactions: Learning Analytics Tools for Student Tracking (MÁCG, ÁHG, FJGP, MLSE), pp. 50–61.
LCT-2015-HoffmannPLSMJ #learning #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 #learning: 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 #learning #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 #learning #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-KlockCCRAG #adaptation #concept #gamification #student: Gamification in e-Learning Systems: A Conceptual Model to Engage Students and Its Application in an Adaptive e-Learning System (ACTK, LFDC, MFdC, BER, AJA, IG), pp. 595–607.
LCT-2015-LambropoulosMFK #design #experience #learning #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 #learning #programming #quality: Inspecting Quality of Games Designed for Learning Programming (TO, SB), pp. 620–631.
LCT-2015-RodriguezOD #hybrid #learning #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 #learning #novel #word: Design and Implementation of Novel Word Learning System “Überall” (RS, KO), pp. 148–159.
LCT-2015-TamuraTHN #generative #learning #wiki: Generating Quizzes for History Learning Based on Wikipedia Articles (YT, YT, YH, YIN), pp. 337–346.
LCT-2015-VallsRF #architecture #design #education #game studies #roadmap: E-Learning and Serious Games — New Trends in Architectural and Urban Design Education (FV, ER, DF), pp. 632–643.
LCT-2015-VielRTP #design #interactive #learning #multi: Design Solutions for Interactive Multi-video Multimedia Learning Objects (CCV, KRHR, CACT, MdGCP), pp. 160–171.
LCT-2015-YusoffK #design #game studies #interactive #learning #persuasion: Game Rhetoric: Interaction Design Model of Persuasive Learning for Serious Games (ZY, AK), pp. 644–654.
ICEIS-v1-2015-PecliGPMFTTDFCG #learning #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 #learning: A Learning Model for Intelligent Agents Applied to Poultry Farming (RR, MT, ALW, APB, FE), pp. 495–503.
ICEIS-v1-2015-SouzaBGBE #learning #online: Applying Ensemble-based Online Learning Techniques on Crime Forecasting (AJdS, APB, HMG, JPB, FE), pp. 17–24.
ICEIS-v2-2015-Judrups #analysis #information management #integration: Analysis of Knowledge Management and E-Learning Integration Approaches (JJ), pp. 451–456.
ECIR-2015-HuynhHR #analysis #learning #sentiment #strict: Learning Higher-Level Features with Convolutional Restricted Boltzmann Machines for Sentiment Analysis (TH, YH, SMR), pp. 447–452.
ECIR-2015-KingI #clustering #generative #music: Generating Music Playlists with Hierarchical Clustering and Q-Learning (JK, VI), pp. 315–326.
ECIR-2015-LiHLZ #twitter: Selecting Training Data for Learning-Based Twitter Search (DL, BH, TL, XZ), pp. 501–506.
ECIR-2015-NicosiaBM #learning #rank: Learning to Rank Aggregated Answers for Crossword Puzzles (MN, GB, AM), pp. 556–561.
ECIR-2015-PasinatoMZ #elicitation #learning #rating: Active Learning Applied to Rating Elicitation for Incentive Purposes (MBP, CEM, GZ), pp. 291–302.
ECIR-2015-PelejaM #learning #retrieval #sentiment: Learning Sentiment Based Ranked-Lexicons for Opinion Retrieval (FP, JM), pp. 435–440.
ICML-2015-AmidU #learning #multi: Multiview Triplet Embedding: Learning Attributes in Multiple Maps (EA, AU), pp. 1472–1480.
ICML-2015-BachHBG #learning #performance: Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs (SHB, BH, JLBG, LG), pp. 381–390.
ICML-2015-BlumH #contest #machine learning #reliability: The Ladder: A Reliable Leaderboard for Machine Learning Competitions (AB, MH), pp. 1006–1014.
ICML-2015-Bou-AmmarTE #learning #policy #sublinear: Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret (HBA, RT, EE), pp. 2361–2369.
ICML-2015-ChangKADL #education #learning: Learning to Search Better than Your Teacher (KWC, AK, AA, HDI, JL), pp. 2058–2066.
ICML-2015-ChenSYU #learning #modelling: Learning Deep Structured Models (LCC, AGS, ALY, RU), pp. 1785–1794.
ICML-2015-CilibertoMPR #learning #multi: Convex Learning of Multiple Tasks and their Structure (CC, YM, TAP, LR), pp. 1548–1557.
ICML-2015-CohenH #learning #online: Following the Perturbed Leader for Online Structured Learning (AC, TH), pp. 1034–1042.
ICML-2015-DanielyGS #adaptation #learning #online: Strongly Adaptive Online Learning (AD, AG, SSS), pp. 1405–1411.
ICML-2015-FetayaU #invariant #learning: Learning Local Invariant Mahalanobis Distances (EF, SU), pp. 162–168.
ICML-2015-GarberHM #learning #online: Online Learning of Eigenvectors (DG, EH, TM), pp. 560–568.
ICML-2015-GuptaAGN #learning #precise: Deep Learning with Limited Numerical Precision (SG, AA, KG, PN), pp. 1737–1746.
ICML-2015-HallakSMM #learning #modelling: Off-policy Model-based Learning under Unknown Factored Dynamics (AH, FS, TAM, SM), pp. 711–719.
ICML-2015-Hernandez-Lobato15b #learning #network #probability #scalability: Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks (JMHL, RA), pp. 1861–1869.
ICML-2015-HockingRB #detection #learning #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 #learning #network #online: Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network (SH, TY, SK, BH), pp. 597–606.
ICML-2015-HsiehND #learning #matrix: PU Learning for Matrix Completion (CJH, NN, ISD), pp. 2445–2453.
ICML-2015-HuangWSLC #classification #image #learning #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 #learning #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 #learning #modelling: Abstraction Selection in Model-based Reinforcement Learning (NJ, AK, SS), pp. 179–188.
ICML-2015-Kandemir #learning #process #symmetry: Asymmetric Transfer Learning with Deep Gaussian Processes (MK), pp. 730–738.
ICML-2015-KvetonSWA #learning #rank: Cascading Bandits: Learning to Rank in the Cascade Model (BK, CS, ZW, AA), pp. 767–776.
ICML-2015-LakshmananOR #bound #learning: Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning (KL, RO, DR), pp. 524–532.
ICML-2015-LeC #learning #metric #using: Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations (TL, MC), pp. 2002–2011.
ICML-2015-LiuY #graph #learning #predict: Bipartite Edge Prediction via Transductive Learning over Product Graphs (HL, YY), pp. 1880–1888.
ICML-2015-LondonHG #approximate #learning: The Benefits of Learning with Strongly Convex Approximate Inference (BL, BH, LG), pp. 410–418.
ICML-2015-LongC0J #adaptation #learning #network: Learning Transferable Features with Deep Adaptation Networks (ML, YC, JW, MJ), pp. 97–105.
ICML-2015-Lopez-PazMST #learning #towards: Towards a Learning Theory of Cause-Effect Inference (DLP, KM, BS, IT), pp. 1452–1461.
ICML-2015-MaclaurinDA #learning #optimisation: Gradient-based Hyperparameter Optimization through Reversible Learning (DM, DKD, RPA), pp. 2113–2122.
ICML-2015-MarietS #algorithm #fixpoint #learning #process: Fixed-point algorithms for learning determinantal point processes (ZM, SS), pp. 2389–2397.
ICML-2015-MenonROW #estimation #learning: Learning from Corrupted Binary Labels via Class-Probability Estimation (AKM, BvR, CSO, BW), pp. 125–134.
ICML-2015-PerrotH #analysis #learning #metric: A Theoretical Analysis of Metric Hypothesis Transfer Learning (MP, AH), pp. 1708–1717.
ICML-2015-PhamRFA #learning #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 #learning #student: Learning Program Embeddings to Propagate Feedback on Student Code (CP, JH, AN, MP, MS, LJG), pp. 1093–1102.
ICML-2015-PlessisNS #learning: Convex Formulation for Learning from Positive and Unlabeled Data (MCdP, GN, MS), pp. 1386–1394.
ICML-2015-Romera-ParedesT #approach #learning: An embarrassingly simple approach to zero-shot learning (BRP, PHST), pp. 2152–2161.
ICML-2015-SerrurierP #evaluation #learning: Entropy evaluation based on confidence intervals of frequency estimates : Application to the learning of decision trees (MS, HP), pp. 1576–1584.
ICML-2015-SibonyCJ #learning #ranking #statistics: MRA-based Statistical Learning from Incomplete Rankings (ES, SC, JJ), pp. 1432–1441.
ICML-2015-Sohl-DicksteinW #learning #using: Deep Unsupervised Learning using Nonequilibrium Thermodynamics (JSD, EAW, NM, SG), pp. 2256–2265.
ICML-2015-SrivastavaMS #learning #using #video: Unsupervised Learning of Video Representations using LSTMs (NS, EM, RS), pp. 843–852.
ICML-2015-SteinhardtL15a #learning #modelling #predict: Learning Fast-Mixing Models for Structured Prediction (JS, PL), pp. 1063–1072.
ICML-2015-SwaminathanJ #feedback #learning: Counterfactual Risk Minimization: Learning from Logged Bandit Feedback (AS, TJ), pp. 814–823.
ICML-2015-TangSX #learning #network: Learning Scale-Free Networks by Dynamic Node Specific Degree Prior (QT, SS, JX), pp. 2247–2255.
ICML-2015-TewariC #bound #documentation #fault #learning #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 #learning: A Deeper Look at Planning as Learning from Replay (HV, RS), pp. 2314–2322.
ICML-2015-WangALB #learning #multi #on the #representation: On Deep Multi-View Representation Learning (WW, RA, KL, JAB), pp. 1083–1092.
ICML-2015-WangWLCW #learning #multi #segmentation: Multi-Task Learning for Subspace Segmentation (YW, DPW, QL, WC, IJW), pp. 1209–1217.
ICML-2015-WangY #learning #matrix #multi: Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices (JW, JY), pp. 1747–1756.
ICML-2015-WeiIB #learning #set: Submodularity in Data Subset Selection and Active Learning (KW, RKI, JAB), pp. 1954–1963.
ICML-2015-WeissN #alias #learning: Learning Parametric-Output HMMs with Two Aliased States (RW, BN), pp. 635–644.
ICML-2015-WenKA #combinator #learning #performance #scalability: Efficient Learning in Large-Scale Combinatorial Semi-Bandits (ZW, BK, AA), pp. 1113–1122.
ICML-2015-WuS #algorithm #learning #modelling #online: An Online Learning Algorithm for Bilinear Models (YW, SS), pp. 890–898.
ICML-2015-YogatamaFDS #learning #word: Learning Word Representations with Hierarchical Sparse Coding (DY, MF, CD, NAS), pp. 87–96.
ICML-2015-YuB #learning: Learning Submodular Losses with the Lovasz Hinge (JY, MBB), pp. 1623–1631.
ICML-2015-YuCL #learning #multi #online #rank: Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams (RY, DC, YL), pp. 238–247.
KDD-2015-Agarwal #machine learning #scalability #statistics #web: Scaling Machine Learning and Statistics for Web Applications (DA), p. 1621.
KDD-2015-Athey #evaluation #machine learning #policy: Machine Learning and Causal Inference for Policy Evaluation (SA), pp. 5–6.
KDD-2015-ChakrabortyBSPY #classification #framework #learning #named #novel: BatchRank: A Novel Batch Mode Active Learning Framework for Hierarchical Classification (SC, VNB, ARS, SP, JY), pp. 99–108.
KDD-2015-Durrant-Whyte #machine learning: Data, Knowledge and Discovery: Machine Learning meets Natural Science (HDW), p. 7.
KDD-2015-DuS #adaptation #feature model #learning: Unsupervised Feature Selection with Adaptive Structure Learning (LD, YDS), pp. 209–218.
KDD-2015-GaoYCH #integration #learning #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 #learning #using: Using Local Spectral Methods to Robustify Graph-Based Learning Algorithms (DFG, MWM), pp. 359–368.
KDD-2015-Gomez-Rodriguez #machine learning #modelling #network #probability #problem #research #social: Diffusion in Social and Information Networks: Research Problems, Probabilistic Models and Machine Learning Methods (MGR, LS), pp. 2315–2316.
KDD-2015-HanZ #learning #multi: Learning Tree Structure in Multi-Task Learning (LH, YZ), pp. 397–406.
KDD-2015-JohanssonD #geometry #graph #learning #similarity #using: Learning with Similarity Functions on Graphs using Matchings of Geometric Embeddings (FDJ, DPD), pp. 467–476.
KDD-2015-Koller #named #question #what: MOOCS: What Have We Learned? (DK), p. 3.
KDD-2015-LakkarajuASMBGA #framework #identification #machine learning #student: A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes (HL, EA, CS, DM, NB, RG, KLA), pp. 1909–1918.
KDD-2015-LanH #complexity #learning #multi: Reducing the Unlabeled Sample Complexity of Semi-Supervised Multi-View Learning (CL, JH), pp. 627–634.
KDD-2015-LinLC #framework #multi #network #social: A Learning-based Framework to Handle Multi-round Multi-party Influence Maximization on Social Networks (SCL, SDL, MSC), pp. 695–704.
KDD-2015-MaoWGS #graph #learning #reduction: Dimensionality Reduction Via Graph Structure Learning (QM, LW, SG, YS), pp. 765–774.
KDD-2015-NairRKBSKHD #detection #learning #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 #learning #multi: Discovering and Exploiting Deterministic Label Relationships in Multi-Label Learning (CP, GT, IT), pp. 915–924.
KDD-2015-Pratt #machine learning #predict #protocol #proving: Proof Protocol for a Machine Learning Technique Making Longitudinal Predictions in Dynamic Contexts (KBP), pp. 2049–2058.
KDD-2015-RiondatoU15a #algorithm #learning #statistics: VC-Dimension and Rademacher Averages: From Statistical Learning Theory to Sampling Algorithms (MR, EU), pp. 2321–2322.
KDD-2015-Schleier-Smith #agile #architecture #machine learning #realtime: An Architecture for Agile Machine Learning in Real-Time Applications (JSS), pp. 2059–2068.
KDD-2015-SethiYRVR #classification #machine learning #scalability #using: Scalable Machine Learning Approaches for Neighborhood Classification Using Very High Resolution Remote Sensing Imagery (MS, YY, AR, RRV, SR), pp. 2069–2078.
KDD-2015-ShashidharPA #machine learning: Spoken English Grading: Machine Learning with Crowd Intelligence (VS, NP, VA), pp. 2089–2097.
KDD-2015-SunAYMMBY #classification #learning: Transfer Learning for Bilingual Content Classification (QS, MSA, BY, CM, VM, AB, JY), pp. 2147–2156.
KDD-2015-TanSZ0 #learning #transitive: Transitive Transfer Learning (BT, YS, EZ, QY), pp. 1155–1164.
KDD-2015-VeeriahDQ #architecture #learning #predict: Deep Learning Architecture with Dynamically Programmed Layers for Brain Connectome Prediction (VV, RD, GJQ), pp. 1205–1214.
KDD-2015-WangWY #collaboration #learning #recommendation: Collaborative Deep Learning for Recommender Systems (HW, NW, DYY), pp. 1235–1244.
KDD-2015-XingHDKWLZXKY #big data #distributed #framework #machine learning #named: Petuum: A New Platform for Distributed Machine Learning on Big Data (EPX, QH, WD, JKK, JW, SL, XZ, PX, AK, YY), pp. 1335–1344.
KDD-2015-XuSB #learning #predict: Longitudinal LASSO: Jointly Learning Features and Temporal Contingency for Outcome Prediction (TX, JS, JB), pp. 1345–1354.
KDD-2015-YangH #learning #multi: Model Multiple Heterogeneity via Hierarchical Multi-Latent Space Learning (PY, JH), pp. 1375–1384.
KDD-2015-YangSJWDY #learning #visual notation: Structural Graphical Lasso for Learning Mouse Brain Connectivity (SY, QS, SJ, PW, ID, JY), pp. 1385–1394.
KDD-2015-YanRHC #distributed #learning #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 #learning #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 #learning #multi: Multi-Task Learning for Spatio-Temporal Event Forecasting (LZ, QS, JY, FC, CTL, NR), pp. 1503–1512.
MLDM-2015-Chou #data-driven #geometry #learning: Data Driven Geometry for Learning (EPC), pp. 395–402.
MLDM-2015-DhulekarNOY #graph #learning #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: Learning Heuristics to Reduce the Overestimation of Bipartite Graph Edit Distance Approximation (MF, FS, KR), pp. 17–31.
MLDM-2015-GovadaJMS #approach #hybrid #induction #learning #using: Hybrid Approach for Inductive Semi Supervised Learning Using Label Propagation and Support Vector Machine (AG, PJ, SM, SKS), pp. 199–213.
MLDM-2015-KrasotkinaM #approach #optimisation #ranking: A Bayesian Approach to Sparse Learning-to-Rank for Search Engine Optimization (OK, VM), pp. 382–394.
MLDM-2015-MoldovanM #data mining #learning #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: Learning Distributed Representations from Reviews for Collaborative Filtering (AA, KK, KC, ACC), pp. 147–154.
RecSys-2015-HuD #machine learning #recommendation #scalability: Scalable Recommender Systems: Where Machine Learning Meets Search (SYDH, JD), pp. 365–366.
RecSys-2015-SongCL #incremental #matrix #recommendation: Incremental Matrix Factorization via Feature Space Re-learning for Recommender System (QS, JC, HL), pp. 277–280.
SEKE-2015-AffonsoLON #adaptation #framework #learning #self: A Framework Based on Learning Techniques for Decision-making in Self-adaptive Software (FJA, GL, RAPO, EYN), pp. 24–29.
SEKE-2015-GoswamiWS #learning #performance #using: Using Learning Styles of Software Professionals to Improve their Inspection Team Performance (AG, GSW, AS), pp. 680–685.
SEKE-2015-LiuXC #learning #recommendation: Context-aware Recommendation System with Anonymous User Profile Learning (YL, YX, MC), pp. 93–98.
SEKE-2015-Murillo-MoreraJ #algorithm #approach #framework #learning #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 #learning #research: Reflecting, adapting and learning in small software organizations: an action research approach (SS, MM, AL, HPM), pp. 46–50.
SEKE-2015-SaputriL #analysis #machine learning #perspective: Are We Living in a Happy Country: An Analysis of National Happiness from Machine Learning Perspective (TRDS, SWL), pp. 174–177.
SEKE-2015-TironiMRM #approach #identification #learning: 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: Learning Folksonomies for Trend Detection in Task-Oriented Dialogues (GW, ECP), pp. 483–488.
SEKE-2015-ZegarraCW #graph #learning #visualisation: Facilitating Peer Learning and Knowledge Sharing in STEM Courses via Pattern Based Graph Visualization (EZ, SKC, JW), pp. 284–289.
SIGIR-2015-Arora #learning: Promoting User Engagement and Learning in Amorphous Search Tasks (PA), p. 1051.
SIGIR-2015-CormackG #bibliography #learning #multi #perspective: Multi-Faceted Recall of Continuous Active Learning for Technology-Assisted Review (GVC, MRG), pp. 763–766.
SIGIR-2015-FoleyBJ #learning #web: Learning to Extract Local Events from the Web (JF, MB, VJ), pp. 423–432.
SIGIR-2015-HarveyHE #learning #query: Learning by Example: Training Users with High-quality Query Suggestions (MH, CH, DE), pp. 133–142.
SIGIR-2015-Li15a #information retrieval #learning: Transfer Learning for Information Retrieval (PL), p. 1061.
SIGIR-2015-LiuW #collaboration #learning: Learning Context-aware Latent Representations for Context-aware Collaborative Filtering (XL, WW), pp. 887–890.
SIGIR-2015-MehrotraY #learning #query #rank #using: Representative & Informative Query Selection for Learning to Rank using Submodular Functions (RM, EY), pp. 545–554.
SIGIR-2015-SeverynM #learning #network #rank: Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks (AS, AM), pp. 373–382.
SIGIR-2015-SongNZAC #learning #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 #learning #microblog: Active Learning for Entity Filtering in Microblog Streams (DS, MHP, MdR), pp. 975–978.
SIGIR-2015-WangGLXWC #learning #recommendation #representation: Learning Hierarchical Representation Model for NextBasket Recommendation (PW, JG, YL, JX, SW, XC), pp. 403–412.
SIGIR-2015-WangLWZZ #learning #named: LBMCH: Learning Bridging Mapping for Cross-modal Hashing (YW, XL, LW, WZ, QZ), pp. 999–1002.
SIGIR-2015-XiaXLGC #evaluation #learning #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 #learning #multi: Adaptive User Engagement Evaluation via Multi-task Learning (HZ, PM, AS), pp. 1011–1014.
SIGIR-2015-ZhengC #distributed #learning: Learning to Reweight Terms with Distributed Representations (GZ, JC), pp. 575–584.
MoDELS-2015-HajriGBS #approach #case study #embedded #industrial #lessons learnt #modelling #product line: Applying product line Use case modeling in an industrial automotive embedded system: Lessons learned and a refined approach (IH, AG, LCB, TS), pp. 338–347.
MoDELS-2015-LettnerEGP #case study #experience #feature model #industrial #lessons learnt #modelling #scalability: Feature modeling of two large-scale industrial software systems: Experiences and lessons learned (DL, KE, PG, HP), pp. 386–395.
OOPSLA-2015-OhYY #adaptation #learning #optimisation #program analysis: Learning a strategy for adapting a program analysis via bayesian optimisation (HO, HY, KY), pp. 572–588.
SAC-2015-BarrosCMP #education #learning #repository #reuse: Integrating educational repositories to improve the reuse of learning objects (HB, EC, JM, RP), pp. 270–272.
SAC-2015-Brefeld #learning #multi: Multi-view learning with dependent views (UB), pp. 865–870.
SAC-2015-CruzPQSSOBO #algorithm #game studies #named #probability #using: Amê: an environment to learn and analyze adversarial search algorithms using stochastic card games (ABC, LP, JQ, US, SS, AO, EB, ESO), pp. 208–213.
SAC-2015-FauconnierKR #approach #machine learning #recognition #taxonomy: A supervised machine learning approach for taxonomic relation recognition through non-linear enumerative structures (JPF, MK, BR), pp. 423–425.
SAC-2015-GomesBE #classification #data type #learning: Pairwise combination of classifiers for ensemble learning on data streams (HMG, JPB, FE), pp. 941–946.
SAC-2015-JeongYAYP #algorithm #interactive #network #search-based #using: Inference of disease-specific gene interaction network using a Bayesian network learned by genetic algorithm (DJ, YY, JA, YY, SP), pp. 47–53.
SAC-2015-LabibPCG #approach #development #learning #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-NascimentoPM #algorithm #machine learning #metaheuristic: A data quality-aware cloud service based on metaheuristic and machine learning provisioning algorithms (DCN, CESP, DGM), pp. 1696–1703.
SAC-2015-OmatuYI #classification #learning #smell: Smell classification of wines by the learning vector quantization method (SO, MY, YI), pp. 195–200.
SAC-2015-PaivaBSIJ #behaviour #learning #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 #learning #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 #learning #social: Smart learning environments for social learning (EP), pp. 273–274.
SAC-2015-ReadPB #data type #learning: Deep learning in partially-labeled data streams (JR, FPC, AB), pp. 954–959.
SAC-2015-ReddySC #approach #aspect-oriented #incremental #learning #performance #weaving: Incremental aspect weaving: an approach for faster AOP learning (YRR, AS, MC), pp. 1480–1485.
SAC-2015-RegoMP #approach #detection #folksonomy #learning: A supervised learning approach to detect subsumption relations between tags in folksonomies (ASdCR, LBM, CESP), pp. 409–415.
SAC-2015-StracciaM #concept #estimation #fuzzy #learning #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 #learning #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: Learning folksonomies from task-oriented dialogues (GMPW, ECP), pp. 360–367.
ESEC-FSE-2015-JingWDQX #fault #learning #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-KoskiM #architecture #lessons learnt #quality #requirements: Requirements, architecture, and quality in a mission critical system: 12 lessons learned (AK, TM), pp. 1018–1021.
ESEC-FSE-2015-SunXLLQ #abstraction #learning #named #testing #validation: TLV: abstraction through testing, learning, and validation (JS, HX, YL, SWL, SQ), pp. 698–709.
ICSE-v1-2015-FilieriGL #adaptation #learning #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 #learning #testing #using: Learning Combinatorial Interaction Test Generation Strategies Using Hyperheuristic Search (YJ, MBC, MH, JP), pp. 540–550.
ICSE-v1-2015-ZhuHFZLZ #developer #learning: Learning to Log: Helping Developers Make Informed Logging Decisions (JZ, PH, QF, HZ, MRL, DZ), pp. 415–425.
ICSE-v2-2015-Hanakawa #contest #learning #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 #learning #mining #simulation #statistics: Statistical Learning and Software Mining for Agent Based Simulation of Software Evolution (VH), pp. 863–866.
ICSE-v2-2015-JankeBW #education #learning #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 #learning #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 #learning: 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 #learning #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 #learning #re-engineering: Active and Inductive Learning in Software Engineering Education (YS, DL), pp. 418–427.
ICSE-v2-2015-SoundarajanJR #collaboration #re-engineering: Collaborative and Cooperative-Learning in Software Engineering Courses (NS, SJ, RR), pp. 319–322.
ICSE-v2-2015-WilkinsG #design #learning #student: Drawing Insight from Student Perceptions of Reflective Design Learning (TVW, JCG), pp. 253–262.
ASPLOS-2015-LiuCLZZTFZC #machine learning #named: PuDianNao: A Polyvalent Machine Learning Accelerator (DFL, TC, SL, JZ, SZ, OT, XF, XZ, YC), pp. 369–381.
CGO-2015-McAfeeO #framework #generative #learning #multi #named: EMEURO: a framework for generating multi-purpose accelerators via deep learning (LCM, KO), pp. 125–135.
HPCA-2015-WuGLJC #estimation #machine learning #performance #using: GPGPU performance and power estimation using machine learning (GYW, JLG, AL, NJ, DC), pp. 564–576.
PPoPP-2015-AshariTBRCKS #kernel #machine learning #on the #optimisation: On optimizing machine learning workloads via kernel fusion (AA, ST, MB, BR, KC, JK, PS), pp. 173–182.
CAV-2015-BrazdilCCFK #learning #markov #process: Counterexample Explanation by Learning Small Strategies in Markov Decision Processes (TB, KC, MC, AF, JK), pp. 158–177.
CAV-2015-GehrDV #commutative #learning #specification: Learning Commutativity Specifications (TG, DD, MTV), pp. 307–323.
CAV-2015-IsbernerHS #automaton #framework #learning #open source: The Open-Source LearnLib — A Framework for Active Automata Learning (MI, FH, BS), pp. 487–495.
CAV-2015-Saha0M #learning #named: Alchemist: Learning Guarded Affine Functions (SS, PG, PM), pp. 440–446.
ICLP-2015-MartinezRIAT #learning #modelling #probability: Learning Probabilistic Action Models from Interpretation Transitions (DM, TR, KI, GA, CT).
ICLP-J-2015-LawRB #constraints #learning #programming #set: Learning weak constraints in answer set programming (ML, AR, KB), pp. 511–525.
SAT-2015-TuHJ #learning #named #reasoning #satisfiability: QELL: QBF Reasoning with Extended Clause Learning and Levelized SAT Solving (KHT, TCH, JHRJ), pp. 343–359.
WICSA-2014-UusitaloRKMM #architecture #automation #lessons learnt #safety: Lessons Learned from Safety-Critical Software-Based Automation Architectures of Nuclear Power Plants (EJU, MR, MK, VM, TM), pp. 45–48.
ASE-2014-NguyenNNN #api #approach #learning #migration #mining #statistics: Statistical learning approach for mining API usage mappings for code migration (ATN, HAN, TTN, TNN), pp. 457–468.
CASE-2014-HabibDBHP #android #learning: Learning human-like facial expressions for Android Phillip K. Dick (AH, SKD, ICB, DH, DOP), pp. 1159–1165.
CASE-2014-HwangLW #adaptation #learning: Adaptive reinforcement learning in box-pushing robots (KSH, JLL, WHW), pp. 1182–1187.
CASE-2014-KernWGBM #estimation #machine learning #using: COD and NH4-N estimation in the inflow of Wastewater Treatment Plants using Machine Learning Techniques (PK, CW, DG, MB, SFM), pp. 812–817.
CASE-2014-MaDLZ #learning #modelling #simulation: Modeling and simulation of product diffusion considering learning effect (KPM, XD, CFL, JZ), pp. 665–670.
CASE-2014-MahlerKLSMKPWFAG #learning #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 #learning: Groundhog Day: Iterative learning for building temperature control (MM, SM, JTW), pp. 948–953.
CASE-2014-MurookaNNKOI #learning #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.
CASE-2014-SustoWPZJOM #adaptation #flexibility #machine learning #maintenance #predict: An adaptive machine learning decision system for flexible predictive maintenance (GAS, JW, SP, MZ, ABJ, PGO, SFM), pp. 806–811.
DAC-2014-0001SMAKV #manycore #optimisation: Reinforcement Learning-Based Inter- and Intra-Application Thermal Optimization for Lifetime Improvement of Multicore Systems (AD, RAS, GVM, BMAH, AK, BV), p. 6.
DAC-2014-AlbalawiLL #algorithm #classification #design #fixpoint #implementation #machine learning #power management: Computer-Aided Design of Machine Learning Algorithm: Training Fixed-Point Classifier for On-Chip Low-Power Implementation (HA, YL, XL), p. 6.
DAC-2014-FarkashHB #incremental #validation: Coverage Learned Targeted Validation for Incremental HW Changes (MF, BGH, MB), p. 6.
DATE-2014-HanKNV #learning: A deep learning methodology to proliferate golden signoff timing (SSH, ABK, SN, ASV), pp. 1–6.
DATE-2014-XuB #hybrid #question: Hybrid side-channel/machine-learning attacks on PUFs: A new threat? (XX, WB), pp. 1–6.
DRR-2014-CartonLC #interactive #learning #named: LearnPos: a new tool for interactive learning positioning (CC, AL, BC), p. ?–12.
DRR-2014-MaXA #algorithm #machine learning #segmentation #video: A machine learning based lecture video segmentation and indexing algorithm (DM, BX, GA), p. ?–8.
DRR-2014-TaoTX #documentation #learning #random #using: Document page structure learning for fixed-layout e-books using conditional random fields (XT, ZT, CX), p. ?–9.
HT-2014-AbbasiTL #learning #scalability #using: Scalable learning of users’ preferences using networked data (MAA, JT, HL), pp. 4–12.
SIGMOD-2014-CaiGLPVJ #algorithm #comparison #implementation #machine learning #scalability: A comparison of platforms for implementing and running very large scale machine learning algorithms (ZC, ZJG, SL, LLP, ZV, CMJ), pp. 1371–1382.
SIGMOD-2014-Sedlar #compilation #how: How i learned to stop worrying and love compilers (ES), pp. 1–2.
VLDB-2014-BoehmTRSTBV #hybrid #machine learning #parallel #scalability: Hybrid Parallelization Strategies for Large-Scale Machine Learning in SystemML (MB, ST, BR, PS, YT, DB, SV), pp. 553–564.
VLDB-2014-SunRYD #classification #crowdsourcing #machine learning #named #scalability #using: Chimera: Large-Scale Classification using Machine Learning, Rules, and Crowdsourcing (CS, NR, FY, AD), pp. 1529–1540.
VLDB-2014-ZouJLGWX #framework #learning #named: Mariana: Tencent Deep Learning Platform and its Applications (YZ, XJ, YL, ZG, EW, BX), pp. 1772–1777.
VLDB-2015-MozafariSFJM14 #dataset #learning #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 #learning #re-engineering: An active learning module for an introduction to software engineering course (AFA), pp. 190–191.
CSEET-2014-BoeschS #automation #learning: Automated mentor assignment in blended learning environments (CB, KS), pp. 94–98.
CSEET-2014-Ding #learning #re-engineering #self: Self-guided learning environment for undergraduate software engineering (JD), pp. 188–189.
CSEET-2014-FranklBK #development #learning: 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 #learning: Teaching and learning agile collaboration (MK, AM, MM, CGZ), pp. 139–148.
CSEET-2014-PotterSDW #game studies #learning #named: InspectorX: A game for software inspection training and learning (HP, MS, LD, VW), pp. 55–64.
CSEET-2014-Wong #challenge #education #experience #lessons learnt #re-engineering: Experience of teaching Executive Master’s program in Software Engineering: Challenges, lessons learned, and path forward (WEW), pp. 186–187.
CSEET-2014-YamadaIWKFYOKT #development #education #effectiveness #learning: 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.
ITiCSE-2014-BainB #programming #question #why: Why is programming so hard to learn? (GB, IB), p. 356.
ITiCSE-2014-BerryK #game studies #learning #programming: The state of play: a notional machine for learning programming (MB, MK), pp. 21–26.
ITiCSE-2014-EckerdalKTNSM #education #learning: Teaching and learning with MOOCs: computing academics’ perspectives and engagement (AE, PK, NT, AN, JS, LM), pp. 9–14.
ITiCSE-2014-EllisH #learning #open source #re-engineering: Structuring software engineering learning within open source software participation (HJCE, GWH), p. 326.
ITiCSE-2014-EllisJBPHD #learning: Learning within a professional environment: shared ownership of an HFOSS project (HJCE, SJ, DB, LP, GWH, JD), p. 337.
ITiCSE-2014-FalknerVF #identification #learning #self: Identifying computer science self-regulated learning strategies (KF, RV, NJGF), pp. 291–296.
ITiCSE-2014-GroverCP #learning: Assessing computational learning in K-12 (SG, SC, RP), pp. 57–62.
ITiCSE-2014-Hidalgo-CespedesRL #concept #design #game studies #learning #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 #learning #programming: Game programming for improving learning experience (RBHN, JÁVI, CPR, LC), pp. 225–230.
ITiCSE-2014-Jasute #education #geometry #interactive #learning #visualisation: An interactive visualization method of constructionist teaching and learning of geometry (EJ), p. 349.
ITiCSE-2014-KothiyalMI #learning #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 #learning #programming: Learning computer programming: a study of the effectiveness of a COAC# (JMA), p. 333.
ITiCSE-2014-MedinaSGG #learning #student #using: Learning outcomes using objectives with computer science students (JAM, JJS, EGL, AGC), p. 339.
ITiCSE-2014-PirkerRG #education #learning #student: Motivational active learning: engaging university students in computer science education (JP, MRS, CG), pp. 297–302.
ITiCSE-2014-PriorCL #case study #experience #learning: Things coming together: learning experiences in a software studio (JP, AC, JL), pp. 129–134.
ITiCSE-2014-Rogers #learning #question: New technology, new learning? (YR), p. 1.
ITiCSE-2014-TaubBA #learning #physics: The effect of computer science on the learning of computational physics (RT, MBA, MA), p. 352.
ITiCSE-2014-Urquiza-FuentesCHMH #framework #learning #social #student #video: A social platform supporting learning through video creation by students (JUF, JC, IH, EM, PAH), p. 330.
ITiCSE-2014-Verwaal #learning: Team based learning in theoretical computer science (NV), p. 331.
ITiCSE-2014-WartVP #design #learning #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 #learning: 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.
TFPIE-2014-Walck #haskell #physics #programming: Learn Physics by Programming in Haskell (SNW), pp. 67–77.
TACAS-2014-MalerM #learning #regular expression #scalability: Learning Regular Languages over Large Alphabets (OM, IEM), pp. 485–499.
CSMR-WCRE-2014-XiaFLCW #behaviour #learning #multi #towards: Towards more accurate multi-label software behavior learning (XX, YF, DL, ZC, XW), pp. 134–143.
ICPC-2014-KaulgudAMT #comprehension #learning: Comprehension support during knowledge transitions: learning from field (VSK, KMA, JM, GT), pp. 205–206.
ICSME-2014-BinkleyL #information retrieval #learning #rank: Learning to Rank Improves IR in SE (DB, DJL), pp. 441–445.
ICSME-2014-XuanM #fault #learning #locality #metric #multi #ranking: Learning to Combine Multiple Ranking Metrics for Fault Localization (JX, MM), pp. 191–200.
STOC-2014-AwasthiBL #learning #linear #locality #power of: The power of localization for efficiently learning linear separators with noise (PA, MFB, PML), pp. 449–458.
STOC-2014-Christiano #learning #online #programming: Online local learning via semidefinite programming (PC), pp. 468–474.
STOC-2014-DanielyLS #complexity #learning: From average case complexity to improper learning complexity (AD, NL, SSS), pp. 441–448.
ICALP-v1-2014-Volkovich #bound #learning #on the: On Learning, Lower Bounds and (un)Keeping Promises (IV), pp. 1027–1038.
ICALP-v2-2014-DamsHK #learning #network: Jamming-Resistant Learning in Wireless Networks (JD, MH, TK), pp. 447–458.
LATA-2014-LaurenceLNST #learning #transducer: Learning Sequential Tree-to-Word Transducers (GL, AL, JN, SS, MT), pp. 490–502.
FM-2014-LinH #composition #concurrent #learning #model checking #synthesis: Compositional Synthesis of Concurrent Systems through Causal Model Checking and Learning (SWL, PAH), pp. 416–431.
SEFM-2014-CasselHJS #finite #learning #state machine: Learning Extended Finite State Machines (SC, FH, BJ, BS), pp. 250–264.
CHI-2014-DontchevaMBG #crowdsourcing #learning #performance: Combining crowdsourcing and learning to improve engagement and performance (MD, RRM, JRB, EMG), pp. 3379–3388.
CHI-2014-DunwellFPHALS #approach #game studies #learning #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 #learning #online: Learning to fail: experiencing public failure online through crowdfunding (MDG, EG), pp. 581–590.
CHI-2014-KovacsM #learning: Smart subtitles for vocabulary learning (GK, RCM), pp. 853–862.
CHI-2014-KuleszaACFC #concept #evolution #machine learning: Structured labeling for facilitating concept evolution in machine learning (TK, SA, RC, DF, DXC), pp. 3075–3084.
CHI-2014-MentisCS #learning: Learning to see the body: supporting instructional practices in laparoscopic surgical procedures (HMM, AC, SDS), pp. 2113–2122.
CHI-2014-MonserratLZC #interactive #learning: L.IVE: an integrated interactive video-based learning environment (TJKPM, YL, SZ, XC), pp. 3399–3402.
CHI-2014-PilliasRL #design #game studies #lessons learnt #video: Designing tangible video games: lessons learned from the sifteo cubes (CP, RRB, GL), pp. 3163–3166.
CHI-2014-Ruggiero #game studies #learning #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 #learning #people #research: Pair research: matching people for collaboration, learning, and productivity (RCM, HZ, EG, EG), pp. 1043–1048.
CSCW-2014-YuAKK #comparison #learning #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 #learning #performance: Reviewing versus doing: learning and performance in crowd assessment (HZ, SPD, REK, AK), pp. 1445–1455.
DHM-2014-GotoYTWS: Application of E-learning System Reality in Kyoto-style Earthen Wall Training (AG, HY, YT, ZW, HS), pp. 247–253.
DUXU-DI-2014-ChangH: Effect of Perception-Compatibility, Learning-Factor, and Symbol-Carrier on Single LED Symbol System Recognizing (CCC, TKPH), pp. 417–424.
DUXU-DI-2014-GencerBZV #detection #machine learning #mobile: Detection of Churned and Retained Users with Machine Learning Methods for Mobile Applications (MG, GB, ÖZ, TV), pp. 234–245.
DUXU-DI-2014-ShafiqICRAAR #analysis #case study #learning #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 #learning #user interface #visualisation: Interactive Visualizations in Learning Mathematics: Implications for Information Design and User Experience (VTS), pp. 472–480.
DUXU-ELAS-2014-KarlinPC #experience #learning #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 #learning #prototype: Prototyping in a Learning Environment — Digital Publishing Projects from the Escola Superior de Desenho Industrial (MAFM), pp. 195–206.
DUXU-ELAS-2014-MedeirosJG #learning #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 #learning #multi: Rural Area Development through Multi-interface Technology and Virtual Learning System (FuM, AM, SM, SA, UA, SM, HE, TAB, MFK), pp. 442–451.
DUXU-ELAS-2014-Portugal #design: Design, User-Experience and Teaching-Learning (CP), pp. 230–241.
DUXU-TMT-2014-BrangierD #case study #heuristic #persuasion: Heuristic Inspection to Assess Persuasiveness: A Case Study of a Mathematics E-learning Program (EB, MCD), pp. 425–436.
HCI-AIMT-2014-AlkhashramiAA #design #interface #learning: 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 #learning: Effectiveness of Virtual Hands in 3D Learning Material (TM, SM), pp. 93–101.
HCI-AIMT-2014-YanikTMMBGW #gesture #learning: A Method for Lifelong Gesture Learning Based on Growing Neural Gas (PMY, AT, JM, JM, JOB, KEG, IDW), pp. 191–202.
HCI-AS-2014-DiasDH #fuzzy #interactive #modelling #quality #using: Exploring B-Learning Scenarios Using Fuzzy Logic-Based Modeling of Users’ LMS Quality of Interaction in Ergonomics and Psychomotor Rehabilitation Academic Courses (SBD, JAD, LJH), pp. 233–243.
HCI-AS-2014-JanssonSBAT #automation #design: Authority and Level of Automation — Lessons to Be Learned in Design of In-vehicle Assistance Systems (AJ, PS, IB, AA, ST), pp. 413–424.
HCI-AS-2014-SchwallerKAL #feedback #gesture #learning #visual notation: Improving In-game Gesture Learning with Visual Feedback (MS, JK, LA, DL), pp. 643–653.
HCI-TMT-2014-MatsumotoKKA #adaptation #automation #learning #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 #learning #online: Teaching and Learning HCI Online (EM, MGD, EH, NF), pp. 230–241.
HCI-TMT-2014-SilvaCP #education #human-computer #interactive #learning: Studio-Based Learning as a Natural Fit to Teaching Human-Computer Interaction (PAS, MEC, BJP), pp. 251–258.
HCI-TMT-2014-YajimaTS #collaboration #learning: Proposal of Collaborative Learning Support Method in Risk Communications (HY, NT, RS), pp. 457–465.
HIMI-AS-2014-AraiKTKA #comprehension #development #learning #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 #learning #problem #word: Triplet Structure Model of Arithmetical Word Problems for Learning by Problem-Posing (TH, SY, YH), pp. 42–50.
HIMI-AS-2014-HirokawaFSY #learning #mindmap: Learning Winespeak from Mind Map of Wine Blogs (SH, BF, TS, CY), pp. 383–393.
HIMI-AS-2014-MatsuiHKA #behaviour #case study #education #learning: 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 #learning #music #performance: A Music Search System for Expressive Music Performance Learning (TM, KT), pp. 80–89.
HIMI-AS-2014-TogawaK #collaboration #framework #smarttech #using: Private Cloud Collaboration Framework for e-Learning Environment for Disaster Recovery Using Smartphone Alert Notification (ST, KK), pp. 118–126.
HIMI-AS-2014-UeiFKNKS #design #education #evaluation #learning: 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 #learning #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 #learning: 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-Choffat-Durr #distance #process: Distance Exchange Projects at Elementary School: A Focus on a Co-learning Process (ACD), pp. 380–387.
LCT-NLE-2014-KaprosP #learning: Empowering L&D Managers through Customisation of Inline Learning Analytics (EK, NP), pp. 282–291.
LCT-NLE-2014-Kim #feedback #learning #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-LimongelliS #fuzzy #modelling #personalisation #student: Fuzzy Student Modeling for Personalization of e-Learning Courses (CL, FS), pp. 292–301.
LCT-NLE-2014-Milde #editing #html #learning #online: An HTML5-Based Online Editor for Creating Annotated Learning Videos (JTM), pp. 172–179.
LCT-NLE-2014-MirandaIC #framework #information management: From Information Systems to e-Learning 3.0 Systems’s Critical Success Factors: A Framework Proposal (PM, PTI, CJC), pp. 180–191.
LCT-NLE-2014-MoissaCG #adaptation #behaviour #student #visualisation #web: A Web Analytics and Visualization Tool to Understand Students’ Behavior in an Adaptive E-Learning System (BM, LSdC, IG), pp. 312–321.
LCT-NLE-2014-MorGHH #assessment #design #learning #tool support: Designing Learning Tools: The Case of a Competence Assessment Tool (EM, AEGR, EH, MAH), pp. 83–94.
LCT-NLE-2014-MoriT #development #learning: Development of a Fieldwork Support System for Group Work in Project-Based Learning (MM, AT), pp. 429–440.
LCT-NLE-2014-Piki #collaboration #learning #process #question: Learner Engagement in Computer-Supported Collaborative Learning Activities: Natural or Nurtured? (AP), pp. 107–118.
LCT-NLE-2014-Said #sorting: Card Sorting Assessing User Attitude in E-Learning (GRES), pp. 261–272.
LCT-NLE-2014-TaraghiSES #classification #learning #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 #learning #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 #learning #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 #learning #multimodal #student: Measuring Students’ Flow Experience in a Multimodal Learning Environment: A Case Study (CV, AI, PZ), pp. 346–357.
LCT-NLE-2014-WangLC #learning #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 #learning #online #process: Enhancing Online Learning Activities for Groups in Flipped Classrooms (RB), pp. 269–276.
LCT-TRE-2014-BraunhoferEGR #learning #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 #learning #named: Mosca — A Case Study on Collaborative Work — Combining Dimensions while Learning (SC), pp. 388–396.
LCT-TRE-2014-EradzeL #design #interactive #learning: 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 #learning #simulation: An Approach to Holistic Development of Serious Games and Learning Simulations (ATH), pp. 42–49.
LCT-TRE-2014-HiramatsuIFS #development #learning #using: Development of the Learning System for Outdoor Study Using Zeigarnik Effect (YH, AI, MF, FS), pp. 127–137.
LCT-TRE-2014-IkedaS #learning: Dream Drill: A Bedtime Learning Application (AI, IS), pp. 138–145.
LCT-TRE-2014-IshikawaAKSTD #learning #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 #learning: Supporting Learning with 3D Interactive Applications in Early Years (ACM, MJMS, MLS, DCPL, MC), pp. 11–22.
LCT-TRE-2014-MartinWH #interactive #learning #mobile: Sensor Based Interaction Mechanisms in Mobile Learning (KUM, MW, WH), pp. 165–172.
LCT-TRE-2014-OliveiraM #learning #network #research: Digital Identity of Researchers and Their Personal Learning Network (NRO, LM), pp. 467–477.
LCT-TRE-2014-ShahoumianSZPH #education #learning #simulation: Blended Simulation Based Medical Education: A Complex Learning/Training Opportunity (AS, MS, MZ, GP, JH), pp. 478–485.
LCT-TRE-2014-ShimizuO #effectiveness #learning #question: Which Is More Effective for Learning German and Japanese Language, Paper or Digital? (RS, KO), pp. 309–318.
LCT-TRE-2014-SzklannyW #learning #prototype: Prototyping M-Learning Course on the Basis of Puzzle Learning Methodology (KS, MW), pp. 215–226.
LCT-TRE-2014-YamaguchiSYNSM #collaboration #detection #distance #learning: Posture and Face Detection with Dynamic Thumbnail Views for Collaborative Distance Learning (TY, HS, MY, YN, HS, TM), pp. 227–236.
AdaEurope-2014-Laine #lessons learnt: Lessons Learned and Easily Forgotten (RL), pp. 1–6.
HILT-2014-BarnesT #ada #design #lessons learnt: Ada 83 to Ada 2012: lessons learned over 30 years of language design (JB, STT), pp. 3–4.
ICEIS-v1-2014-ShakirIB #machine learning #topic: Machine Learning Techniques for Topic Spotting (NS, EI, ISB), pp. 450–455.
ICEIS-v2-2014-MahmoudBAG #approach #learning: A New Approach Based on Learning Services to Generate Appropriate Learning Paths (CBM, FB, MHA, FG), pp. 643–646.
ICEIS-v2-2014-OtonBGGB #learning #metadata #using: Description of Accessible Learning Resources by Using Metadata (SO, CB, EG, AGC, RB), pp. 620–626.
ICEIS-v2-2014-ZhengJL #hybrid #learning #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 #learning #process #student: The Response Systems in the Student’s Learning/Teaching Process — A Case Study in a Portuguese School (PA, MJF), pp. 79–86.
ICEIS-v3-2014-BalinaAMS #development #metamodelling: Meta Model of e-Learning Materials Development (SB, IA, IM, ES), pp. 150–155.
ICEIS-v3-2014-PaulinsBA #visualisation: e-Learning Material Presentation and Visualization Types and Schemes (NP, SB, IA), pp. 138–143.
CIKM-2014-DeBBGC #learning #linear: Learning a Linear Influence Model from Transient Opinion Dynamics (AD, SB, PB, NG, SC), pp. 401–410.
CIKM-2014-DeveaudAMO #learning #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 #learning #multi: Multi-task Sparse Structure Learning (ARG, PD, SC, VS, FJVZ, AB), pp. 451–460.
CIKM-2014-JinZXDLH #learning #multi: Multi-task Multi-view Learning for Heterogeneous Tasks (XJ, FZ, HX, CD, PL, QH), pp. 441–450.
CIKM-2014-MaoWHO #classification #learning #linear #multi: Nonlinear Classification via Linear SVMs and Multi-Task Learning (XM, OW, WH, PO), pp. 1955–1958.
CIKM-2014-PfeifferNB #learning #network #probability #using: Active Exploration in Networks: Using Probabilistic Relationships for Learning and Inference (JJPI, JN, PNB), pp. 639–648.
CIKM-2014-PimplikarGBP #learning: Learning to Propagate Rare Labels (RP, DG, DB, GRP), pp. 201–210.
CIKM-2014-ShiKBLH #learning #named #recommendation: CARS2: Learning Context-aware Representations for Context-aware Recommendations (YS, AK, LB, ML, AH), pp. 291–300.
CIKM-2014-VinzamuriLR #learning: Active Learning based Survival Regression for Censored Data (BV, YL, CKR), pp. 241–250.
CIKM-2014-WangMC #learning #parametricity: Structure Learning via Parameter Learning (WYW, KM, WWC), pp. 1199–1208.
CIKM-2014-WuHPZCZ #feature model #learning #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 #learning #multi: A Cross-modal Multi-task Learning Framework for Image Annotation (LX, PP, YL, SW), pp. 431–440.
CIKM-2014-YangTZ #learning #streaming: Active Learning for Streaming Networked Data (ZY, JT, YZ), pp. 1129–1138.
CIKM-2014-YaoRSLF #locality #probability: Exploring Tag-Free RFID-Based Passive Localization and Tracking via Learning-Based Probabilistic Approaches (LY, WR, QZS, XL, NJGF), pp. 1799–1802.
CIKM-2014-YuX #interactive #learning #network #predict #scalability #social: Learning Interactions for Social Prediction in Large-scale Networks (XY, JX), pp. 161–170.
CIKM-2014-ZhongPXYM #adaptation #collaboration #learning #recommendation: Adaptive Pairwise Preference Learning for Collaborative Recommendation with Implicit Feedbacks (HZ, WP, CX, ZY, ZM), pp. 1999–2002.
CIKM-2014-ZhuSY #information retrieval #learning #taxonomy: Cross-Modality Submodular Dictionary Learning for Information Retrieval (FZ, LS, MY), pp. 1479–1488.
ECIR-2014-BauerCRG #corpus #formal method #learning #web: Learning a Theory of Marriage (and Other Relations) from a Web Corpus (SB, SC, LR, TG), pp. 591–597.
ECIR-2014-BreussT #interactive #learning #recommendation #social #social media: Learning from User Interactions for Recommending Content in Social Media (MB, MT), pp. 598–604.
ECIR-2014-FiliceCCB #effectiveness #kernel #learning #online: Effective Kernelized Online Learning in Language Processing Tasks (SF, GC, DC, RB), pp. 347–358.
ECIR-2014-NainiA #feature model #learning #rank: Exploiting Result Diversification Methods for Feature Selection in Learning to Rank (KDN, ISA), pp. 455–461.
ECIR-2014-QiDCW #information management #learning: Deep Learning for Character-Based Information Extraction (YQ, SGD, RC, JW), pp. 668–674.
ICML-c1-2014-AroraBGM #bound #learning: Provable Bounds for Learning Some Deep Representations (SA, AB, RG, TM), pp. 584–592.
ICML-c1-2014-DenisGH #bound #learning #matrix: Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning (FD, MG, AH), pp. 449–457.
ICML-c1-2014-DickGS #learning #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 #learning: (Near) Dimension Independent Risk Bounds for Differentially Private Learning (PJ, AGT), pp. 476–484.
ICML-c1-2014-LacosteMLL #learning: Agnostic Bayesian Learning of Ensembles (AL, MM, FL, HL), pp. 611–619.
ICML-c1-2014-LajugieBA #clustering #learning #metric #problem: Large-Margin Metric Learning for Constrained Partitioning Problems (RL, FRB, SA), pp. 297–305.
ICML-c1-2014-LuoS #learning #online #towards: Towards Minimax Online Learning with Unknown Time Horizon (HL, RES), pp. 226–234.
ICML-c1-2014-MohriM #algorithm #learning #optimisation: Learning Theory and Algorithms for revenue optimization in second price auctions with reserve (MM, AMM), pp. 262–270.
ICML-c1-2014-RooshenasL #interactive #learning #network: Learning Sum-Product Networks with Direct and Indirect Variable Interactions (AR, DL), pp. 710–718.
ICML-c1-2014-ShalitC #coordination #learning #matrix #orthogonal: Coordinate-descent for learning orthogonal matrices through Givens rotations (US, GC), pp. 548–556.
ICML-c1-2014-ShiZ #learning #online: Online Bayesian Passive-Aggressive Learning (TS, JZ), pp. 378–386.
ICML-c1-2014-SolomonRGB #learning: Wasserstein Propagation for Semi-Supervised Learning (JS, RMR, LJG, AB), pp. 306–314.
ICML-c1-2014-TandonR #graph #learning: Learning Graphs with a Few Hubs (RT, PDR), pp. 602–610.
ICML-c1-2014-Yu0KD #learning #multi #scalability: Large-scale Multi-label Learning with Missing Labels (HFY, PJ, PK, ISD), pp. 593–601.
ICML-c2-2014-AffandiFAT #kernel #learning #parametricity #process: Learning the Parameters of Determinantal Point Process Kernels (RHA, EBF, RPA, BT), pp. 1224–1232.
ICML-c2-2014-AminHK #learning: Learning from Contagion (Without Timestamps) (KA, HH, MK), pp. 1845–1853.
ICML-c2-2014-AndoniPV0 #learning #network: Learning Polynomials with Neural Networks (AA, RP, GV, LZ), pp. 1908–1916.
ICML-c2-2014-AziziAG #composition #learning #network: Learning Modular Structures from Network Data and Node Variables (EA, EA, JEG), pp. 1440–1448.
ICML-c2-2014-BalleHP #comparison #empirical #learning #probability: Methods of Moments for Learning Stochastic Languages: Unified Presentation and Empirical Comparison (BB, WLH, JP), pp. 1386–1394.
ICML-c2-2014-Bou-AmmarERT #learning #multi #online #policy: Online Multi-Task Learning for Policy Gradient Methods (HBA, EE, PR, MET), pp. 1206–1214.
ICML-c2-2014-BrunskillL #learning: PAC-inspired Option Discovery in Lifelong Reinforcement Learning (EB, LL), pp. 316–324.
ICML-c2-2014-Chen0 #big data #learning #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: Learning the Irreducible Representations of Commutative Lie Groups (TC, MW), pp. 1755–1763.
ICML-c2-2014-DuLBS #information management #learning #network: Influence Function Learning in Information Diffusion Networks (ND, YL, MFB, LS), pp. 2016–2024.
ICML-c2-2014-FangCL #graph #learning: Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness Probabilistically (YF, KCCC, HWL), pp. 406–414.
ICML-c2-2014-GrandeWH #learning #performance #process: Sample Efficient Reinforcement Learning with Gaussian Processes (RCG, TJW, JPH), pp. 1332–1340.
ICML-c2-2014-HoangLJK #learning #process: Nonmyopic ϵ-Bayes-Optimal Active Learning of Gaussian Processes (TNH, BKHL, PJ, MSK), pp. 739–747.
ICML-c2-2014-HoulsbyHG #learning #matrix #robust: Cold-start Active Learning with Robust Ordinal Matrix Factorization (NH, JMHL, ZG), pp. 766–774.
ICML-c2-2014-HuS #machine learning #multi #predict: Multi-period Trading Prediction Markets with Connections to Machine Learning (JH, AJS), pp. 1773–1781.
ICML-c2-2014-JawanpuriaVN #feature model #kernel #learning #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 #learning #on the: On the convergence of no-regret learning in selfish routing (WK, BD, AMB), pp. 163–171.
ICML-c2-2014-LevineK #learning #network #optimisation #policy: Learning Complex Neural Network Policies with Trajectory Optimization (SL, VK), pp. 829–837.
ICML-c2-2014-LiG #classification #learning #representation #semantics: Latent Semantic Representation Learning for Scene Classification (XL, YG), pp. 532–540.
ICML-c2-2014-LimL #learning #metric #performance #ranking: Efficient Learning of Mahalanobis Metrics for Ranking (DL, GRGL), pp. 1980–1988.
ICML-c2-2014-LinK #constraints #learning #performance #representation: Stable and Efficient Representation Learning with Nonnegativity Constraints (THL, HTK), pp. 1323–1331.
ICML-c2-2014-LinYHY #distance #learning: Geodesic Distance Function Learning via Heat Flow on Vector Fields (BL, JY, XH, JY), pp. 145–153.
ICML-c2-2014-LiuD #learning #problem #set: Learnability of the Superset Label Learning Problem (LPL, TGD), pp. 1629–1637.
ICML-c2-2014-LiZ #higher-order #learning #problem: High Order Regularization for Semi-Supervised Learning of Structured Output Problems (YL, RSZ), pp. 1368–1376.
ICML-c2-2014-LiZ0 #learning #multi: Bayesian Max-margin Multi-Task Learning with Data Augmentation (CL, JZ, JC), pp. 415–423.
ICML-c2-2014-MengEH #learning #modelling #visual notation: Learning Latent Variable Gaussian Graphical Models (ZM, BE, AOHI), pp. 1269–1277.
ICML-c2-2014-MizrahiDF #learning #linear #markov #parallel #random: Linear and Parallel Learning of Markov Random Fields (YDM, MD, NdF), pp. 199–207.
ICML-c2-2014-MnihG #learning #network: Neural Variational Inference and Learning in Belief Networks (AM, KG), pp. 1791–1799.
ICML-c2-2014-NiuDPS #approximate #learning #multi: Transductive Learning with Multi-class Volume Approximation (GN, BD, MCdP, MS), pp. 1377–1385.
ICML-c2-2014-PandeyD #learning #network: Learning by Stretching Deep Networks (GP, AD), pp. 1719–1727.
ICML-c2-2014-PentinaL #bound #learning: A PAC-Bayesian bound for Lifelong Learning (AP, CHL), pp. 991–999.
ICML-c2-2014-QinLJ #learning #optimisation: Sparse Reinforcement Learning via Convex Optimization (ZQ, WL, FJ), pp. 424–432.
ICML-c2-2014-ReedSZL #interactive #learning: Learning to Disentangle Factors of Variation with Manifold Interaction (SR, KS, YZ, HL), pp. 1431–1439.
ICML-c2-2014-RippelGA #learning #order: Learning Ordered Representations with Nested Dropout (OR, MAG, RPA), pp. 1746–1754.
ICML-c2-2014-RodriguesPR #classification #learning #multi #process: Gaussian Process Classification and Active Learning with Multiple Annotators (FR, FCP, BR), pp. 433–441.
ICML-c2-2014-SantosZ #learning: Learning Character-level Representations for Part-of-Speech Tagging (CNdS, BZ), pp. 1818–1826.
ICML-c2-2014-SilvaKB #learning: Active Learning of Parameterized Skills (BCdS, GK, AGB), pp. 1737–1745.
ICML-c2-2014-SongGJMHD #learning #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 #learning #linear: Learning Mixtures of Linear Classifiers (YS, SI, AM), pp. 721–729.
ICML-c2-2014-SunM #geometry #learning #statistics: An Information Geometry of Statistical Manifold Learning (KS, SMM), pp. 1–9.
ICML-c2-2014-TrigeorgisBZS #learning: A Deep Semi-NMF Model for Learning Hidden Representations (GT, KB, SZ, BWS), pp. 1692–1700.
ICML-c2-2014-WangHS #learning: Active Transfer Learning under Model Shift (XW, TKH, JS), pp. 1305–1313.
ICML-c2-2014-WangNH #distance #learning #metric #robust: Robust Distance Metric Learning via Simultaneous L1-Norm Minimization and Maximization (HW, FN, HH), pp. 1836–1844.
ICML-c2-2014-WangSSMK #learning #metric: Two-Stage Metric Learning (JW, KS, FS, SMM, AK), pp. 370–378.
ICML-c2-2014-WenYG #learning #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 #learning #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 #learning #online: Online Learning and Detection with Part-Based, Circulant Structure (OA, KM), pp. 4229–4233.
ICPR-2014-Al-HalahRS #learning #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 #learning #video: Correntropy-Based Adaptive Learning to Support Video Surveillance Systems (AMÁM, SMG, GCD), pp. 2590–2595.
ICPR-2014-AodhaSBTGJ #interactive #machine learning: Putting the Scientist in the Loop — Accelerating Scientific Progress with Interactive Machine Learning (OMA, VS, GJB, MT, MAG, KEJ), pp. 9–17.
ICPR-2014-ArvanitopoulosBT #analysis #learning: Laplacian Support Vector Analysis for Subspace Discriminative Learning (NA, DB, AT), pp. 1609–1614.
ICPR-2014-BargiXP #adaptation #classification #infinity #learning #online #segmentation #streaming: An Infinite Adaptive Online Learning Model for Segmentation and Classification of Streaming Data (AB, RYDX, MP), pp. 3440–3445.
ICPR-2014-BayramogluKEANKH #approach #detection #image #machine learning #using: Detection of Tumor Cell Spheroids from Co-cultures Using Phase Contrast Images and Machine Learning Approach (NB, MK, LE, MA, MN, JK, JH), pp. 3345–3350.
ICPR-2014-BertonL #graph #learning: Graph Construction Based on Labeled Instances for Semi-supervised Learning (LB, AdAL), pp. 2477–2482.
ICPR-2014-BouillonA #classification #evolution #gesture #learning #online: Supervision Strategies for the Online Learning of an Evolving Classifier for Gesture Commands (MB, ÉA), pp. 2029–2034.
ICPR-2014-CaiTF #learning #recognition #taxonomy: Learning Pose Dictionary for Human Action Recognition (JxC, XT, GCF), pp. 381–386.
ICPR-2014-CaoHS #approach #classification #kernel #learning #multi: Optimization-Based Extreme Learning Machine with Multi-kernel Learning Approach for Classification (LlC, WbH, FS), pp. 3564–3569.
ICPR-2014-ChengZHT #learning #recognition: Semi-supervised Learning for RGB-D Object Recognition (YC, XZ, KH, TT), pp. 2377–2382.
ICPR-2014-ChenK14a #learning: Learning to Count with Back-propagated Information (KC, JKK), pp. 4672–4677.
ICPR-2014-ChenZW #identification #learning #metric: Relevance Metric Learning for Person Re-identification by Exploiting Global Similarities (JC, ZZ, YW), pp. 1657–1662.
ICPR-2014-CheplyginaSTPLB #classification #learning #multi: Classification of COPD with Multiple Instance Learning (VC, LS, DMJT, JJHP, ML, MdB), pp. 1508–1513.
ICPR-2014-CruzSC #on the: On Meta-learning for Dynamic Ensemble Selection (RMOC, RS, GDCC), pp. 1230–1235.
ICPR-2014-DengZS #learning #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 #learning #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 #learning: Cross Domain Shared Subspace Learning for Unsupervised Transfer Classification (ZF, ZZ), pp. 3927–3932.
ICPR-2014-FanSCD #framework #learning #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 #learning #representation: Representation Learning for Contextual Object and Region Detection in Remote Sensing (OF, GC, FTYV), pp. 3708–3713.
ICPR-2014-FornoniC #learning #naive bayes #recognition: Scene Recognition with Naive Bayes Non-linear Learning (MF, BC), pp. 3404–3409.
ICPR-2014-GanSZ #learning: An Extended Isomap for Manifold Topology Learning with SOINN Landmarks (QG, FS, JZ), pp. 1579–1584.
ICPR-2014-GeDGC #learning: Background Subtraction with Dynamic Noise Sampling and Complementary Learning (WG, YD, ZG, YC), pp. 2341–2346.
ICPR-2014-GengWX #adaptation #estimation #learning: Facial Age Estimation by Adaptive Label Distribution Learning (XG, QW, YX), pp. 4465–4470.
ICPR-2014-GienTCL #fuzzy #learning #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 #learning #multi: Multiple Kernel Learning Based Multi-view Spectral Clustering (DG, JZ, XL, YC, CZ), pp. 3774–3779.
ICPR-2014-HooKPC #comprehension #image #learning #random: Enhanced Random Forest with Image/Patch-Level Learning for Image Understanding (WLH, TKK, YP, CSC), pp. 3434–3439.
ICPR-2014-HouYW #adaptation #learning #recognition #self: Domain Adaptive Self-Taught Learning for Heterogeneous Face Recognition (CAH, MCY, YCFW), pp. 3068–3073.
ICPR-2014-HuDG #experience #learning #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 #learning #multi #video: Video Event Detection via Multi-modality Deep Learning (IHJ, DTL), pp. 666–671.
ICPR-2014-KhoshrouCT #learning #multi #video: Active Learning from Video Streams in a Multi-camera Scenario (SK, JSC, LFT), pp. 1248–1253.
ICPR-2014-KrauseGDLF #fine-grained #learning #recognition: Learning Features and Parts for Fine-Grained Recognition (JK, TG, JD, LJL, FFL), pp. 26–33.
ICPR-2014-KumarG #documentation #keyword #learning: Bayesian Active Learning for Keyword Spotting in Handwritten Documents (GK, VG), pp. 2041–2046.
ICPR-2014-LeiSLCXP #learning #metric #similarity: Humanoid Robot Imitation with Pose Similarity Metric Learning (JL, MS, ZNL, CC, XX, SP), pp. 4240–4245.
ICPR-2014-LiuL0L #classification #image #learning: 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 #learning: Automatic Image Attribute Selection for Zero-Shot Learning of Object Categories (LL, AW, SC, BCL), pp. 2619–2624.
ICPR-2014-LiuYHTH #learning #recognition #visual notation: Semi-supervised Learning for Cross-Device Visual Location Recognition (PL, PY, KH, TT, HWH), pp. 2873–2878.
ICPR-2014-LiuZC #identification #learning #metric #multi #parametricity: Parametric Local Multi-modal Metric Learning for Person Re-identification (KL, ZCZ, AC), pp. 2578–2583.
ICPR-2014-LuoJ #encoding #image #learning #retrieval #semantics: Learning Semantic Binary Codes by Encoding Attributes for Image Retrieval (JL, ZJ), pp. 279–284.
ICPR-2014-ManfrediGC #energy #graph #image #learning #segmentation: Learning Graph Cut Energy Functions for Image Segmentation (MM, CG, RC), pp. 960–965.
ICPR-2014-MarcaciniDHR #approach #clustering #documentation #learning #metric: Privileged Information for Hierarchical Document Clustering: A Metric Learning Approach (RMM, MAD, ERH, SOR), pp. 3636–3641.
ICPR-2014-MontagnerjH #machine learning: A Machine Learning Based Method for Staff Removal (IdSM, RHJ, NSTH), pp. 3162–3167.
ICPR-2014-NegrelPG #image #learning #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 #learning #linear #using: Feature Learning Using Bayesian Linear Regression Model (SN, QJ), pp. 1502–1507.
ICPR-2014-NieKZ #learning #recognition #using: Periocular Recognition Using Unsupervised Convolutional RBM Feature Learning (LN, AK, SZ), pp. 399–404.
ICPR-2014-NilufarP #detection #learning #programming: Learning to Detect Contours with Dynamic Programming Snakes (SN, TJP), pp. 984–989.
ICPR-2014-OHarneyMRCSCBF #kernel #learning #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 #learning #multi #performance: Multi-source Adaptive Learning for Fast Control of Prosthetics Hand (NP, TT, BC), pp. 2769–2774.
ICPR-2014-PengWQP #encoding #evaluation #learning #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 #learning: Semi-supervised Learning on Bi-relational Graph for Image Annotation (HDP, KHK, SC), pp. 2465–2470.
ICPR-2014-PillaiFR #classification #learning #multi: Learning of Multilabel Classifiers (IP, GF, FR), pp. 3452–3456.
ICPR-2014-RenYZH #classification #image #learning #nearest neighbour: Learning Convolutional Nonlinear Features for K Nearest Neighbor Image Classification (WR, YY, JZ, KH), pp. 4358–4363.
ICPR-2014-RiabchenkoKC #generative #learning #modelling: Learning Generative Models of Object Parts from a Few Positive Examples (ER, JKK, KC), pp. 2287–2292.
ICPR-2014-RozzaMP #graph #kernel #learning #novel: A Novel Graph-Based Fisher Kernel Method for Semi-supervised Learning (AR, MM, AP), pp. 3786–3791.
ICPR-2014-SaitoAFRSGC #learning #using: Active Semi-supervised Learning Using Optimum-Path Forest (PTMS, WPA, AXF, PJdR, CTNS, JFG, MHdC), pp. 3798–3803.
ICPR-2014-SatoKSK #classification #learning #multi: Learning Multiple Complex Features Based on Classification Results (YS, KK, YS, MK), pp. 3369–3373.
ICPR-2014-SavakisRP #difference #gesture #learning #using: Gesture Control Using Active Difference Signatures and Sparse Learning (AES, RR, RWP), pp. 3969–3974.
ICPR-2014-ShenHSGM #framework #interactive #learning: Interactive Framework for Insect Tracking with Active Learning (MS, WH, PS, CGG, DM), pp. 2733–2738.
ICPR-2014-StraehleKKH #learning #multi #random: Multiple Instance Learning with Response-Optimized Random Forests (CNS, MK, UK, FAH), pp. 3768–3773.
ICPR-2014-UmakanthanDFS #learning #multi #process #representation #taxonomy: Multiple Instance Dictionary Learning for Activity Representation (SU, SD, CF, SS), pp. 1377–1382.
ICPR-2014-VellankiDVP #learning #parametricity: Nonparametric Discovery of Learning Patterns and Autism Subgroups from Therapeutic Data (PV, TVD, SV, DQP), pp. 1828–1833.
ICPR-2014-WalhaDLGA #approach #image #learning #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 #learning #using: Learning with Hidden Information Using a Max-Margin Latent Variable Model (ZW, TG, QJ), pp. 1389–1394.
ICPR-2014-WangWH #framework #learning #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: Learning with Hidden Information (ZW, XW, QJ), pp. 238–243.
ICPR-2014-WangZWB #learning #modelling: Learning Room Occupancy Patterns from Sparsely Recovered Light Transport Models (QW, XZ, MW, KLB), pp. 1987–1992.
ICPR-2014-WanHA #image #learning #recognition: Indoor Scene Recognition from RGB-D Images by Learning Scene Bases (SW, CH, JKA), pp. 3416–3421.
ICPR-2014-WatanabeW #analysis #component #distance #learning #metric #performance: Logistic Component Analysis for Fast Distance Metric Learning (KW, TW), pp. 1278–1282.
ICPR-2014-WuHYWT #image #network #segmentation: Early Hierarchical Contexts Learned by Convolutional Networks for Image Segmentation (ZW, YH, YY, LW, TT), pp. 1538–1543.
ICPR-2014-WuJ #detection #learning: Learning the Deep Features for Eye Detection in Uncontrolled Conditions (YW, QJ), pp. 455–459.
ICPR-2014-WuLWHJ #learning #multi: Multi-label Learning with Missing Labels (BW, ZL, SW, BGH, QJ), pp. 1964–1968.
ICPR-2014-WuS #learning #multi #recognition: Regularized Multi-view Multi-metric Learning for Action Recognition (XW, SKS), pp. 471–476.
ICPR-2014-WuTS #3d #learning #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 #learning: Incremental Learning with Support Vector Data Description (WX, SU, SK, MG), pp. 3904–3909.
ICPR-2014-XuS #learning #network #using: Bayesian Network Structure Learning Using Causality (ZX, SNS), pp. 3546–3551.
ICPR-2014-YangN #integration #learning #multi: Semi-supervised Learning of Geospatial Objects through Multi-modal Data Integration (YY, SN), pp. 4062–4067.
ICPR-2014-YangXWL #learning #realtime: Real-Time Tracking via Deformable Structure Regression Learning (XY, QX, SW, PL), pp. 2179–2184.
ICPR-2014-YangYH #learning: Diversity-Based Ensemble with Sample Weight Learning (CY, XCY, HWH), pp. 1236–1241.
ICPR-2014-YanSRLS #classification #interactive #learning #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 #learning #metric: Deep Metric Learning for Person Re-identification (DY, ZL, SL, SZL), pp. 34–39.
ICPR-2014-YinYPH #case study #classification #learning: Shallow Classification or Deep Learning: An Experimental Study (XCY, CY, WYP, HWH), pp. 1904–1909.
ICPR-2014-YooJKC #learning #optimisation: Transfer Learning of Motion Patterns in Traffic Scene via Convex Optimization (YJY, HJ, SWK, JYC), pp. 4158–4163.
ICPR-2014-ZenRS #distance #learning #matrix #metric: Simultaneous Ground Metric Learning and Matrix Factorization with Earth Mover’s Distance (GZ, ER, NS), pp. 3690–3695.
ICPR-2014-ZhangM14a #detection #learning #multi: Simultaneous Detection of Multiple Facial Action Units via Hierarchical Task Structure Learning (XZ, MHM), pp. 1863–1868.
ICPR-2014-ZhangQWL #classification #learning #online: Object Classification in Traffic Scene Surveillance Based on Online Semi-supervised Active Learning (ZZ, JQ, YW, ML), pp. 3086–3091.
ICPR-2014-ZhouIWBPKO #learning #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 #learning #recognition #taxonomy: Correspondence-Free Dictionary Learning for Cross-View Action Recognition (FZ, LS), pp. 4525–4530.
ICPR-2014-ZhuWYJ #learning #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 #learning #scalability: Scaling up deep learning (YB), p. 1966.
KDD-2014-BensonRS #learning #multi #network #scalability: Learning multifractal structure in large networks (ARB, CR, SS), pp. 1326–1335.
KDD-2014-DalessandroCRPWP #learning #online #scalability: Scalable hands-free transfer learning for online advertising (BD, DC, TR, CP, MHW, FJP), pp. 1573–1582.
KDD-2014-GaddeAO #graph #learning #using: Active semi-supervised learning using sampling theory for graph signals (AG, AA, AO), pp. 492–501.
KDD-2014-GohR #learning: Box drawings for learning with imbalanced data (STG, CR), pp. 333–342.
KDD-2014-GongZFY #learning #multi #performance: Efficient multi-task feature learning with calibration (PG, JZ, WF, JY), pp. 761–770.
KDD-2014-GrabockaSWS #learning: Learning time-series shapelets (JG, NS, MW, LST), pp. 392–401.
KDD-2014-Kushnir #adaptation #kernel #learning: Active-transductive learning with label-adapted kernels (DK), pp. 462–471.
KDD-2014-LanSB #analysis #learning: Time-varying learning and content analytics via sparse factor analysis (ASL, CS, RGB), pp. 452–461.
KDD-2014-LiangRR #learning #personalisation: Personalized search result diversification via structured learning (SL, ZR, MdR), pp. 751–760.
KDD-2014-Mullainathan #machine learning #question #social: Bugbears or legitimate threats?: (social) scientists’ criticisms of machine learning? (SM), p. 4.
KDD-2014-PerozziAS #learning #named #online #social: DeepWalk: online learning of social representations (BP, RAR, SS), pp. 701–710.
KDD-2014-PrabhuV #classification #learning #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 #learning #modelling: Factorized sparse learning models with interpretable high order feature interactions (SP, MRM, CCJK, RO), pp. 552–561.
KDD-2014-QianHJPZ #approach #distance #learning #metric #using: Distance metric learning using dropout: a structured regularization approach (QQ, JH, RJ, JP, SZ), pp. 323–332.
KDD-2014-Rudin #algorithm #machine learning: Algorithms for interpretable machine learning (CR), p. 1519.
KDD-2014-Salakhutdinov #learning: Deep learning (RS), p. 1973.
KDD-2014-ShaoAK #concept #data type #learning #prototype: Prototype-based learning on concept-drifting data streams (JS, ZA, SK), pp. 412–421.
KDD-2014-SrikantA #machine learning #programming #using: A system to grade computer programming skills using machine learning (SS, VA), pp. 1887–1896.
KDD-2014-TayebiEGB #embedded #learning #predict #using: Spatially embedded co-offence prediction using supervised learning (MAT, ME, UG, PLB), pp. 1789–1798.
KDD-2014-VasishtDVK #classification #learning #multi: Active learning for sparse bayesian multilabel classification (DV, ACD, MV, AK), pp. 472–481.
KDD-2014-WangNH #adaptation #induction #learning #scalability: Large-scale adaptive semi-supervised learning via unified inductive and transductive model (DW, FN, HH), pp. 482–491.
KDD-2014-WangSE #collaboration #learning #permutation: Active collaborative permutation learning (JW, NS, JE), pp. 502–511.
KDD-2014-WangSW #learning #modelling: Unsupervised learning of disease progression models (XW, DS, FW), pp. 85–94.
KDD-2014-XuL #behaviour #learning #problem: Product selection problem: improve market share by learning consumer behavior (SX, JCSL), pp. 851–860.
KDD-2014-YangH #learning #parametricity: Learning with dual heterogeneity: a nonparametric bayes model (HY, JH), pp. 582–590.
KDD-2014-ZhangTMF #learning #network: Supervised deep learning with auxiliary networks (JZ, GT, YM, WF), pp. 353–361.
KDD-2014-ZhouC #adaptation #documentation #learning #rank: Unifying learning to rank and domain adaptation: enabling cross-task document scoring (MZ, KCCC), pp. 781–790.
KDIR-2014-Bleiweiss #execution #machine learning #using: SoC Processor Discovery for Program Execution Matching Using Unsupervised Machine Learning (AB), pp. 192–201.
KDIR-2014-DistanteCVL #learning #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 #learning #word: Learning Good Opinions from Just Two Words Is Not Bad (DAS, VVI, ACC, MD, RP), pp. 233–241.
KEOD-2014-KarkalasS #concept #learning #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 #learning: Supporting Organizational Learning with Collaborative Annotation (AA, MHA, CM), pp. 237–244.
KMIS-2014-BartuskovaK #information management #learning: Knowledge Management and Sharing in E-Learning — Hierarchical System for Managing Learning Resources (AB, OK), pp. 179–185.
KMIS-2014-HisakaneOSK #multi: A Tutoring Rule Selection Method for Case-based e-Learning by Multi-class Support Vector Machine (DH, MO, MS, NK), pp. 119–125.
KMIS-2014-HisakaneS #learning #visualisation: A Visualization System of Discussion Structure in Case Method Learning (DH, MS), pp. 126–132.
KMIS-2014-SmirnovS #implementation #information management #lessons learnt: Role-Driven Knowledge Management Implementation — Lessons Learned (AVS, NS), pp. 36–43.
KR-2014-KonevLOW #learning #lightweight #logic #ontology: Exact Learning of Lightweight Description Logic Ontologies (BK, CL, AO, FW).
KR-2014-Michael #learning #predict: Simultaneous Learning and Prediction (LM).
MLDM-2014-AlbarrakCZ #classification #image #taxonomy: Dictionary Learning-Based Volumetric Image Classification for the Diagnosis of Age-Related Macular Degeneration (AA, FC, YZ), pp. 272–284.
MLDM-2014-BugaychenkoZ #diagrams #learning #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 #learning #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 #learning #mining: Manifold Learning in Data Mining Tasks (APK, AVB), pp. 119–133.
MLDM-2014-NeumannHRL #case study #experience #learning: A Robot Waiter Learning from Experiences (BN, LH, PR, JL), pp. 285–299.
MLDM-2014-SandovalH #learning #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 #learning #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 #learning #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 #learning #recommendation: Item cold-start recommendations: learning local collective embeddings (MS, AM), pp. 89–96.
SEKE-2014-GaoKN #estimation #learning #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 #learning #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 #learning #programming #semantics #source code: A Semantic Analyzer for Simple Games Source Codes to Programming Learning (ECOdS, GBB, VHVdS, EC), pp. 522–527.
SEKE-2014-SinghS #machine learning #requirements #using: Software Requirement Prioritization using Machine Learning (DS, AS), pp. 701–704.
SIGIR-2014-CanCM #feedback #modelling #ranking: Incorporating query-specific feedback into learning-to-rank models (EFC, WBC, RM), pp. 1035–1038.
SIGIR-2014-CormackG #bibliography #evaluation #protocol: Evaluation of machine-learning protocols for technology-assisted review in electronic discovery (GVC, MRG), pp. 153–162.
SIGIR-2014-CostaCS #learning #modelling #ranking: Learning temporal-dependent ranking models (MC, FMC, MJS), pp. 757–766.
SIGIR-2014-EfronWS #learning #query: Learning sufficient queries for entity filtering (ME, CW, GS), pp. 1091–1094.
SIGIR-2014-FangWYZ #information retrieval #learning #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 #learning #query: Learning user reformulation behavior for query auto-completion (JYJ, YYK, PYC, PJC), pp. 445–454.
SIGIR-2014-LengCL #image #learning #random #retrieval #scalability: Random subspace for binary codes learning in large scale image retrieval (CL, JC, HL), pp. 1031–1034.
SIGIR-2014-LiuL #learning #probability #segmentation #word: Probabilistic ensemble learning for vietnamese word segmentation (WL, LL), pp. 931–934.
SIGIR-2014-NiuLGCG #data analysis #learning #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 #learning: Click-through-based cross-view learning for image search (YP, TY, TM, HL, CWN, YR), pp. 717–726.
SIGIR-2014-QiuCYLL #learning #personalisation #ranking: Item group based pairwise preference learning for personalized ranking (SQ, JC, TY, CL, HL), pp. 1219–1222.
SIGIR-2014-SokolovHR #learning #query: Learning to translate queries for CLIR (AS, FH, SR), pp. 1179–1182.
SIGIR-2014-SpinaGA #detection #learning #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 #learning #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: 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 #learning #personalisation: Learning to personalize trending image search suggestion (CCW, TM, WHH, YR), pp. 727–736.
SIGIR-2014-YuWZTSZ #learning #rank: Hashing with List-Wise learning to rank (ZY, FW, YZ, ST, JS, YZ), pp. 999–1002.
SIGIR-2014-ZhuLGCN #learning: Learning for search result diversification (YZ, YL, JG, XC, SN), pp. 293–302.
SIGIR-2014-ZhuNG #adaptation #learning #random #social: An adaptive teleportation random walk model for learning social tag relevance (XZ, WN, MG), pp. 223–232.
MoDELS-2014-BakiSCMF #learning #model transformation: Learning Implicit and Explicit Control in Model Transformations by Example (IB, HAS, QC, PM, MF), pp. 636–652.
MoDELS-2014-RabiserVGDSL #case study #experience #lessons learnt #modelling #multi: Supporting Multiplicity and Hierarchy in Model-Based Configuration: Experiences and Lessons Learned (RR, MV, PG, DD, HS, ML), pp. 320–336.
MoDELS-2014-BakiSCMF #learning #model transformation: Learning Implicit and Explicit Control in Model Transformations by Example (IB, HAS, QC, PM, MF), pp. 636–652.
MoDELS-2014-RabiserVGDSL #case study #experience #lessons learnt #modelling #multi: Supporting Multiplicity and Hierarchy in Model-Based Configuration: Experiences and Lessons Learned (RR, MV, PG, DD, HS, ML), pp. 320–336.
RE-2014-MaalejG #lessons learnt: Capturing and sharing domain knowledge with business rules lessons learned from a global software vendor (WM, SG), pp. 364–373.
SAC-2014-ChallcoI #authoring #design #learning #personalisation #towards: Towards a learning design authoring tool that generates personalized units of learning for CSCL (GCC, SI), pp. 778–780.
SAC-2014-DhanjalC #learning #network: Learning reputation in an authorship network (CD, SC), pp. 1724–1726.
SAC-2014-LiWL #learning #mobile #online #recognition: Online learning with mobile sensor data for user recognition (HGL, XW, ZL), pp. 64–70.
SAC-2014-PaivaBSBS #case study #lessons learnt #online: Lessons learned from an online open course: a Brazilian case study (ROAP, DB, JS, IIB, APdS), pp. 229–234.
SAC-2014-PessinOUWMV #evolution #learning #network #self: Self-localisation in indoor environments combining learning and evolution with wireless networks (GP, FSO, JU, DFW, RCM, PAV), pp. 661–666.
FSE-2014-AllamanisBBS #learning: Learning natural coding conventions (MA, ETB, CB, CAS), pp. 281–293.
FSE-2014-Joseph #framework #interactive #machine learning: Software programmer management: a machine learning and human computer interaction framework for optimal task assignment (HRJ), pp. 826–828.
FSE-2014-YeBL #debugging #learning #rank #using: Learning to rank relevant files for bug reports using domain knowledge (XY, RCB, CL), pp. 689–699.
ICSE-2014-HeWYZ #learning #reasoning: Symbolic assume-guarantee reasoning through BDD learning (FH, BYW, LY, LZ), pp. 1071–1082.
ICSE-2014-JingYZWL #fault #learning #predict #taxonomy: Dictionary learning based software defect prediction (XYJ, SY, ZWZ, SSW, JL), pp. 414–423.
ICSE-2014-LeeJP #behaviour #detection #machine learning #memory management #modelling #using: Detecting memory leaks through introspective dynamic behavior modelling using machine learning (SL, CJ, SP), pp. 814–824.
ICSE-2014-Monperrus #automation #bibliography #evaluation #generative #problem: A critical review of “automatic patch generation learned from human-written patches”: essay on the problem statement and the evaluation of automatic software repair (MM), pp. 234–242.
ASPLOS-2014-ChenDSWWCT #named #ubiquitous: DianNao: a small-footprint high-throughput accelerator for ubiquitous machine-learning (TC, ZD, NS, JW, CW, YC, OT), pp. 269–284.
HPCA-2014-WonCGHS #learning #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 #learning #performance #scalability: Project Adam: Building an Efficient and Scalable Deep Learning Training System (TMC, YS, JA, KK), pp. 571–582.
OSDI-2014-LiAPSAJLSS #distributed #machine learning #parametricity #scalability: Scaling Distributed Machine Learning with the Parameter Server (ML, DGA, JWP, AJS, AA, VJ, JL, EJS, BYS), pp. 583–598.
CAV-2014-0001LMN #framework #invariant #learning #named #robust: ICE: A Robust Framework for Learning Invariants (PG, CL, PM, DN), pp. 69–87.
CAV-2014-HeizmannHP #analysis #learning #source code #termination: Termination Analysis by Learning Terminating Programs (MH, JH, AP), pp. 797–813.
SMT-2014-KorovinKS #learning #towards: Towards Conflict-Driven Learning for Virtual Substitution (KK, MK, TS), p. 71.
ASE-2013-DietrichCS #effectiveness #learning #query #requirements #retrieval: Learning effective query transformations for enhanced requirements trace retrieval (TD, JCH, YS), pp. 586–591.
ASE-2013-GuoCASW #approach #learning #performance #predict #statistics #variability: Variability-aware performance prediction: A statistical learning approach (JG, KC, SA, NS, AW), pp. 301–311.
ASE-2013-Xiao0LLS #learning #named #type system: TzuYu: Learning stateful typestates (HX, JS, YL, SWL, CS), pp. 432–442.
CASE-2013-LiX #adaptation #learning: Off-line learning based adaptive dispatching rule for semiconductor wafer fabrication facility (LL, HX), pp. 1028–1033.
CASE-2013-OFlahertyE #bound #learning #sequence: Learning to locomote: Action sequences and switching boundaries (RO, ME), pp. 7–12.
CASE-2013-SharabianiDBCND #machine learning #predict: Machine learning based prediction of warfarin optimal dosing for African American patients (AS, HD, AB, LC, EN, KD), pp. 623–628.
DAC-2013-LiuC #on the #synthesis: On learning-based methods for design-space exploration with high-level synthesis (HYL, LPC), p. 7.
DAC-2013-YuLJC #classification #detection #feature model #using: Machine-learning-based hotspot detection using topological classification and critical feature extraction (YTY, GHL, IHRJ, CC), p. 6.
DATE-2013-DeOrioLBB #debugging #detection #machine learning: Machine learning-based anomaly detection for post-silicon bug diagnosis (AD, QL, MB, VB), pp. 491–496.
DATE-2013-QianJBTMM #analysis #named #performance #using: SVR-NoC: a performance analysis tool for network-on-chips using learning-based support vector regression model (ZQ, DCJ, PB, CYT, DM, RM), pp. 354–357.
DocEng-2013-DoTT #documentation #taxonomy #using: Document noise removal using sparse representations over learned dictionary (THD, ST, ORT), pp. 161–168.
DocEng-2013-Esposito #documentation #machine learning: Symbolic machine learning methods for historical document processing (FE), pp. 1–2.
ICDAR-2013-AgarwalGC #learning: Greedy Search for Active Learning of OCR (AA, RG, SC), pp. 837–841.
ICDAR-2013-BougueliaBB #approach #classification #documentation #learning: A Stream-Based Semi-supervised Active Learning Approach for Document Classification (MRB, YB, AB), pp. 611–615.
ICDAR-2013-BouillonLAR #gesture #learning #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 #learning #using: Learning to Detect Tables in Scanned Document Images Using Line Information (TK, PB, SA, CC, TP), pp. 1185–1189.
ICDAR-2013-LvHWL #online #realtime #recognition #segmentation: Learning-Based Candidate Segmentation Scoring for Real-Time Recognition of Online Overlaid Chinese Handwriting (YFL, LLH, DHW, CLL), pp. 74–78.
ICDAR-2013-NguyenCBO #image #interactive #learning: Interactive Knowledge Learning for Ancient Images (NVN, MC, AB, JMO), pp. 300–304.
ICDAR-2013-PuriST #learning #network: Bayesian Network Structure Learning and Inference Methods for Handwriting (MP, SNS, YT), pp. 1320–1324.
ICDAR-2013-SchambachR #learning #network #sequence: Stabilize Sequence Learning with Recurrent Neural Networks by Forced Alignment (MPS, SFR), pp. 1270–1274.
ICDAR-2013-SuL #learning #recognition: Discriminative Weighting and Subspace Learning for Ensemble Symbol Recognition (FS, TL), pp. 1088–1092.
ICDAR-2013-SuTLDT #classification #documentation #image #learning #representation: Self Learning Classification for Degraded Document Images by Sparse Representation (BS, ST, SL, TAD, CLT), pp. 155–159.
ICDAR-2013-TeradaHFU #detection #on the: On the Possibility of Structure Learning-Based Scene Character Detector (YT, RH, YF, SU), pp. 472–476.
ICDAR-2013-TuarobBMG #automation #detection #documentation #machine learning #pseudo #using: Automatic Detection of Pseudocodes in Scholarly Documents Using Machine Learning (ST, SB, PM, CLG), pp. 738–742.
ICDAR-2013-WalhaDLGA #clustering #image #multi: Multiple Learned Dictionaries Based Clustered Sparse Coding for the Super-Resolution of Single Text Image (RW, FD, FL, CG, AMA), pp. 484–488.
ICDAR-2013-ZhouYL #learning #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 #learning #recognition: Sub-structure Learning Based Handwritten Chinese Text Recognition (YZ, JS, SN), pp. 295–299.
PODS-2013-AbouziedAPHS #learning #quantifier #query #verification: Learning and verifying quantified boolean queries by example (AA, DA, CHP, JMH, AS), pp. 49–60.
SIGMOD-2013-CondieMPW #big data #machine learning: Machine learning for big data (TC, PM, NP, MW), pp. 939–942.
SIGMOD-2013-MullerKLM #named #what: WOW: what the world of (data) warehousing can learn from the World of Warcraft (RM, TK, GML, JM), pp. 961–964.
VLDB-2013-BergamaschiGILV #data-driven #database #keyword #machine learning #named #relational #semantics: QUEST: A Keyword Search System for Relational Data based on Semantic and Machine Learning Techniques (SB, FG, MI, RTL, YV), pp. 1222–1225.
VLDB-2013-BrunatoB #learning #optimisation: Learning and Intelligent Optimization (LION): One Ring to Rule Them All (MB, RB), pp. 1176–1177.
VLDB-2013-Hoppe #automation #big data #learning #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 #learning #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 #learning #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 #learning #towards: Toward infusing modular and reflective design learning throughout the curriculum (JCG), pp. 274–278.
CSEET-2013-RibaudS #cost analysis #information management #learning #problem: The cost of problem-based learning: An example in information systems engineering (VR, PS), pp. 259–263.
CSEET-2013-StejskalS #learning #testing: Test-driven learning in high school computer science (RS, HPS), pp. 289–293.
CSEET-2013-Vallino #question #re-engineering #student #what: What should students learn in their first (and often only) software engineering course? (JV), pp. 335–337.
ITiCSE-2013-Alshaigy #development #education #interactive #learning #programming language #python: Development of an interactive learning tool to teach python programming language (BA), p. 344.
ITiCSE-2013-BeltranGP #architecture #concept #in the cloud #using: Using CloudSim to learn cloud computing architecture/system concepts in a graduate course (MB, AG, MP), pp. 82–87.
ITiCSE-2013-CalvoGII #content management #evaluation #heuristic #learning: 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 #learning: A pilot project on non-conventional learning (SF, AC, LSB), p. 346.
ITiCSE-2013-German #learning: Jump-starting team-based learning in the computer science classroom (DAG), p. 323.
ITiCSE-2013-GorlatovaSKKZ #learning #research #scalability: Project-based learning within a large-scale interdisciplinary research effort (MG, JS, PRK, IK, GZ), pp. 207–212.
ITiCSE-2013-HawthorneC #learning #source code: ACM core IT learning outcomes for associate-degree programs (EKH, RDC), p. 357.
ITiCSE-2013-JalilPWL #design #interactive #learning #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: Learning elsewhere: tales from an extracurricular game development competition (CJ, AC, SH), pp. 70–75.
ITiCSE-2013-MedinaPGR #data mining #education #learning #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 #learning #student #using: Using the arduino platform to enhance student learning experiences (PM, IR), p. 338.
ITiCSE-2013-Paule-RuizGPG #evaluation #framework #interactive #learning: Voice interactive learning: a framework and evaluation (MPPR, VMÁG, JRPP, MRG), pp. 34–39.
ITiCSE-2013-QianYGBT #authentication #learning #mobile #network #security: Mobile device based authentic learning for computer network and security (KQ, MY, MG, PB, LT), p. 335.
ITiCSE-2013-ReedZ #framework #learning: A hierarchical framework for mapping and quantitatively assessing program and learning outcomes (JR, HZ), pp. 52–57.
ITiCSE-2013-RowanD #bibliography #learning #mobile #using: A systematic literature review on using mobile computing as a learning intervention (MR, JD), p. 339.
ITiCSE-2013-Sanchez-Nielsen #learning #multi #student: Producing multimedia pills to stimulate student learning and engagement (ESN), pp. 165–170.
ITiCSE-2013-ScottG #learning #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-ShiQC #adaptation #design #personalisation #social: Designing social personalized adaptive e-learning (LS, DAQ, AIC), p. 341.
ITiCSE-2013-VihavainenVLP #learning #student #using: Scaffolding students’ learning using test my code (AV, TV, ML, MP), pp. 117–122.
ITiCSE-2013-Wildsmith #learning #named: Kinetic: a learning environment within business (CW), p. 3.
TACAS-2013-ChenW #algorithm #learning #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 #learning #memory management: Identifying Dynamic Data Structures by Learning Evolving Patterns in Memory (DHW, GL), pp. 354–369.
CSMR-2013-MinelliL #lessons learnt #mobile: Software Analytics for Mobile Applications-Insights & Lessons Learned (RM, ML), pp. 144–153.
CSMR-2013-XiaLWYLS #algorithm #case study #comparative #debugging #learning #predict: A Comparative Study of Supervised Learning Algorithms for Re-opened Bug Prediction (XX, DL, XW, XY, SL, JS), pp. 331–334.
ICSM-2013-FontanaZMM #approach #detection #machine learning #smell #towards: Code Smell Detection: Towards a Machine Learning-Based Approach (FAF, MZ, AM, MM), pp. 396–399.
ICSM-2013-OsmanCP #algorithm #analysis #diagrams #machine learning: An Analysis of Machine Learning Algorithms for Condensing Reverse Engineered Class Diagrams (MHO, MRVC, PvdP), pp. 140–149.
ICSM-2013-Perez #design #lessons learnt #refactoring #smell #summary: Refactoring Planning for Design Smell Correction: Summary, Opportunities and Lessons Learned (JP), pp. 572–577.
ICSM-2013-SemenenkoDS #image #machine learning #named #testing: Browserbite: Accurate Cross-Browser Testing via Machine Learning over Image Features (NS, MD, TS), pp. 528–531.
ICSM-2013-SiebraMSS #framework: The Adventure of Developing a Software Application on a Pre-release Platform: Features and Learned Lessons (CdS, AM, FQBdS, ALMS), pp. 556–559.
ICSM-2013-SorOTS #approach #detection #machine learning #memory management #statistics #using: Improving Statistical Approach for Memory Leak Detection Using Machine Learning (VS, PO, TT, SNS), pp. 544–547.
WCRE-2013-KhadkaSJHH #challenge #legacy #lessons learnt #migration #scalability: Migrating a large scale legacy application to SOA: Challenges and lessons learned (RK, AS, SJ, JH, GPH), pp. 425–432.
WCRE-2013-MontandonBFV #api #framework #lessons learnt: Documenting APIs with examples: Lessons learned with the APIMiner platform (JEM, HB, DF, MTV), pp. 401–408.
SAS-2013-0001GHAN #concept #geometry #learning #verification: Verification as Learning Geometric Concepts (RS, SG, BH, AA, AVN), pp. 388–411.
STOC-2013-BrakerskiLPRS #fault #learning: Classical hardness of learning with errors (ZB, AL, CP, OR, DS), pp. 575–584.
DLT-2013-BolligHLM #approach #automaton #learning: A Fresh Approach to Learning Register Automata (BB, PH, ML, BM), pp. 118–130.
ICALP-v2-2013-FuscoPP #learning #performance: Learning a Ring Cheaply and Fast (EGF, AP, RP), pp. 557–568.
LATA-2013-BjorklundFK #automaton #learning: MAT Learning of Universal Automata (JB, HF, AK), pp. 141–152.
GT-VMT-2013-AlshanqitiHK #graph transformation #learning: Learning Minimal and Maximal Rules from Observations of Graph Transformations (AMA, RH, TAK).
CHI-2013-AndersonB #gesture #learning #performance: Learning and performance with gesture guides (FA, WFB), pp. 1109–1118.
CHI-2013-EdgeCW #learning #named: SpatialEase: learning language through body motion (DE, KYC, MW), pp. 469–472.
CHI-2013-HarpsteadMA #data analysis #education #game studies #learning: In search of learning: facilitating data analysis in educational games (EH, BAM, VA), pp. 79–88.
CHI-2013-KharrufaBHLDO #deployment #lessons learnt #multi #scalability: Tables in the wild: lessons learned from a large-scale multi-tabletop deployment (AK, MB, PH, DL, PD, PO), pp. 1021–1030.
CHI-2013-RauARR #design #interactive #learning #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 #bibliography #learning #named: ARTFul: adaptive review technology for flipped learning (DS, BM), pp. 1001–1010.
CSCW-2013-KowY #community #learning: Media technologies and learning in the starcraft esport community (YMK, TY), pp. 387–398.
CSCW-2013-LinF #learning #network: Opportunities via extended networks for teens’ informal learning (PL, SDF), pp. 1341–1352.
DHM-HB-2013-NakamuraKOOHNAKMK #artificial reality #learning #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-BlanklBH #development #human-computer #lessons learnt: Lessons Learned from Projects in Japan and Korea Relevant for Intercultural HCI Development (MB, PB, RH), pp. 20–27.
DUXU-CXC-2013-ChoensawatSKH #education #learning: Desirability of a Teaching and Learning Tool for Thai Dance Body Motion (WC, KS, CK, KH), pp. 171–179.
DUXU-CXC-2013-LeraAVG #experience #user interface: Improving User Experience in e-Learning, the Case of the Open University of Catalonia (EdL, MA, LV, MG), pp. 180–188.
DUXU-CXC-2013-MarchettiB #game studies #learning: Setting Conditions for Learning: Mediated Play and Socio-material Dialogue (EM, EPB), pp. 238–246.
DUXU-CXC-2013-MarcusPL #design #learning #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 #learning #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-GencerBZV #framework #machine learning #mobile #using: A New Framework for Increasing User Engagement in Mobile Applications Using Machine Learning Techniques (MG, GB, ÖZ, TV), pp. 651–659.
DUXU-WM-2013-SasajimaNKHHNTTM #learning #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 #learning: 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 #learning #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 #learning: Evaluating Engagement Physiologically and Knowledge Retention Subjectively through Two Different Learning Techniques (MA, JIE, JEG, PM), pp. 335–342.
HCI-AS-2013-BitontoLRR #collaboration #process #recommendation: Recommendation of Collaborative Activities in E-learning Environments (PDB, ML, TR, VR), pp. 484–492.
HCI-AS-2013-CharoenpitO #biology #using: A New E-learning System Focusing on Emotional Aspect Using Biological Signals (SC, MO), pp. 343–350.
HCI-AS-2013-EskildsenR #challenge #integration #learning: Challenges for Contextualizing Language Learning — Supporting Cultural Integration (SE, MR), pp. 361–369.
HCI-AS-2013-FrajhofACLLM #collaboration #framework #learning #network #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 #learning #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 #learning #using: Refining Rules Learning Using Evolutionary PD (AFH, SB, CO, NLMN), pp. 376–385.
HCI-AS-2013-HuangC13a #education #interface #learning #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 #learning #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 #learning: Innovation in Learning — The Use of Avatar for Sign Language (TL, MSR, TAS, AB, ES, HSdO), pp. 428–433.
HCI-AS-2013-MajimaMSS #learning: 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-MarsicoST #framework #personalisation: A Framework to Support Social-Collaborative Personalized e-Learning (MDM, AS, MT), pp. 351–360.
HCI-AS-2013-MatsumotoAK #development #email #learning #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 #learning #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 #learning #mobile #performance: Mobile Inquiry-Based Learning — A Study of Collaborative Scaffolding and Performance (JN, TCP, KZ), pp. 464–473.
HCI-AS-2013-TakanoS #learning: Nature Sound Ensemble Learning in Narrative-Episode Creation with Pictures (KT, SS), pp. 493–502.
HCI-AS-2013-TogawaK #framework: Private Cloud Cooperation Framework for Reducing the Earthquake Damage on e-Learning Environment (ST, KK), pp. 503–510.
HCI-III-2013-StorzRMLE #analysis #detection #machine learning #visualisation #workflow: Annotate. Train. Evaluate. A Unified Tool for the Analysis and Visualization of Workflows in Machine Learning Applied to Object Detection (MS, MR, RM, HL, ME), pp. 196–205.
HCI-IMT-2013-DruryPKL #design #lessons learnt #visualisation: Decision Space Visualization: Lessons Learned and Design Principles (JLD, MSP, GLK, YL), pp. 658–667.
HCI-UC-2013-LinHW #learning #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 #learning: Agility Based on Stakeholder Interaction — Blending Organizational Learning with Interactive BPM (CS, WS, AF), pp. 456–465.
HIMI-D-2013-TakemoriYST #interactive #learning #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 #learning: Augmented Reality System for Measuring and Learning Tacit Artisan Skills (AH, HO, MM, EE, MS, MH), pp. 85–91.
HIMI-HSM-2013-SaitohI #detection #learning #using #visualisation: Visualization of Anomaly Data Using Peculiarity Detection on Learning Vector Quantization (FS, SI), pp. 181–188.
HIMI-LCCB-2013-Canter #hybrid #student: A Hybrid Model for an E-learning System Which Develops Metacognitive Skills at Students (MC), pp. 9–15.
HIMI-LCCB-2013-Frederick-RecascinoLDKL #case study #game studies #learning: 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 #learning #tool support: Psychophysiological Assessment Tools for Evaluation of Learning Technologies (RHH, NSL, HS), pp. 33–42.
HIMI-LCCB-2013-HayashiON #collaboration #interactive #learning: An Experimental Environment for Analyzing Collaborative Learning Interaction (YH, YO, YIN), pp. 43–52.
HIMI-LCCB-2013-KanamoriTA #development #learning #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 #learning #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-WatabeMH #process: Application to Help Learn the Process of Transforming Mathematical Expressions with a Focus on Study Logs (TW, YM, YH), pp. 157–164.
HIMI-LCCB-2013-YamamotoKYMH #learning #online #problem: Learning by Problem-Posing with Online Connected Media Tablets (SY, TK, YY, KM, TH), pp. 165–174.
HIMI-LCCB-2013-YuL #approach #feedback #mining: Exploring User Feedback of a E-Learning System: A Text Mining Approach (WBY, RL), pp. 182–191.
OCSC-2013-Eustace #learning #network: Building and Sustaining a Lifelong Adult Learning Network (KE), pp. 260–268.
OCSC-2013-StieglitzES #behaviour #education #learning #student: Influence of Monetary and Non-monetary Incentives on Students’ Behavior in Blended Learning Settings in Higher Education (SS, AE, MS), pp. 104–112.
VISSOFT-2013-Wijk #case study #experience #lessons learnt #visualisation: Keynote talk: Information visualization: Experiences and lessons learned (JJvW), p. 1.
EDOC-2013-Swenson #design #learning: Designing for an Innovative Learning Organization (KDS), pp. 209–213.
ICEIS-J-2013-KalsingITN13a #incremental #legacy #mining #modelling #process #using: Re-learning of Business Process Models from Legacy System Using Incremental Process Mining (ACK, CI, LHT, GSdN), pp. 314–330.
ICEIS-v2-2013-EomA #testing: Developing and Testing a Model to Understand Relationships between e-Learning Outcomes and Human Factors (SBE, NJA), pp. 361–370.
ICEIS-v2-2013-KalsingITN #incremental #learning #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-LiL #agile #network #object-oriented #predict #process #using: Predicting Fault-proneness of Object-Oriented System Developed with Agile Process using Learned Bayesian Network (LL, HL), pp. 5–16.
ICEIS-v2-2013-MoreiraF #learning #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 #learning #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 #learning #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 #learning #named #predict: LearNext: learning to predict tourists movements (RB, CIM, FMN, FS), pp. 751–756.
CIKM-2013-CeccarelliLOPT #learning #metric: Learning relatedness measures for entity linking (DC, CL, SO, RP, ST), pp. 139–148.
CIKM-2013-ChengCLWAC #data type #learning #multi: Feedback-driven multiclass active learning for data streams (YC, ZC, LL, JW, AA, ANC), pp. 1311–1320.
CIKM-2013-ChenW #classification #learning #scalability: Cost-sensitive learning for large-scale hierarchical classification (JC, DW), pp. 1351–1360.
CIKM-2013-FangZ #feature model #learning #multi: Discriminative feature selection for multi-view cross-domain learning (ZF, Z(Z), pp. 1321–1330.
CIKM-2013-Guestrin #machine learning #scalability #usability: Usability in machine learning at scale with graphlab (CG), pp. 5–6.
CIKM-2013-HashemiNB #approach #learning #network #retrieval #topic: Expertise retrieval in bibliographic network: a topic dominance learning approach (SHH, MN, HB), pp. 1117–1126.
CIKM-2013-KamathC #learning #predict #what: Spatio-temporal meme prediction: learning what hashtags will be popular where (KYK, JC), pp. 1341–1350.
ECIR-2013-DangBC #information retrieval #learning #rank: Two-Stage Learning to Rank for Information Retrieval (VD, MB, WBC), pp. 423–434.
ECIR-2013-JuMJ #classification #learning #rank: Learning to Rank from Structures in Hierarchical Text Classification (QJ, AM, RJ), pp. 183–194.
ECIR-2013-NguyenTT #classification #learning #rank #using: Folktale Classification Using Learning to Rank (DN, DT, MT), pp. 195–206.
ICML-c1-2013-0005LSL #feature model #learning #modelling #online: Online Feature Selection for Model-based Reinforcement Learning (TTN, ZL, TS, TYL), pp. 498–506.
ICML-c1-2013-AbernethyAKD #learning #problem #scalability: Large-Scale Bandit Problems and KWIK Learning (JA, KA, MK, MD), pp. 588–596.
ICML-c1-2013-AfkanpourGSB #algorithm #kernel #learning #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 #learning #linear #network: Learning Linear Bayesian Networks with Latent Variables (AA, DH, AJ, SK), pp. 249–257.
ICML-c1-2013-BalcanBEL #learning #performance: Efficient Semi-supervised and Active Learning of Disjunctions (NB, CB, SE, YL), pp. 633–641.
ICML-c1-2013-BootsG #approach #learning: A Spectral Learning Approach to Range-Only SLAM (BB, GJG), pp. 19–26.
ICML-c1-2013-ChenK #adaptation #learning #optimisation: Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization (YC, AK), pp. 160–168.
ICML-c1-2013-CotterSS #learning: Learning Optimally Sparse Support Vector Machines (AC, SSS, NS), pp. 266–274.
ICML-c1-2013-GiguereLMS #algorithm #approach #bound #learning #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: Learning an Internal Dynamics Model from Control Demonstration (MG, SC, BY), pp. 606–614.
ICML-c1-2013-GonenSS #approach #learning #performance: Efficient Active Learning of Halfspaces: an Aggressive Approach (AG, SS, SSS), pp. 480–488.
ICML-c1-2013-GongGS #adaptation #invariant #learning: 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 #learning: A Generalized Kernel Approach to Structured Output Learning (HK, MG, PP), pp. 471–479.
ICML-c1-2013-KarbasiSS #learning: Iterative Learning and Denoising in Convolutional Neural Associative Memories (AK, AHS, AS), pp. 445–453.
ICML-c1-2013-KumarB #bound #graph #learning: Convex Relaxations for Learning Bounded-Treewidth Decomposable Graphs (KSSK, FRB), pp. 525–533.
ICML-c1-2013-LiLSHD #generative #learning #using: Learning Hash Functions Using Column Generation (XL, GL, CS, AvdH, ARD), pp. 142–150.
ICML-c1-2013-LimLM #learning #metric #robust: Robust Structural Metric Learning (DL, GRGL, BM), pp. 615–623.
ICML-c1-2013-MaatenCTW #learning: Learning with Marginalized Corrupted Features (LvdM, MC, ST, KQW), pp. 410–418.
ICML-c1-2013-MaillardNOR #bound #learning #representation: Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning (OAM, PN, RO, DR), pp. 543–551.
ICML-c1-2013-MenonTGLK #framework #machine learning #programming: A Machine Learning Framework for Programming by Example (AKM, OT, SG, BWL, AK), pp. 187–195.
ICML-c1-2013-RuvoloE #algorithm #learning #named #performance: ELLA: An Efficient Lifelong Learning Algorithm (PR, EE), pp. 507–515.
ICML-c1-2013-ZuluagaSKP #learning #multi #optimisation: Active Learning for Multi-Objective Optimization (MZ, GS, AK, MP), pp. 462–470.
ICML-c2-2013-GaneshapillaiGL #learning: Learning Connections in Financial Time Series (GG, JVG, AL), pp. 109–117.
ICML-c2-2013-GolovinSMY #learning #ram #scalability: Large-Scale Learning with Less RAM via Randomization (DG, DS, HBM, MY), pp. 325–333.
ICML-c2-2013-KrummenacherOB #learning #multi: Ellipsoidal Multiple Instance Learning (GK, CSO, JMB), pp. 73–81.
ICML-c2-2013-MaurerPR #learning #multi: Sparse coding for multitask and transfer learning (AM, MP, BRP), pp. 343–351.
ICML-c2-2013-MeentBWGW #learning #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 #learning #multi: A unifying framework for vector-valued manifold regularization and multi-view learning (HQM, LB, VM), pp. 100–108.
ICML-c2-2013-RanganathWBX #adaptation #learning #probability: An Adaptive Learning Rate for Stochastic Variational Inference (RR, CW, DMB, EPX), pp. 298–306.
ICML-c2-2013-SohnZLL #learning: 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 #learning #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 #learning #multi: Thurstonian Boltzmann Machines: Learning from Multiple Inequalities (TT, DQP, SV), pp. 46–54.
ICML-c2-2013-YangH #classification #learning: Activized Learning with Uniform Classification Noise (LY, SH), pp. 370–378.
ICML-c3-2013-0002T #kernel #learning: Differentially Private Learning with Kernels (PJ, AT), pp. 118–126.
ICML-c3-2013-AlmingolML #behaviour #learning #multi: Learning Multiple Behaviors from Unlabeled Demonstrations in a Latent Controller Space (JA, LM, ML), pp. 136–144.
ICML-c3-2013-BalasubramanianYL #learning: Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations (KB, KY, GL), pp. 289–297.
ICML-c3-2013-BalcanBM #learning #ontology: Exploiting Ontology Structures and Unlabeled Data for Learning (NB, AB, YM), pp. 1112–1120.
ICML-c3-2013-BellemareVB #learning #recursion: Bayesian Learning of Recursively Factored Environments (MGB, JV, MB), pp. 1211–1219.
ICML-c3-2013-BrechtelGD #incremental #learning #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 #learning: Joint Transfer and Batch-mode Active Learning (RC, WF, ID, SP, JY), pp. 253–261.
ICML-c3-2013-Cheng #learning #similarity: Riemannian Similarity Learning (LC), pp. 540–548.
ICML-c3-2013-CoatesHWWCN #learning #off the shelf: Deep learning with COTS HPC systems (AC, BH, TW, DJW, BCC, AYN), pp. 1337–1345.
ICML-c3-2013-DalalyanHMS #learning #modelling #programming: Learning Heteroscedastic Models by Convex Programming under Group Sparsity (ASD, MH, KM, JS), pp. 379–387.
ICML-c3-2013-DimitrakakisT #learning: ABC Reinforcement Learning (CD, NT), pp. 684–692.
ICML-c3-2013-GensD #learning #network: Learning the Structure of Sum-Product Networks (RG, PMD), pp. 873–880.
ICML-c3-2013-GittensM #machine learning #scalability: Revisiting the Nystrom method for improved large-scale machine learning (AG, MWM), pp. 567–575.
ICML-c3-2013-GuptaPV #approach #learning #multi #parametricity: Factorial Multi-Task Learning : A Bayesian Nonparametric Approach (SKG, DQP, SV), pp. 657–665.
ICML-c3-2013-HockingRVB #detection #learning #using: Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression (TH, GR, JPV, FRB), pp. 172–180.
ICML-c3-2013-HoXV #learning #on the #taxonomy: On A Nonlinear Generalization of Sparse Coding and Dictionary Learning (JH, YX, BCV), pp. 1480–1488.
ICML-c3-2013-HuangS #learning #markov #modelling: Spectral Learning of Hidden Markov Models from Dynamic and Static Data (TKH, JGS), pp. 630–638.
ICML-c3-2013-JancsaryNR #learning #predict: Learning Convex QP Relaxations for Structured Prediction (JJ, SN, CR), pp. 915–923.
ICML-c3-2013-JoseGAV #kernel #learning #performance #predict: Local Deep Kernel Learning for Efficient Non-linear SVM Prediction (CJ, PG, PA, MV), pp. 486–494.
ICML-c3-2013-JoulaniGS #feedback #learning #online: Online Learning under Delayed Feedback (PJ, AG, CS), pp. 1453–1461.
ICML-c3-2013-JunZSR #learning: Learning from Human-Generated Lists (KSJ, X(Z, BS, TTR), pp. 181–189.
ICML-c3-2013-KarS0K #algorithm #learning #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 #learning #on the: On learning parametric-output HMMs (AK, BN, RW), pp. 702–710.
ICML-c3-2013-KoppulaS #detection #learning #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 #learning #parametricity #random: Parameter Learning and Convergent Inference for Dense Random Fields (PK, VK), pp. 513–521.
ICML-c3-2013-KuzborskijO #learning: Stability and Hypothesis Transfer Learning (IK, FO), pp. 942–950.
ICML-c3-2013-LattimoreHS #learning: The Sample-Complexity of General Reinforcement Learning (TL, MH, PS), pp. 28–36.
ICML-c3-2013-MalioutovV #learning: Exact Rule Learning via Boolean Compressed Sensing (DMM, KRV), pp. 765–773.
ICML-c3-2013-MemisevicE #invariant #learning #problem: Learning invariant features by harnessing the aperture problem (RM, GE), pp. 100–108.
ICML-c3-2013-NiuJDHS #approach #learning #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 #learning: Stable Coactive Learning via Perturbation (KR, TJ, PS, TS), pp. 837–845.
ICML-c3-2013-Romera-ParedesABP #learning #multi: Multilinear Multitask Learning (BRP, HA, NBB, MP), pp. 1444–1452.
ICML-c3-2013-RossZYDB #learning #policy #predict: Learning Policies for Contextual Submodular Prediction (SR, JZ, YY, DD, DB), pp. 1364–1372.
ICML-c3-2013-SchaulZL #learning: No more pesky learning rates (TS, SZ, YL), pp. 343–351.
ICML-c3-2013-SilverNBWM #concurrent #interactive #learning: Concurrent Reinforcement Learning from Customer Interactions (DS, LN, DB, SW, JM), pp. 924–932.
ICML-c3-2013-SimsekliCY #learning #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 #learning #named #probability: Coco-Q: Learning in Stochastic Games with Side Payments (ES, EH, MLL, AG), pp. 1471–1479.
ICML-c3-2013-SutskeverMDH #learning #on the: On the importance of initialization and momentum in deep learning (IS, JM, GED, GEH), pp. 1139–1147.
ICML-c3-2013-TarlowSCSZ #learning #probability: Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning (DT, KS, LC, IS, RSZ), pp. 199–207.
ICML-c3-2013-WangNH #learning #robust #self: Robust and Discriminative Self-Taught Learning (HW, FN, HH), pp. 298–306.
ICML-c3-2013-WangNH13a #clustering #learning #multi: Multi-View Clustering and Feature Learning via Structured Sparsity (HW, FN, HH), pp. 352–360.
ICML-c3-2013-WangWBLT #learning #multi #taxonomy: Max-Margin Multiple-Instance Dictionary Learning (XW, BW, XB, WL, ZT), pp. 846–854.
ICML-c3-2013-XuKHW #learning #representation: Anytime Representation Learning (ZEX, MJK, GH, KQW), pp. 1076–1084.
ICML-c3-2013-YangLZ #learning #matrix #multi: Multi-Task Learning with Gaussian Matrix Generalized Inverse Gaussian Model (MY, YL, ZZ), pp. 423–431.
ICML-c3-2013-YuLKJC #learning: ∝SVM for Learning with Label Proportions (FXY, DL, SK, TJ, SFC), pp. 504–512.
ICML-c3-2013-ZemelWSPD #learning: Learning Fair Representations (RSZ, YW, KS, TP, CD), pp. 325–333.
ICML-c3-2013-ZhangYJLH #bound #kernel #learning #online: Online Kernel Learning with a Near Optimal Sparsity Bound (LZ, JY, RJ, ML, XH), pp. 621–629.
ICML-c3-2013-ZhouZS #kernel #learning #multi #process: Learning Triggering Kernels for Multi-dimensional Hawkes Processes (KZ, HZ, LS), pp. 1301–1309.
ICML-c3-2013-ZweigW #learning: Hierarchical Regularization Cascade for Joint Learning (AZ, DW), pp. 37–45.
KDD-2013-BahadoriLX #learning #performance #probability #process: Fast structure learning in generalized stochastic processes with latent factors (MTB, YL, EPX), pp. 284–292.
KDD-2013-ChakrabartiH #learning #scalability #social: Speeding up large-scale learning with a social prior (DC, RH), pp. 650–658.
KDD-2013-ChenHKB #learning #named: DTW-D: time series semi-supervised learning from a single example (YC, BH, EJK, GEAPAB), pp. 383–391.
KDD-2013-DasMGW #learning: Learning to question: leveraging user preferences for shopping advice (MD, GDFM, AG, IW), pp. 203–211.
KDD-2013-FeiKSNMH #detection #learning: 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 #learning #multi: Multi-source deep learning for information trustworthiness estimation (LG, JG, XL, AZ), pp. 766–774.
KDD-2013-GilpinED #algorithm #framework #learning: Guided learning for role discovery (GLRD): framework, algorithms, and applications (SG, TER, IND), pp. 113–121.
KDD-2013-HaoCZ0RK #learning #towards: Towards never-ending learning from time series streams (YH, YC, JZ, BH, TR, EJK), pp. 874–882.
KDD-2013-Howard #learning: The business impact of deep learning (JH), p. 1135.
KDD-2013-KongY #automation #classification #distance #learning: Discriminant malware distance learning on structural information for automated malware classification (DK, GY), pp. 1357–1365.
KDD-2013-KutzkovBBG #learning #named: STRIP: stream learning of influence probabilities (KK, AB, FB, AG), pp. 275–283.
KDD-2013-LinWHY #information management #learning #modelling #social: Extracting social events for learning better information diffusion models (SL, FW, QH, PSY), pp. 365–373.
KDD-2013-LiuFYX #learning #recommendation: Learning geographical preferences for point-of-interest recommendation (BL, YF, ZY, HX), pp. 1043–1051.
KDD-2013-MorenoNK #graph #learning #modelling: Learning mixed kronecker product graph models with simulated method of moments (SM, JN, SK), pp. 1052–1060.
KDD-2013-SutherlandPS #learning #matrix #rank: Active learning and search on low-rank matrices (DJS, BP, JGS), pp. 212–220.
KDD-2013-TanXGW #learning #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 #learning #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 #learning #query: Querying discriminative and representative samples for batch mode active learning (ZW, JY), pp. 158–166.
KDD-2013-Wright #data analysis #learning #optimisation: Optimization in learning and data analysis (SJW), p. 3.
KDD-2013-XiangYFWTY #learning #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 #learning #multi #named: MI2LS: multi-instance learning from multiple informationsources (DZ, JH, RDL), pp. 149–157.
KDD-2013-ZhaoH #detection #learning #online: Cost-sensitive online active learning with application to malicious URL detection (PZ, SCHH), pp. 919–927.
KDD-2013-ZhaoYNG #framework #learning #twitter: A transfer learning based framework of crowd-selection on twitter (ZZ, DY, WN, SG), pp. 1514–1517.
KDIR-KMIS-2013-AtrashAM #enterprise #learning #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 #learning #recommendation #semantics: Semantic Collaborative Filtering for Learning Objects Recommendation (LB, ON), pp. 52–63.
KDIR-KMIS-2013-CastellanoS: Developing Innovative e-Learning Solutions (MC, FAS), pp. 484–489.
KDIR-KMIS-2013-Dessne #learning: Learning in an Organisation — Exploring the Nature of Relationships (KD), pp. 496–501.
KDIR-KMIS-2013-Eardley #information management #learning: 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 #learning #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 #learning: A Cognitive Reference based Model for Learning Compositional Hierarchies with Whole-composite Tags (ABS, AB, AW), pp. 119–127.
KEOD-2013-WohlgenanntBS #automation #evolution #learning #ontology #prototype: A Prototype for Automating Ontology Learning and Ontology Evolution (GW, SB, MS), pp. 407–412.
MLDM-2013-BouillonAA #evolution #fuzzy #gesture #learning #recognition: Decremental Learning of Evolving Fuzzy Inference Systems: Application to Handwritten Gesture Recognition (MB, ÉA, AA), pp. 115–129.
MLDM-2013-ElGibreenA #learning #multi #product line: Multi Model Transfer Learning with RULES Family (HE, MSA), pp. 42–56.
MLDM-2013-GopalakrishnaOLL #algorithm #machine learning #metric: Relevance as a Metric for Evaluating Machine Learning Algorithms (AKG, TO, AL, JJL), pp. 195–208.
MLDM-2013-KoharaS #learning #self: Typhoon Damage Scale Forecasting with Self-Organizing Maps Trained by Selective Presentation Learning (KK, IS), pp. 16–26.
MLDM-2013-MaziluCGRHT #detection #learning #predict: Feature Learning for Detection and Prediction of Freezing of Gait in Parkinson’s Disease (SM, AC, EG, DR, JMH, GT), pp. 144–158.
MLDM-2013-Suthaharan #big data #classification #network: A Single-Domain, Representation-Learning Model for Big Data Classification of Network Intrusion (SS), pp. 296–310.
RecSys-2013-HuY #learning #process #recommendation: Interview process learning for top-n recommendation (FH, YY), pp. 331–334.
RecSys-2013-KaratzoglouBS #learning #rank #recommendation: Learning to rank for recommender systems (AK, LB, YS), pp. 493–494.
RecSys-2013-KucharK #case study #learning #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 #learning #recommendation: Pairwise learning in recommendation: experiments with community recommendation on linkedin (AS, BY), pp. 193–200.
RecSys-2013-WestonYW #learning #rank #recommendation #statistics: Learning to rank recommendations with the k-order statistic loss (JW, HY, RJW), pp. 245–248.
SEKE-2013-BarbosaFNM #architecture #learning #towards: Towards the Establishment of a Reference Architecture for Developing Learning Environments (EFB, MLF, EYN, JCM), pp. 350–355.
SEKE-2013-CarrerasZO #machine learning: A Machine Learning Based File Archival Tool (S) (RC, DZ, JO), pp. 73–76.
SEKE-2013-HoritaHGB #development #maturity #quality: Maturity Model and Lesson Learned for improve the Quality of Organizational Knowledge and Human Resources Management in Software Development (S) (FEAH, MIH, FHG, RMdB), pp. 552–555.
SIGIR-2013-DalipGCC #case study #feedback #rank #stack overflow: Exploiting user feedback to learn to rank answers in q&a forums: a case study with stack overflow (DHD, MAG, MC, PC), pp. 543–552.
SIGIR-2013-LimsopathamMO #learning: Learning to combine representations for medical records search (NL, CM, IO), pp. 833–836.
SIGIR-2013-Moschitti #kernel #learning #rank #semantics: Kernel-based learning to rank with syntactic and semantic structures (AM), p. 1128.
SIGIR-2013-Shokouhi #learning #personalisation #query: Learning to personalize query auto-completion (MS), pp. 103–112.
SIGIR-2013-WangHWZ0M #learning #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 #learning #network #predict: Learning latent friendship propagation networks with interest awareness for link prediction (JZ, CW, PSY, JW), pp. 63–72.
ICMT-2013-FaunesSB #approach #model transformation: Genetic-Programming Approach to Learn Model Transformation Rules from Examples (MF, HAS, MB), pp. 17–32.
OOPSLA-2013-ChoiNS #android #approximate #learning #testing #user interface: Guided GUI testing of android apps with minimal restart and approximate learning (WC, GCN, KS), pp. 623–640.
POPL-2013-BotincanB #learning #specification: Sigma*: symbolic learning of input-output specifications (MB, DB), pp. 443–456.
POPL-2013-DSilvaHK #learning: Abstract conflict driven learning (VD, LH, DK), pp. 143–154.
RE-2013-ShiWL #evolution #learning #predict: Learning from evolution history to predict future requirement changes (LS, QW, ML), pp. 135–144.
RE-2013-SultanovH #learning #requirements: Application of reinforcement learning to requirements engineering: requirements tracing (HS, JHH), pp. 52–61.
REFSQ-2013-TjongB #ambiguity #design #lessons learnt #prototype #requirements #specification: The Design of SREE — A Prototype Potential Ambiguity Finder for Requirements Specifications and Lessons Learned (SFT, DMB), pp. 80–95.
SAC-2013-AkritidisB #algorithm #classification #machine learning #research: A supervised machine learning classification algorithm for research articles (LA, PB), pp. 115–120.
SAC-2013-BerralGT #automation #machine learning: Empowering automatic data-center management with machine learning (JLB, RG, JT), pp. 170–172.
SAC-2013-BlondelSU #classification #constraints #learning #using: Learning non-linear classifiers with a sparsity constraint using L1 regularization (MB, KS, KU), pp. 167–169.
SAC-2013-FilhoB #learning #mobile #requirements: A requirements catalog for mobile learning environments (NFDF, EFB), pp. 1266–1271.
SAC-2013-LinCLG #approach #data-driven #distributed #learning #predict: Distributed dynamic data driven prediction based on reinforcement learning approach (SYL, KMC, CCL, NG), pp. 779–784.
SAC-2013-LommatzschKA #hybrid #learning #modelling #recommendation #semantics: Learning hybrid recommender models for heterogeneous semantic data (AL, BK, SA), pp. 275–276.
SAC-2013-SeelandKP #graph #kernel #learning: Model selection based product kernel learning for regression on graphs (MS, SK, BP), pp. 136–143.
SAC-2013-SinghR #algorithm #architecture #optimisation #predict: Meta-learning based architectural and algorithmic optimization for achieving green-ness in predictive workload analytics (NS, SR), pp. 1169–1176.
ICSE-2013-CotroneoPR #testing: A learning-based method for combining testing techniques (DC, RP, SR), pp. 142–151.
ICSE-2013-Jonsson #machine learning #performance #scalability #using: Increasing anomaly handling efficiency in large organizations using applied machine learning (LJ), pp. 1361–1364.
ICSE-2013-KimNSK #automation #generative: Automatic patch generation learned from human-written patches (DK, JN, JS, SK), pp. 802–811.
ICSE-2013-MengKM #learning #named: LASE: locating and applying systematic edits by learning from examples (NM, MK, KSM), pp. 502–511.
ICSE-2013-NamPK #fault #learning: Transfer defect learning (JN, SJP, SK), pp. 382–391.
ICSE-2013-SykesCMKRI #adaptation #learning #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 #learning #programming #re-engineering: Teaching and learning programming and software engineering via interactive gaming (NT, JdH, TX, SG, JB), pp. 1117–1126.
CC-2013-MooreC #automation #generative #machine learning #policy #using: Automatic Generation of Program Affinity Policies Using Machine Learning (RWM, BRC), pp. 184–203.
CGO-2013-KulkarniCWS #automation #heuristic #machine learning #using: Automatic construction of inlining heuristics using machine learning (SK, JC, CW, DS), p. 12.
CAV-2013-0001LMN #data type #invariant #learning #linear #quantifier: Learning Universally Quantified Invariants of Linear Data Structures (PG, CL, PM, DN), pp. 813–829.
CAV-2013-ChagantyLNR #learning #relational #smt #using: Combining Relational Learning with SMT Solvers Using CEGAR (ATC, AL, AVN, SKR), pp. 447–462.
ICST-2013-BotellaBCLLS #component #encryption #experience #lessons learnt #modelling #testing: Model-Based Testing of Cryptographic Components — Lessons Learned from Experience (JB, FB, JFC, FL, BL, FS), pp. 192–201.
ICST-2013-MeinkeS #named #testing: LBTest: A Learning-Based Testing Tool for Reactive Systems (KM, MAS), pp. 447–454.
ICTSS-2013-FengLMNSW #case study #testing: Case Studies in Learning-Based Testing (LF, SL, KM, FN, MAS, PYHW), pp. 164–179.
ISSTA-2013-HowarGR #analysis #generative #hybrid #interface #learning: Hybrid learning: interface generation through static, dynamic, and symbolic analysis (FH, DG, ZR), pp. 268–279.
ISSTA-2013-TrippWG #approach #learning #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-Ben-Ari #education #named #satisfiability: LearnSAT: A SAT Solver for Education (MBA), pp. 403–407.
SAT-2013-Johannsen #exponential #learning #proving: Exponential Separations in a Hierarchy of Clause Learning Proof Systems (JJ), pp. 40–51.
SAT-2013-LonsingEG #learning #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 #learning #repository: Learning from the future of component repositories (PA, RDC, RT, SZ), pp. 51–60.
ASE-2012-LuCC #fault #learning #predict #reduction #using: Software defect prediction using semi-supervised learning with dimension reduction (HL, BC, MC), pp. 314–317.
CASE-2012-AnKP #learning #modelling #process: Grasp motion learning with Gaussian Process Dynamic Models (BA, HK, FCP), pp. 1114–1119.
CASE-2012-YamamotoD #interface #learning: Robot interface learning user-defined voice instructions (DY, MD), pp. 926–929.
DAC-2012-WardDP #automation #evaluation #learning #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 #learning #multi #reliability #using: Hierarchical analog circuit reliability analysis using multivariate nonlinear regression and active learning sample selection (EM, DdJ, GGEG), pp. 745–750.
DocEng-2012-MoulderM #how #layout #learning: Learning how to trade off aesthetic criteria in layout (PM, KM), pp. 33–36.
HT-2012-SchofeggerKSG #behaviour #learning #social: Learning user characteristics from social tagging behavior (KS, CK, PS, MG), pp. 207–212.
SIGMOD-2012-AbiteboulAMS #learning #xml: Auto-completion learning for XML (SA, YA, TM, PS), pp. 669–672.
SIGMOD-2012-LinK #machine learning #scalability #twitter: Large-scale machine learning at twitter (JL, AK), pp. 793–804.
VLDB-2012-IseleB #learning #programming #search-based #using: Learning Expressive Linkage Rules using Genetic Programming (RI, CB), pp. 1638–1649.
VLDB-2012-KanagalAPJYP #behaviour #learning #recommendation #taxonomy #using: Supercharging Recommender Systems using Taxonomies for Learning User Purchase Behavior (BK, AA, SP, VJ, JY, LGP), pp. 956–967.
VLDB-2012-LowGKBGH #distributed #framework #in the cloud #machine learning: Distributed GraphLab: A Framework for Machine Learning in the Cloud (YL, JG, AK, DB, CG, JMH), pp. 716–727.
VLDB-2012-SinghG #learning #semantics #string: Learning Semantic String Transformations from Examples (RS, SG), pp. 740–751.
CSEET-2012-AroraG #collaboration #learning #programming #source code: Learning to Write Programs with Others: Collaborative Quadruple Programming (RA, SG), pp. 32–41.
CSEET-2012-BareissS: A Gentle Introduction to Learn by Doing (RB, TS), pp. 81–84.
CSEET-2012-TillmannHXB #education #game studies #learning #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 #learning: Are children capable of learning image processing concepts?: cognitive and affective aspects (KA, MB), pp. 227–231.
ITiCSE-2012-BaghdadiAR #case study #distance #learning #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 #learning: 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 #learning #programming: Evaluation of a collaborative instructional framework for programming learning (LMSC, MPV, JÁVI), pp. 162–167.
ITiCSE-2012-ChristensenC #learning: Lectures abandoned: active learning by active seminars (HBC, AVC), pp. 16–21.
ITiCSE-2012-GomesSM #behaviour #case study #learning #student #towards: A study on students’ behaviours and attitudes towards learning to program (AJG, ÁNS, AJM), pp. 132–137.
ITiCSE-2012-GovenderG #learning #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 #learning: A learning tool for MP3 audio compression (MH, HN), p. 397.
ITiCSE-2012-HiltonJ #array #education #learning #on the #testing: On teaching arrays with test-driven learning in WebIDE (MH, DSJ), pp. 93–98.
ITiCSE-2012-KrausePR #learning: Formal learning groups in an introductory CS course: a qualitative exploration (JK, IP, CR), pp. 315–320.
ITiCSE-2012-Larraza-MendiluzeGMMRALS: Nintendo® DS projects to learn computer input-output (ELM, NGV, JIM, JM, TRV, ISA, JFL, KS), p. 373.
ITiCSE-2012-Luxton-ReillyDPS #how #learning #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 #learning #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 #learning #network: Forming project groups while learning about matching and network flows in algorithms (DPM, TMK, IP), pp. 40–45.
ITiCSE-2012-MeyerW #lessons learnt #programming: Programming studio: advances and lessons learned (CM, MW), p. 369.
ITiCSE-2012-MussaiL #animation #concept #learning #object-oriented: An animation as an illustrate tool for learning concepts in oop (YM, NL), p. 386.
ITiCSE-2012-MyllymakiH #case study #learning: Choosing a study mode in blended learning (MM, IH), pp. 291–296.
ITiCSE-2012-SperlingL #machine learning #re-engineering #student: Integrating AI and machine learning in software engineering course for high school students (AS, DL), pp. 244–249.
ITiCSE-2012-Sudol-DeLyserSC #comprehension #learning #problem: Code comprehension problems as learning events (LASD, MS, SC), pp. 81–86.
ITiCSE-2012-Velazquez-Iturbide #algorithm #approach #learning #refinement: Refinement of an experimental approach tocomputer-based, active learning of greedy algorithms (JÁVI), pp. 46–51.
FASE-2012-AlrajehKRU #learning #satisfiability #specification: Learning from Vacuously Satisfiable Scenario-Based Specifications (DA, JK, AR, SU), pp. 377–393.
TACAS-2012-DSilvaHKT #analysis #bound #learning: Numeric Bounds Analysis with Conflict-Driven Learning (VD, LH, DK, MT), pp. 48–63.
TACAS-2012-MertenHSCJ #automaton #learning: Demonstrating Learning of Register Automata (MM, FH, BS, SC, BJ), pp. 466–471.
ICPC-2012-Sajnani #approach #architecture #automation #machine learning: Automatic software architecture recovery: A machine learning approach (HS), pp. 265–268.
ICSM-2012-BoomsmaHG #industrial #lessons learnt #php #web: Dead code elimination for web systems written in PHP: Lessons learned from an industry case (HB, BVH, HGG), pp. 511–515.
SAS-2012-GiannakopoulouRR #component #interface #learning: Symbolic Learning of Component Interfaces (DG, ZR, VR), pp. 248–264.
STOC-2012-DaskalakisDS #learning: Learning poisson binomial distributions (CD, ID, RAS), pp. 709–728.
DLT-2012-BoiretLN #learning: Learning Rational Functions (AB, AL, JN), pp. 273–283.
LATA-2012-GeilkeZ #algorithm #learning #pattern matching #polynomial: Polynomial-Time Algorithms for Learning Typed Pattern Languages (MG, SZ), pp. 277–288.
LATA-2012-Yoshinaka #context-free grammar #integration #learning: Integration of the Dual Approaches in the Distributional Learning of Context-Free Grammars (RY), pp. 538–550.
FM-2012-AartsHKOV #abstraction #automaton #learning #refinement: Automata Learning through Counterexample Guided Abstraction Refinement (FA, FH, HK, PO, FWV), pp. 10–27.
CHI-2012-AmershiFW #interactive #machine learning #named #network #on-demand #social: Regroup: interactive machine learning for on-demand group creation in social networks (SA, JF, DSW), pp. 21–30.
CHI-2012-ChinF #difference #health #learning: Age differences in exploratory learning from a health information website (JC, WTF), pp. 3031–3040.
CHI-2012-DongDJKNA #game studies #learning: Discovery-based games for learning software (TD, MD, DJ, KK, MWN, MSA), pp. 2083–2086.
CHI-2012-JainB #learning #performance: User learning and performance with bezel menus (MJ, RB), pp. 2221–2230.
CHI-2012-OganFMDMC #exclamation #interactive #learning #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 #learning: Adaptation as design: learning from an EMR deployment study (SYP, YC), pp. 2097–2106.
CHI-2012-VitakIDEG #learning: Gaze-augmented think-aloud as an aid to learning (SAV, JEI, ATD, SE, AKG), pp. 2991–3000.
CHI-2012-XuBRTM #communication #how #learning #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 #bibliography #community #gender #learning: Learning the lingo?: gender, prestige and linguistic adaptation in review communities (LH, JO), pp. 305–314.
CSCW-2012-LeeTH #coordination #named: Micro-coordination: because we did not already learn everything we need to know about working with others in kindergarten (JSL, DGT, SH), pp. 1135–1144.
CSCW-2012-RzeszotarskiK #learning #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 #learning: “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 #learning #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 #learning: 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 #learning #rank #robust: Learning to rank for robust question answering (AA, HR, KS, PM, RDL, DG, JF), pp. 833–842.
CIKM-2012-AnHS #learning #ontology #web: Learning to discover complex mappings from web forms to ontologies (YA, XH, IYS), pp. 1253–1262.
CIKM-2012-CaiZ #injection #learning #rank: Variance maximization via noise injection for active sampling in learning to rank (WC, YZ), pp. 1809–1813.
CIKM-2012-ChaliHI #learning #performance: Improving the performance of the reinforcement learning model for answering complex questions (YC, SAH, KI), pp. 2499–2502.
CIKM-2012-ChengZXAC #classification #learning #on the: On active learning in hierarchical classification (YC, KZ, YX, AA, ANC), pp. 2467–2470.
CIKM-2012-Cohen #learning #metric #random #similarity: Learning similarity measures based on random walks (WWC), p. 3.
CIKM-2012-CuiMWGL #image #keyword #semantics: Semantically coherent image annotation with a learning-based keyword propagation strategy (CC, JM, SW, SG, TL), pp. 2423–2426.
CIKM-2012-FangS #approach #feedback #learning #recommendation: A latent pairwise preference learning approach for recommendation from implicit feedback (YF, LS), pp. 2567–2570.
CIKM-2012-GuoMCJ #learning #recommendation #social: Learning to recommend with social relation ensemble (LG, JM, ZC, HJ), pp. 2599–2602.
CIKM-2012-KanhabuaN #learning #query #rank: Learning to rank search results for time-sensitive queries (NK, KN), pp. 2463–2466.
CIKM-2012-LiBCH #clustering #learning #relational: Relational co-clustering via manifold ensemble learning (PL, JB, CC, ZH), pp. 1687–1691.
CIKM-2012-LuZZX #image #learning #scalability #semantics #set: Semantic context learning with large-scale weakly-labeled image set (YL, WZ, KZ, XX), pp. 1859–1863.
CIKM-2012-MacdonaldSO #learning #on the #query #rank: On the usefulness of query features for learning to rank (CM, RLTS, IO), pp. 2559–2562.
CIKM-2012-MetzgerSHS #interactive #learning: LUKe and MIKe: learning from user knowledge and managing interactive knowledge extraction (SM, MS, KH, RS), pp. 2671–2673.
CIKM-2012-MorenoSRS #learning #multi #named: TALMUD: transfer learning for multiple domains (OM, BS, LR, GS), pp. 425–434.
CIKM-2012-NegahbanRG #learning #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 #learning #multi #named: CoNet: feature generation for multi-view semi-supervised learning with partially observed views (BQ, JH), pp. 1273–1282.
CIKM-2012-RamanSGB #algorithm #learning #towards: Learning from mistakes: towards a correctable learning algorithm (KR, KMS, RGB, CJCB), pp. 1930–1934.
CIKM-2012-RenCJ #learning #topic: Topic based pose relevance learning in dance archives (RR, JPC, JMJ), pp. 2571–2574.
CIKM-2012-ShangJLW #learning: Learning spectral embedding via iterative eigenvalue thresholding (FS, LCJ, YL, FW), pp. 1507–1511.
CIKM-2012-SunG #learning: Active learning for relation type extension with local and global data views (AS, RG), pp. 1105–1112.
CIKM-2012-SunSL #learning #multi #performance #query: Fast multi-task learning for query spelling correction (XS, AS, PL), pp. 285–294.
CIKM-2012-SunWGM #hybrid #learning #rank #recommendation: Learning to rank for hybrid recommendation (JS, SW, BJG, JM), pp. 2239–2242.
CIKM-2012-VolkovsLZ #learning #rank: Learning to rank by aggregating expert preferences (MV, HL, RSZ), pp. 843–851.
CIKM-2012-WangC #learning #predict #word: Learning to predict the cost-per-click for your ad words (CJW, HHC), pp. 2291–2294.
CIKM-2012-WangH0 #framework #image #learning #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 #learning #modelling #novel: A novel local patch framework for fixing supervised learning models (YW, BW, JY, YH, ZHD, ZC), pp. 1233–1242.
CIKM-2012-WangXY #learning: Importance weighted passive learning (SW, XX, YY), pp. 2243–2246.
CIKM-2012-YangTKZLDLW #learning #mining #network: Mining competitive relationships by learning across heterogeneous networks (YY, JT, JK, YZ, JL, YD, TL, LW), pp. 1432–1441.
CIKM-2012-YaoS #learning #relational #ubiquitous: Exploiting latent relevance for relational learning of ubiquitous things (LY, QZS), pp. 1547–1551.
CIKM-2012-ZhangHLL #learning #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 #learning #ontology: An interaction framework of service-oriented ontology learning (JZ, YW, HW), pp. 2303–2306.
CIKM-2012-ZhouLZ #community #learning #quality: Joint relevance and answer quality learning for question routing in community QA (GZ, KL, JZ), pp. 1492–1496.
CIKM-2012-ZhouZ #debugging #learning #rank: Learning to rank duplicate bug reports (JZ, HZ), pp. 852–861.
ECIR-2012-Lubell-DoughtieH #feedback #learning #rank: Learning to Rank from Relevance Feedback for e-Discovery (PLD, KH), pp. 535–539.
ECIR-2012-LungleyKS #adaptation #domain model #interactive #learning #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 #learning #on the: On the Sample Complexity of Reinforcement Learning with a Generative Model (MGA, RM, BK), p. 222.
ICML-2012-AzimiFFBH #coordination #learning: Batch Active Learning via Coordinated Matching (JA, AF, XZF, GB, BH), p. 44.
ICML-2012-BalleQC #learning #modelling #optimisation: Local Loss Optimization in Operator Models: A New Insight into Spectral Learning (BB, AQ, XC), p. 236.
ICML-2012-BelletHS #classification #learning #linear #similarity: Similarity Learning for Provably Accurate Sparse Linear Classification (AB, AH, MS), p. 193.
ICML-2012-BonillaR #learning #probability #prototype: Discriminative Probabilistic Prototype Learning (EVB, ARK), p. 155.
ICML-2012-BronsteinSS #learning #modelling #performance: Learning Efficient Structured Sparse Models (AMB, PS, GS), p. 33.
ICML-2012-ChambersJ #learning: Learning the Central Events and Participants in Unlabeled Text (NC, DJ), p. 3.
ICML-2012-CharlinZB #learning #problem: Active Learning for Matching Problems (LC, RSZ, CB), p. 23.
ICML-2012-DekelTA #adaptation #learning #online #policy: Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret (OD, AT, RA), p. 227.
ICML-2012-DuanXT #adaptation #learning: Learning with Augmented Features for Heterogeneous Domain Adaptation (LD, DX, IWT), p. 89.
ICML-2012-DundarAQR #learning #modelling #online: Bayesian Nonexhaustive Learning for Online Discovery and Modeling of Emerging Classes (MD, FA, AQ, BR), p. 18.
ICML-2012-EbanBSG #learning #online #predict #sequence: Learning the Experts for Online Sequence Prediction (EE, AB, SSS, AG), p. 38.
ICML-2012-FarabetCNL #learning #multi #parsing: Scene parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers (CF, CC, LN, YL), p. 241.
ICML-2012-GeistSLG #approach #difference #learning: A Dantzig Selector Approach to Temporal Difference Learning (MG, BS, AL, MG), p. 49.
ICML-2012-Gonen #kernel #learning #multi #performance: Bayesian Efficient Multiple Kernel Learning (MG), p. 17.
ICML-2012-GongZM #learning #multi #robust: Robust Multiple Manifold Structure Learning (DG, XZ, GGM), p. 7.
ICML-2012-GoodfellowCB #learning #scalability: Large-Scale Feature Learning With Spike-and-Slab Sparse Coding (IJG, ACC, YB), p. 180.
ICML-2012-GuoX #classification #learning #multi: Cross Language Text Classification via Subspace Co-regularized Multi-view Learning (YG, MX), p. 120.
ICML-2012-HanLC #learning #modelling #multi: Cross-Domain Multitask Learning with Latent Probit Models (SH, XL, LC), p. 51.
ICML-2012-HazanK #learning #online: Projection-free Online Learning (EH, SK), p. 239.
ICML-2012-HoiWZ #learning: Exact Soft Confidence-Weighted Learning (SCHH, JW, PZ), p. 19.
ICML-2012-HoiWZJW #algorithm #bound #kernel #learning #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 #learning #modelling #optimisation #probability: Convergence Rates of Biased Stochastic Optimization for Learning Sparse Ising Models (JH), p. 144.
ICML-2012-JalaliS #dependence #graph #learning: Learning the Dependence Graph of Time Series with Latent Factors (AJ, SS), p. 83.
ICML-2012-JawanpuriaN #learning: A Convex Feature Learning Formulation for Latent Task Structure Discovery (PJ, JSN), p. 199.
ICML-2012-JiangLS #3d #learning #using: Learning Object Arrangements in 3D Scenes using Human Context (YJ, ML, AS), p. 119.
ICML-2012-JiYLJH #algorithm #bound #fault #learning: A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound (MJ, TY, BL, RJ, JH), p. 110.
ICML-2012-KalakrishnanRPS #learning #policy: Learning Force Control Policies for Compliant Robotic Manipulation (MK, LR, PP, SS), p. 10.
ICML-2012-KarbasiIM #learning #rank: Comparison-Based Learning with Rank Nets (AK, SI, LM), p. 161.
ICML-2012-KumarD #learning #multi: Learning Task Grouping and Overlap in Multi-task Learning (AK, HDI), p. 224.
ICML-2012-KumarNKD #classification #framework #kernel #learning #multi: A Binary Classification Framework for Two-Stage Multiple Kernel Learning (AK, ANM, KK, HDI), p. 173.
ICML-2012-KumarPK #learning #modelling #nondeterminism: Modeling Latent Variable Uncertainty for Loss-based Learning (MPK, BP, DK), p. 29.
ICML-2012-LanctotGBB #game studies #learning: No-Regret Learning in Extensive-Form Games with Imperfect Recall (ML, RGG, NB, MB), p. 135.
ICML-2012-LeRMDCCDN #learning #scalability #using: Building high-level features using large scale unsupervised learning (QVL, MR, RM, MD, GC, KC, JD, AYN), p. 69.
ICML-2012-LinXWZ #learning: Total Variation and Euler’s Elastica for Supervised Learning (TL, HX, LW, HZ), p. 82.
ICML-2012-LouH #learning: Structured Learning from Partial Annotations (XL, FAH), p. 52.
ICML-2012-MakinoT #learning #parametricity: Apprenticeship Learning for Model Parameters of Partially Observable Environments (TM, JT), p. 117.
ICML-2012-MatuszekFZBF #learning: A Joint Model of Language and Perception for Grounded Attribute Learning (CM, NF, LSZ, LB, DF), p. 186.
ICML-2012-Memisevic #learning #multi #on the: On multi-view feature learning (RM), p. 140.
ICML-2012-MnihH #image #learning #semistructured data: Learning to Label Aerial Images from Noisy Data (VM, GEH), p. 31.
ICML-2012-MohamedHG #learning: Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning (SM, KAH, ZG), p. 91.
ICML-2012-NiuDYS #learning #metric: Information-theoretic Semi-supervised Metric Learning via Entropy Regularization (GN, BD, MY, MS), p. 136.
ICML-2012-Painter-WakefieldP #algorithm #learning: Greedy Algorithms for Sparse Reinforcement Learning (CPW, RP), p. 114.
ICML-2012-PassosRWD #flexibility #learning #modelling #multi: Flexible Modeling of Latent Task Structures in Multitask Learning (AP, PR, JW, HDI), p. 167.
ICML-2012-PeharzP #learning #network: Exact Maximum Margin Structure Learning of Bayesian Networks (RP, FP), p. 102.
ICML-2012-PiresS #estimation #learning #linear #statistics: Statistical linear estimation with penalized estimators: an application to reinforcement learning (BAP, CS), p. 228.
ICML-2012-PlessisS #learning: Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching (MCdP, MS), p. 159.
ICML-2012-PrasseSLS #email #identification #learning #regular expression: Learning to Identify Regular Expressions that Describe Email Campaigns (PP, CS, NL, TS), p. 146.
ICML-2012-RossB #identification #learning #modelling: Agnostic System Identification for Model-Based Reinforcement Learning (SR, DB), p. 247.
ICML-2012-SamdaniR #learning #performance #predict: Efficient Decomposed Learning for Structured Prediction (RS, DR), p. 200.
ICML-2012-ScholkopfJPSZM #learning #on the: On causal and anticausal learning (BS, DJ, JP, ES, KZ, JMM), p. 63.
ICML-2012-ShiS #adaptation #clustering #learning: Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation (YS, FS), p. 166.
ICML-2012-ShivaswamyJ #learning #online #predict: Online Structured Prediction via Coactive Learning (PS, TJ), p. 12.
ICML-2012-SilvaKB #learning: Learning Parameterized Skills (BCdS, GK, AGB), p. 187.
ICML-2012-SohnL #invariant #learning: Learning Invariant Representations with Local Transformations (KS, HL), p. 174.
ICML-2012-StorkeyMG #machine learning: Isoelastic Agents and Wealth Updates in Machine Learning Markets (AJS, JM, KG), p. 133.
ICML-2012-Wagstaff #machine learning #matter: Machine Learning that Matters (KW), p. 240.
ICML-2012-WangWHL #learning #monte carlo: Monte Carlo Bayesian Reinforcement Learning (YW, KSW, DH, WSL), p. 105.
ICML-2012-XieHS #approach #automation #generative #learning: Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting (NX, HH, MS), p. 139.
ICML-2012-XuWC #learning: The Greedy Miser: Learning under Test-time Budgets (ZEX, KQW, OC), p. 169.
ICML-2012-YackleyL #learning: Smoothness and Structure Learning by Proxy (BY, TL), p. 57.
ICML-2012-YangMJZZ #kernel #learning #multi #probability #programming: Multiple Kernel Learning from Noisy Labels by Stochastic Programming (TY, MM, RJ, LZ, YZ), p. 21.
ICML-2012-ZhongK #clustering #flexibility #learning #multi: Convex Multitask Learning with Flexible Task Clusters (WZ, JTYK), p. 66.
ICPR-2012-AbeOD #image #learning #rank: Recognizing surface qualities from natural images based on learning to rank (TA, TO, KD), pp. 3712–3715.
ICPR-2012-AntoniukFH #learning #markov #network: Learning Markov Networks by Analytic Center Cutting Plane Method (KA, VF, VH), pp. 2250–2253.
ICPR-2012-BaccoucheMWGB #2d #invariant #learning #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: Learning global cost function for face alignment (KB, MM, PP, EB), pp. 1112–1115.
ICPR-2012-BanerjeeN #kernel #learning #multi #process #recognition #using: Pose based activity recognition using Multiple Kernel learning (PB, RN), pp. 445–448.
ICPR-2012-CermanH #learning #problem: Tracking with context as a semi-supervised learning and labeling problem (LC, VH), pp. 2124–2127.
ICPR-2012-ChernoffLN #fault #learning #metric: Metric learning by directly minimizing the k-NN training error (KC, ML, MN), pp. 1265–1268.
ICPR-2012-DahmaneLDB #estimation #learning #symmetry: Learning symmetrical model for head pose estimation (AD, SL, CD, IMB), pp. 3614–3617.
ICPR-2012-DAmbrosioIS #learning #re-engineering: A One-per-Class reconstruction rule for class imbalance learning (RD, GI, PS), pp. 1310–1313.
ICPR-2012-DoTT #multi #representation #using: Text/graphic separation using a sparse representation with multi-learned dictionaries (THD, ST, ORT), pp. 689–692.
ICPR-2012-DuanWLDC #learning #named: K-CPD: Learning of overcomplete dictionaries for tensor sparse coding (GD, HW, ZL, JD, YWC), pp. 493–496.
ICPR-2012-FangZ #learning: I don’t know the label: Active learning with blind knowledge (MF, XZ), pp. 2238–2241.
ICPR-2012-FiaschiKNH #learning: Learning to count with regression forest and structured labels (LF, UK, RN, FAH), pp. 2685–2688.
ICPR-2012-GhanemKFZ #automation #learning #recognition: Context-aware learning for automatic sports highlight recognition (BG, MK, MF, TZ), pp. 1977–1980.
ICPR-2012-GhoseMOMLFVCSM12a #3d #energy #framework #graph #learning #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 #learning #segmentation: Learning non-target items for interesting clothes segmentation in fashion images (CG, SC, DB, RC), pp. 3317–3320.
ICPR-2012-GuK #learning #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: Learning a selectivity-invariance-selectivity feature extraction architecture for images (MG, AH), pp. 918–921.
ICPR-2012-HidoK #graph #learning #similarity: Hash-based structural similarity for semi-supervised Learning on attribute graphs (SH, HK), pp. 3009–3012.
ICPR-2012-HinoO #kernel #learning #multi: An improved entropy-based multiple kernel learning (HH, TO), pp. 1189–1192.
ICPR-2012-HiradeY #learning #predict: Ensemble learning for change-point prediction (RH, TY), pp. 1860–1863.
ICPR-2012-HuangLT #invariant #learning #recognition: Learning modality-invariant features for heterogeneous face recognition (LH, JL, YPT), pp. 1683–1686.
ICPR-2012-JinGYZ #algorithm #learning #multi: Multi-label learning vector quantization algorithm (XBJ, GG, JY, DZ), pp. 2140–2143.
ICPR-2012-JiS12a #3d #estimation #learning #robust: Robust 3D human pose estimation via dual dictionaries learning (HJ, FS), pp. 3370–3373.
ICPR-2012-KhanT #learning #taxonomy: Stable discriminative dictionary learning via discriminative deviation (NK, MFT), pp. 3224–3227.
ICPR-2012-KongW #clustering #learning #multi: A multi-task learning strategy for unsupervised clustering via explicitly separating the commonality (SK, DW), pp. 771–774.
ICPR-2012-KrijtheHL #classification #using: Improving cross-validation based classifier selection using meta-learning (JHK, TKH, ML), pp. 2873–2876.
ICPR-2012-KumarRS #learning #predict: Learning to predict super resolution wavelet coefficients (NK, NKR, AS), pp. 3468–3471.
ICPR-2012-KumarYD #classification #documentation #learning #retrieval: Learning document structure for retrieval and classification (JK, PY, DSD), pp. 1558–1561.
ICPR-2012-LeeKD #induction #learning: Learning action symbols for hierarchical grammar induction (KL, TKK, YD), pp. 3778–3782.
ICPR-2012-LiCHWM #3d #kernel #learning #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 #learning #multi #online #people: Online adaptive learning for multi-camera people counting (JL, LH, CL), pp. 3415–3418.
ICPR-2012-LiLLL #distance #estimation #learning #metric: Learning distance metric regression for facial age estimation (CL, QL, JL, HL), pp. 2327–2330.
ICPR-2012-LinLZ #learning #representation #taxonomy: Incoherent dictionary learning for sparse representation (TL, SL, HZ), pp. 1237–1240.
ICPR-2012-LiPMH #classification #email #incremental #learning #using: Business email classification using incremental subspace learning (ML, YP, RM, HYH), pp. 625–628.
ICPR-2012-LiuCSTN #learning #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 #learning #multi: Unsupervised multi-target trajectory detection, learning and analysis in complicated environments (HL, JL), pp. 3716–3720.
ICPR-2012-LiuLWZ #learning #linear: Locally linear embedding based example learning for pan-sharpening (QL, LL, YW, ZZ), pp. 1928–1931.
ICPR-2012-LiuLYZ #composition #learning #visual notation: Learning to describe color composition of visual objects (YL, YL, ZY, NZ), pp. 3337–3340.
ICPR-2012-LiuML #learning #multi: Training data recycling for multi-level learning (JL, SM, YL), pp. 2314–2318.
ICPR-2012-LiuSW #learning #recognition #taxonomy: Facial expression recognition based on discriminative dictionary learning (WL, CS, YW), pp. 1839–1842.
ICPR-2012-LiVBB #clustering #learning #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 #learning: Adaptive kernel learning based on centered alignment for hierarchical classification (YL, JL, JY), pp. 569–572.
ICPR-2012-LuLZSCO #using: Learning-based deformable registration using weighted mutual information (YL, RL, LZ, YS, CC, SHO), pp. 2626–2629.
ICPR-2012-MarcaciniCR #approach #clustering #learning: An active learning approach to frequent itemset-based text clustering (RMM, GNC, SOR), pp. 3529–3532.
ICPR-2012-MogelmoseTM #comparative #dataset #detection #evaluation #learning: Learning to detect traffic signs: Comparative evaluation of synthetic and real-world datasets (AM, MMT, TBM), pp. 3452–3455.
ICPR-2012-MoZW #classification #learning: Enhancing cross-view object classification by feature-based transfer learning (YM, ZZ, YW), pp. 2218–2221.
ICPR-2012-Nagy #learning #web: Learning the characteristics of critical cells from web tables (GN), pp. 1554–1557.
ICPR-2012-NamA #image #learning: Learning human preferences to sharpen images (MN, NA), pp. 2173–2176.
ICPR-2012-NayefAB #learning: Learning feature weights of symbols, with application to symbol spotting (NN, MZA, TMB), pp. 2371–2374.
ICPR-2012-Noh #analysis #classification #learning #metric #nearest neighbour: χ2 Metric learning for nearest neighbor classification and its analysis (SN), pp. 991–995.
ICPR-2012-PangHYQW #analysis #classification #learning: Theoretical analysis of learning local anchors for classification (JP, QH, BY, LQ, DW), pp. 1803–1806.
ICPR-2012-PanLS #kernel #learning: Learning kernels from labels with ideal regularization (BP, JHL, LS), pp. 505–508.
ICPR-2012-PourdamghaniRZ #estimation #graph #learning #metric: Metric learning for graph based semi-supervised human pose estimation (NP, HRR, MZ), pp. 3386–3389.
ICPR-2012-QinZCW #learning #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 #learning #multi: A multiple kernel learning approach to multi-modal pedestrian classification (MSB, AU, MC, MC, UC, VM), pp. 2412–2415.
ICPR-2012-SchauerteS #image #learning #modelling #robust #web: Learning robust color name models from web images (BS, RS), pp. 3598–3601.
ICPR-2012-SharmaHN #classification #detection #incremental #learning #performance: Efficient incremental learning of boosted classifiers for object detection (PS, CH, RN), pp. 3248–3251.
ICPR-2012-ShenMZ #analysis #graph #learning #online: Unsupervised online learning trajectory analysis based on weighted directed graph (YS, ZM, JZ), pp. 1306–1309.
ICPR-2012-SommerFHG #detection #image: Learning-based mitotic cell detection in histopathological images (CS, LF, FAH, DG), pp. 2306–2309.
ICPR-2012-SuLT #documentation #framework #image #learning #markov #random #using: A learning framework for degraded document image binarization using Markov Random Field (BS, SL, CLT), pp. 3200–3203.
ICPR-2012-SunBM #learning: Unsupervised skeleton learning for manifold denoising (KS, EB, SMM), pp. 2719–2722.
ICPR-2012-TabernikKBL #learning #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 #learning #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 #learning #multi: A Multiple Kernel Learning framework for detecting altered fingerprints (MT, MP, PV, ER), pp. 3402–3405.
ICPR-2012-TuS #adaptation #classification #learning: Dynamical ensemble learning with model-friendly classifiers for domain adaptation (WT, SS), pp. 1181–1184.
ICPR-2012-VillamizarGSM #learning #online #random #using: Online human-assisted learning using Random Ferns (MV, AG, AS, FMN), pp. 2821–2824.
ICPR-2012-VuralA #machine learning #video: A machine learning system for human-in-the-loop video surveillance (UV, YSA), pp. 1092–1095.
ICPR-2012-WangJ12b #learning #network #process #recognition: Learning dynamic Bayesian network discriminatively for human activity recognition (XW, QJ), pp. 3553–3556.
ICPR-2012-WangL12b #learning #recognition #string: String-level learning of confidence transformation for Chinese handwritten text recognition (DHW, CLL), pp. 3208–3211.
ICPR-2012-WeberBLS #learning #segmentation: Unsupervised motion pattern learning for motion segmentation (MW, GB, ML, DS), pp. 202–205.
ICPR-2012-XiaTWLL #categorisation #learning: Object categorization based on hierarchical learning (TX, YYT, YW, HL, LL), pp. 1419–1422.
ICPR-2012-YangLZC #image #learning #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 #learning: Automatic annotation of court games with structured output learning (FY, JK, KM, DW), pp. 3577–3580.
ICPR-2012-YanRLS #classification #learning #multi: Active transfer learning for multi-view head-pose classification (YY, SR, OL, NS), pp. 1168–1171.
ICPR-2012-YeD #learning #predict: Learning features for predicting OCR accuracy (PY, DSD), pp. 3204–3207.
ICPR-2012-ZhangHR #classification #gender #learning: Hypergraph based semi-supervised learning for gender classification (ZZ, ERH, PR), pp. 1747–1750.
ICPR-2012-ZhangZNH #learning #multi #recognition: Joint dynamic sparse learning and its application to multi-view face recognition (HZ, YZ, NMN, TSH), pp. 1671–1674.
ICPR-2012-ZhaoSS #learning #predict: Importance-weighted label prediction for active learning with noisy annotations (LZ, GS, RS), pp. 3476–3479.
ICPR-2012-ZhaoXY #learning #network #speech: Unsupervised Tibetan speech features Learning based on Dynamic Bayesian Networks (YZ, XX, GY), pp. 2319–2322.
ICPR-2012-ZhaoYXJ #learning: A near-optimal non-myopic active learning method (YZ, GY, XX, QJ), pp. 1715–1718.
ICPR-2012-ZhouWXZM #learning #recognition: Learning weighted features for human action recognition (WZ, CW, BX, ZZ, LM), pp. 1160–1163.
ICPR-2012-ZhuoCQYX #algorithm #classification #image #learning #using: Image classification using HTM cortical learning algorithms (WZ, ZC, YQ, ZY, YX), pp. 2452–2455.
KDD-2012-GongYZ #learning #multi #robust: Robust multi-task feature learning (PG, JY, CZ), pp. 895–903.
KDD-2012-HalawiDGK #constraints #learning #scalability #word: Large-scale learning of word relatedness with constraints (GH, GD, EG, YK), pp. 1406–1414.
KDD-2012-HoensC #learning: Learning in non-stationary environments with class imbalance (TRH, NVC), pp. 168–176.
KDD-2012-JainVV #kernel #learning #multi #named: SPF-GMKL: generalized multiple kernel learning with a million kernels (AJ, SVNV, MV), pp. 750–758.
KDD-2012-LiJPS #classification #learning #multi: Multi-domain active learning for text classification (LL, XJ, SJP, JTS), pp. 1086–1094.
KDD-2012-Lin #case study #data mining #experience #machine learning #mining: Experiences and lessons in developing industry-strength machine learning and data mining software (CJL), p. 1176.
KDD-2012-PatroDSWFK #approach #data-driven #how #learning #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-Posse #lessons learnt #network #recommendation #scalability #social: Key lessons learned building recommender systems for large-scale social networks (CP), p. 587.
KDD-2012-RamanSJ #feedback #learning #online: Online learning to diversify from implicit feedback (KR, PS, TJ), pp. 705–713.
KDD-2012-SeelandKK #clustering #graph #kernel #learning: A structural cluster kernel for learning on graphs (MS, AK, SK), pp. 516–524.
KDD-2012-ShangJW #learning: Semi-supervised learning with mixed knowledge information (FS, LCJ, FW), pp. 732–740.
KDD-2012-ShenJ #learning #recommendation #social: Learning personal + social latent factor model for social recommendation (YS, RJ), pp. 1303–1311.
KDD-2012-SilvaC #learning #matrix #online: Active learning for online bayesian matrix factorization (JGS, LC), pp. 325–333.
KDD-2012-SindhwaniG #distributed #learning #scalability #taxonomy: Large-scale distributed non-negative sparse coding and sparse dictionary learning (VS, AG), pp. 489–497.
KDD-2012-TianZ #learning: Learning from crowds in the presence of schools of thought (YT, JZ), pp. 226–234.
KDD-2012-XiongJXC #dependence #learning #metric #random: Random forests for metric learning with implicit pairwise position dependence (CX, DMJ, RX, JJC), pp. 958–966.
KDD-2012-YuanWTNY #analysis #learning #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 #learning #multi: Inductive multi-task learning with multiple view data (JZ, JH), pp. 543–551.
KDD-2012-ZhenY #learning #multimodal #probability: A probabilistic model for multimodal hash function learning (YZ, DYY), pp. 940–948.
KDD-2012-ZhouKTX #machine learning: Adversarial support vector machine learning (YZ, MK, BMT, BX), pp. 1059–1067.
KDD-2012-ZhouZ #collaboration #learning: Learning binary codes for collaborative filtering (KZ, HZ), pp. 498–506.
KDIR-2012-AbdullinN #clustering #data type #framework #learning: A Semi-supervised Learning Framework to Cluster Mixed Data Types (AA, ON), pp. 45–54.
KDIR-2012-BressoGDNS #3d #concept analysis #learning #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-Dagnino #approach #grid #information management #machine learning #smarttech: Knowledge Discovery in the Smart Grid — A Machine Learning Approach (AD), pp. 366–369.
KDIR-2012-IkebeKT #learning #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 #learning #maintenance #parsing #random: Parsing and Maintaining Bibliographic References — Semi-supervised Learning of Conditional Random Fields with Constraints (SL, WH), pp. 233–238.
KEOD-2012-GarciaAGG #case study #metadata #ontology: Case Study: Ontology for Metadata in e-Learning (AMFG, SSA, MEBG, RBG), pp. 317–320.
KEOD-2012-RuizHM #education #evaluation #learning #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 #learning #ontology #web: Confidence Management for Learning Ontologies from Dynamic Web Sources (GW, AW, AS, MS), pp. 172–177.
KMIS-2012-AkiyoshiSK #learning #problem #towards: A Project Manager Skill-up Simulator Towards Problem Solving-based Learning (MA, MS, NK), pp. 190–195.
KMIS-2012-AtkociunieneG #convergence #learning: 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 #learning: Management of Collaboration — Impacts of Virtualization to Learning & Knowledge (GH, MM, PH, GH, MM), pp. 235–239.
KMIS-2012-HamadaAS #generative #learning #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 #learning #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 #learning #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 #learning #network: A New Learning Structure Heuristic of Bayesian Networks from Data (HB, AM, TL, AR), pp. 183–197.
MLDM-2012-ChanguelL #independence #machine learning #metadata #problem: Content Independent Metadata Production as a Machine Learning Problem (SC, NL), pp. 306–320.
MLDM-2012-HoaD #learning: A New Learning Strategy of General BAMs (NTH, TDB), pp. 213–221.
MLDM-2012-PitelisT #learning: Discriminant Subspace Learning Based on Support Vectors Machines (NP, AT), pp. 198–212.
MLDM-2012-TabatabaeiAKK #classification #internet #machine learning: Machine Learning-Based Classification of Encrypted Internet Traffic (TST, MA, FK, MK), pp. 578–592.
MLDM-2012-ToussaintB #comparison #empirical #learning: Proximity-Graph Instance-Based Learning, Support Vector Machines, and High Dimensionality: An Empirical Comparison (GTT, CB), pp. 222–236.
MLDM-2012-XuCG #concept #learning #multi #using: Constructing Target Concept in Multiple Instance Learning Using Maximum Partial Entropy (TX, DKYC, IG), pp. 169–182.
RecSys-2012-DeDGM #difference #learning #using: Local learning of item dissimilarity using content and link structure (AD, MSD, NG, PM), pp. 221–224.
RecSys-2012-Herbrich #distributed #learning #online #realtime: Distributed, real-time bayesian learning in online services (RH), pp. 203–204.
RecSys-2012-KarimiFNS #learning #matrix #recommendation: Exploiting the characteristics of matrix factorization for active learning in recommender systems (RK, CF, AN, LST), pp. 317–320.
RecSys-2012-Kohavi #online #statistics: Online controlled experiments: introduction, learnings, and humbling statistics (RK), pp. 1–2.
RecSys-2012-SalimansPG #collaboration #learning #ranking: Collaborative learning of preference rankings (TS, UP, TG), pp. 261–264.
RecSys-2012-ShiKBLOH #collaboration #learning #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 #learning #named: CLAT: Collaborative Learning Adaptive Tutor (AMHA, CA, LL), pp. 747–752.
SEKE-2012-DagninoSR #fault #machine learning #using: Forecasting Fault Events in Power Distribution Grids Using Machine Learning (AD, KS, LR), pp. 458–463.
SEKE-2012-El-SherifFE #concept #learning #multi #network #social #using: Using Social Networks for Learning New Concepts in Multi-Agent Systems (SMES, BHF, AE), pp. 261–266.
SEKE-2012-HaoWZ #classification #empirical #machine learning: An Empirical Study of Execution-Data Classification Based on Machine Learning (DH, XW, LZ), pp. 283–288.
SEKE-2012-XavierOC #fuzzy #learning #logic: Evolutionary Learning and Fuzzy Logic Applied to a Load Balancer (FCX, MGdO, CLdC), pp. 256–260.
SEKE-2012-XiePDMRTR #categorisation #clustering #grid #power management: Progressive Clustering with Learned Seeds: An Event Categorization System for Power Grid (BX, RJP, HD, JYM, AR, AT, CR), pp. 100–105.
SEKE-2012-Zhang #bias #learning #named: i2Learning: Perpetual Learning through Bias Shifting (DZ), pp. 249–255.
SIGIR-2012-BilgicB #learning #query: Active query selection for learning rankers (MB, PNB), pp. 1033–1034.
SIGIR-2012-ChangHYLC #ranking #web: Learning-based time-sensitive re-ranking for web search (PTC, YCH, CLY, SDL, PJC), pp. 1101–1102.
SIGIR-2012-GaoWL #graph #information retrieval #learning #mining #scalability: Large-scale graph mining and learning for information retrieval (BG, TW, TYL), pp. 1194–1195.
SIGIR-2012-HongBAD #learning #rank #social: Learning to rank social update streams (LH, RB, JA, BDD), pp. 651–660.
SIGIR-2012-JiangWLAW #alias #approach #detection #learning #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 #learning #retrieval: Learning to select a time-aware retrieval model (NK, KB, KN), pp. 1099–1100.
SIGIR-2012-KovesiGA #categorisation #learning #multi #online #performance: Fast on-line learning for multilingual categorization (MK, CG, MRA), pp. 1071–1072.
SIGIR-2012-LiX #machine learning #web: Beyond bag-of-words: machine learning for query-document matching in web search (HL, JX), p. 1177.
SIGIR-2012-MacdonaldTO #learning #online #predict #query #scheduling: Learning to predict response times for online query scheduling (CM, NT, IO), pp. 621–630.
SIGIR-2012-MacdonaldTO12a #effectiveness #learning #rank #safety: Effect of dynamic pruning safety on learning to rank effectiveness (CM, NT, IO), pp. 1051–1052.
SIGIR-2012-NiuGLC #evaluation #learning #rank #ranking: Top-k learning to rank: labeling, ranking and evaluation (SN, JG, YL, XC), pp. 751–760.
SIGIR-2012-OzertemCDV #framework #machine learning #query #ranking: Learning to suggest: a machine learning framework for ranking query suggestions (UO, OC, PD, EV), pp. 25–34.
SIGIR-2012-SeverynM #learning #ranking #scalability: Structural relationships for large-scale learning of answer re-ranking (AS, AM), pp. 741–750.
SIGIR-2012-ZhangWDH #detection #learning #performance #reuse: Learning hash codes for efficient content reuse detection (QZ, YW, ZD, XH), pp. 405–414.
OOPSLA-2012-KulkarniC #compilation #machine learning #optimisation #problem #using: Mitigating the compiler optimization phase-ordering problem using machine learning (SK, JC), pp. 147–162.
OOPSLA-2012-St-AmourTF #communication #optimisation: Optimization coaching: optimizers learn to communicate with programmers (VSA, STH, MF), pp. 163–178.
TOOLS-EUROPE-2012-Sureka #component #debugging #learning: Learning to Classify Bug Reports into Components (AS), pp. 288–303.
PADL-2012-ZhuFW #ad hoc #incremental: LearnPADS + + : Incremental Inference of Ad Hoc Data Formats (KQZ, KF, DW), pp. 168–182.
REFSQ-2012-EngelsmanW #architecture #case study #enterprise #lessons learnt #requirements: Goal-Oriented Requirements Engineering and Enterprise Architecture: Two Case Studies and Some Lessons Learned (WE, RW), pp. 306–320.
REFSQ-2012-KnaussS #documentation #heuristic #learning #requirements: Supporting Learning Organisations in Writing Better Requirements Documents Based on Heuristic Critiques (EK, KS), pp. 165–171.
SAC-2012-MinervinidF #concept #learning #logic #probability: Learning probabilistic Description logic concepts: under different Assumptions on missing knowledge (PM, Cd, NF), pp. 378–383.
SAC-2012-NunesCM #learning #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 #learning #multi #performance #testing: Multilayer Fuzzy ARTMAP: fast learning and fast testing for pattern classification (THO, NAMI), pp. 27–32.
ICSE-2012-Chioasca #automation #machine learning #model transformation #requirements #using: Using machine learning to enhance automated requirements model transformation (EVC), pp. 1487–1490.
ICSE-2012-DagenaisR #api #learning #traceability: Recovering traceability links between an API and its learning resources (BD, MPR), pp. 47–57.
ICSE-2012-FengC #behaviour #learning #multi: Multi-label software behavior learning (YF, ZC), pp. 1305–1308.
ICSE-2012-GrechanikFX #automation #learning #performance #problem #testing: Automatically finding performance problems with feedback-directed learning software testing (MG, CF, QX), pp. 156–166.
ICSE-2012-StaatsGH #automation #fault #how #mutation testing #testing: Automated oracle creation support, or: How I learned to stop worrying about fault propagation and love mutation testing (MS, GG, MPEH), pp. 870–880.
CAV-2012-ChenW #incremental #learning: Learning Boolean Functions Incrementally (YFC, BYW), pp. 55–70.
CAV-2012-LeeWY #algorithm #analysis #learning #termination: Termination Analysis with Algorithmic Learning (WL, BYW, KY), pp. 88–104.
CAV-2012-SinhaSCS: Alternate and Learn: Finding Witnesses without Looking All over (NS, NS, SC, MS), pp. 599–615.
CSL-2012-Berardid #learning: Knowledge Spaces and the Completeness of Learning Strategies (SB, Ud), pp. 77–91.
ICLP-2012-BlockeelBBCP #data mining #machine learning #mining #modelling #problem: Modeling Machine Learning and Data Mining Problems with FO(·) (HB, BB, MB, BdC, SDP, MD, AL, JR, SV), pp. 14–25.
ICLP-2012-MarateaPR #machine learning: Applying Machine Learning Techniques to ASP Solving (MM, LP, FR), pp. 37–48.
ICST-2012-RamlerKP #combinator #design #lessons learnt: Combinatorial Test Design in the TOSCA Testsuite: Lessons Learned and Practical Implications (RR, TK, WP), pp. 569–572.
ICST-2012-SunSPR #cost analysis #learning #named #reliability: CARIAL: Cost-Aware Software Reliability Improvement with Active Learning (BS, GS, AP, SR), pp. 360–369.
ICTSS-2012-StrugS #approach #machine learning #mutation testing #testing: Machine Learning Approach in Mutation Testing (JS, BS), pp. 200–214.
ICTSS-2012-Vaandrager #finite #learning #state machine: Active Learning of Extended Finite State Machines (FWV), pp. 5–7.
LICS-2012-KomuravelliPC #learning #probability: Learning Probabilistic Systems from Tree Samples (AK, CSP, EMC), pp. 441–450.
SAT-2012-BonetB #learning: An Improved Separation of Regular Resolution from Pool Resolution and Clause Learning (MLB, SRB), pp. 44–57.
SAT-2012-KatsirelosS #learning #satisfiability: Learning Polynomials over GF(2) in a SAT Solver — (Poster Presentation) (GK, LS), pp. 496–497.
SAT-2012-LaitinenJN #learning: Conflict-Driven XOR-Clause Learning (TL, TAJ, IN), pp. 383–396.
SAT-2012-MatsliahSS #learning: Augmenting Clause Learning with Implied Literals — (Poster Presentation) (AM, AS, HS), pp. 500–501.
SAT-2012-SabharwalSS #learning #satisfiability: Learning Back-Clauses in SAT — (Poster Presentation) (AS, HS, MS), pp. 498–499.
SMT-2012-AzizWD #estimation #machine learning #problem #smt: A Machine Learning Technique for Hardness Estimation of QFBV SMT Problems (MAA, AGW, NMD), pp. 57–66.
CBSE-2011-AletiM #component #deployment #learning #optimisation: Component deployment optimisation with bayesian learning (AA, IM), pp. 11–20.
ECSA-2011-JrCCGOFMG #architecture #component #lessons learnt #product line #uml: Extending UML Components to Develop Software Product-Line Architectures: Lessons Learned (ACCJ, GGC, TEC, IMdSG, EAOJ, SF, PCM, AFG), pp. 130–138.
ASE-2011-ChenHX #approach #evaluation #machine learning #process: Software process evaluation: A machine learning approach (NC, SCHH, XX), pp. 333–342.
DAC-2011-DingGYP #detection #learning #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-GeQ #machine learning #multi #using: Dynamic thermal management for multimedia applications using machine learning (YG, QQ), pp. 95–100.
DAC-2011-KatzRZS #architecture #behaviour #generative #learning #quality: Learning microarchitectural behaviors to improve stimuli generation quality (YK, MR, AZ, GS), pp. 848–853.
DAC-2011-WangXAP #classification #learning #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 #learning #optimisation #realtime: Adaptive test optimization through real time learning of test effectiveness (BA, AO), pp. 1430–1435.
DocEng-2011-ChidlovskiiB #learning #metric #network #recommendation #social: Local metric learning for tag recommendation in social networks (BC, AB), pp. 205–208.
ICDAR-2011-CoatesCCSSWWN #detection #image #learning #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 #learning #multi #using: Document Image Classification and Labeling Using Multiple Instance Learning (JK, JP, DSD), pp. 1059–1063.
ICDAR-2011-ShaoWXZZ11a #learning #multi: Multiple Instance Learning Based Method for Similar Handwritten Chinese Characters Discrimination (YS, CW, BX, RZ, YZ), pp. 1002–1006.
ICDAR-2011-SuLZ #learning #polynomial: Perceptron Learning of Modified Quadratic Discriminant Function (THS, CLL, XYZ), pp. 1007–1011.
ICDAR-2011-TaoLJG #learning #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 #learning: A Semi-supervised Ensemble Learning Approach for Character Labeling with Minimal Human Effort (SV, AJ, GAF), pp. 259–263.
ICDAR-2011-WangDL #learning #recognition: MQDF Discriminative Learning Based Offline Handwritten Chinese Character Recognition (YW, XD, CL), pp. 1100–1104.
SIGMOD-2011-GetoorM #learning #modelling #relational #statistics: Learning statistical models from relational data (LG, LM), pp. 1195–1198.
CSEET-2011-AndrianoMBR #assessment: A quantitative assessment method for simulation-based e-learnings (NA, MGM, CB, DR), pp. 159–168.
CSEET-2011-ChimalakondaN #education #learning #question #re-engineering: Can we make software engineering education better by applying learning theories? (SC, KVN), p. 561.
CSEET-2011-EngM #assessment #communication #experience #learning #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 #learning #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-Garousi #challenge #industrial #lessons learnt #testing: Incorporating real-world industrial testing projects in software testing courses: Opportunities, challenges, and lessons learned (VG), pp. 396–400.
CSEET-2011-GimenesBB #distance #learning #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 #learning: Erase and rewind — Learning by replaying examples (LH, AB, ML, ML), p. 558.
CSEET-2011-HoskingSKJ #learning #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 #learning #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 #learning #named #social: Pex4Fun: Teaching and learning computer science via social gaming (NT, JdH, TX), pp. 546–548.
CSEET-2011-TuTOBHKY #learning: Turning real-world systems into verification-driven learning cases (ST, ST, SO, BB, BH, AK, ZY), pp. 129–138.
CSEET-2011-Virseda #education #learning #re-engineering #semantics: A learning methodology based on semantic tableaux for software engineering education (RdVV), pp. 401–405.
CSEET-2011-WongBDMOV #case study #education #experience #lessons learnt #testing: Teaching software testing: Experiences, lessons learned and the path forward (WEW, AB, VD, APM, JO, MAV), pp. 530–534.
ITiCSE-2011-AnjorinGR #collaboration #framework #learning #named #web: CROKODIL: a platform supporting the collaborative management of web resources for learning purposes (MA, RDG, CR), p. 361.
ITiCSE-2011-BowerM #comparison #learning: 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 #learning #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 #learning #programming: Facilitating learning dynamic programming through a previous introduction of exhaustive search (AC, AM), p. 355.
ITiCSE-2011-ChanK: Do educational software systems provide satisfactory learning opportunities for “multi-sensory learning” methodology? (PC, GK), p. 358.
ITiCSE-2011-ChuaB #framework: Integrating scholarly articles within e-learning courses: a framework (BBC, DVB), p. 392.
ITiCSE-2011-EllisH #learning #named #student: Courseware: student learning via FOSS field trips (HJCE, GWH), p. 329.
ITiCSE-2011-GarciaMGH #interface #learning #unification: A system for usable unification of interfaces of learning objects in m-learning (EG, LdM, AGC, JRH), p. 347.
ITiCSE-2011-Goldweber #learning #process #turing machine: Two kinesthetic learning activities: turing machines and basic computer organization (MG), p. 335.
ITiCSE-2011-Goldweber11a #learning #social: Computing for the social good: a service learning project (MG), p. 379.
ITiCSE-2011-HarrachA #collaboration #learning #optimisation #process #recommendation #using: Optimizing collaborative learning processes by using recommendation systems (SH, MA), p. 389.
ITiCSE-2011-Hijon-NeiraV11a #design #learning: A first step mapping IMS learning design and Merlin-Mo (RHN, JÁVI), p. 365.
ITiCSE-2011-HoverHR #collaboration #learning: A collaborative linked learning space (KMH, MH, GR), p. 380.
ITiCSE-2011-HoverHRM #collaboration #how #learning #student: Evaluating how students would use a collaborative linked learning space (KMH, MH, GR, MM), pp. 88–92.
ITiCSE-2011-JourjonKY #framework: Impact of an e-learning platform on CSE lectures (GJ, SSK, JY), pp. 83–87.
ITiCSE-2011-KonertRGSB #ad hoc #community #learning: Supporting peer learning with ad-hoc communities (JK, KR, SG, RS, RB), p. 393.
ITiCSE-2011-LasserreS #learning: Effects of team-based learning on a CS1 course (PL, CS), pp. 133–137.
ITiCSE-2011-LuLJZJ #student: A bioinformatics e-learning lab for undergraduate students (FL, HL, YJ, YZ, ZJ), p. 356.
ITiCSE-2011-MartinezC #algebra #education #relational: A cooperative learning-based strategy for teaching relational algebra (AM, AC), pp. 263–267.
ITiCSE-2011-MesserK #problem #process: The use of mediating artifacts in embedding problem solving processes in an e-learning environment (OMM, AK), p. 390.
ITiCSE-2011-MothVB #learning #named #syntax: SyntaxTrain: relieving the pain of learning syntax (ALAM, JV, MBA), p. 387.
ITiCSE-2011-OliveiraMR #learning #problem #programming: From concrete to abstract?: problem domain in the learning of introductory programming (OLO, AMM, NTR), pp. 173–177.
ITiCSE-2011-PollockH #learning #multi: Combining multiple pedagogies to boost learning and enthusiasm (LLP, TH), pp. 258–262.
ITiCSE-2011-RussellMD #approach #learning #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 #learning: GLMP for automatic assessment of DFS algorithm learning (MGST, CTB, GT), p. 351.
ITiCSE-2011-ShuhidanHD #comprehension #learning: Understanding novice programmer difficulties via guided learning (SMS, MH, DJD), pp. 213–217.
ITiCSE-2011-VanoM #learning #quote: “Computer science and nursery rhymes”: a learning path for the middle school (DDV, CM), pp. 238–242.
ITiCSE-2011-WolzMS #learning #process: Kinesthetic learning of computing via “off-beat” activities (UW, MM, MS), pp. 68–72.
ESOP-2011-BorgstromGGMG #machine learning #semantics: Measure Transformer Semantics for Bayesian Machine Learning (JB, ADG, MG, JM, JVG), pp. 77–96.
FASE-2011-FengKP #automation #composition #learning #probability #reasoning: Automated Learning of Probabilistic Assumptions for Compositional Reasoning (LF, MZK, DP), pp. 2–17.
TACAS-2011-JungLWY #generative #invariant #quantifier: Predicate Generation for Learning-Based Quantifier-Free Loop Invariant Inference (YJ, WL, BYW, KY), pp. 205–219.
TACAS-2011-MertenSHM #generative: Next Generation LearnLib (MM, BS, FH, TMS), pp. 220–223.
ICPC-J-2009-Sanz-RodriguezDA11 #evaluation #learning #reuse: Metrics-based evaluation of learning object reusability (JSR, JMD, SSA), pp. 121–140.
CSMR-2011-Borchers #assessment #re-engineering: Invited Talk: Reengineering from a Practitioner’s View — A Personal Lesson’s Learned Assessment (JB), pp. 1–2.
SAS-2011-NoriR #machine learning #program analysis: Program Analysis and Machine Learning: A Win-Win Deal (AVN, SKR), pp. 2–3.
STOC-2011-BalcanH #learning: Learning submodular functions (MFB, NJAH), pp. 793–802.
DLT-2011-Yoshinaka #concept #context-free grammar #learning #towards: Towards Dual Approaches for Learning Context-Free Grammars Based on Syntactic Concept Lattices (RY), pp. 429–440.
ICALP-v1-2011-AroraG #algorithm #fault #learning: New Algorithms for Learning in Presence of Errors (SA, RG), pp. 403–415.
ICALP-v1-2011-HarkinsH #algorithm #bound #game studies #learning: Exact Learning Algorithms, Betting Games, and Circuit Lower Bounds (RCH, JMH), pp. 416–423.
LATA-2011-CaseJLOSS #automation #learning #pattern matching #subclass: Automatic Learning of Subclasses of Pattern Languages (JC, SJ, TDL, YSO, PS, FS), pp. 192–203.
SFM-2011-Jonsson #automaton #learning #modelling: Learning of Automata Models Extended with Data (BJ), pp. 327–349.
SFM-2011-Moschitti #automation #kernel #learning #modelling: Kernel-Based Machines for Abstract and Easy Modeling of Automatic Learning (AM), pp. 458–503.
SFM-2011-SteffenHM #automaton #learning #perspective: Introduction to Active Automata Learning from a Practical Perspective (BS, FH, MM), pp. 256–296.
CHI-2011-ChauKHF #interactive #machine learning #named #network #scalability: Apolo: making sense of large network data by combining rich user interaction and machine learning (DHC, AK, JIH, CF), pp. 167–176.
CHI-2011-DavidoffZZD #coordination #learning #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 #learning #mobile #named: MicroMandarin: mobile language learning in context (DE, ES, KC, JZ, JAL), pp. 3169–3178.
CHI-2011-FiebrinkCT #evaluation #interactive #learning: Human model evaluation in interactive supervised learning (RF, PRC, DT), pp. 147–156.
CHI-2011-HowisonTRA #concept #interactive #learning: The mathematical imagery trainer: from embodied interaction to conceptual learning (MH, DT, DR, DA), pp. 1989–1998.
CHI-2011-JamilOPKS #collaboration #interactive #learning: 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-LindenJBRS #feedback #game studies #lessons learnt #realtime: Buzzing to play: lessons learned from an in the wild study of real-time vibrotactile feedback (JvdL, RMGJ, JB, YR, ES), pp. 533–542.
CHI-2011-MoravejiMMCR #development #learning #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 #learning: Enhancing genomic learning through tabletop interaction (OS, MS, CV, TF, ML, HW), pp. 2817–2826.
CHI-2011-ToupsKHS #coordination #learning #simulation: Zero-fidelity simulation of fire emergency response: improving team coordination learning (ZOT, AK, WAH, NS), pp. 1959–1968.
CHI-2011-TrustyT #learning #web: Augmenting the web for second language vocabulary learning (AT, KNT), pp. 3179–3188.
CSCW-2011-NawahdahI #automation #education #learning: Automatic adjustment of a virtual teacher’s model in a learning support system (MN, TI), pp. 693–696.
DHM-2011-EilersM #composition #learning #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 #learning #polymorphism: Polymorphic Cumulative Learning in Integrated Cognitive Architectures for Analysis of Pilot-Aircraft Dynamic Environment (TY, SF), pp. 409–416.
DUXU-v1-2011-BjorndalRM #industrial #lessons learnt #requirements #specification #using: Lessons Learned from Using Personas and Scenarios for Requirements Specification of Next-Generation Industrial Robots (PB, MJR, SM), pp. 378–387.
DUXU-v1-2011-ChenT #design #industrial #learning #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 #learning #multi: Multitouch Tables for Collaborative Object-Based Learning (JG, EdA, DD, DSM, GW), pp. 237–246.
DUXU-v1-2011-Innes #design #enterprise #why: Why Enterprises Can’t Innovate: Helping Companies Learn Design Thinking (JI), pp. 442–448.
DUXU-v1-2011-LeeR #architecture #collaboration #concept #learning #mobile: Suggested Collaborative Learning Conceptual Architecture and Applications for Mobile Devices (KL, AR), pp. 611–620.
DUXU-v1-2011-Schmid #analysis #development #feedback #learning: 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 #learning #pervasive: Designing Pervasive Games for Learning (CA, RL, DR, CS, NY, NMA, MFC), pp. 99–108.
HCD-2011-ChoiS #approach #design #implementation #process: A Design-Supporting Tool for Implementing the Learning-Based Approach: Accommodating Users’ Domain Knowledge into Design Processes (JMC, KS), pp. 369–378.
HCD-2011-KamihiraAN #communication #community #design #education #learning #visual notation: Building a Shared Cross-Cultural Learning Community for Visual Communication Design Education (TK, MA, TN), pp. 397–406.
HCI-MIIE-2011-KarthikP #adaptation #approach #classification #email #machine learning: Adaptive Machine Learning Approach for Emotional Email Classification (KK, RP), pp. 552–558.
HCI-MIIE-2011-MajimaNMHNHA #evaluation #learning #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 #learning: A Web-Based Learning Environment to Support Chemistry (CA, CS), pp. 3–11.
HCI-UA-2011-EverardJM #learning #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 #learning: Introducing Mobility in Serious Games: Enhancing Situated and Collaborative Learning (SG, AS), pp. 12–20.
HCI-UA-2011-HayakawaNOFN #framework #learning #visualisation: Visualization Framework for Computer System Learning (EH, YN, HO, MF, YN), pp. 21–26.
HCI-UA-2011-Huseyinov #adaptation #fuzzy #learning #modelling #multi: Fuzzy Linguistic Modelling Cognitive / Learning Styles for Adaptation through Multi-level Granulation (IH), pp. 39–47.
HCI-UA-2011-Klenner-Moore #learning #process: Creating a New Context for Activity in Blended Learning Environments: Engaging the Twitchy Fingers (JKM), pp. 61–67.
HCI-UA-2011-LiJN #learning #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 #learning #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 #learning #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 #learning: Proposal of Collaborative Learning Support Method in Risk Communications (HY, NT), pp. 113–120.
HCI-UA-2011-YamaguchiMT #evaluation #learning #online: Evaluation of Online Handwritten Characters for Penmanship Learning Support System (TY, NM, MT), pp. 121–130.
HCI-UA-2011-YangCS #analysis #learning #recognition: Facial Expression Recognition for Learning Status Analysis (MTY, YJC, YCS), pp. 131–138.
HIMI-v2-2011-PohlML #hybrid #learning #standard: Transforming a Standard Lecture into a Hybrid Learning Scenario (HMP, JTM, JL), pp. 55–61.
OCSC-2011-AhmadL #learning: Promoting Reflective Learning: The Role of Blogs in the Classroom (RA, WGL), pp. 3–11.
OCSC-2011-PujariK #approach #machine learning #predict #recommendation: A Supervised Machine Learning Link Prediction Approach for Tag Recommendation (MP, RK), pp. 336–344.
OCSC-2011-PuseyM #collaboration #design #learning #recommendation #wiki: Assessments in Large- and Small-Scale Wiki Collaborative Learning Environments: Recommendations for Educators and Wiki Designers (PP, GM), pp. 60–68.
SIGAda-2011-Booch #ada: Everything i know i learned from ada (GB), pp. 17–18.
CAiSE-2011-DornD #process #self: Supporting Dynamic, People-Driven Processes through Self-learning of Message Flows (CD, SD), pp. 657–671.
ICEIS-J-2011-Li11f #analysis #approach #case study #machine learning #type system #using: A Study on Noisy Typing Stream Analysis Using Machine Learning Approach (JL0), pp. 149–161.
ICEIS-J-2011-NganBL11a #framework #learning #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 #learning #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 #learning #student: Students’ Acceptance of E-Group Collaboration Learning (AM), pp. 269–274.
CIKM-2011-ArguelloDC #learning #web: Learning to aggregate vertical results into web search results (JA, FD, JC), pp. 201–210.
CIKM-2011-CoffmanW #keyword #learning #rank #relational: Learning to rank results in relational keyword search (JC, ACW), pp. 1689–1698.
CIKM-2011-DhillonSS #information management #learning #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 #learning #multi #predict #social: Content based social behavior prediction: a multi-task learning approach (HF, RJ, YY, BL, JH), pp. 995–1000.
CIKM-2011-FuLZZ #learning #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 #learning #rank: Learning to rank user intent (GG, UB, TD, TKS), pp. 195–200.
CIKM-2011-KasiviswanathanMBS #detection #learning #taxonomy #topic #using: Emerging topic detection using dictionary learning (SPK, PM, AB, VS), pp. 745–754.
CIKM-2011-LauLBW #learning #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 #learning #online: Collaborative online learning of user generated content (GL, KC, SCHH, WL, RJ), pp. 285–290.
CIKM-2011-LinC #data fusion #learning #query: Query sampling for learning data fusion (TCL, PJC), pp. 141–146.
CIKM-2011-LinLWX #learning #rank: Learning to rank with cross entropy (YL, HL, JW, KX), pp. 2057–2060.
CIKM-2011-LiSZ #feedback: Learning-based relevance feedback for web-based relation completion (ZL, LS, XZ), pp. 1535–1540.
CIKM-2011-LiuCZH #learning #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: Learning kernels with upper bounds of leave-one-out error (YL, SL, YH), pp. 2205–2208.
CIKM-2011-NavigliFSLA #ambiguity #categorisation #learning #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 #learning #named: SILA: a spatial instance learning approach for deep webpages (EO, MR), pp. 2329–2332.
CIKM-2011-PandeyABHCRZ #behaviour #learning #what: Learning to target: what works for behavioral targeting (SP, MA, AB, AOH, PC, AR, MZ), pp. 1805–1814.
CIKM-2011-QianHCZN #ambiguity #machine learning: Combining machine learning and human judgment in author disambiguation (YnQ, YH, JC, QZ, ZN), pp. 1241–1246.
CIKM-2011-RamanJS #learning #ranking: Structured learning of two-level dynamic rankings (KR, TJ, PS), pp. 291–296.
CIKM-2011-SellamanickamGS #approach #learning #ranking: A pairwise ranking based approach to learning with positive and unlabeled examples (SS, PG, SKS), pp. 663–672.
CIKM-2011-SzummerY #learning #rank: Semi-supervised learning to rank with preference regularization (MS, EY), pp. 269–278.
CIKM-2011-TangLYSGGYZ #behaviour #learning #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 #learning #query #rank #web: Learning to rank categories for web queries (PU, VV), pp. 2065–2068.
CIKM-2011-WangCWLWO #learning #similarity: Coupled nominal similarity in unsupervised learning (CW, LC, MW, JL, WW, YO), pp. 973–978.
CIKM-2011-WangHJT #categorisation #image #learning #metric #multi #performance: Efficient lp-norm multiple feature metric learning for image categorization (SW, QH, SJ, QT), pp. 2077–2080.
CIKM-2011-WangHLCH #learning #recommendation: Learning to recommend questions based on public interest (JW, XH, ZL, WHC, BH), pp. 2029–2032.
CIKM-2011-WangL #framework #learning #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 #learning #query #recommendation: Context-aware query recommendation by learning high-order relation in query logs (XY, JG, XC), pp. 2073–2076.
CIKM-2011-YangZKL #how #learning #question #why: Can irrelevant data help semi-supervised learning, why and how? (HY, SZ, IK, MRL), pp. 937–946.
CIKM-2011-YanTLSL #learning #predict: Citation count prediction: learning to estimate future citations for literature (RY, JT, XL, DS, XL), pp. 1247–1252.
CIKM-2011-ZhangYCT #detection: A machine-learned proactive moderation system for auction fraud detection (LZ, JY, WC, BLT), pp. 2501–2504.
CIKM-2011-ZhaoYX #independence #information management #learning #web: Max margin learning on domain-independent web information extraction (BZ, XY, EPX), pp. 1305–1310.
CIKM-2011-ZhuZYGX #learning: Transfer active learning (ZZ, XZ, YY, YFG, XX), pp. 2169–2172.
ECIR-2011-BuffoniTG #ranking: The Importance of the Depth for Text-Image Selection Strategy in Learning-To-Rank (DB, ST, PG), pp. 743–746.
ECIR-2011-HofmannWR #learning #online #rank: Balancing Exploration and Exploitation in Learning to Rank Online (KH, SW, MdR), pp. 251–263.
ECIR-2011-LeonardLZTCD #data fusion #information retrieval #machine learning #metric: Applying Machine Learning Diversity Metrics to Data Fusion in Information Retrieval (DL, DL, LZ, FT, RWC, JD), pp. 695–698.
ECIR-2011-MacdonaldO #learning #modelling #ranking: Learning Models for Ranking Aggregates (CM, IO), pp. 517–529.
ECIR-2011-ZhouH #comprehension #learning #natural language #random: Learning Conditional Random Fields from Unaligned Data for Natural Language Understanding (DZ, YH), pp. 283–288.
ICML-2011-BabenkoVDB #learning #multi: Multiple Instance Learning with Manifold Bags (BB, NV, PD, SB), pp. 81–88.
ICML-2011-BabesMLS #learning #multi: Apprenticeship Learning About Multiple Intentions (MB, VNM, KS, MLL), pp. 897–904.
ICML-2011-BazzaniFLMT #learning #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 #learning #standard: Learning Scoring Functions with Order-Preserving Losses and Standardized Supervision (DB, CC, PG, NU), pp. 825–832.
ICML-2011-Bylander #learning #linear #multi #polynomial: Learning Linear Functions with Quadratic and Linear Multiplicative Updates (TB), pp. 505–512.
ICML-2011-ChakrabortyS #learning: Structure Learning in Ergodic Factored MDPs without Knowledge of the Transition Function’s In-Degree (DC, PS), pp. 737–744.
ICML-2011-ChenPSDC #analysis #learning #process: The Hierarchical Beta Process for Convolutional Factor Analysis and Deep Learning (BC, GP, GS, DBD, LC), pp. 361–368.
ICML-2011-ChoRI #adaptation #learning #strict: Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines (KC, TR, AI), pp. 105–112.
ICML-2011-DauphinGB #learning #re-engineering #scalability: Large-Scale Learning of Embeddings with Reconstruction Sampling (YD, XG, YB), pp. 945–952.
ICML-2011-DinuzzoOGP #coordination #kernel #learning: Learning Output Kernels with Block Coordinate Descent (FD, CSO, PVG, GP), pp. 49–56.
ICML-2011-DudikLL #evaluation #learning #policy #robust: Doubly Robust Policy Evaluation and Learning (MD, JL, LL), pp. 1097–1104.
ICML-2011-GlorotBB #adaptation #approach #classification #learning #scalability #sentiment: Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach (XG, AB, YB), pp. 513–520.
ICML-2011-Gould #learning #linear #markov #random: Max-margin Learning for Lower Linear Envelope Potentials in Binary Markov Random Fields (SG), pp. 193–200.
ICML-2011-GuilloryB #learning: Simultaneous Learning and Covering with Adversarial Noise (AG, JAB), pp. 369–376.
ICML-2011-HarelM #learning #multi: Learning from Multiple Outlooks (MH, SM), pp. 401–408.
ICML-2011-HeL #framework #learning #multi: A Graphbased Framework for Multi-Task Multi-View Learning (JH, RL), pp. 25–32.
ICML-2011-HuWC #coordination #kernel #learning #named #parametricity #scalability #using: BCDNPKL: Scalable Non-Parametric Kernel Learning Using Block Coordinate Descent (EH, BW, SC), pp. 209–216.
ICML-2011-JawanpuriaNR #kernel #learning #performance #using: Efficient Rule Ensemble Learning using Hierarchical Kernels (PJ, JSN, GR), pp. 161–168.
ICML-2011-KangGS #learning #multi: Learning with Whom to Share in Multi-task Feature Learning (ZK, KG, FS), pp. 521–528.
ICML-2011-KuwadekarN #classification #learning #modelling #relational: Relational Active Learning for Joint Collective Classification Models (AK, JN), pp. 385–392.
ICML-2011-LeeW #identification #learning #online #probability: Manifold Identification of Dual Averaging Methods for Regularized Stochastic Online Learning (SL, SJW), pp. 1121–1128.
ICML-2011-LeNCLPN #learning #on the #optimisation: On optimization methods for deep learning (QVL, JN, AC, AL, BP, AYN), pp. 265–272.
ICML-2011-LiZSC #integration #learning #modelling #on the #taxonomy #topic: On the Integration of Topic Modeling and Dictionary Learning (LL, MZ, GS, LC), pp. 625–632.
ICML-2011-LuB #learning #modelling: Learning Mallows Models with Pairwise Preferences (TL, CB), pp. 145–152.
ICML-2011-Maaten #kernel #learning: Learning Discriminative Fisher Kernels (LvdM), pp. 217–224.
ICML-2011-MachartPARG #kernel #learning #probability #rank: Stochastic Low-Rank Kernel Learning for Regression (PM, TP, SA, LR, HG), pp. 969–976.
ICML-2011-MartensS #learning #network #optimisation: Learning Recurrent Neural Networks with Hessian-Free Optimization (JM, IS), pp. 1033–1040.
ICML-2011-NgiamCKN #energy #learning #modelling: Learning Deep Energy Models (JN, ZC, PWK, AYN), pp. 1105–1112.
ICML-2011-NgiamKKNLN #learning #multimodal: Multimodal Deep Learning (JN, AK, MK, JN, HL, AYN), pp. 689–696.
ICML-2011-NickelTK #learning #multi: A Three-Way Model for Collective Learning on Multi-Relational Data (MN, VT, HPK), pp. 809–816.
ICML-2011-OrabonaL #algorithm #kernel #learning #multi #optimisation: Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning (FO, JL), pp. 249–256.
ICML-2011-QuadriantoL #learning #multi: Learning Multi-View Neighborhood Preserving Projections (NQ, CHL), pp. 425–432.
ICML-2011-RobbianoC #learning #plugin #ranking: Minimax Learning Rates for Bipartite Ranking and Plug-in Rules (SR, SC), pp. 441–448.
ICML-2011-SaxeKCBSN #learning #on the #random: On Random Weights and Unsupervised Feature Learning (AMS, PWK, ZC, MB, BS, AYN), pp. 1089–1096.
ICML-2011-SmallWBT #learning: The Constrained Weight Space SVM: Learning with Ranked Features (KS, BCW, CEB, TAT), pp. 865–872.
ICML-2011-Sohl-DicksteinBD #learning #probability: Minimum Probability Flow Learning (JSD, PB, MRD), pp. 905–912.
ICML-2011-SujeethLBRCWAOO #domain-specific language #machine learning #named #parallel: OptiML: An Implicitly Parallel Domain-Specific Language for Machine Learning (AKS, HL, KJB, TR, HC, MW, ARA, MO, KO), pp. 609–616.
ICML-2011-TamuzLBSK #adaptation #kernel #learning: Adaptively Learning the Crowd Kernel (OT, CL, SB, OS, AK), pp. 673–680.
ICML-2011-WellingT #learning #probability: Bayesian Learning via Stochastic Gradient Langevin Dynamics (MW, YWT), pp. 681–688.
ICML-2011-YangR #learning #on the #visual notation: On the Use of Variational Inference for Learning Discrete Graphical Model (EY, PDR), pp. 1009–1016.
ICML-2011-YanRFD #learning: Active Learning from Crowds (YY, RR, GF, JGD), pp. 1161–1168.
KDD-2011-AttenbergP #learning #online: Online active inference and learning (JA, FJP), pp. 186–194.
KDD-2011-Boire #case study #data mining #lessons learnt #mining: The practitioner’s viewpoint to data mining: key lessons learned in the trenches and case studies (RB), p. 785.
KDD-2011-ChakiCG #learning: Supervised learning for provenance-similarity of binaries (SC, CC, AG), pp. 15–23.
KDD-2011-ChauKHF #graph #interactive #machine learning #named #scalability #visualisation: Apolo: interactive large graph sensemaking by combining machine learning and visualization (DHC, AK, JIH, CF), pp. 739–742.
KDD-2011-ChenRT #adaptation #detection #incremental #learning: Detecting bots via incremental LS-SVM learning with dynamic feature adaptation (FC, SR, PNT), pp. 386–394.
KDD-2011-ChenZY #learning #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 #learning #online: Unbiased online active learning in data streams (WC, MZ, LL, AT, BLT), pp. 195–203.
KDD-2011-Cormode #learning #privacy: Personal privacy vs population privacy: learning to attack anonymization (GC), pp. 1253–1261.
KDD-2011-GhaniK #detection #fault #interactive #learning: Interactive learning for efficiently detecting errors in insurance claims (RG, MK), pp. 325–333.
KDD-2011-GhotingKPK #algorithm #data mining #implementation #machine learning #mining #named #parallel #pipes and filters #tool support: NIMBLE: a toolkit for the implementation of parallel data mining and machine learning algorithms on mapreduce (AG, PK, EPDP, RK), pp. 334–342.
KDD-2011-GuerraVMA #analysis #approach #bias #realtime #sentiment: From bias to opinion: a transfer-learning approach to real-time sentiment analysis (PHCG, AV, WMJ, VA), pp. 150–158.
KDD-2011-JiangBSZL #adaptation #concept #learning #ontology: Ontology enhancement and concept granularity learning: keeping yourself current and adaptive (SJ, LB, BS, YZ, WL), pp. 1244–1252.
KDD-2011-MesterharmP #algorithm #learning #online #using: Active learning using on-line algorithms (CM, MJP), pp. 850–858.
KDD-2011-MooreYZRL #classification #learning #network: Active learning for node classification in assortative and disassortative networks (CM, XY, YZ, JBR, TL), pp. 841–849.
KDD-2011-RashidiC #induction #learning #query: Ask me better questions: active learning queries based on rule induction (PR, DJC), pp. 904–912.
KDD-2011-ValizadeganJW #learning #multi #predict: Learning to trade off between exploration and exploitation in multiclass bandit prediction (HV, RJ, SW), pp. 204–212.
KDD-2011-VijayaraghavanK #data mining #machine learning #mining #online: Applications of data mining and machine learning in online customer care (RV, PVK), p. 779.
KDD-2011-ZhangHLSL #approach #learning #multi #scalability: Multi-view transfer learning with a large margin approach (DZ, JH, YL, LS, RDL), pp. 1208–1216.
KDD-2011-ZhangLS #learning: Serendipitous learning: learning beyond the predefined label space (DZ, YL, LS), pp. 1343–1351.
KDD-2011-ZhouYLY #learning #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 #learning: 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 #learning #named #rank: XHITS: Learning to Rank in a Hyperlinked Structure (FBF, RPR, RLM), pp. 385–389.
KDIR-2011-GriffithOS #collaboration #learning #parametricity: Learning Neighbourhood-based Collaborative Filtering Parameters (JG, CO, HS), pp. 452–455.
KDIR-2011-Liebowitz #information management: Knowledge Management and e-Learning: Putting Theory into Practice (JL), p. 5.
KDIR-2011-LiVM #graph #learning #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 #learning #similarity #using: Learning Similarity Functions for Event Identification using Support Vector Machines (TR, PC), pp. 208–215.
KEOD-2011-AbbesZN #learning #ontology #semantics: Evaluating Semantic Classes Used for Ontology Building and Learning from Texts (SBA, HZ, AN), pp. 445–448.
KEOD-2011-IshakLA #approach #learning #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 #learning #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.
KEOD-2011-YamasakiS #graph: A Graph Manipulation System Abstracted from e-Learning (SY, MS), pp. 466–469.
KMIS-2011-BerkaniCN #community #online #semantics: Semantics and Knowledge Capitalization in Online Communities of Practice of e-Learning (LB, AC, ON), pp. 96–104.
KMIS-2011-BuresPCO #framework #interactive #towards: Interactive Digital TV as the e-Learning Platform — Towards Supportive Environments for Elderly (VB, DP, PC, TO), pp. 107–113.
KMIS-2011-Silva #approach #concept #learning: Learning Organization — Concept and Proposal of a New Approach (AFdS), pp. 384–389.
MLDM-2011-CelibertoM #learning: Investigation in Transfer Learning: Better Way to Apply Transfer Learning between Agents (LACJ, JPM), pp. 210–223.
MLDM-2011-LahbibBL #learning #multi: Informative Variables Selection for Multi-relational Supervised Learning (DL, MB, DL), pp. 75–87.
MLDM-2011-Sullins #smarttech: Exploration Strategies for Learned Probabilities in Smart Terrain (JS), pp. 224–238.
MLDM-2011-TalbertHT #data mining #framework #machine learning #mining: A Machine Learning and Data Mining Framework to Enable Evolutionary Improvement in Trauma Triage (DAT, MH, ST), pp. 348–361.
MLDM-2011-XuGC #adaptation #kernel #learning #multi: Adaptive Kernel Diverse Density Estimate for Multiple Instance Learning (TX, IG, DKYC), pp. 185–198.
MLDM-2011-XuM #learning #taxonomy: Dictionary Learning Based on Laplacian Score in Sparse Coding (JX, HM), pp. 253–264.
RecSys-2011-Makrehchi #learning #recommendation #social #topic: Social link recommendation by learning hidden topics (MM), pp. 189–196.
RecSys-2011-PaparrizosCG #recommendation: Machine learned job recommendation (IKP, BBC, AG), pp. 325–328.
RecSys-2011-WuCMW #detection #learning #named: Semi-SAD: applying semi-supervised learning to shilling attack detection (ZW, JC, BM, YW), pp. 289–292.
SEKE-2011-GaoZHL #learning #modelling: Learning action models with indeterminate effects (JG, HHZ, DjH, LL), pp. 159–162.
SEKE-2011-NoorianBD #classification #framework #machine learning #testing #towards: Machine Learning-based Software Testing: Towards a Classification Framework (MN, EB, WD), pp. 225–229.
SEKE-2011-SantosGSF #agile #empirical #implementation #learning #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 #learning #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 #learning #testing: Empirical study upon software testing learning with support from educational game (MT, AZ, ACdS), pp. 481–484.
SIGIR-2011-AminiU #automation #detection #learning #multi #summary: Transductive learning over automatically detected themes for multi-document summarization (MRA, NU), pp. 1193–1194.
SIGIR-2011-AsadiMEL #learning #pseudo #ranking #web: Pseudo test collections for learning web search ranking functions (NA, DM, TE, JJL), pp. 1073–1082.
SIGIR-2011-DaiSD #learning #rank: Learning to rank for freshness and relevance (ND, MS, BDD), pp. 95–104.
SIGIR-2011-DaiSD11a #learning #multi #optimisation #rank: Multi-objective optimization in learning to rank (ND, MS, BDD), pp. 1241–1242.
SIGIR-2011-GaoZLLW #feedback #learning: Learning features through feedback for blog distillation (DG, RZ, WL, RYKL, KFW), pp. 1085–1086.
SIGIR-2011-Hofmann #online: Search engines that learn online (KH), pp. 1313–1314.
SIGIR-2011-JiYGHHZC #graph #learning #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-KanoulasSMPA #algorithm #ranking #scalability #set: A large-scale study of the effect of training set characteristics over learning-to-rank algorithms (EK, SS, PM, VP, JAA), pp. 1243–1244.
SIGIR-2011-KumarL #learning #rank: Learning to rank from a noisy crowd (AK, ML), pp. 1221–1222.
SIGIR-2011-LeeHWHS #dataset #graph #image #learning #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: Learning for graphs with annotated edges (FL), pp. 1259–1260.
SIGIR-2011-LinLJY #approach #machine learning #query #social: Social annotation in query expansion: a machine learning approach (YL, HL, SJ, ZY), pp. 405–414.
SIGIR-2011-MoghaddamE #aspect-oriented #learning #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 #learning #rank: Learning to rank under tight budget constraints (CP, RS), pp. 1173–1174.
SIGIR-2011-SantosMO11a #metric #on the #ranking: On the suitability of diversity metrics for learning-to-rank for diversity (RLTS, CM, IO), pp. 1185–1186.
SIGIR-2011-ShiYGN #machine learning #network #recommendation #scalability #social: A large scale machine learning system for recommending heterogeneous content in social networks (YS, DY, AG, SN), pp. 1337–1338.
SIGIR-2011-SiJ #information retrieval #machine learning: Machine learning for information retrieval (LS, RJ), pp. 1293–1294.
SIGIR-2011-TianL #information retrieval #interactive #learning: Active learning to maximize accuracy vs. effort in interactive information retrieval (AT, ML), pp. 145–154.
SIGIR-2011-WangGWL #information retrieval #learning #parallel #rank: Parallel learning to rank for information retrieval (SW, BJG, KW, HWL), pp. 1083–1084.
SIGIR-2011-WangRFZHB #learning #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 #learning #online #random: Learning online discussion structures by conditional random fields (HW, CW, CZ, JH), pp. 435–444.
SIGIR-2011-WuYLLYX #learning #rank #using: Learning to rank using query-level regression (JW, ZY, YL, HL, ZY, KX), pp. 1091–1092.
SIGIR-2011-YangLSZZ #collaboration #learning #recommendation #using: Collaborative competitive filtering: learning recommender using context of user choice (SHY, BL, AJS, HZ, ZZ), pp. 295–304.
ECMFA-2011-DolquesDFHNP #automation #learning #model transformation: Easing Model Transformation Learning with Automatically Aligned Examples (XD, AD, JRF, MH, CN, FP), pp. 189–204.
PADL-2011-Mooney #learning: Learning Language from Its Perceptual Context (RJM), pp. 2–4.
POPL-2011-LiangTN #abstraction #learning: Learning minimal abstractions (PL, OT, MN), pp. 31–42.
RE-2011-Waldmann #agile #constraints #development #requirements #what: There’s never enough time: Doing requirements under resource constraints, and what requirements engineering can learn from agile development (BW), pp. 301–305.
SAC-2011-BhaskaranNFG #behaviour #detection #learning #online: Deceit detection via online behavioral learning (NB, IN, MGF, VG), pp. 29–30.
SAC-2011-FontesNPC #architecture #detection #learning #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: Learning recurring concepts from data streams with a context-aware ensemble (JBG, EMR, PACS), pp. 994–999.
SAC-2011-LiuLTL #framework #game studies #interactive #learning: A cognition-based interactive game platform for learning Chinese characters (CLL, CYL, JLT, CLL), pp. 1181–1186.
SAC-2011-NawahdahI #education #learning #physics: Positioning a virtual teacher in an MR physical task learning support system (MN, TI), pp. 1169–1174.
SAC-2011-SimoesO #behaviour #game studies #learning #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-VerasBBSC #education #framework #personalisation #web: A solution for personalized t-learning applications integrated with a web educational platform (DV, IIB, HB, MS, EdBC), pp. 1187–1193.
SAC-2011-ZhangZZZX #detection #learning #web: Harmonic functions based semi-supervised learning for web spam detection (WZ, DZ, YZ, GZ, BX), pp. 74–75.
ICSE-2011-BorgesGLN #adaptation #evolution #learning #requirements #specification: Learning to adapt requirements specifications of evolving systems (RVB, ASdG, LCL, BN), pp. 856–859.
CGO-2011-SanchezASPS #compilation #using: Using machines to learn method-specific compilation strategies (RNS, JNA, DS, MP, MGS), pp. 257–266.
ICLP-J-2011-CorapiRVPS #design #induction #learning #using: Normative design using inductive learning (DC, AR, MDV, JAP, KS), pp. 783–799.
ICTSS-2011-MeinkeN #term rewriting #testing #using: Learning-Based Testing for Reactive Systems Using Term Rewriting Technology (KM, FN), pp. 97–114.
SAT-2011-SilverthornM #learning #satisfiability: Learning Polarity from Structure in SAT (BS, RM), pp. 377–378.
TAP-2011-MeinkeS #incremental #testing: Incremental Learning-Based Testing for Reactive Systems (KM, MAS), pp. 134–151.
VMCAI-2011-HowarSM #abstraction #automation #automaton #learning #refinement: Automata Learning with Automated Alphabet Abstraction Refinement (FH, BS, MM), pp. 263–277.
ECSA-2010-MarcoGII #adaptation #learning #lifecycle #paradigm #self: Learning from the Cell Life-Cycle: A Self-adaptive Paradigm (ADM, FG, PI, RI), pp. 485–488.
CASE-2010-DoroodgarN #architecture #learning: A hierarchical reinforcement learning based control architecture for semi-autonomous rescue robots in cluttered environments (BD, GN), pp. 948–953.
CASE-2010-LiYG #learning: Learning compliance control of robot manipulators in contact with the unknown environment (YL, CY, SSG), pp. 644–649.
DAC-2010-CallegariDWA #classification #learning #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 #learning #named: BooM: a decision procedure for boolean matching with abstraction and dynamic learning (CFL, JHRJ, KHW), pp. 499–504.
DATE-2010-HuangSM #fault #machine learning: Fault diagnosis of analog circuits based on machine learning (KH, HGDS, SM), pp. 1761–1766.
DATE-2010-LiuTQ #algorithm #constraints #performance #power management: Enhanced Q-learning algorithm for dynamic power management with performance constraint (WL, YT, QQ), pp. 602–605.
DATE-2010-ShenHH #adaptation #configuration management: Learning-based adaptation to applications and environments in a reconfigurable Network-on-Chip (JSS, CHH, PAH), pp. 381–386.
DRR-2010-LiuZ #detection #documentation #image #learning: Semi-supervised learning for detecting text-lines in noisy document images (ZL, HZ), pp. 1–10.
DRR-2010-Obafemi-AjayiAF #documentation #learning: Learning shape features for document enhancement (TOA, GA, OF), pp. 1–10.
DRR-2010-ZhangZLT #learning #recognition: A stacked sequential learning method for investigator name recognition from web-based medical articles (XZ, JZ, DXL, GRT), pp. 1–10.
HT-2010-HsiaoBYO #adaptation #approach #case study: The value of adaptive link annotation in e-learning: a study of a portal-based approach (IHH, PB, MY, AO), pp. 223–228.
HT-2010-PaekHS #hypermedia #learning: Spatial contiguity and implicit learning in hypertext (SP, DH, AS), pp. 291–292.
HT-2010-PrataGC #learning #personalisation: Crossmedia personalized learning contexts (AP, NG, TC), pp. 305–306.
HT-2010-TielletPRLC #design #evaluation #learning: Design and evaluation of a hypervideo environment to support veterinary surgery learning (CABT, AGP, EBR, JVdL, TC), pp. 213–222.
HT-2010-TielletPRLC10a #learning #named: HVet: a hypervideo environment to support veterinary surgery learning (CABT, AGP, EBR, JVdL, TC), pp. 313–314.
PODS-2010-LemayMN #algorithm #learning #top-down #xml: A learning algorithm for top-down XML transformations (AL, SM, JN), pp. 285–296.
SIGMOD-2010-ArasuGK #learning #on the: On active learning of record matching packages (AA, MG, RK), pp. 783–794.
SIGMOD-2010-CortezSGM #information management #learning #named #on-demand: ONDUX: on-demand unsupervised learning for information extraction (EC, ASdS, MAG, ESdM), pp. 807–818.
ITiCSE-2010-AydinolG10a #learning #spreadsheet #video: The effect of video tutorials on learning spreadsheets (ABA, ÖG), p. 323.
ITiCSE-2010-CoconF #education #learning #named #online: LOMOLEHEA: learning object model for online learning based on the european higher education area (FC, EF), pp. 78–82.
ITiCSE-2010-Cross #learning: Promoting active learning through assignments (GWC), p. 306.
ITiCSE-2010-Denny #collaboration #learning #online: Motivating online collaborative learning (PD), p. 300.
ITiCSE-2010-EganJ #learning: Service learning in introductory computer science (MALE, MJ), pp. 8–12.
ITiCSE-2010-HamadaS #learning: Lego NXT as a learning tool (MH, SS), p. 321.
ITiCSE-2010-HowardJN #behaviour #design #learning #online #using: Reflecting on online learning designs using observed behavior (LH, JJ, CN), pp. 179–183.
ITiCSE-2010-Larraza-MendiluzeG #game studies #learning #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 #learning #visualisation: Integrating categories of algorithm learning objective into algorithm visualization design: a proposal (MHL, GR), pp. 289–293.
ITiCSE-2010-MarcosHGGMGBOGVME #learning #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 #learning #multi #recursion #student: Learning (through) recursion: a multidimensional analysis of the competences achieved by CS1 students (CM), pp. 160–164.
ITiCSE-2010-QianLYL #learning #programming: Inquiry-based active learning in introductory programming courses (KQ, CTDL, LY, JL), p. 312.
ITiCSE-2010-TuOKKT #learning: Developing verification-driven learning cases (ST, SJO, RK, AK, ST), pp. 58–62.
ICSM-2010-BhattacharyaN #debugging #fine-grained #graph #incremental #learning #multi: Fine-grained incremental learning and multi-feature tossing graphs to improve bug triaging (PB, IN), pp. 1–10.
PASTE-2010-FengG #fault #learning #locality #modelling #probability: Learning universal probabilistic models for fault localization (MF, RG), pp. 81–88.
SCAM-2010-Zeller #in the large #learning #mining #modelling: Learning from 6,000 Projects: Mining Models in the Large (AZ), pp. 3–6.
STOC-2010-KalaiMV #learning: Efficiently learning mixtures of two Gaussians (ATK, AM, GV), pp. 553–562.
LATA-2010-KasprzikK #learning #string #using: String Extension Learning Using Lattices (AK, TK), pp. 380–391.
CHI-2010-AmershiFKT #concept #interactive #learning #modelling #multi: Examining multiple potential models in end-user interactive concept learning (SA, JF, AK, DST), pp. 1357–1360.
CHI-2010-CapraMVM #collaboration #learning #multi: Tools-at-hand and learning in multi-session, collaborative search (RGC, GM, JVM, KM), pp. 951–960.
CHI-2010-DornG #design #learning #programming #web: Learning on the job: characterizing the programming knowledge and learning strategies of web designers (BD, MG), pp. 703–712.
CHI-2010-DuganGM #lessons learnt: Lessons learned from blog muse: audience-based inspiration for bloggers (CD, WG, DRM), pp. 1965–1974.
CHI-2010-HuangSDWKAL #learning #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 #learning: Designing games for learning: insights from conversations with designers (KI, MF, CH), pp. 2041–2044.
CHI-2010-KumarTSCKC #case study #learning #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 #learning #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 #learning #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 #learning #network #performance: Efficient Learning of Dynamic Bayesian Networks from Timed Data (AA, MLG), pp. 226–231.
ICEIS-AIDSS-2010-CacoveanuBP #framework #predict: Evaluating Prediction Strategies in an Enhanced Meta-learning Framework (SC, CVB, RP), pp. 148–156.
ICEIS-AIDSS-2010-MasvoulaKM #bibliography #learning: A Review of Learning Methods Enhanced in Strategies of Negotiating Agents (MM, PK, DM), pp. 212–219.
ICEIS-AIDSS-2010-MoriyasuYN #learning #self #using: Supervised Learning for Agent Positioning by using Self-organizing Map (KM, TY, HN), pp. 368–372.
ICEIS-HCI-2010-Cruz-LaraOGBB #chat #communication #interface #multi #standard: Standards for Communication and e-Learning in Virtual Worlds — The Multilingual-assisted Chat Interface (SCL, TO, JG, NB, LB), pp. 45–52.
ICEIS-HCI-2010-DelceaDC #collaboration #enterprise #performance: A Model for Improving Enterprise’s Performance based on Collaborative e-Learning (CD, MD, CC), pp. 5–12.
ICEIS-HCI-2010-DiosERR #collaboration #learning: Virtual and Collaborative Environment for Learning Maths (AQD, AHE, IVR, ÁMdR), pp. 86–90.
ICEIS-HCI-2010-FardounVGRG #mobile #tool support: New Era of m-Learning Tools — Creation of MPrinceTool a Mobile Educative Tool (HF, PGV, JEG, GSR, EdlG), pp. 161–167.
ICEIS-J-2010-Cruz-LaraOGBBC #chat #communication #interface #standard #using: A Chat Interface Using Standards for Communication and e-Learning in Virtual Worlds (SCL, TO, JG, NB, LB, JPC), pp. 541–554.
ICEIS-J-2010-DiosERR10a #collaboration #student: A Virtual Collaborative Environment Helps University Students to Learn Maths (AQD, AHE, IVR, ÁMdR), pp. 600–606.
ICEIS-J-2010-PotoleaCL #evaluation #framework #predict: Meta-learning Framework for Prediction Strategy Evaluation (RP, SC, CL), pp. 280–295.
ICEIS-SAIC-2010-MaximianoF #case study #mobile: Mobile e-Learning — Support Services Case Study (CM, VBF), pp. 106–113.
CIKM-2010-BethardJ #behaviour #learning #modelling: Who should I cite: learning literature search models from citation behavior (SB, DJ), pp. 609–618.
CIKM-2010-BilottiECN #constraints #learning #rank #semantics: Rank learning for factoid question answering with linguistic and semantic constraints (MWB, JLE, JGC, EN), pp. 459–468.
CIKM-2010-BingSJZL #documentation #learning #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 #learning #parallel: Active learning in parallel universes (NC, MRB), pp. 1621–1624.
CIKM-2010-ComarTJ #learning #multi #network: Multi task learning on multiple related networks (PMC, PNT, AKJ), pp. 1737–1740.
CIKM-2010-DuNL #adaptation #learning: Adapting cost-sensitive learning for reject option (JD, EAN, CXL), pp. 1865–1868.
CIKM-2010-EatondJ #clustering #constraints #learning #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 #learning #multi: Multilevel manifold learning with application to spectral clustering (HrF, SS, YS), pp. 419–428.
CIKM-2010-FujinoUN #classification #learning #robust: A robust semi-supervised classification method for transfer learning (AF, NU, MN), pp. 379–388.
CIKM-2010-He #classification #learning #sentiment: Learning sentiment classification model from labeled features (YH), pp. 1685–1688.
CIKM-2010-HeMW #algorithm #evaluation #learning #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 #learning #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 #learning #novel #rank: Learning to rank relevant and novel documents through user feedback (AL, YY), pp. 469–478.
CIKM-2010-LinLYJS #learning #rank: Learning to rank with groups (YL, HL, ZY, SJ, XS), pp. 1589–1592.
CIKM-2010-MoonLCLZC #feedback #learning #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 #learning #multi #ranking #using: Experiences with using SVM-based learning for multi-objective ranking (LTN, WGY, RL, OF), pp. 1917–1920.
CIKM-2010-ShiZT #learning: Combining link and content for collective active learning (LS, YZ, JT), pp. 1829–1832.
CIKM-2010-SonPS #classification #estimation #learning #naive bayes: Learning naïve bayes transfer classifier throughclass-wise test distribution estimation (JWS, SBP, HJS), pp. 1729–1732.
CIKM-2010-TakamuraO #learning #summary: Learning to generate summary as structured output (HT, MO), pp. 1437–1440.
CIKM-2010-YangKL #feature model #learning #multi #online: Online learning for multi-task feature selection (HY, IK, MRL), pp. 1693–1696.
CIKM-2010-ZhangWWCZHZ #learning #modelling: Learning click models via probit bayesian inference (YZ, DW, GW, WC, ZZ, BH, LZ), pp. 439–448.
CIKM-2010-ZhaoBCGWZ #concurrent #learning #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 #learning: Transfer incremental learning for pattern classification (ZZ, XZ, YFG, XX), pp. 1709–1712.
ECIR-2010-MendozaMFP #learning #query #web: Learning to Distribute Queries into Web Search Nodes (MM, MM, FF, BP), pp. 281–292.
ECIR-2010-PengMO #learning #ranking: Learning to Select a Ranking Function (JP, CM, IO), pp. 114–126.
ICML-2010-Apte #machine learning #optimisation: The Role of Machine Learning in Business Optimization (CA), pp. 1–2.
ICML-2010-BilgicMG #learning: Active Learning for Networked Data (MB, LM, LG), pp. 79–86.
ICML-2010-BordesUW #ambiguity #learning #ranking #semantics: Label Ranking under Ambiguous Supervision for Learning Semantic Correspondences (AB, NU, JW), pp. 103–110.
ICML-2010-BouzyM #game studies #learning #matrix #multi: Multi-agent Learning Experiments on Repeated Matrix Games (BB, MM), pp. 119–126.
ICML-2010-BradleyG #learning #random: Learning Tree Conditional Random Fields (JKB, CG), pp. 127–134.
ICML-2010-CaniniSG #categorisation #learning #modelling #process: Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process (KRC, MMS, TLG), pp. 151–158.
ICML-2010-CaoLY #learning #multi #predict: Transfer Learning for Collective Link Prediction in Multiple Heterogenous Domains (BC, NNL, QY), pp. 159–166.
ICML-2010-Cesa-BianchiSS #learning #performance: Efficient Learning with Partially Observed Attributes (NCB, SSS, OS), pp. 183–190.
ICML-2010-ChakrabortyS #convergence #learning #multi #safety: Convergence, Targeted Optimality, and Safety in Multiagent Learning (DC, PS), pp. 191–198.
ICML-2010-ChangSGR #learning: Structured Output Learning with Indirect Supervision (MWC, VS, DG, DR), pp. 199–206.
ICML-2010-CortesMR #algorithm #kernel #learning: Two-Stage Learning Kernel Algorithms (CC, MM, AR), pp. 239–246.
ICML-2010-CortesMR10a #bound #kernel #learning: Generalization Bounds for Learning Kernels (CC, MM, AR), pp. 247–254.
ICML-2010-CumminsN #named #recognition #using #visual notation: FAB-MAP: Appearance-Based Place Recognition and Mapping using a Learned Visual Vocabulary Model (MJC, PMN), pp. 3–10.
ICML-2010-DavisD #bottom-up #learning #markov #network: Bottom-Up Learning of Markov Network Structure (JD, PMD), pp. 271–278.
ICML-2010-DeselaersF #learning #multi #random: A Conditional Random Field for Multiple-Instance Learning (TD, VF), pp. 287–294.
ICML-2010-DillonBL #analysis #generative #learning: Asymptotic Analysis of Generative Semi-Supervised Learning (JVD, KB, GL), pp. 295–302.
ICML-2010-DruckM #generative #learning #modelling #using: High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models (GD, AM), pp. 319–326.
ICML-2010-GavishNC #graph #learning #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 #learning #parametricity: Budgeted Nonparametric Learning from Data Streams (RG, AK), pp. 391–398.
ICML-2010-GregorL #approximate #learning #performance: Learning Fast Approximations of Sparse Coding (KG, YL), pp. 399–406.
ICML-2010-GrubbB #composition #learning #network: Boosted Backpropagation Learning for Training Deep Modular Networks (AG, JAB), pp. 407–414.
ICML-2010-HarpaleY #adaptation #learning #multi: Active Learning for Multi-Task Adaptive Filtering (AH, YY), pp. 431–438.
ICML-2010-HonorioS #learning #modelling #multi #visual notation: Multi-Task Learning of Gaussian Graphical Models (JH, DS), pp. 447–454.
ICML-2010-HuangG #independence #learning #ranking: Learning Hierarchical Riffle Independent Groupings from Rankings (JH, CG), pp. 455–462.
ICML-2010-HueV #kernel #learning #on the: On learning with kernels for unordered pairs (MH, JPV), pp. 463–470.
ICML-2010-JenattonMOB #learning #taxonomy: Proximal Methods for Sparse Hierarchical Dictionary Learning (RJ, JM, GO, FRB), pp. 487–494.
ICML-2010-KimT10a #learning #multi #process: Gaussian Processes Multiple Instance Learning (MK, FDlT), pp. 535–542.
ICML-2010-KokD #learning #logic #markov #network #using: Learning Markov Logic Networks Using Structural Motifs (SK, PMD), pp. 551–558.
ICML-2010-KulisB #learning #online: Implicit Online Learning (BK, PLB), pp. 575–582.
ICML-2010-LazaricG #learning #multi: Bayesian Multi-Task Reinforcement Learning (AL, MG), pp. 599–606.
ICML-2010-LiangJK #approach #learning #source code: Learning Programs: A Hierarchical Bayesian Approach (PL, MIJ, DK), pp. 639–646.
ICML-2010-LiangS #interactive #learning #multi #on the: On the Interaction between Norm and Dimensionality: Multiple Regimes in Learning (PL, NS), pp. 647–654.
ICML-2010-LiPSG #learning #parametricity: Budgeted Distribution Learning of Belief Net Parameters (LL, BP, CS, RG), pp. 879–886.
ICML-2010-LiuHC #graph #learning #scalability: Large Graph Construction for Scalable Semi-Supervised Learning (WL, JH, SFC), pp. 679–686.
ICML-2010-LiuNLL #analysis #graph #learning #relational: Learning Temporal Causal Graphs for Relational Time-Series Analysis (YL, ANM, ACL, YL), pp. 687–694.
ICML-2010-LizotteBM #analysis #learning #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 #learning #towards: Toward Off-Policy Learning Control with Function Approximation (HRM, CS, SB, RSS), pp. 719–726.
ICML-2010-Mahmud #learning: Constructing States for Reinforcement Learning (MMHM), pp. 727–734.
ICML-2010-Martens #learning #optimisation: Deep learning via Hessian-free optimization (JM), pp. 735–742.
ICML-2010-Martens10a #learning #linear: Learning the Linear Dynamical System with ASOS (JM), pp. 743–750.
ICML-2010-McFeeL #learning #metric #rank: Metric Learning to Rank (BM, GRGL), pp. 775–782.
ICML-2010-MeshiSJG #approximate #learning: Learning Efficiently with Approximate Inference via Dual Losses (OM, DS, TSJ, AG), pp. 783–790.
ICML-2010-MorimuraSKHT #approximate #learning #parametricity: Nonparametric Return Distribution Approximation for Reinforcement Learning (TM, MS, HK, HH, TT), pp. 799–806.
ICML-2010-OntanonP #approach #induction #learning #multi: Multiagent Inductive Learning: an Argumentation-based Approach (SO, EP), pp. 839–846.
ICML-2010-Raphael #machine learning #music: Music Plus One and Machine Learning (CR), pp. 21–28.
ICML-2010-Salakhutdinov #adaptation #learning #using: Learning Deep Boltzmann Machines using Adaptive MCMC (RS), pp. 943–950.
ICML-2010-ShoebG #detection #machine learning: Application of Machine Learning To Epileptic Seizure Detection (AHS, JVG), pp. 975–982.
ICML-2010-SlivkinsRG #documentation #learning #ranking #scalability: Learning optimally diverse rankings over large document collections (AS, FR, SG), pp. 983–990.
ICML-2010-SnyderB #learning #multi: Climbing the Tower of Babel: Unsupervised Multilingual Learning (BS, RB), pp. 29–36.
ICML-2010-SzitaS #bound #complexity #learning #modelling: Model-based reinforcement learning with nearly tight exploration complexity bounds (IS, CS), pp. 1031–1038.
ICML-2010-TanWT #dataset #feature model #learning: Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets (MT, LW, IWT), pp. 1047–1054.
ICML-2010-TomiokaSSK #algorithm #learning #matrix #performance #rank: A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices (RT, TS, MS, HK), pp. 1087–1094.
ICML-2010-WalshSLD #learning: Generalizing Apprenticeship Learning across Hypothesis Classes (TJW, KS, MLL, CD), pp. 1119–1126.
ICML-2010-WangKC #learning: Sequential Projection Learning for Hashing with Compact Codes (JW, SK, SFC), pp. 1127–1134.
ICML-2010-WunderLB #multi: Classes of Multiagent Q-learning Dynamics with epsilon-greedy Exploration (MW, MLL, MB), pp. 1167–1174.
ICML-2010-XuJYKL #kernel #learning #multi #performance: Simple and Efficient Multiple Kernel Learning by Group Lasso (ZX, RJ, HY, IK, MRL), pp. 1175–1182.
ICML-2010-YangJJ #learning: Learning from Noisy Side Information by Generalized Maximum Entropy Model (TY, RJ, AKJ), pp. 1199–1206.
ICML-2010-YangXKL #learning #online: Online Learning for Group Lasso (HY, ZX, IK, MRL), pp. 1191–1198.
ICML-2010-ZhaoH #framework #learning #named #online: OTL: A Framework of Online Transfer Learning (PZ, SCHH), pp. 1231–1238.
ICML-2010-ZhuGJRHK #learning #modelling: Cognitive Models of Test-Item Effects in Human Category Learning (XZ, BRG, KSJ, TTR, JH, CK), pp. 1247–1254.
ICPR-2010-Al-HuseinyMN #approach #set: Gait Learning-Based Regenerative Model: A Level Set Approach (MSAH, SM, MSN), pp. 2644–2647.
ICPR-2010-AlmaksourAQC #classification #evolution #fuzzy #gesture #incremental #learning #recognition: Evolving Fuzzy Classifiers: Application to Incremental Learning of Handwritten Gesture Recognition Systems (AA, ÉA, SQ, MC), pp. 4056–4059.
ICPR-2010-AmateR #learning #modelling #probability: Learning Probabilistic Models of Contours (LA, MJR), pp. 645–648.
ICPR-2010-AroraS #algorithm #learning #performance: An Efficient and Stable Algorithm for Learning Rotations (RA, WAS), pp. 2993–2996.
ICPR-2010-AtmosukartoSH #3d #learning #programming #search-based: The Use of Genetic Programming for Learning 3D Craniofacial Shape Quantifications (IA, LGS, CH), pp. 2444–2447.
ICPR-2010-BaghshahS #constraints #kernel #learning #performance: Efficient Kernel Learning from Constraints and Unlabeled Data (MSB, SBS), pp. 3364–3367.
ICPR-2010-BalujaC #learning #performance #retrieval: Beyond “Near Duplicates”: Learning Hash Codes for Efficient Similar-Image Retrieval (SB, MC), pp. 543–547.
ICPR-2010-BanderaMM #incremental #learning #mobile #visual notation: Incremental Learning of Visual Landmarks for Mobile Robotics (AB, RM, RVM), pp. 4255–4258.
ICPR-2010-BlondelSU #learning #online #recognition: Unsupervised Learning of Stroke Tagger for Online Kanji Handwriting Recognition (MB, KS, KU), pp. 1973–1976.
ICPR-2010-BoltonG #framework #learning #multi #optimisation #random #set: Cross Entropy Optimization of the Random Set Framework for Multiple Instance Learning (JB, PDG), pp. 3907–3910.
ICPR-2010-BuyssensR #learning #verification: Learning Sparse Face Features: Application to Face Verification (PB, MR), pp. 670–673.
ICPR-2010-CamposZJ: An Improved Structural EM to Learn Dynamic Bayesian Nets (CPdC, ZZ, QJ), pp. 601–604.
ICPR-2010-CarneiroN #architecture #learning: The Fusion of Deep Learning Architectures and Particle Filtering Applied to Lip Tracking (GC, JCN), pp. 2065–2068.
ICPR-2010-Casarrubias-VargasPB #machine learning #navigation #visual notation: EKF-SLAM and Machine Learning Techniques for Visual Robot Navigation (HCV, APB, EBC), pp. 396–399.
ICPR-2010-Cevikalp #distance #learning #metric #polynomial #programming: Semi-supervised Distance Metric Learning by Quadratic Programming (HC), pp. 3352–3355.
ICPR-2010-ChenF #graph #learning: Semi-supervised Graph Learning: Near Strangers or Distant Relatives (WC, GF), pp. 3368–3371.
ICPR-2010-CiompiPR #approach #random #using: A Meta-Learning Approach to Conditional Random Fields Using Error-Correcting Output Codes (FC, OP, PR), pp. 710–713.
ICPR-2010-CohenP #learning #performance #robust: Reinforcement Learning for Robust and Efficient Real-World Tracking (AC, VP), pp. 2989–2992.
ICPR-2010-DagAKS #categorisation #learning: Learning Affordances for Categorizing Objects and Their Properties (ND, IA, SK, ES), pp. 3089–3092.
ICPR-2010-DitzlerPC #algorithm #incremental #learning: An Incremental Learning Algorithm for Non-stationary Environments and Class Imbalance (GD, RP, NVC), pp. 2997–3000.
ICPR-2010-DundarBRJSG #approach #classification #learning #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 #learning #linear: A Unifying Framework for Learning the Linear Combiners for Classifier Ensembles (HE, MUS), pp. 2985–2988.
ICPR-2010-FanHM #classification #learning #metric: Learning Metrics for Shape Classification and Discrimination (YF, DH, WM), pp. 2652–2655.
ICPR-2010-FausserS #approximate #learning: Learning a Strategy with Neural Approximated Temporal-Difference Methods in English Draughts (SF, FS), pp. 2925–2928.
ICPR-2010-FengZH #detection #learning #online #self: Online Learning with Self-Organizing Maps for Anomaly Detection in Crowd Scenes (JF, CZ, PH), pp. 3599–3602.
ICPR-2010-FuLTZ #classification #learning #music #naive bayes #retrieval: Learning Naive Bayes Classifiers for Music Classification and Retrieval (ZF, GL, KMT, DZ), pp. 4589–4592.
ICPR-2010-GuoBC #approach #learning #using: Support Vectors Selection for Supervised Learning Using an Ensemble Approach (LG, SB, NC), pp. 37–40.
ICPR-2010-GuoZCZG #documentation #learning: Unsupervised Learning from Linked Documents (ZG, SZ, YC, ZZ, YG), pp. 730–733.
ICPR-2010-HanCR #categorisation #image #low level: Image Categorization by Learned Nonlinear Subspace of Combined Visual-Words and Low-Level Features (XHH, YWC, XR), pp. 3037–3040.
ICPR-2010-HanCR10a #concept #interactive #learning #recognition #semantics: Semi-supervised and Interactive Semantic Concept Learning for Scene Recognition (XHH, YWC, XR), pp. 3045–3048.
ICPR-2010-HanFD #learning #prototype #recognition #set: Discriminative Prototype Learning in Open Set Face Recognition (ZH, CF, XD), pp. 2696–2699.
ICPR-2010-HuangY #learning #recognition: Learning Virtual HD Model for Bi-model Emotional Speaker Recognition (TH, YY), pp. 1614–1617.
ICPR-2010-HurWL #estimation #invariant #learning: View Invariant Body Pose Estimation Based on Biased Manifold Learning (DH, CW, SWL), pp. 3866–3869.
ICPR-2010-JhuoL #kernel #learning #multi #recognition: Boosted Multiple Kernel Learning for Scene Category Recognition (IHJ, DTL), pp. 3504–3507.
ICPR-2010-JiaCLW #image #learning #performance: Efficient Learning to Label Images (KJ, LC, NL, LW), pp. 942–945.
ICPR-2010-JokoKY #learning #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 #learning: Scene-Adaptive Human Detection with Incremental Active Learning (AJJ, FP), pp. 2760–2763.
ICPR-2010-KamarainenI #canonical #detection #learning: Learning and Detection of Object Landmarks in Canonical Object Space (JKK, JI), pp. 1409–1412.
ICPR-2010-KappSM #adaptation #incremental #learning: Adaptive Incremental Learning with an Ensemble of Support Vector Machines (MNK, RS, PM), pp. 4048–4051.
ICPR-2010-KimuraKSNMSI #canonical #correlation #learning #named #performance: SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations (AK, HK, MS, TN, EM, HS, KI), pp. 2933–2936.
ICPR-2010-LiLD #learning #using: Learning GMM Using Elliptically Contoured Distributions (BL, WL, LD), pp. 511–514.
ICPR-2010-LiuA #learning #semantics #using: Learning Scene Semantics Using Fiedler Embedding (JL, SA), pp. 3627–3630.
ICPR-2010-LiuLH #learning #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 #learning #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 #learning #multi #online: Particle Filter Tracking with Online Multiple Instance Learning (ZN, SS, AR, BSM), pp. 2616–2619.
ICPR-2010-OhH #learning #process #using #video: Unsupervised Learning of Activities in Video Using Scene Context (SO, AH), pp. 3579–3582.
ICPR-2010-PapadopoulosMKS #analysis #approach #image #learning #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 #learning #network #online: Bayesian Networks Learning Algorithms for Online Form Classification (EP, YB, AB), pp. 1981–1984.
ICPR-2010-PuS #learning #probability #verification: Probabilistic Measure for Signature Verification Based on Bayesian Learning (DP, SNS), pp. 1188–1191.
ICPR-2010-RevaudLAB #graph #learning #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: Learning Pedestrian Trajectories with Kernels (ER, FT, GZ), pp. 149–152.
ICPR-2010-SangWW #learning #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: Learning Image Anchor Templates for Document Classification and Data Extraction (PS), pp. 3428–3431.
ICPR-2010-Sato #classification #design #kernel #learning: A New Learning Formulation for Kernel Classifier Design (AS), pp. 2897–2900.
ICPR-2010-ShamiliBA #detection #distributed #machine learning #mobile #using: Malware Detection on Mobile Devices Using Distributed Machine Learning (ASS, CB, TA), pp. 4348–4351.
ICPR-2010-ShenYS #learning: Learning Discriminative Features Based on Distribution (JS, WY, CS), pp. 1401–1404.
ICPR-2010-SodaI #composition #dataset #integration #learning: Decomposition Methods and Learning Approaches for Imbalanced Dataset: An Experimental Integration (PS, GI), pp. 3117–3120.
ICPR-2010-SternigRB #classification #learning #multi: Inverse Multiple Instance Learning for Classifier Grids (SS, PMR, HB), pp. 770–773.
ICPR-2010-SuLT10a #documentation #framework #learning #self: A Self-Training Learning Document Binarization Framework (BS, SL, CLT), pp. 3187–3190.
ICPR-2010-SunSHE #learning #locality #metric: Localized Supervised Metric Learning on Temporal Physiological Data (JS, DMS, JH, SE), pp. 4149–4152.
ICPR-2010-TaxHVP #clustering #concept #detection #learning #multi #using: The Detection of Concept Frames Using Clustering Multi-instance Learning (DMJT, EH, MFV, MP), pp. 2917–2920.
ICPR-2010-TorkiEL #learning #multi #representation #set: Learning a Joint Manifold Representation from Multiple Data Sets (MT, AME, CSL), pp. 1068–1071.
ICPR-2010-TsagkatakisS #distance #modelling #random #recognition: Manifold Modeling with Learned Distance in Random Projection Space for Face Recognition (GT, AES), pp. 653–656.
ICPR-2010-TsaiHTC #detection #pipes and filters #predict #scalability #using: Learning-Based Vehicle Detection Using Up-Scaling Schemes and Predictive Frame Pipeline Structures (YMT, KYH, CCT, LGC), pp. 3101–3104.
ICPR-2010-WangAYL #bottom-up #estimation #learning #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 #learning #multi: Multiple Kernel Learning with High Order Kernels (SW, SJ, QH, QT), pp. 2138–2141.
ICPR-2010-WangM #learning #order #process #using: Gaussian Process Learning from Order Relationships Using Expectation Propagation (RW, SJM), pp. 605–608.
ICPR-2010-WidhalmB #learning: Learning Major Pedestrian Flows in Crowded Scenes (PW, NB), pp. 4064–4067.
ICPR-2010-WuLW #image #learning #retrieval #using: Enhancing SVM Active Learning for Image Retrieval Using Semi-supervised Bias-Ensemble (JW, ML, CLW), pp. 3175–3178.
ICPR-2010-XingAL #detection #learning #multi: Multiple Human Tracking Based on Multi-view Upper-Body Detection and Discriminative Learning (JX, HA, SL), pp. 1698–1701.
ICPR-2010-YaegashiY #kernel #learning #multi #recognition #using: Geotagged Photo Recognition Using Corresponding Aerial Photos with Multiple Kernel Learning (KY, KY), pp. 3272–3275.
ICPR-2010-ZhangLD #approach #kernel #learning #multi #named #novel: AdaMKL: A Novel Biconvex Multiple Kernel Learning Approach (ZZ, ZNL, MSD), pp. 2126–2129.
ICPR-2010-ZhangWL #categorisation #kernel #learning: Learning the Kernel Combination for Object Categorization (DZ, XW, BL), pp. 2929–2932.
ICPR-2010-ZhangZYK #classification #detection #learning #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 #learning #visual notation: Canonical Image Selection by Visual Context Learning (WZ, YL, HL, QT), pp. 834–837.
ICPR-2010-ZhuHYL #behaviour #learning #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: Learning the Relationship Between High and Low Resolution Images in Kernel Space for Face Super Resolution (WWWZ, PCY), pp. 1152–1155.
KDD-2010-AbeMPRJTBACKDG #learning #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 #learning #online #performance #recommendation: Fast online learning through offline initialization for time-sensitive recommendation (DA, BCC, PE), pp. 703–712.
KDD-2010-AttenbergP #classification #learning #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 #learning #predict: Beyond heuristics: learning to classify vulnerabilities and predict exploits (MB, LKS, SS, GMV), pp. 105–114.
KDD-2010-ChapelleSVWZT #learning #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 #learning #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 #learning #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 #learning #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 #learning #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 #learning #modelling #topic: Discriminative topic modeling based on manifold learning (SH, SEF), pp. 653–662.
KDD-2010-KhoslaCLCHL #approach #machine learning #predict: An integrated machine learning approach to stroke prediction (AK, YC, CCYL, HKC, JH, HL), pp. 183–192.
KDD-2010-Lee #classification #learning: Learning to combine discriminative classifiers: confidence based (CHL), pp. 743–752.
KDD-2010-LiuMTLL #learning #metric #optimisation #using: Semi-supervised sparse metric learning using alternating linearization optimization (WL, SM, DT, JL, PL), pp. 1139–1148.
KDD-2010-LiuZ #learning: Learning with cost intervals (XYL, ZHZ), pp. 403–412.
KDD-2010-SomaiyaJR #learning #modelling: Mixture models for learning low-dimensional roles in high-dimensional data (MS, CMJ, SR), pp. 909–918.
KDD-2010-WallaceSBT #learning: Active learning for biomedical citation screening (BCW, KS, CEB, TAT), pp. 173–182.
KDD-2010-ZhangY #learning #metric: Transfer metric learning by learning task relationships (YZ, DYY), pp. 1199–1208.
KDD-2010-ZhangZ #dependence #learning #multi: Multi-label learning by exploiting label dependency (MLZ, KZ), pp. 999–1008.
KDD-2010-ZhuLX #feature model #incremental #learning #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-CarulloB #analysis #machine learning #mining #web: Machine Learning and Link Analysis for Web Content Mining (MC, EB), pp. 156–161.
KDIR-2010-Cebron #learning #representation #towards: Towards Learning with Objects in a Hierarchical Representation (NC), pp. 326–329.
KDIR-2010-LourencoF #clustering #learning #multi: Selectively Learning Clusters in Multi-EAC (AL, ALNF), pp. 491–499.
KDIR-2010-ParviainenRML #approximate #infinity #learning #network: Interpreting Extreme Learning Machine as an Approximation to an Infinite Neural Network (EP, JR, YM, AL), pp. 65–73.
KDIR-2010-WangSFR #concept: A Meta-learning Method for Concept Drift (RW, LS, MÓF, ER), pp. 257–262.
KEOD-2010-ArdilaAL #kernel #learning #multi #ontology: Multiple Kernel Learning for Ontology Instance Matching (DA, JA, FL), pp. 311–318.
KEOD-2010-Braham #assessment #learning #metric: A Knowledge Metric with Applications to Learning Assessment (RB), pp. 5–9.
KEOD-2010-EynardMM #analysis #on the #ontology: On the Use of Correspondence Analysis to Learn Seed Ontologies from Text (DE, FM, MM), pp. 430–437.
KEOD-2010-GilCM #case study #evaluation #learning #ontology: A Systemic Methodology for Ontology Learning — An Academic Case Study and Evaluation (RG, LC, MJMB), pp. 206–212.
KEOD-2010-Girardi #learning #ontology: Guiding Ontology Learning and Population by Knowledge System Goals (RG), pp. 480–484.
KMIS-2010-FersiniMTAC #generative #machine learning #semantics: Semantics and Machine Learning for Building the Next Generation of Judicial Court Management Systems (EF, EM, DT, FA, MC), pp. 51–60.
KMIS-2010-JuvonenO #learning: Studying IT Team Entrepreneurship as a Learning Organization (PJ, PO), pp. 332–337.
KMIS-2010-LiDFF #behaviour #comprehension: Understanding Behavioral Intention of e-Learning System Re-use (YL, YD, ZF, WF), pp. 218–223.
RecSys-2010-BenchettaraKR #approach #collaboration #machine learning #predict #recommendation: A supervised machine learning link prediction approach for academic collaboration recommendation (NB, RK, CR), pp. 253–256.
RecSys-2010-LipczakM #learning #performance #recommendation: Learning in efficient tag recommendation (ML, EEM), pp. 167–174.
RecSys-2010-MelloAZ #impact analysis #learning #rating: Active learning driven by rating impact analysis (CERdM, MAA, GZ), pp. 341–344.
RecSys-2010-ShiLH #collaboration #learning #matrix #rank: List-wise learning to rank with matrix factorization for collaborative filtering (YS, ML, AH), pp. 269–272.
SEKE-2010-JuniorLAMW #impact analysis #learning #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-KhoshgoftaarG #machine learning #metric #novel #re-engineering #using: Software Engineering with Computational Intelligence and Machine Learning A Novel Software Metric Selection Technique Using the Area Under ROC Curves (TMK, KG), pp. 203–208.
SEKE-2010-Yeh #animation #human-computer #interactive #learning: The effects of human-computer interaction modes for weak learners in an animation learning environment (YFY), pp. 18–23.
SIGIR-2010-BalasubramanianA #learning: Learning to select rankers (NB, JA), pp. 855–856.
SIGIR-2010-DangBC #learning #query #rank: Learning to rank query reformulations (VD, MB, WBC), pp. 807–808.
SIGIR-2010-DaveV #learning: Learning the click-through rate for rare/new ads from similar ads (KSD, VV), pp. 897–898.
SIGIR-2010-GaoCWZ #learning #rank #using: Learning to rank only using training data from related domain (WG, PC, KFW, AZ), pp. 162–169.
SIGIR-2010-HajishirziYK #adaptation #detection #learning #similarity: Adaptive near-duplicate detection via similarity learning (HH, WtY, AK), pp. 419–426.
SIGIR-2010-LeeCW #machine learning #social: Uncovering social spammers: social honeypots + machine learning (KL, JC, SW), pp. 435–442.
SIGIR-2010-Liu #information retrieval #learning #rank: Learning to rank for information retrieval (TYL), p. 904.
SIGIR-2010-LiuW #email #learning #multi: Multi-field learning for email spam filtering (WL, TW), pp. 745–746.
SIGIR-2010-LiuYSCCL #behaviour #learning #rank: Learning to rank audience for behavioral targeting (NL, JY, DS, DC, ZC, YL), pp. 719–720.
SIGIR-2010-LongCZCZT #learning #optimisation #ranking: Active learning for ranking through expected loss optimization (BL, OC, YZ, YC, ZZ, BLT), pp. 267–274.
SIGIR-2010-MojdehC #consistency #learning #using: Semi-supervised spam filtering using aggressive consistency learning (MM, GVC), pp. 751–752.
SIGIR-2010-Wang #learning #modelling #retrieval: Learning hidden variable models for blog retrieval (MW), p. 922.
SIGIR-2010-WangLM #learning #rank: Learning to efficiently rank (LW, JJL, DM), pp. 138–145.
SIGIR-2010-WangWVL #clustering #documentation #learning #metric: Text document clustering with metric learning (JW, SW, HQV, GL), pp. 783–784.
SIGIR-2010-YanZJLYC #framework #learning: 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 #learning #retrieval #statistics: Learning more powerful test statistics for click-based retrieval evaluation (YY, YG, OC, YZ, TJ), pp. 507–514.
SIGIR-2010-ZwolPMS #ranking: Machine learned ranking of entity facets (RvZ, LGP, MM, BS), pp. 879–880.
MoDELS-v2-2010-FernandezPKB #lessons learnt #metamodelling #requirements: A Meta Model for Artefact-Orientation: Fundamentals and Lessons Learned in Requirements Engineering (DMF, BP, MK, MB), pp. 183–197.
RE-2010-MashkoorJ #domain model #lessons learnt: Domain Engineering with Event-B: Some Lessons We Learned (AM, JPJ), pp. 252–261.
REFSQ-2010-KomssiKTSU #collaboration #lessons learnt #specification: Lessons Learned from Integrating Specification Templates, Collaborative Workshops, and Peer Reviews (MK, MK, KT, RS, EJU), pp. 158–172.
SAC-2010-AppiceCM #learning: Transductive learning for spatial regression with co-training (AA, MC, DM), pp. 1065–1070.
SAC-2010-AyyappanWN #algorithm #constraints #learning #named #network #scalability: MICHO: a scalable constraint-based algorithm for learning Bayesian networks (MA, YKW, WKN), pp. 985–989.
SAC-2010-CostaFGMO #learning #mining #modelling: Mining models of exceptional objects through rule learning (GC, FF, MG, GM, RO), pp. 1078–1082.
FSE-2010-Elkhodary #adaptation #approach #feature model #self: A learning-based approach for engineering feature-oriented self-adaptive software systems (AME), pp. 345–348.
ICSE-2010-Cleland-HuangCGE #approach #machine learning #requirements: A machine learning approach for tracing regulatory codes to product specific requirements (JCH, AC, MG, JE), pp. 155–164.
HPDC-2010-KettimuthuSGABBCCDFHHLLLMNPRRWWW #grid #lessons learnt #network #set: Lessons learned from moving earth system grid data sets over a 20 Gbps wide-area network (RK, AS, DG, BA, PTB, JB, AC, LC, ED, ITF, KH, JH, JL, ML, JL, KM, VN, VP, KR, DR, DNW, LW, LW), pp. 316–319.
CAV-2010-BolligKKLNP #automaton #framework #learning #named: libalf: The Automata Learning Framework (BB, JPK, CK, ML, DN, DRP), pp. 360–364.
CAV-2010-ChenCFTTW #automation #learning #reasoning: Automated Assume-Guarantee Reasoning through Implicit Learning (YFC, EMC, AF, MHT, YKT, BYW), pp. 511–526.
CAV-2010-SinghGP #abstraction #component #interface #learning: Learning Component Interfaces with May and Must Abstractions (RS, DG, CSP), pp. 527–542.
ICLP-2010-Balduccini10 #heuristic #learning #set: Learning Domain-Specific Heuristics for Answer Set Solvers (MB), pp. 14–23.
ICLP-2010-Pahlavi10 #higher-order #learning #logic: Higher-order Logic Learning and λ-Progol (NP), pp. 281–285.
ICLP-J-2010-SneyersMVKS #learning #logic #probability: CHR(PRISM)-based probabilistic logic learning (JS, WM, JV, YK, TS), pp. 433–447.
ICST-2010-SilvaJA #cost analysis #execution #machine learning #symmetry #testing: Machine Learning Methods and Asymmetric Cost Function to Estimate Execution Effort of Software Testing (DGeS, MJ, BTdA), pp. 275–284.
ICTSS-2010-MeinkeN #approach #testing: A Learning-Based Approach to Unit Testing of Numerical Software (KM, FN), pp. 221–235.
ISSTA-2010-GruskaWZ #detection #learning #lightweight: Learning from 6, 000 projects: lightweight cross-project anomaly detection (NG, AW, AZ), pp. 119–130.
SAT-2010-Ben-SassonJ #bound #learning #strict: Lower Bounds for Width-Restricted Clause Learning on Small Width Formulas (EBS, JJ), pp. 16–29.
SAT-2010-KlieberSGC #learning: A Non-prenex, Non-clausal QBF Solver with Game-State Learning (WK, SS, SG, EMC), pp. 128–142.
VMCAI-2010-JungKWY #abstraction #algorithm #invariant #learning: Deriving Invariants by Algorithmic Learning, Decision Procedures, and Predicate Abstraction (YJ, SK, BYW, KY), pp. 180–196.
CASE-2009-BountourelisR #algorithm #learning: Customized learning algorithms for episodic tasks with acyclic state spaces (TB, SR), pp. 627–634.
CASE-2009-Ray #lessons learnt #standard: Healthcare interoperability — lessons learned from the manufacturing standards sector (SRR), pp. 88–89.
CASE-2009-SolisT #comprehension #learning #towards: Towards enhancing the understanding of human motor learning (JS, AT), pp. 591–596.
DAC-2009-MarrBBH #learning: A learning digital computer (BM, AB, SB, PEH), pp. 617–618.
DATE-2009-RichterJE #framework #learning #verification: Learning early-stage platform dimensioning from late-stage timing verification (KR, MJ, RE), pp. 851–857.
DATE-2009-StratigopoulosMM #set: Enrichment of limited training sets in machine-learning-based analog/RF test (HGDS, SM, YM), pp. 1668–1673.
DATE-2009-WangW #machine learning: Machine learning-based volume diagnosis (SW, WW), pp. 902–905.
DRR-2009-ZhangZLT #learning: A semi-supervised learning method to classify grant-support zone in web-based medical articles (XZ, JZ, DXL, GRT), pp. 1–10.
HT-2009-AlAghaB #approach #hypermedia #learning #towards: Towards a constructivist approach to learning from hypertext (IA, LB), pp. 51–56.
HT-2009-MorishimaNISK #approach #lessons learnt: Bringing your dead links back to life: a comprehensive approach and lessons learned (AM, AN, TI, SS, HK), pp. 15–24.
ICDAR-2009-AbdulkaderC #fault #learning #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 #learning #online #performance #recognition: Fast Incremental Learning Strategy Driven by Confusion Reject for Online Handwriting Recognition (AA, ÉA), pp. 81–85.
ICDAR-2009-BallS #learning #recognition: Semi-supervised Learning for Handwriting Recognition (GRB, SNS), pp. 26–30.
ICDAR-2009-FrinkenB #learning #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 #learning #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 #learning #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 #learning #markov #modelling: Learning Rich Hidden Markov Models in Document Analysis: Table Location (ACeS), pp. 843–847.
ICDAR-2009-StefanoFFM #classification #evolution #learning #network: Learning Bayesian Networks by Evolution for Classifier Combination (CDS, FF, ASdF, AM), pp. 966–970.
ICDAR-2009-TewariN #adaptation #learning: Learning and Adaptation for Improving Handwritten Character Recognizers (NCT, AMN), pp. 86–90.
ICDAR-2009-WangLJ #learning #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 #learning #online #probability #recognition: A Probabilistic Framework for Soft Target Learning in Online Cursive Handwriting Recognition (XZ, YG, FJG, LXZ), pp. 1246–1250.
SIGMOD-2009-BabuGM #learning #nondeterminism #scalability: Large-scale uncertainty management systems: learning and exploiting your data (SB, SG, KM), pp. 995–998.
VLDB-2009-ArasuCK #learning #string: Learning String Transformations From Examples (AA, SC, RK), pp. 514–525.
VLDB-2009-Ley #lessons learnt #named: DBLP — Some Lessons Learned (ML), pp. 1493–1500.
VLDB-2009-PandaHBB #learning #named #parallel #pipes and filters: PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce (BP, JH, SB, RJB), pp. 1426–1437.
CSEET-2009-Armarego #learning #student: Displacing the Sage on the Stage: Student Control of Learning (JA), pp. 198–201.
CSEET-2009-ChaoR #agile #learning #student: Agile Software Factory for Student Service Learning (JC, MR), pp. 34–40.
CSEET-2009-Goel #education #learning #re-engineering: Enriching the Culture of Software Engineering Education through Theories of Knowledge and Learning (SG), p. 279.
CSEET-2009-PadminiR #challenge #development: Issues in SE E-learning Development — Changing Phases and Challenges Going Forward (HAP, SSR), pp. 130–137.
CSEET-2009-RichardsonD #learning #problem #re-engineering: Problem Based Learning in the Software Engineering Classroom (IR, YD), pp. 174–181.
CSEET-2009-Rosso-Llopart #education #learning #re-engineering: An Examination of Learning Technologies That Support Software Engineering and Education (MRL), pp. 294–295.
ITiCSE-2009-AltinBEKOSSSMPR09a #experience #learning #student #tool support #user interface: 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 #learning #student: Exploring technologies for building collaborative learning communities among diverse student populations (NA, CCL), pp. 243–247.
ITiCSE-2009-BlasGMB #analysis #automation #composition #using: Automatic E-learning contents composition by using gap analysis techniques (JMdB, JMG, LdM, RB), p. 369.
ITiCSE-2009-BuendiaCB #approach #learning: An instructional approach to drive computer science courses through virtual learning environments (FB, JCC, JVB), pp. 6–10.
ITiCSE-2009-CukiermanT #learning #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-DoerschukLM #experience #lessons learnt: INSPIRED broadening participation: first year experience and lessons learned (PID, JL, JM), pp. 238–242.
ITiCSE-2009-Draganova #learning #mobile: Use of mobile phone technologies in learning (CD), p. 399.
ITiCSE-2009-Ginat #composition #learning: Interleaved pattern composition and scaffolded learning (DG), pp. 109–113.
ITiCSE-2009-Hwang09a #education #learning #operating system: Blended learning for teaching operating systems with Windows (SwH), p. 380.
ITiCSE-2009-Lasserre #adaptation #learning #programming: Adaptation of team-based learning on a first term programming class (PL), pp. 186–190.
ITiCSE-2009-Martin #learning: Cooperative learning to support the lacks of PBL (JGM), p. 343.
ITiCSE-2009-MhiriR #development #learning #named: AARTIC: development of an intelligent environment for human learning (FM, SR), p. 359.
ITiCSE-2009-MoraPJC #assessment #collaboration #learning #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 #learning: Guided learning via diagnostic feedback to question responses (DPB, CD, SWL), p. 362.
ITiCSE-2009-Pantaleev #learning #named #visual notation: Dzver: a visual computer science learning environment (AP), p. 387.
ITiCSE-2009-Radenski #learning: Freedom of choice as motivational factor for active learning (AR), pp. 21–25.
ITiCSE-2009-Sondergaard #learning #student: Learning from and with peers: the different roles of student peer reviewing (HS), pp. 31–35.
ITiCSE-2009-TsengHH #collaboration #education #framework #learning #ubiquitous: A collaborative ubiquitous learning platform for computer science education (JCRT, SYYH, GJH), p. 368.
ITiCSE-2009-Velazquez-IturbideP #algorithm #interactive #learning: Active learning of greedy algorithms by means of interactive experimentation (JÁVI, APC), pp. 119–123.
ITiCSE-2009-VillalobosCJ #interactive #learning #programming #using: Developing programming skills by using interactive learning objects (JV, NAC, CJ), pp. 151–155.
ITiCSE-2009-WangHCT #behaviour #collaboration #learning: 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 #learning #research: Relating research and teaching: learning from experiences and beliefs (SW, AI), pp. 75–79.
ITiCSE-2009-WiesnerB #concept #how #learning #question: How do robots foster the learning of basic concepts in informatics? (BW, TB), p. 403.
ITiCSE-2009-ZanderTSMMHF #learning: Learning styles: novices decide (CZ, LT, BS, LM, RM, BH, SF), pp. 223–227.
ESOP-2009-Eber #contract #design #programming language #question #specification #tool support #what: The Financial Crisis, a Lack of Contract Specification Tools: What Can Finance Learn from Programming Language Design? (JME), pp. 205–206.
TACAS-2009-ChenFCTW #automaton #composition #learning #verification: Learning Minimal Separating DFA’s for Compositional Verification (YFC, AF, EMC, YKT, BYW), pp. 31–45.
ICPC-2009-JeffreyFGG #debugging #developer #named: BugFix: A learning-based tool to assist developers in fixing bugs (DJ, MF, NG, RG), pp. 70–79.
MSR-2009-AyewahP #fault #learning: Learning from defect removals (NA, WP), pp. 179–182.
PLDI-2009-TournavitisWFO #approach #detection #parallel #towards: Towards a holistic approach to auto-parallelization: integrating profile-driven parallelism detection and machine-learning based mapping (GT, ZW, BF, MFPO), pp. 177–187.
STOC-2009-KleinbergPT #game studies #learning #multi: Multiplicative updates outperform generic no-regret learning in congestion games: extended abstract (RK, GP, ÉT), pp. 533–542.
STOC-2009-Sellie #learning #random: Exact learning of random DNF over the uniform distribution (LS), pp. 45–54.
ICALP-v1-2009-KlivansLS #learning: Learning Halfspaces with Malicious Noise (ARK, PML, RAS), pp. 609–621.
LATA-2009-Akama #commutative #learning: Commutative Regular Shuffle Closed Languages, Noetherian Property, and Learning Theory (YA), pp. 93–104.
LATA-2009-Gierasimczuk #learning #logic: Learning by Erasing in Dynamic Epistemic Logic (NG), pp. 362–373.
LATA-2009-Jain #learning: Hypothesis Spaces for Learning (SJ), pp. 43–58.
CHI-2009-BrandtGLDK #learning #programming #web: Two studies of opportunistic programming: interleaving web foraging, learning, and writing code (JB, PJG, JL, MD, SRK), pp. 1589–1598.
CHI-2009-GaverBKBJ #design #how #what: Anatomy of a failure: how we knew when our design went wrong, and what we learned from it (WWG, JB, TK, AB, NJ), pp. 2213–2222.
CHI-2009-HaradaWMBL #learning #people: Longitudinal study of people learning to use continuous voice-based cursor control (SH, JOW, JM, JAB, JAL), pp. 347–356.
CHI-2009-KammererNPC #learning #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 #learning #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: Learning from IKEA hacking: I’m not one to decoupage a tabletop and call it a day (DR, JB), pp. 419–422.
CHI-2009-TalbotLKT #classification #interactive #machine learning #multi #named #visualisation: EnsembleMatrix: interactive visualization to support machine learning with multiple classifiers (JT, BL, AK, DST), pp. 1283–1292.
CHI-2009-Thom-SantelliM #learning: Learning by seeing: photo viewing in the workplace (JTS, DRM), pp. 2081–2090.
CHI-2009-TorreyCM #how #internet #learning: Learning how: the search for craft knowledge on the internet (CT, EFC, DWM), pp. 1371–1380.
DHM-2009-FallonCP #assessment #learning #risk management: Learning from Risk Assessment in Radiotherapy (EFF, LC, WJvdP), pp. 502–511.
DHM-2009-HashagenZSZ #adaptation #implementation #interactive #learning #pattern matching #pattern recognition #recognition: Adaptive Motion Pattern Recognition: Implementing Playful Learning through Embodied Interaction (AH, CZ, HS, SZ), pp. 105–114.
DHM-2009-KuramotoIST #comparison #education #physics #realtime #self: Augmented Practice Mirror: A Self-learning Support System of Physical Motion with Real-Time Comparison to Teacher’s Model (IK, YI, YS, YT), pp. 123–131.
HCD-2009-BlumK #challenge #e-commerce #lessons learnt #user interface: Evaluating E-Commerce User Interfaces: Challenges and Lessons Learned (RB, KK), pp. 653–660.
HCD-2009-FerranGMM #design #learning #repository: User Centered Design of a Learning Object Repository (NF, AEGR, EM, JM), pp. 679–688.
HCD-2009-NasozB #user interface: Affectively Intelligent User Interfaces for Enhanced E-Learning Applications (FN, MB), pp. 765–774.
HCD-2009-ShibukawaFIN #effectiveness #using: Fundamental Studies on Effective e-Learning Using Physiology Indices (MS, MFF, YI, SPN), pp. 795–804.
HCI-AUII-2009-McMullenW #assessment #design #learning: Relationship Learning Software: Design and Assessment (KAM, GHW), pp. 631–640.
HCI-AUII-2009-ZarraonandiaVDA #learning #protocol: A Virtual Environment for Learning Aiport Emergency Management Protocols (TZ, MRRV, PD, IA), pp. 228–235.
HCI-NIMT-2009-AlexanderAA #framework #gesture #incremental #learning #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-NIMT-2009-NagaiKI: A Basic Study on a Drawing-Learning Support System in the Networked Environment (TN, MK, KI), pp. 860–868.
HCI-NT-2009-Wang09a #concept #design: Learn as Babies Learn: A Conceptual Model of Designing Optimum Learnability (DXW), pp. 745–751.
HCI-VAD-2009-BaldirisFMG #adaptation #machine learning: Adaptation Decisions and Profiles Exchange among Open Learning Management Systems Based on Agent Negotiations and Machine Learning Techniques (SB, RF, CM, SG), pp. 12–20.
HCI-VAD-2009-BuzziBL: Accessing e-Learning Systems via Screen Reader: An Example (MCB, MB, BL), pp. 21–30.
HCI-VAD-2009-ChalfounF #3d #learning: Optimal Affective Conditions for Subconscious Learning in a 3D Intelligent Tutoring System (PC, CF), pp. 39–48.
HCI-VAD-2009-ChenGSEJ #detection #learning: Computer-Based Learning to Improve Breast Cancer Detection Skills (YC, AGG, HJS, AE, JJ), pp. 49–57.
HCI-VAD-2009-DeickeMP #development #editing #interactive: A Web-Based, Interactive Annotation Editor for the eCampus Development Environment for SCORM Compliant E-Learning Modules (BD, JTM, HMP), pp. 88–93.
HCI-VAD-2009-DogusoyC #comprehension #eye tracking #learning #process: An Innovative Way of Understanding Learning Processes: Eye Tracking (BD, KÇ), pp. 94–100.
HCI-VAD-2009-FicarraCV #evaluation #learning: Communicability for Virtual Learning: Evaluation (FVCF, MCF, PMV), pp. 68–77.
HCI-VAD-2009-KashiwagiXSKO #learning #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 #learning: Promoting Metacognition in Immersive Cultural Learning Environments (HCL), pp. 129–139.
HCI-VAD-2009-MampadiCG #adaptation #hypermedia #information management #learning: The Effects of Prior Knowledge on the Use of Adaptive Hypermedia Learning Systems (FM, SYC, GG), pp. 156–165.
HCI-VAD-2009-MazzolaM #adaptation #learning #student: Supporting Learners in Adaptive Learning Environments through the Enhancement of the Student Model (LM, RM), pp. 166–175.
HCI-VAD-2009-SaC #development #learning #mobile #personalisation #tool support: Supporting End-User Development of Personalized Mobile Learning Tools (MdS, LC), pp. 217–225.
HCI-VAD-2009-SuLHC #learning #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 #learning: Interactive Learning Panels (RT, HF, JAG, MDL, VMRP), pp. 236–245.
HCI-VAD-2009-UenoHY #education #framework #named: WebELS: A Content-Centered E-Learning Platform for Postgraduate Education in Engineering (HU, ZH, JY), pp. 246–255.
HCI-VAD-2009-ZhangLBAMY #development #learning #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-DIE-2009-BruinLB #case study #feedback #how: How to Learn from Intelligent Products; The Structuring of Incoherent Field Feedback Data in Two Case Studies (RdB, YL, AB), pp. 227–232.
HIMI-II-2009-AyodeleZK #approach #email #machine learning #predict: Email Reply Prediction: A Machine Learning Approach (TA, SZ, RK), pp. 114–123.
HIMI-II-2009-JacobsonMM #collaboration #interactive #learning #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 #learning #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 #learning #personalisation: Personalization for Specific Users: Designing Decision Support Systems to Support Stimulating Learning Environments (LM, NRF, RJFvH), pp. 660–668.
HIMI-II-2009-NakamuraS #learning: Construction of Systematic Learning Support System of Business Theory and Method (YN, KS), pp. 669–678.
HIMI-II-2009-NishinoH #embedded #learning #named #visualisation: Minato: Integrated Visualization Environment for Embedded Systems Learning (YN, EH), pp. 325–333.
HIMI-II-2009-PrecelEA #design #learning #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 #learning: 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 #learning: Framework for Supporting Decision Making in Learning Management System Selection (YT), pp. 699–707.
HIMI-II-2009-Wang09c #adaptation #design #development #learning: The Design and Development of an Adaptive Web-Based Learning System (CW), pp. 716–725.
IDGD-2009-AlsharaA #case study: The Effect of E-Learning on Business Organizations: A UAE Case Study (OKA, MKA), pp. 437–446.
IDGD-2009-RizvanogluO #case study #comprehension: Cross-Cultural Understanding of the Dual Structure of Metaphorical Icons: An Explorative Study with French and Turkish Users on an E-Learning Site (KR, ÖÖ), pp. 89–98.
IDGD-2009-ZhongLL #learning #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: Learning Computer Science Fundamentals through Virtual Environments (JB, GV, AMAD, AJ), pp. 423–431.
OCSC-2009-ConlonP #distance #learning #video: A Discussion of Video Capturing to Assist in Distance Learning (MC, VP), pp. 432–441.
OCSC-2009-OganAKJ #education #game studies #learning #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-PastorRRHH #adaptation #community #distance #enterprise: Virtual Communities Adapted to the EHEA in an Enterprise Distance e-Learning Based Environment (RPV, TR, SR, RH, RH), pp. 488–497.
OCSC-2009-Pozzi #community #learning #online #social: Evaluating the Social Dimension in Online Learning Communities (FP), pp. 498–506.
OCSC-2009-PuseyM #education #heuristic #implementation #learning #wiki: Heuristics for Implementation of Wiki Technology in Higher Education Learning (PP, GM), pp. 507–514.
VISSOFT-2009-SensalireOT #evaluation #lessons learnt #tool support #visualisation: Evaluation of software visualization tools: Lessons learned (MS, PO, ACT), pp. 19–26.
CAiSE-2009-MouratidisSJ #case study #experience #health #information management #lessons learnt: Secure Information Systems Engineering: Experiences and Lessons Learned from Two Health Care Projects (HM, AS, JJ), pp. 231–245.
ICEIS-DISI-2009-Mao #machine learning #online: Machine Learning in Online Advertising (JM), p. 27.
ICEIS-AIDSS-2009-BombiniMBFE #framework #learning #logic programming: A Logic Programming Framework for Learning by Imitation (GB, NDM, TMAB, SF, FE), pp. 218–223.
ICEIS-AIDSS-2009-YangLSKCGP #graph #learning: Graph Structure Learning for Task Ordering (YY, AL, HS, BK, CMC, RG, KP), pp. 164–169.
ICEIS-HCI-2009-Casalino #aspect-oriented #learning: 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 #learning #named #repository: CrimsonHex: A Service Oriented Repository of Specialised Learning Objects (JPL, RQ), pp. 102–113.
ICEIS-J-2009-PenteadoM #authentication #web: A Video-Based Biometric Authentication for e-Learning Web Applications (BEP, ANM), pp. 770–779.
ICEIS-J-2009-Prokhorov #categorisation #self: A Self-learning System for Object Categorization (DVP), pp. 265–274.
ICEIS-J-2009-SiepermannS #automation #generative: e-Learning in Logistics Cost Accounting Automatic Generation and Marking of Exercises (MS, CS), pp. 665–676.
ICEIS-SAIC-2009-CastroFSC #learning #programming: Fleshing Out Clues on Group Programming Learning (TC, HF, LS, ANdCJ), pp. 68–73.
CIKM-2009-BaiZXZSTZC #learning #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 #learning #query: Learning from past queries for resource selection (SC, LS, HY), pp. 1867–1870.
CIKM-2009-ChenLAA #image #learning #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 #learning #novel #rank: Learning to rank with a novel kernel perceptron method (XwC, HW, XL), pp. 505–512.
CIKM-2009-GargS #classification #learning: Active learning in partially supervised classification (PG, SS), pp. 1783–1786.
CIKM-2009-HeLL #graph #learning: Graph-based transfer learning (JH, YL, RDL), pp. 937–946.
CIKM-2009-KuoCW #learning #rank: Learning to rank from Bayesian decision inference (JWK, PJC, HMW), pp. 827–836.
CIKM-2009-MeloW #learning #towards: Towards a universal wordnet by learning from combined evidence (GdM, GW), pp. 513–522.
CIKM-2009-Paranjpe #documentation #feedback #learning: Learning document aboutness from implicit user feedback and document structure (DP), pp. 365–374.
CIKM-2009-PasternackR #learning: Learning better transliterations (JP, DR), pp. 177–186.
CIKM-2009-QiCKKW #learning: Combining labeled and unlabeled data with word-class distribution learning (YQ, RC, PPK, KK, JW), pp. 1737–1740.
CIKM-2009-QuanzH #learning #scalability: Large margin transductive transfer learning (BQ, JH), pp. 1327–1336.
CIKM-2009-SunCSSWL #learning #recommendation: Learning to recommend questions based on user ratings (KS, YC, XS, YIS, XW, CYL), pp. 751–758.
CIKM-2009-SunMG09a #graph #learning #online #rank: Learning to rank graphs for online similar graph search (BS, PM, CLG), pp. 1871–1874.
CIKM-2009-SvoreB #approach #machine learning #retrieval: A machine learning approach for improved BM25 retrieval (KMS, CJCB), pp. 1811–1814.
CIKM-2009-TangL #behaviour #learning #scalability #social: Scalable learning of collective behavior based on sparse social dimensions (LT, HL), pp. 1107–1116.
CIKM-2009-WangHLS #comprehension #learning #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 #learning #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 #learning #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 #learning: Experiments on pattern-based relation learning (WY, TB), pp. 1657–1660.
CIKM-2009-ZhangMCM #fuzzy #learning #ontology #semantics #uml #web: Fuzzy semantic web ontology learning from fuzzy UML model (FZ, ZMM, JC, XM), pp. 1007–1016.
CIKM-2009-ZhangXSYD #evaluation #learning #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 #learning #query #ranking: To divide and conquer search ranking by learning query difficulty (ZAZ, WC, TW, CZ, GW, ZC), pp. 1883–1886.
CIKM-2009-ZhuWZ #learning: Label correspondence learning for part-of-speech annotation transformation (MZ, HW, JZ), pp. 1461–1464.
ECIR-2009-DonmezC #learning #optimisation #rank: Active Sampling for Rank Learning via Optimizing the Area under the ROC Curve (PD, JGC), pp. 78–89.
ECIR-2009-EsuliS #classification #learning #multi: Active Learning Strategies for Multi-Label Text Classification (AE, FS), pp. 102–113.
ECIR-2009-GeraniCC #learning #retrieval: Investigating Learning Approaches for Blog Post Opinion Retrieval (SG, MJC, FC), pp. 313–324.
ECIR-2009-LeaseAC #learning #query #rank: Regression Rank: Learning to Meet the Opportunity of Descriptive Queries (ML, JA, WBC), pp. 90–101.
ICML-2009-AdamsG #learning #named #parametricity: Archipelago: nonparametric Bayesian semi-supervised learning (RPA, ZG), pp. 1–8.
ICML-2009-BengioLCW #education #learning: Curriculum learning (YB, JL, RC, JW), pp. 41–48.
ICML-2009-BennettBC #information retrieval #machine learning #summary #tutorial: Tutorial summary: Machine learning in IR: recent successes and new opportunities (PNB, MB, KCT), p. 17.
ICML-2009-BeygelzimerDL #learning: Importance weighted active learning (AB, SD, JL), pp. 49–56.
ICML-2009-BeygelzimerLZ #machine learning #reduction #summary #tutorial: Tutorial summary: Reductions in machine learning (AB, JL, BZ), p. 12.
ICML-2009-BurlW #learning: Active learning for directed exploration of complex systems (MCB, EW), pp. 89–96.
ICML-2009-CamposZJ #constraints #learning #network #using: Structure learning of Bayesian networks using constraints (CPdC, ZZ, QJ), pp. 113–120.
ICML-2009-ChengHH #learning #ranking: Decision tree and instance-based learning for label ranking (WC, JCH, EH), pp. 161–168.
ICML-2009-ChenGR #kernel #learning: Learning kernels from indefinite similarities (YC, MRG, BR), pp. 145–152.
ICML-2009-ChenTLY #learning #multi: A convex formulation for learning shared structures from multiple tasks (JC, LT, JL, JY), pp. 137–144.
ICML-2009-ChoS #analysis #learning #modelling: Learning dictionaries of stable autoregressive models for audio scene analysis (YC, LKS), pp. 169–176.
ICML-2009-Cortes #kernel #learning #performance #question: Invited talk: Can learning kernels help performance? (CC), p. 1.
ICML-2009-DaiJXYY #framework #learning #named: EigenTransfer: a unified framework for transfer learning (WD, OJ, GRX, QY, YY), pp. 193–200.
ICML-2009-DasguptaL #learning #summary #tutorial: Tutorial summary: Active learning (SD, JL), p. 18.
ICML-2009-DiukLL #adaptation #feature model #learning #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 #learning #online: Proximal regularization for online and batch learning (CBD, QVL, CSF), pp. 257–264.
ICML-2009-FarhangfarGS #image #learning: Learning to segment from a few well-selected training images (AF, RG, CS), pp. 305–312.
ICML-2009-FooDN #algorithm #learning #multi: A majorization-minimization algorithm for (multiple) hyperparameter learning (CSF, CBD, AYN), pp. 321–328.
ICML-2009-Freund #game studies #learning #online: Invited talk: Drifting games, boosting and online learning (YF), p. 2.
ICML-2009-GermainLLM #classification #learning #linear: PAC-Bayesian learning of linear classifiers (PG, AL, FL, MM), pp. 353–360.
ICML-2009-GomesK #dynamic analysis #multi: Dynamic analysis of multiagent Q-learning with ε-greedy exploration (ERG, RK), pp. 369–376.
ICML-2009-HazanS #algorithm #learning #performance: Efficient learning algorithms for changing environments (EH, CS), pp. 393–400.
ICML-2009-HuangS #learning #linear #sequence: Learning linear dynamical systems without sequence information (TKH, JGS), pp. 425–432.
ICML-2009-HuangZM #learning: Learning with structured sparsity (JH, TZ, DNM), pp. 417–424.
ICML-2009-JebaraWC #graph #learning: Graph construction and b-matching for semi-supervised learning (TJ, JW, SFC), pp. 441–448.
ICML-2009-JetchevT #learning #predict: Trajectory prediction: learning to map situations to robot trajectories (NJ, MT), pp. 449–456.
ICML-2009-KarampatziakisK #learning #predict: Learning prediction suffix trees with Winnow (NK, DK), pp. 489–496.
ICML-2009-KokD #learning #logic #markov #network: Learning Markov logic network structure via hypergraph lifting (SK, PMD), pp. 505–512.
ICML-2009-KolterN09a #difference #feature model #learning: Regularization and feature selection in least-squares temporal difference learning (JZK, AYN), pp. 521–528.
ICML-2009-KotlowskiS #constraints #learning: Rule learning with monotonicity constraints (WK, RS), pp. 537–544.
ICML-2009-KowalskiSR #kernel #learning #multi: Multiple indefinite kernel learning with mixed norm regularization (MK, MS, LR), pp. 545–552.
ICML-2009-KunegisL #graph transformation #learning #predict: Learning spectral graph transformations for link prediction (JK, AL), pp. 561–568.
ICML-2009-LangfordSZ #learning #modelling: Learning nonlinear dynamic models (JL, RS, TZ), pp. 593–600.
ICML-2009-LanLML #algorithm #analysis #ranking: Generalization analysis of listwise learning-to-rank algorithms (YL, TYL, ZM, HL), pp. 577–584.
ICML-2009-LeeGRN #learning #network #scalability: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations (HL, RBG, RR, AYN), pp. 609–616.
ICML-2009-LiangJK #exponential #learning #metric #product line: Learning from measurements in exponential families (PL, MIJ, DK), pp. 641–648.
ICML-2009-LiKZ #learning #using: Semi-supervised learning using label mean (YFL, JTK, ZHZ), pp. 633–640.
ICML-2009-LiYX #collaboration #generative #learning: Transfer learning for collaborative filtering via a rating-matrix generative model (BL, QY, XX), pp. 617–624.
ICML-2009-LuJD #geometry #learning #metric: Geometry-aware metric learning (ZL, PJ, ISD), pp. 673–680.
ICML-2009-MairalBPS #learning #online #taxonomy: Online dictionary learning for sparse coding (JM, FRB, JP, GS), pp. 689–696.
ICML-2009-MaSSV #identification #learning #online #scalability: Identifying suspicious URLs: an application of large-scale online learning (JM, LKS, SS, GMV), pp. 681–688.
ICML-2009-MobahiCW #learning #video: Deep learning from temporal coherence in video (HM, RC, JW), pp. 737–744.
ICML-2009-NeumannMP #learning: Learning complex motions by sequencing simpler motion templates (GN, WM, JP), pp. 753–760.
ICML-2009-Niv #learning #summary #tutorial: Tutorial summary: The neuroscience of reinforcement learning (YN), p. 16.
ICML-2009-NowozinJ #clustering #graph #learning #linear #programming: Solution stability in linear programming relaxations: graph partitioning and unsupervised learning (SN, SJ), pp. 769–776.
ICML-2009-PazisL #learning #policy: Binary action search for learning continuous-action control policies (JP, MGL), pp. 793–800.
ICML-2009-PoczosASGS #exclamation #learning: Learning when to stop thinking and do something! (BP, YAY, CS, RG, NRS), pp. 825–832.
ICML-2009-QiTZCZ #learning #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 #learning #scalability #using: Large-scale deep unsupervised learning using graphics processors (RR, AM, AYN), pp. 873–880.
ICML-2009-RaykarYZJFVBM #learning #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 #learning #modelling #probability #visual notation: Learning structurally consistent undirected probabilistic graphical models (SR, TL, MWW), pp. 905–912.
ICML-2009-SunJY #machine learning #problem: A least squares formulation for a class of generalized eigenvalue problems in machine learning (LS, SJ, JY), pp. 977–984.
ICML-2009-SuttonMPBSSW #approximate #learning #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 #learning #polynomial: Optimistic initialization and greediness lead to polynomial time learning in factored MDPs (IS, AL), pp. 1001–1008.
ICML-2009-TaylorP #approximate #kernel #learning: Kernelized value function approximation for reinforcement learning (GT, RP), pp. 1017–1024.
ICML-2009-Tillman #distributed #independence #learning: Structure learning with independent non-identically distributed data (RET), pp. 1041–1048.
ICML-2009-TrespY #dependence #learning #summary #tutorial: Tutorial summary: Learning with dependencies between several response variables (VT, KY), p. 14.
ICML-2009-VarmaB #kernel #learning #multi #performance: More generality in efficient multiple kernel learning (MV, BRB), pp. 1065–1072.
ICML-2009-VlassisT #learning: Model-free reinforcement learning as mixture learning (NV, MT), pp. 1081–1088.
ICML-2009-VolkovsZ #learning #named #ranking: BoltzRank: learning to maximize expected ranking gain (MV, RSZ), pp. 1089–1096.
ICML-2009-WeinbergerDLSA #learning #multi #scalability: Feature hashing for large scale multitask learning (KQW, AD, JL, AJS, JA), pp. 1113–1120.
ICML-2009-Welling: Herding dynamical weights to learn (MW), pp. 1121–1128.
ICML-2009-XuWS #learning #predict: Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning (LX, MW, DS), pp. 1137–1144.
ICML-2009-YangJY #learning #online: Online learning by ellipsoid method (LY, RJ, JY), pp. 1153–1160.
ICML-2009-YuanH #feature model #learning #robust: Robust feature extraction via information theoretic learning (XY, BGH), pp. 1193–1200.
ICML-2009-YuilleZ #composition #learning: Compositional noisy-logical learning (ALY, SZ), pp. 1209–1216.
ICML-2009-YuJ #learning: Learning structural SVMs with latent variables (CNJY, TJ), pp. 1169–1176.
ICML-2009-ZhangKP #learning #prototype #scalability: Prototype vector machine for large scale semi-supervised learning (KZ, JTK, BP), pp. 1233–1240.
ICML-2009-ZhangSFD #learning: Learning non-redundant codebooks for classifying complex objects (WZ, AS, XF, TGD), pp. 1241–1248.
ICML-2009-ZhanLLZ #learning #metric #using: Learning instance specific distances using metric propagation (DCZ, ML, YFL, ZHZ), pp. 1225–1232.
ICML-2009-ZhouSL #learning #multi: Multi-instance learning by treating instances as non-I.I.D. samples (ZHZ, YYS, YFL), pp. 1249–1256.
ICML-2009-ZhuangTH #kernel #learning #named #parametricity: SimpleNPKL: simple non-parametric kernel learning (JZ, IWT, SCHH), pp. 1273–1280.
KDD-2009-BeygelzimerL #learning: The offset tree for learning with partial labels (AB, JL), pp. 129–138.
KDD-2009-ChenCBT #learning #optimisation #random: Constrained optimization for validation-guided conditional random field learning (MC, YC, MRB, AET), pp. 189–198.
KDD-2009-DonmezCS #learning: Efficiently learning the accuracy of labeling sources for selective sampling (PD, JGC, JGS), pp. 259–268.
KDD-2009-DundarHBRR #case study #dataset #detection #learning #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 #learning: Issues in evaluation of stream learning algorithms (JG, RS, PPR), pp. 329–338.
KDD-2009-GaoFSH #learning: Heterogeneous source consensus learning via decision propagation and negotiation (JG, WF, YS, JH), pp. 339–348.
KDD-2009-GeXZSGW #learning #multi: Multi-focal learning and its application to customer service support (YG, HX, WZ, RKS, XG, WW), pp. 349–358.
KDD-2009-GuptaBR #learning: Catching the drift: learning broad matches from clickthrough data (SG, MB, MR), pp. 1165–1174.
KDD-2009-JinHS #machine learning #mining #named #novel #web: OpinionMiner: a novel machine learning system for web opinion mining and extraction (WJ, HHH, RKS), pp. 1195–1204.
KDD-2009-LiuKJ #graph #learning #monitoring: Learning dynamic temporal graphs for oil-production equipment monitoring system (YL, JRK, OJ), pp. 1225–1234.
KDD-2009-Macskassy #empirical #graph #learning #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 #learning #web: Beyond blacklists: learning to detect malicious web sites from suspicious URLs (JM, LKS, SS, GMV), pp. 1245–1254.
KDD-2009-RendleMNS #learning #ranking #recommendation: Learning optimal ranking with tensor factorization for tag recommendation (SR, LBM, AN, LST), pp. 727–736.
KDD-2009-TangL #learning #relational #social: Relational learning via latent social dimensions (LT, HL), pp. 817–826.
KDD-2009-WangCWPBGZ #independence #question: Can we learn a template-independent wrapper for news article extraction from a single training site? (JW, CC, CW, JP, JB, ZG, WVZ), pp. 1345–1354.
KDD-2009-WangSAL #fault #learning #network: Learning, indexing, and diagnosing network faults (TW, MS, DA, LL), pp. 857–866.
KDD-2009-YangSWC #classification #effectiveness #learning #multi: Effective multi-label active learning for text classification (BY, JTS, TW, ZC), pp. 917–926.
KDD-2009-YouHC #biology #learning #network: Learning patterns in the dynamics of biological networks (CHY, LBH, DJC), pp. 977–986.
KDIR-2009-CallejaFGA #learning #set: A Learning Method for Imbalanced Data Sets (JdlC, OF, JG, RMAP), pp. 307–310.
KDIR-2009-ZhouZK #collaboration #learning: The Collaborative Learning Agent (CLA) in Trident Warrior 08 Exercise (CZ, YZ, CK), pp. 323–328.
KEOD-2009-Aussenac-GillesK #documentation #learning #ontology #xml: Ontology Learning by Analyzing XML Document Structure and Content (NAG, MK), pp. 159–165.
KEOD-2009-FreddoT #evolution #folksonomy #learning #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 #learning #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 #learning #semantics #towards #web: Towards Semantic Web Enhanced Learning (DD, GA), pp. 212–217.
KMIS-2009-MorenoCCAMCG #case study #experience #research: When Knowledge Meets Innovation Technology — The ENEA e-LEARN Experiences through Technology and Research (AM, FC, CC, AA, CM, AC, SG), pp. 161–166.
MLDM-2009-BouthinonSV #ambiguity #concept #learning: Concept Learning from (Very) Ambiguous Examples (DB, HS, VV), pp. 465–478.
MLDM-2009-ChanguelLB #automation #html #learning: A General Learning Method for Automatic Title Extraction from HTML Pages (SC, NL, BBM), pp. 704–718.
MLDM-2009-LeeCWL #learning: Learning with a Quadruped Chopstick Robot (WCL, JCC, SzW, KML), pp. 603–616.
MLDM-2009-Mendes-MoreiraJSS #approach #case study #learning: Ensemble Learning: A Study on Different Variants of the Dynamic Selection Approach (JMM, AMJ, CS, JFdS), pp. 191–205.
MLDM-2009-SeredinKM #machine learning #order #set: Selection of Subsets of Ordered Features in Machine Learning (OS, AK, VM), pp. 16–28.
MLDM-2009-StrumbeljRK #learning: Learning Betting Tips from Users’ Bet Selections (ES, MRS, IK), pp. 678–688.
RecSys-2009-MaLK #learning #recommendation #trust: Learning to recommend with trust and distrust relationships (HM, MRL, IK), pp. 189–196.
RecSys-2009-OMahonyS #learning #recommendation: Learning to recommend helpful hotel reviews (MPO, BS), pp. 305–308.
SEKE-2009-AhsanFW #debugging #estimation #machine learning #using: Program File Bug Fix Effort Estimation Using Machine Learning Methods for OSS (SNA, JF, FW), pp. 129–134.
SEKE-2009-AxelssonBFSK #bibliography #code review #detection #fault #interactive #machine learning #visualisation: Detecting Defects with an Interactive Code Review Tool Based on Visualisation and Machine Learning (SA, DB, RF, DS, DK), pp. 412–417.
SEKE-2009-FarZYA #concept #documentation #learning #semantics #using: Realization of Semantic Search Using Concept Learning and Document Annotation Agents (BHF, CZ, Z(Y, MA), pp. 164–169.
SEKE-2009-LounisAS #approach #impact analysis #maintenance #predict: Predicting Maintainability expressed as Change Impact: A Machine-learning-based Approach (HL, MKA, HAS), pp. 122–128.
SEKE-2009-TianCYL #approach #learning #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 #learning #re-engineering: An Academia-Industry Collaborative Teaching and Learning Model for Software Engineering Education (HY), pp. 301–305.
SIGIR-2009-AslamKPSY #documentation #effectiveness #performance #ranking: Document selection methodologies for efficient and effective learning-to-rank (JAA, EK, VP, SS, EY), pp. 468–475.
SIGIR-2009-BanerjeeCR #learning #query #rank: Learning to rank for quantity consensus queries (SB, SC, GR), pp. 243–250.
SIGIR-2009-CormackCB #learning #rank: Reciprocal rank fusion outperforms condorcet and individual rank learning methods (GVC, CLAC, SB), pp. 758–759.
SIGIR-2009-CumminsO #framework #information retrieval #learning #proximity: Learning in a pairwise term-term proximity framework for information retrieval (RC, CO), pp. 251–258.
SIGIR-2009-HuangH #approach #information retrieval #learning #ranking: A bayesian learning approach to promoting diversity in ranking for biomedical information retrieval (XH, QH), pp. 307–314.
SIGIR-2009-MaKL #learning #recommendation #social #trust: Learning to recommend with social trust ensemble (HM, IK, MRL), pp. 203–210.
SIGIR-2009-SunQTW #learning #metric #rank #ranking #robust: Robust sparse rank learning for non-smooth ranking measures (ZS, TQ, QT, JW), pp. 259–266.
SIGIR-2009-YangWGH #learning #query #ranking #web: Query sampling for ranking learning in web search (LY, LW, BG, XSH), pp. 754–755.
SIGIR-2009-YilmazR #learning #rank: Deep versus shallow judgments in learning to rank (EY, SR), pp. 662–663.
RE-2009-KnaussSS #heuristic #learning #requirements: Learning to Write Better Requirements through Heuristic Critiques (EK, KS, KS), pp. 387–388.
RE-2009-KonradD #industrial #lessons learnt #modelling: Lessons Learned from the Use of Artifact Models in Industrial Projects (SK, HD), pp. 349–354.
REFSQ-2009-LaurentC #lessons learnt #online #open source #process #requirements: Lessons Learned from Open Source Projects for Facilitating Online Requirements Processes (PL, JCH), pp. 240–255.
SAC-2009-GamaRS #algorithm #data type: Evaluating algorithms that learn from data streams (JG, PPR, RS), pp. 1496–1500.
SAC-2009-LeezerZ #simulation: Simulating human intuitive decisions by Q-learning (JL, YZ), pp. 2077–2081.
SAC-2009-LiuTS #classification #complexity #learning #using: Assessing complexity of service-oriented computing using learning classifier systems (LL, ST, HS), pp. 2170–2171.
SAC-2009-Manine #information management #learning #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 #learning #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-MartinsBPS #feedback #information retrieval: Implicit relevance feedback for context-aware information retrieval in UbiLearning environments (DSM, MB, AFdP, WLdS), pp. 659–663.
SAC-2009-RoeslerHC #case study #distance #learning #multi: A new multimedia synchronous distance learning system: the IVA study case (VR, RH, CHC), pp. 1765–1770.
SAC-2009-SchmitzbergerRNRP #architecture #learning: Thin client architecture in support of remote radiology learning (FFS, JER, SN, GDR, DSP), pp. 842–846.
SAC-2009-WangCH #learning #multi #music #retrieval: Music retrieval based on a multi-samples selection strategy for support vector machine active learning (TW, GC, PH), pp. 1750–1751.
ESEC-FSE-2009-BruchMM #code completion #learning: Learning from examples to improve code completion systems (MB, MM, MM), pp. 213–222.
ICSE-2009-AlrajehKRU #learning #modelling #requirements: Learning operational requirements from goal models (DA, JK, AR, SU), pp. 265–275.
SPLC-2009-PechKCSH #case study #development #experience #lessons learnt #variability: Variability management in small development organizations: experiences and lessons learned from a case study (DP, JK, RC, CS, DH), pp. 285–294.
CGO-2009-LeatherBO #automation #compilation #generative #machine learning #optimisation: Automatic Feature Generation for Machine Learning Based Optimizing Compilation (HL, EVB, MFPO), pp. 81–91.
CGO-2009-MaoS #evolution #learning #predict #virtual machine: Cross-Input Learning and Discriminative Prediction in Evolvable Virtual Machines (FM, XS), pp. 92–101.
HPDC-2009-Reeuwijk #data flow #framework #learning #named #peer-to-peer #self #using: Maestro: a self-organizing peer-to-peer dataflow framework using reinforcement learning (CvR), pp. 187–196.
PPoPP-2009-WangO #approach #machine learning #parallel: Mapping parallelism to multi-cores: a machine learning based approach (ZW, MFPO), pp. 75–84.
ICLP-2009-Raedt #learning #logic #probability #tutorial: Probabilistic Logic Learning — A Tutorial Abstract (LDR), p. 39.
ICST-2009-KoochakzadehGM #lessons learnt #metric: Test Redundancy Measurement Based on Coverage Information: Evaluations and Lessons Learned (NK, VG, FM), pp. 220–229.
SAT-2009-AtseriasFT #algorithm #bound: Clause-Learning Algorithms with Many Restarts and Bounded-Width Resolution (AA, JKF, MT), pp. 114–127.
SAT-2009-DilkinaGS #learning: Backdoors in the Context of Learning (BND, CPG, AS), pp. 73–79.
SAT-2009-HaimW #machine learning #using: Restart Strategy Selection Using Machine Learning Techniques (SH, TW), pp. 312–325.
SAT-2009-Johannsen #bound #exponential #learning #strict: An Exponential Lower Bound for Width-Restricted Clause Learning (JJ), pp. 128–140.
SAT-2009-PipatsrisawatD #policy #satisfiability: Width-Based Restart Policies for Clause-Learning Satisfiability Solvers (KP, AD), pp. 341–355.
SAT-2009-SorenssonB: Minimizing Learned Clauses (NS, AB), pp. 237–243.
TLCA-2009-AschieriB #interactive: Interactive Learning-Based Realizability Interpretation for Heyting Arithmetic with EM1 (FA, SB), pp. 20–34.
ASE-2008-GrunbacherRD #lessons learnt #product line #tool support: Product Line Tools are Product Lines Too: Lessons Learned from Developing a Tool Suite (PG, RR, DD), pp. 351–354.
CASE-2008-StabelliniZ #approach #learning #network #self: Interference aware self-organization for wireless sensor networks: A reinforcement learning approach (LS, JZ), pp. 560–565.
CASE-2008-WeiP #implementation #industrial #learning: An implementation of iterative learning control in industrial production machines (DW, RP), pp. 472–477.
DAC-2008-BastaniKWC #learning #predict #set: Speedpath prediction based on learning from a small set of examples (PB, KK, LCW, EC), pp. 217–222.
DAC-2008-CoskunRG #learning #multi #online #using: Temperature management in multiprocessor SoCs using online learning (AKC, TSR, KCG), pp. 890–893.
DAC-2008-OzisikyilmazMC #design #machine learning #performance #using: Efficient system design space exploration using machine learning techniques (BÖ, GM, ANC), pp. 966–969.
DATE-2008-KangK #design #framework #machine learning #manycore #named #optimisation #performance: Magellan: A Search and Machine Learning-based Framework for Fast Multi-core Design Space Exploration and Optimization (SK, RK), pp. 1432–1437.
HT-2008-HeoY #empirical #information management #learning: An empirical study of the learning effect of an ontology-driven information system (MH, MY), pp. 225–226.
HT-2008-KetterlEB #learning #social #web: Social selected learning content out of web lectures (MK, JE, JB), pp. 231–232.
HT-2008-LawlessHW #corpus #education #learning: Enhancing access to open corpus educational content: learning in the wild (SL, LH, VW), pp. 167–174.
SIGMOD-2008-FisherWZ #ad hoc #automation #generative #named: LearnPADS: automatic tool generation from ad hoc data (KF, DW, KQZ), pp. 1299–1302.
VLDB-2008-NguyenNF #learning: Learning to extract form labels (HN, THN, JF), pp. 684–694.
VLDB-2008-TalukdarJMCIPG #learning #query: Learning to create data-integrating queries (PPT, MJ, MSM, KC, ZGI, FCNP, SG), pp. 785–796.
CSEET-2008-BarbosaSM #education #experience #learning #testing: An Experience on Applying Learning Mechanisms for Teaching Inspection and Software Testing (EFB, SdRSdS, JCM), pp. 189–196.
CSEET-2008-RasR #information management #learning #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 #learning #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: Learning through creating learning objects: experiences with a class project in a distributed systems course (CLA), pp. 255–259.
ITiCSE-2008-Bower #learning #online: The “instructed-teacher”: a computer science online learning pedagogical pattern (MB), pp. 189–193.
ITiCSE-2008-Burrell #learning #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 #learning: Extending moodle for collaborative learning (FDC, GD, GS), p. 324.
ITiCSE-2008-CharltonMD #learning #performance #social: Evaluating the extent to which sociability and social presence affects learning performance (TC, LM, MD), p. 342.
ITiCSE-2008-ChidanandanS #learning #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 #learning: Databases, non-majors and collaborative learning: a ternary relationships (DG), pp. 27–31.
ITiCSE-2008-Jackova #learning #programming: Learning for mastery in an introductory programming course (JJ), p. 352.
ITiCSE-2008-Kolikant #education #framework #learning: Computer-science education as a cultural encounter: a socio-cultural framework for articulating learning difficulties (YBDK), pp. 291–295.
ITiCSE-2008-Kolling #ide #learning #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 #learning #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 #learning #programming: A distance learning approach to teaching eXtreme programming (CM, DBP, GEK), pp. 199–203.
ITiCSE-2008-PerezMF #learning #operating system: Cooperative learning in operating systems laboratory (JEP, JGM, IMF), p. 323.
ITiCSE-2008-Shaban-NejadH #education #learning #towards: Web-based dynamic learning through lexical chaining: a step forward towards knowledge-driven education (ASN, VH), p. 375.
ITiCSE-2008-SierraCF #learning: An environment for supporting active learning in courses on language processing (JLS, AMFPC, AFV), pp. 128–132.
ICSM-2008-Hou #design #framework #learning: Investigating the effects of framework design knowledge in example-based framework learning (DH), pp. 37–46.
ICSM-2008-RiccaPT #guidelines #lessons learnt #maintenance: Guidelines on the use of Fit tables in software maintenance tasks: Lessons learned from 8 experiments (FR, MDP, MT), pp. 317–326.
MSR-2008-Bernstein #data mining #how #mining: How to learn enough data mining to be dangerous in 60 minutes (AB), pp. 77–78.
STOC-2008-BlumLR #approach #database #learning #privacy: A learning theory approach to non-interactive database privacy (AB, KL, AR), pp. 609–618.
STOC-2008-Feldman #algorithm #learning: Evolvability from learning algorithms (VF), pp. 619–628.
STOC-2008-GopalanKK #learning: Agnostically learning decision trees (PG, ATK, ARK), pp. 527–536.
STOC-2008-KalaiMV #learning #on the: On agnostic boosting and parity learning (ATK, YM, EV), pp. 629–638.
STOC-2008-KhotS #learning #on the: On hardness of learning intersection of two halfspaces (SK, RS), pp. 345–354.
CIAA-2008-GarciaPAR #automaton #finite #learning #nondeterminism #regular expression #using: Learning Regular Languages Using Nondeterministic Finite Automata (PG, MVdP, GIA, JR), pp. 92–101.
ICALP-A-2008-Dachman-SoledLMSWW #encryption #learning: Optimal Cryptographic Hardness of Learning Monotone Functions (DDS, HKL, TM, RAS, AW, HW), pp. 36–47.
CHI-2008-CostabileALABP #challenge #exclamation #learning #mobile: Explore! possibilities and challenges of mobile learning (MFC, ADA, RL, CA, PB, TP), pp. 145–154.
CHI-2008-FogartyTKW #concept #image #interactive #learning #named: CueFlik: interactive concept learning in image search (JF, DST, AK, SAJW), pp. 29–38.
CHI-2008-Grammenos #game studies #learning: Game over: learning by dying (DG), pp. 1443–1452.
CHI-2008-McQuigganRL #learning: The effects of empathetic virtual characters on presence in narrative-centered learning environments (SWM, JPR, JCL), pp. 1511–1520.
CHI-2008-OganAJ #learning #predict: Pause, predict, and ponder: use of narrative videos to improve cultural discussion and learning (AO, VA, CJ), pp. 155–162.
CHI-2008-PatelFLH #development #machine learning #statistics: Investigating statistical machine learning as a tool for software development (KP, JF, JAL, BLH), pp. 667–676.
CHI-2008-WangM #interactive #learning: Human-Currency Interaction: learning from virtual currency use in China (YW, SDM), pp. 25–28.
ICEIS-AIDSS-2008-MorgadoPR #evaluation #learning #quality: An Evaluation Instrument for Learning Object Quality and Management (EMM, FJGP, ÁBR), pp. 327–332.
ICEIS-AIDSS-2008-StateCRP #algorithm #classification #learning: A New Learning Algorithm for Classification in the Reduced Space (LS, CC, IR, PV), pp. 155–160.
ICEIS-HCI-2008-CarvalhoS #learning #lessons learnt #usability: The Importance of Usability Criteria on Learning Management Systems: Lessons Learned (AFPdC, JCAS), pp. 154–159.
ICEIS-HCI-2008-DamaseviciusT #design #learning #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 #learning #visualisation: An Interface Environment for Learning Object Search and Pre-Visualisation (LSG, ROdOM, AID, MSS), pp. 240–247.
ICEIS-HCI-2008-MileyRM #learning: Traditional Learning Vs. e-LEARNING — Some Results from Training Call Centre Personnel (MM, JAR, CM), pp. 299–307.
ICEIS-ISAS1-2008-GullaBK #concept #ontology #using: Using Association Rules to Learn Concept Relationships in Ontologies (JAG, TB, GSK), pp. 58–65.
ICEIS-ISAS2-2008-LopesA #development #distributed #lessons learnt #process #requirements: A Requirements Engineering Process Model for Distributed Software Development — Lessons Learned (LTL, JLNA), pp. 117–122.
ICEIS-J-2008-GullaBK08a #learning #ontology: Association Rules and Cosine Similarities in Ontology Relationship Learning (JAG, TB, GSK), pp. 201–212.
ICEIS-SAIC-2008-CanalesP #architecture #learning #semantics #web: Learning Technology System Architecture Based on Agents and Semantic Web (ACC, RPV), pp. 127–132.
ICEIS-SAIC-2008-RanW #adaptation #metric #performance #using: Develop Adaptive Workplace E-Learning Environments by Using Performance Measurement Systems (WR, MW), pp. 142–147.
CIKM-2008-BroderCFGJMMP #learning: 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 #learning #multi: Proactive learning: cost-sensitive active learning with multiple imperfect oracles (PD, JGC), pp. 619–628.
CIKM-2008-DouSYW #learning #question #ranking #web: Are click-through data adequate for learning web search rankings? (ZD, RS, XY, JRW), pp. 73–82.
CIKM-2008-HoefelE #classification #learning #sequence: Learning a two-stage SVM/CRF sequence classifier (GH, CE), pp. 271–278.
CIKM-2008-LuoZHXH #learning #multi: Transfer learning from multiple source domains via consensus regularization (PL, FZ, HX, YX, QH), pp. 103–112.
CIKM-2008-MaYKL #learning #query #semantics: Learning latent semantic relations from clickthrough data for query suggestion (HM, HY, IK, MRL), pp. 709–718.
CIKM-2008-MilneW #learning #wiki: Learning to link with wikipedia (DNM, IHW), pp. 509–518.
CIKM-2008-NiXLH #approach #learning: Group-based learning: a boosting approach (WN, JX, HL, YH), pp. 1443–1444.
CIKM-2008-WangCZL #constraints #learning #metric: Semi-supervised metric learning by maximizing constraint margin (FW, SC, CZ, TL), pp. 1457–1458.
ECIR-2008-AyacheQ #corpus #learning #using #video: Video Corpus Annotation Using Active Learning (SA, GQ), pp. 187–198.
ICML-2008-BarrettN #learning #multi #policy: Learning all optimal policies with multiple criteria (LB, SN), pp. 41–47.
ICML-2008-BickelBLS #learning #multi: Multi-task learning for HIV therapy screening (SB, JB, TL, TS), pp. 56–63.
ICML-2008-BryanS #learning: Actively learning level-sets of composite functions (BB, JGS), pp. 80–87.
ICML-2008-CaruanaKY #empirical #evaluation #learning: An empirical evaluation of supervised learning in high dimensions (RC, NK, AY), pp. 96–103.
ICML-2008-ChenM #learning: Learning to sportscast: a test of grounded language acquisition (DLC, RJM), pp. 128–135.
ICML-2008-CoatesAN #learning #multi: Learning for control from multiple demonstrations (AC, PA, AYN), pp. 144–151.
ICML-2008-CollobertW #architecture #learning #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 #learning: Hierarchical sampling for active learning (SD, DH), pp. 208–215.
ICML-2008-DekelS #learning: Learning to classify with missing and corrupted features (OD, OS), pp. 216–223.
ICML-2008-DickHS #infinity #learning #semistructured data: Learning from incomplete data with infinite imputations (UD, PH, TS), pp. 232–239.
ICML-2008-DiukCL #learning #object-oriented #performance #representation: An object-oriented representation for efficient reinforcement learning (CD, AC, MLL), pp. 240–247.
ICML-2008-DonmezC #learning #optimisation #rank #reduction: Optimizing estimated loss reduction for active sampling in rank learning (PD, JGC), pp. 248–255.
ICML-2008-DoshiPR #learning #using: Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs (FD, JP, NR), pp. 256–263.
ICML-2008-DuchiSSC #learning #performance: Efficient projections onto the l1-ball for learning in high dimensions (JCD, SSS, YS, TC), pp. 272–279.
ICML-2008-EpshteynVD #learning: Active reinforcement learning (AE, AV, GD), pp. 296–303.
ICML-2008-FrankMP #learning: Reinforcement learning in the presence of rare events (JF, SM, DP), pp. 336–343.
ICML-2008-GonenA #kernel #learning #locality #multi: Localized multiple kernel learning (MG, EA), pp. 352–359.
ICML-2008-GordonGM #game studies #learning: No-regret learning in convex games (GJG, AG, CM), pp. 360–367.
ICML-2008-HamL #analysis #learning: Grassmann discriminant analysis: a unifying view on subspace-based learning (JH, DDL), pp. 376–383.
ICML-2008-HoiJ #kernel #learning: Active kernel learning (SCHH, RJ), pp. 400–407.
ICML-2008-HuynhM #learning #logic #markov #network #parametricity: Discriminative structure and parameter learning for Markov logic networks (TNH, RJM), pp. 416–423.
ICML-2008-KolterCNGD #learning #programming: Space-indexed dynamic programming: learning to follow trajectories (JZK, AC, AYN, YG, CD), pp. 488–495.
ICML-2008-LanLQML #learning #rank: Query-level stability and generalization in learning to rank (YL, TYL, TQ, ZM, HL), pp. 512–519.
ICML-2008-LazaricRB #learning: Transfer of samples in batch reinforcement learning (AL, MR, AB), pp. 544–551.
ICML-2008-LiLW #framework #learning #self #what: Knows what it knows: a framework for self-aware learning (LL, MLL, TJW), pp. 568–575.
ICML-2008-LoeffFR #approximate #learning #named: ManifoldBoost: stagewise function approximation for fully-, semi- and un-supervised learning (NL, DAF, DR), pp. 600–607.
ICML-2008-MekaJCD #learning #online #rank: Rank minimization via online learning (RM, PJ, CC, ISD), pp. 656–663.
ICML-2008-MeloMR #analysis #approximate #learning: An analysis of reinforcement learning with function approximation (FSM, SPM, MIR), pp. 664–671.
ICML-2008-NowozinB #approach #learning: A decoupled approach to exemplar-based unsupervised learning (SN, GHB), pp. 704–711.
ICML-2008-OuyangG #learning #ranking: Learning dissimilarities by ranking: from SDP to QP (HO, AGG), pp. 728–735.
ICML-2008-ParrLTPL #analysis #approximate #feature model #learning #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 #learning #query: Learning to learn implicit queries from gaze patterns (KP, AA, SK), pp. 760–767.
ICML-2008-RadlinskiKJ #learning #multi #ranking: Learning diverse rankings with multi-armed bandits (FR, RK, TJ), pp. 784–791.
ICML-2008-RanzatoS #documentation #learning #network: Semi-supervised learning of compact document representations with deep networks (MR, MS), pp. 792–799.
ICML-2008-RaykarKBDR #automation #feature model #induction #learning #multi: Bayesian multiple instance learning: automatic feature selection and inductive transfer (VCR, BK, JB, MD, RBR), pp. 808–815.
ICML-2008-ReisingerSM #kernel #learning #online: Online kernel selection for Bayesian reinforcement learning (JR, PS, RM), pp. 816–823.
ICML-2008-SakumaKW #learning #privacy: Privacy-preserving reinforcement learning (JS, SK, RNW), pp. 864–871.
ICML-2008-ShiBY #learning #modelling #using: Data spectroscopy: learning mixture models using eigenspaces of convolution operators (TS, MB, BY), pp. 936–943.
ICML-2008-SilverSM #learning: Sample-based learning and search with permanent and transient memories (DS, RSS, MM), pp. 968–975.
ICML-2008-SindhwaniR #learning #multi: An RKHS for multi-view learning and manifold co-regularization (VS, DSR), pp. 976–983.
ICML-2008-SokolovskaCY #learning #modelling #probability: The asymptotics of semi-supervised learning in discriminative probabilistic models (NS, OC, FY), pp. 984–991.
ICML-2008-SuZLM #learning #network #parametricity: Discriminative parameter learning for Bayesian networks (JS, HZ, CXL, SM), pp. 1016–1023.
ICML-2008-SyedBS #learning #linear #programming #using: Apprenticeship learning using linear programming (US, MHB, RES), pp. 1032–1039.
ICML-2008-SzafranskiGR #kernel #learning: Composite kernel learning (MS, YG, AR), pp. 1040–1047.
ICML-2008-WangYZ #adaptation #kernel #learning #multi: Adaptive p-posterior mixture-model kernels for multiple instance learning (HYW, QY, HZ), pp. 1136–1143.
ICML-2008-WangZ #learning #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 #learning #metric #performance: Fast solvers and efficient implementations for distance metric learning (KQW, LKS), pp. 1160–1167.
ICML-2008-WestonRC #learning: Deep learning via semi-supervised embedding (JW, FR, RC), pp. 1168–1175.
ICML-2008-WingateS #exponential #learning #predict #product line: Efficiently learning linear-linear exponential family predictive representations of state (DW, SPS), pp. 1176–1183.
ICML-2008-XiaLWZL #algorithm #approach #learning #rank: Listwise approach to learning to rank: theory and algorithm (FX, TYL, JW, WZ, HL), pp. 1192–1199.
ICML-2008-YaoL #difference #learning: Preconditioned temporal difference learning (HY, ZQL), pp. 1208–1215.
ICPR-2008-AlpcanB #algorithm #distributed #learning #parallel: A discrete-time parallel update algorithm for distributed learning (TA, CB), pp. 1–4.
ICPR-2008-Arevalillo-HerraezFD #image #learning #metric #retrieval #similarity: Learning combined similarity measures from user data for image retrieval (MAH, FJF, JD), pp. 1–4.
ICPR-2008-BasakLC #learning #summary #video: Video summarization with supervised learning (JB, VL, SC), pp. 1–4.
ICPR-2008-CamposJ #constraints #learning #network #parametricity #using: Improving Bayesian Network parameter learning using constraints (CPdC, QJ), pp. 1–4.
ICPR-2008-ChangLAH08a #collaboration #image #learning #using: Using collaborative learning for image contrast enhancement (YC, DJL, JKA, YH), pp. 1–4.
ICPR-2008-DehzangiMCL #classification #fuzzy #learning #speech #using: Fuzzy rule selection using Iterative Rule Learning for speech data classification (OD, BM, CES, HL), pp. 1–4.
ICPR-2008-DuinP #difference #learning #matrix #on the: On refining dissimilarity matrices for an improved NN learning (RPWD, EP), pp. 1–4.
ICPR-2008-FabletLSMCB #learning #using: Weakly supervised learning using proportion-based information: An application to fisheries acoustics (RF, RL, CS, JM, PC, JMB), pp. 1–4.
ICPR-2008-FerilliBBE #comprehension #documentation #incremental #layout #machine learning: Incremental machine learning techniques for document layout understanding (SF, MB, TMAB, FE), pp. 1–4.
ICPR-2008-FuR #learning #multi #performance: Fast multiple instance learning via L1, 2 logistic regression (ZF, ARK), pp. 1–4.
ICPR-2008-FuSHLT #image #kernel #learning #multi #set: Multiple kernel learning from sets of partially matching image features (SYF, GS, ZGH, ZzL, MT), pp. 1–4.
ICPR-2008-GhanemVW #learning #relational: Learning in imbalanced relational data (ASG, SV, GAWW), pp. 1–4.
ICPR-2008-GongC #graph #learning #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 #learning: An improvement on learning with local and global consistency (JG, DSH, ZY), pp. 1–4.
ICPR-2008-HuAS08a #learning #using: Learning motion patterns in crowded scenes using motion flow field (MH, SA, MS), pp. 1–5.
ICPR-2008-HuWJHG #detection #learning #online: Human reappearance detection based on on-line learning (LH, YW, SJ, QH, WG), pp. 1–4.
ICPR-2008-JinLH #learning #prototype: Prototype learning with margin-based conditional log-likelihood loss (XJ, CLL, XH), pp. 1–4.
ICPR-2008-JradGB #constraints #learning #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 #learning #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 #learning #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 #learning #novel #robust: A novel robust kernel for appearance-based learning (CTL, SHL), pp. 1–4.
ICPR-2008-LiDM #feature model #learning #locality #using: Localized feature selection for Gaussian mixtures using variational learning (YL, MD, YM), pp. 1–4.
ICPR-2008-LiuWBM #kernel #learning #linear: Semi-supervised learning by locally linear embedding in kernel space (RL, YW, TB, DM), pp. 1–4.
ICPR-2008-LiuZDY #detection #learning #sequence #video: Video attention: Learning to detect a salient object sequence (TL, NZ, WD, ZY), pp. 1–4.
ICPR-2008-LuFJW #classification #framework #learning #metric #reduction #visualisation: Metric Learning: A general dimension reduction framework for classification and visualization (CL, GF, JJ, PSPW), pp. 1–4.
ICPR-2008-NaYKC #learning: Relevant pattern selection for subspace learning (JHN, SMY, MK, JYC), pp. 1–4.
ICPR-2008-NguyenBP #approach #learning #set: A supervised learning approach for imbalanced data sets (GHN, AB, SLP), pp. 1–4.
ICPR-2008-NingXZGH #detection #difference #learning: Temporal difference learning to detect unsafe system states (HN, WX, YZ, YG, TSH), pp. 1–4.
ICPR-2008-PerezO #invariant #learning #programming #search-based: Learning invariant region descriptor operators with genetic programming and the F-measure (CBP, GO), pp. 1–4.
ICPR-2008-QuQY #learning: Learning a discriminative sparse tri-value transform (ZQ, GQ, PCY), pp. 1–4.
ICPR-2008-SudoOTKA #detection #incremental #learning #online: Online anomal movement detection based on unsupervised incremental learning (KS, TO, HT, HK, KA), pp. 1–4.
ICPR-2008-TorselloD #generative #graph #learning: Supervised learning of a generative model for edge-weighted graphs (AT, DLD), pp. 1–4.
ICPR-2008-WangWCW #algorithm #clustering #learning: 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 #learning: Collaborative learning by boosting in distributed environments (SW, CZ), pp. 1–4.
ICPR-2008-WuF #3d #classification #learning #multi #using: Multiple view based 3D object classification using ensemble learning of local subspaces (JW, KF), pp. 1–4.
ICPR-2008-ZhaoGLJ #learning #modelling: Spatio-temporal patches for night background modeling by subspace learning (YZ, HG, LL, YJ), pp. 1–4.
ICPR-2008-Zhu #documentation #image #learning: Augment document image binarization by learning (YZ), pp. 1–4.
ICPR-2008-ZhuBQ #lazy evaluation #learning: Bagging very weak learners with lazy local learning (XZ, CB, WQ), pp. 1–4.
KDD-2008-ChakrabartiKSB #learning #ranking: Structured learning for non-smooth ranking losses (SC, RK, US, CB), pp. 88–96.
KDD-2008-ChengT #learning: Semi-supervised learning with data calibration for long-term time series forecasting (HC, PNT), pp. 133–141.
KDD-2008-ChenJCLWY #classification #kernel #learning: Learning subspace kernels for classification (JC, SJ, BC, QL, MW, JY), pp. 106–114.
KDD-2008-CuiDSAJ #learning: Learning methods for lung tumor markerless gating in image-guided radiotherapy (YC, JGD, GCS, BMA, SBJ), pp. 902–910.
KDD-2008-DavisD #learning #metric #problem: Structured metric learning for high dimensional problems (JVD, ISD), pp. 195–203.
KDD-2008-ElkanN #classification #learning: Learning classifiers from only positive and unlabeled data (CE, KN), pp. 213–220.
KDD-2008-LiFGMF #learning #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 #learning #query: Active learning with direct query construction (CXL, JD), pp. 480–487.
KDD-2008-LingDXYY #learning: Spectral domain-transfer learning (XL, WD, GRX, QY, YY), pp. 488–496.
KDD-2008-MadaniH #learning #on the: On updates that constrain the features’ connections during learning (OM, JH), pp. 515–523.
KDD-2008-SinghG #learning #matrix #relational: Relational learning via collective matrix factorization (APS, GJG), pp. 650–658.
KDD-2008-SunJY #classification #learning #multi: Hypergraph spectral learning for multi-label classification (LS, SJ, JY), pp. 668–676.
KDD-2008-WuLCC #learning #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 #learning #named: SAIL: summation-based incremental learning for information-theoretic clustering (JW, HX, JC), pp. 740–748.
KDD-2008-ZhangSPN #documentation #learning #multi #topic #web: Learning from multi-topic web documents for contextual advertisement (YZ, ACS, JCP, MN), pp. 1051–1059.
KR-2008-Rintanen #graph: Planning Graphs and Propositional Clause-Learning (JR), pp. 535–543.
RecSys-2008-DrachslerHK #learning #navigation: Navigation support for learners in informal learning environments (HD, HGKH, RK), pp. 303–306.
SEKE-2008-MurphyKHW #machine learning #testing: Properties of Machine Learning Applications for Use in Metamorphic Testing (CM, GEK, LH, LW), pp. 867–872.
SEKE-2008-Zhang #machine learning #re-engineering #research: Machine Learning and Value-based Software Engineering: a Research Agenda (DZ), pp. 285–290.
SEKE-2008-ZhongYAF #using: Ontology-learning Supported Sematic Search Using Cooperative Agents (CZ, Z(Y, MA, BHF), pp. 123–128.
SIGIR-2008-AminiTG #algorithm #learning #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 #learning: Information retrieval on bug locations by learning co-located bug report clusters (IXC, HJ, CZY, PJW), pp. 801–802.
SIGIR-2008-DruckMM #learning #using: Learning from labeled features using generalized expectation criteria (GD, GSM, AM), pp. 595–602.
SIGIR-2008-DuhK #learning #rank: Learning to rank with partially-labeled data (KD, KK), pp. 251–258.
SIGIR-2008-GuiverS #learning #process #rank: Learning to rank with SoftRank and Gaussian processes (JG, ES), pp. 259–266.
SIGIR-2008-HarpaleY #collaboration #learning #personalisation: Personalized active learning for collaborative filtering (AH, YY), pp. 91–98.
SIGIR-2008-LeeKJ #algorithm #constraints #learning: Fixed-threshold SMO for Joint Constraint Learning Algorithm of Structural SVM (CL, HK, MGJ), pp. 829–830.
SIGIR-2008-LiWA #graph #learning #query: Learning query intent from regularized click graphs (XL, YYW, AA), pp. 339–346.
SIGIR-2008-TanWC #detection #sentiment #using: Combining learn-based and lexicon-based techniques for sentiment detection without using labeled examples (ST, YW, XC), pp. 743–744.
SIGIR-2008-TsaiWC #case study #information retrieval #learning #multi: A study of learning a merge model for multilingual information retrieval (MFT, YTW, HHC), pp. 195–202.
SIGIR-2008-VelosoAGM #learning #rank #using: Learning to rank at query-time using association rules (AV, HMdA, MAG, WMJ), pp. 267–274.
SIGIR-2008-WangZZ #image #learning #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 #learning #metric #optimisation #rank: Directly optimizing evaluation measures in learning to rank (JX, TYL, ML, HL, WYM), pp. 107–114.
SIGIR-2008-YuZXG #categorisation #design #learning #using: trNon-greedy active learning for text categorization using convex ansductive experimental design (KY, SZ, WX, YG), pp. 635–642.
SIGIR-2008-ZhangL #learning #multi: Learning with support vector machines for query-by-multiple-examples (DZ, WSL), pp. 835–836.
SIGIR-2008-ZhouXZY #learning #rank: Learning to rank with ties (KZ, GRX, HZ, YY), pp. 275–282.
OOPSLA-2008-SimpkinsBIM #adaptation #learning #programming language #towards: Towards adaptive programming: integrating reinforcement learning into a programming language (CS, SB, CLIJ, MM), pp. 603–614.
RE-2008-JonesLML #learning #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 #learning #requirements: Requirements Engineering Education in the 21st Century, An Experiential Learning Approach (GR, DCG, AW), pp. 85–94.
RE-2008-SimAA #experience #requirements #what: Marginal Notes on Amethodical Requirements Engineering: What Experts Learned from Experience (SES, TAA, BAA), pp. 105–114.
SAC-2008-CarvalhoAZ #health #learning #process: Learning activities on health care supported by common sense knowledge (AFPdC, JCAS, SZM), pp. 1385–1389.
SAC-2008-CorreaLSM #composition #learning #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 #learning #ubiquitous: Context-aware information retrieval on a ubiquitous medical learning environment (DSM, LHZS, MB, AFdP, WLdS), pp. 2348–2349.
SAC-2008-StrapparavaM #identification #learning: Learning to identify emotions in text (CS, RM), pp. 1556–1560.
SAC-2008-SuKZG #classification #collaboration #machine learning #using: Imputation-boosted collaborative filtering using machine learning classifiers (XS, TMK, XZ, RG), pp. 949–950.
SAC-2008-SungCM #clustering #concept #learning #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.
SAC-2008-TaghipourK #hybrid #recommendation #web: A hybrid web recommender system based on Q-learning (NT, AAK), pp. 1164–1168.
ATEM-J-2006-DubeyJA #context-free grammar #learning #set: Learning context-free grammar rules from a set of program (AD, PJ, SKA), pp. 223–240.
ASPLOS-2008-LuPSZ #concurrent #debugging #learning: Learning from mistakes: a comprehensive study on real world concurrency bug characteristics (SL, SP, ES, YZ), pp. 329–339.
ISSTA-2008-SankaranarayananCIG #learning: Dynamic inference of likely data preconditions over predicates by tree learning (SS, SC, FI, AG), pp. 295–306.
SAT-2008-StachniakB #learning #satisfiability: Speeding-Up Non-clausal Local Search for Propositional Satisfiability with Clause Learning (ZS, AB), pp. 257–270.
DATE-2007-Huang #learning: Dynamic learning based scan chain diagnosis (YH0), pp. 510–515.
HT-2007-BrownFB #learning: Real users, real results: examining the limitations of learning styles within AEH (EJB, TF, TJB), pp. 57–66.
HT-2007-FigueiraL #interactive #learning #using #visualisation: Interaction visualization in web-based learning using igraphs (ÁRF, JBL), pp. 45–46.
HT-2007-GodboleJMR #concept #interactive #learning #towards: Toward interactive learning by concept ordering (SG, SJ, SM, GR), pp. 149–150.
HT-2007-LeblancA #learning #using: Using forum in an organizational learning context (AL, MHA), pp. 41–42.
ICDAR-2007-ChenLJ #learning #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: Learning of Pattern-Based Rules for Document Classification (AD), pp. 123–127.
ICDAR-2007-EspositoFMB #automation #documentation #first-order #incremental #learning #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: Learning to Group Text Lines and Regions in Freeform Handwritten Notes (MY, PAV, SR, HS, CL), pp. 28–32.
CSEET-2007-Armarego #learning: Learning from Reflection: Practitioners as Adult Learners (JA), pp. 55–63.
CSEET-2007-DistanteH #challenge #education #lessons learnt #programming #re-engineering #student: Challenges and Lessons Learned in Teaching Software Engineering and Programming to Hearing-Impaired Students (DD, SH), pp. 344–354.
CSEET-2007-KanerP #education #learning #testing: Practice and Transfer of Learning in the Teaching of Software Testing (CK, SP), pp. 157–166.
CSEET-2007-KrogstieB #collaboration #learning #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 #learning #re-engineering: Laptop Enabled Active Learning in the Software Engineering Classroom: An Experience Report (DP, RK), pp. 262–274.
CSEET-2007-Staron #analysis #learning #student #using: Using Students as Subjects in Experiments--A Quantitative Analysis of the Influence of Experimentation on Students’ Learning Proces (MS), pp. 221–228.
CSEET-2007-Zeid #lessons learnt #re-engineering: Lessons Learned from Establishing a Software Engineering Academic Programme in Developing Countries (AZ), pp. 11–18.
ITiCSE-2007-AlstesL #learning #named #network #online #programming: VERKKOKE: learning routing and network programming online (AA, JL), pp. 91–95.
ITiCSE-2007-AmelungPR #component #named: eduComponents: a component-based e-learning environment (MA, MP, DFR), p. 352.
ITiCSE-2007-Arnold #interactive #learning #logic: Introducing propositional logic and queueing theory with the infotraffic interactive learning environments (RA), p. 356.
ITiCSE-2007-BagleyC #collaboration #java #learning #programming: Collaboration and the importance for novices in learning java computer programming (CAB, CCC), pp. 211–215.
ITiCSE-2007-BarnesRPCG #game studies #learning #named: Game2Learn: building CS1 learning games for retention (TB, HR, EP, AC, AG), pp. 121–125.
ITiCSE-2007-BuenoAC #adaptation #education #student: Assisting lecturers to adapt e-learning content for deaf students (FJB, MGA, JRFdC), p. 335.
ITiCSE-2007-BuenoCGB #adaptation #student: E-learning content adaptation for deaf students (FJB, JRFdC, SG, RB), pp. 271–275.
ITiCSE-2007-CassenSALN #generative #interactive #learning #visual notation: A visual learning engine for interactive generation ofinstructional materials (TC, KRS, JA, DL, AN), p. 319.
ITiCSE-2007-CukiermanT #learning: Learning strategies sessions within the classroom in computing science university courses (DC, DMT), p. 341.
ITiCSE-2007-GalpinSC #learning #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 #learning #outsourcing #re-engineering: A classroom outsourcing experience for software engineering learning (WLH, TP), pp. 181–185.
ITiCSE-2007-KorteAPG #approach #education #learning #novel: Learning by game-building: a novel approach to theoretical computer science education (LK, SA, HP, JG), pp. 53–57.
ITiCSE-2007-LeidlR #how #learning #question: How will future learning work in the third dimension? (ML, GR), p. 329.
ITiCSE-2007-OliverGMA #learning #using: Using disruptive technology for explorative learning (IO, KG, AM, CA), pp. 96–100.
ITiCSE-2007-Sanchez-TorrubiaTC #algorithm #graph #interactive #learning #tool support: New interactive tools for graph algorithms active learning (MGST, CTB, JC), p. 337.
TACAS-2007-BolligKKL #design #game studies #learning #modelling #synthesis: Replaying Play In and Play Out: Synthesis of Design Models from Scenarios by Learning (BB, JPK, CK, ML), pp. 435–450.
TACAS-2007-Cleaveland #lessons learnt: THERE AND BACK AGAIN: Lessons Learned on the Way to the Market (RC), p. 1.
ICSM-2007-CorboGP #learning #source code: Smart Formatter: Learning Coding Style from Existing Source Code (FC, CDG, MDP), pp. 525–526.
WCRE-2007-Kienle #component #lessons learnt #reverse engineering #tool support: Building Reverse Engineering Tools with Software Components: Ten Lessons Learned (HMK), pp. 289–292.
STOC-2007-GuhaM #algorithm #approximate #learning #problem: Approximation algorithms for budgeted learning problems (SG, KM), pp. 104–113.
IFM-2007-OostdijkRTVW #encryption #learning #protocol #testing #verification: Integrating Verification, Testing, and Learning for Cryptographic Protocols (MO, VR, JT, RGdV, TACW), pp. 538–557.
CHI-2007-CockburnKAZ #interface #learning: Hard lessons: effort-inducing interfaces benefit spatial learning (AC, POK, JA, SZ), pp. 1571–1580.
CHI-2007-GrossmanDB #learning #online: Strategies for accelerating on-line learning of hotkeys (TG, PD, RB), pp. 1591–1600.
CHI-2007-KamRDTC #design #framework #learning #locality: Localized iterative design for language learning in underdeveloped regions: the PACE framework (MK, DR, VD, AT, JFC), pp. 1097–1106.
CHI-2007-KelleherPK #motivation #programming #women: Storytelling alice motivates middle school girls to learn computer programming (CK, RFP, SBK), pp. 1455–1464.
CHI-2007-ZimmermanTSHMCM #approach #automation #learning #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 #learning #process: An Essay About the Relevance of Educational Interactive Systems in the Learning Process (AC, CRM), pp. 183–189.
HCI-AS-2007-ChenL #assessment #usability: Usability Assessment of an E-Learning Courseware for Basic Cataloging (XSC, TL), pp. 198–207.
HCI-AS-2007-ChoK #collaboration #contest #learning: Suppressing Competition in a Computer-Supported Collaborative Learning System (KC, BK), pp. 208–214.
HCI-AS-2007-Furukawa #challenge #effectiveness #fault #injection #question #what: Challenge for Preventing Medication Errors -Learn from Errors- : What Is the Most Effective Label Display to Prevent Medication Error for Injectable Drug ? (HF), pp. 437–442.
HCI-AS-2007-KimJCHH #learning: 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 #learning: Breaking the Traditional E-Learning Mould: Support for the Learning Preference Approach (FL, JK, LL), pp. 294–301.
HCI-AS-2007-LuYTHY #difference #learning #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 #learning #ubiquitous: Detecting Learning Difficulties on Ubiquitous Scenarios (MdS, LC), pp. 235–244.
HCI-AS-2007-SanchezSS #game studies #learning #mobile: Mobile Game-Based Methodology for Science Learning (JS, AS, MS), pp. 322–331.
HCI-AS-2007-ShenHB #collaboration #comparison #learning #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 #learning #usability: Perceived Usefulness and Usability of Weblogs for Collaborating Learning (YLT, ELYW), pp. 361–370.
HCI-AS-2007-XiaoCR #authentication #collaboration #learning #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 #learning #process: Creating Computer Supported Collaborative Learning Activities with IMS LD (DY, XC), pp. 391–400.
HCI-MIE-2007-FabriEM #learning: Emotionally Expressive Avatars for Chatting, Learning and Therapeutic Intervention (MF, SYAE, DJM), pp. 275–285.
HCI-MIE-2007-SerbanTM #behaviour #interface #learning #predict: A Learning Interface Agent for User Behavior Prediction (GS, AT, GSM), pp. 508–517.
HCI-MIE-2007-WangYCI #interactive #interface #multimodal #realtime #using: Character Agents in E-Learning Interface Using Multimodal Real-Time Interaction (HW, JY, MHC, MI), pp. 225–231.
HCI-MIE-2007-ZhuL #case study #recognition #speech: Study on Speech Emotion Recognition System in E-Learning (AZ, QL), pp. 544–552.
HIMI-IIE-2007-AlsharaI #integration #learning #using: Business Integration Using the Interdisciplinary Project Based Learning Model (IPBL) (OKA, MI), pp. 823–833.
HIMI-IIE-2007-AnseT #evaluation: Evaluation Method of e-Learning Materials by α-Wave and β-Wave of EEG (MA, TT), pp. 252–259.
HIMI-IIE-2007-BaeckerBCLRMWW #distributed #interactive #learning #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 #learning #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-DavcevAIK #human-computer #image: HCI for m-Learning in Image Processing by Handhelds (DD, MA, DI, AK), pp. 299–308.
HIMI-IIE-2007-HorinouchiWAT #case study #effectiveness: A Study of an Effective Rehearsal Method in e-Learning (TH, SW, MA, TT), pp. 328–336.
HIMI-IIE-2007-IbrahimA #interactive #learning: Impact of Interactive Learning on Knowledge Retention (MI, OAS), pp. 347–355.
HIMI-IIE-2007-JeongL #interactive #learning #ubiquitous: Context Aware Human Computer Interaction for Ubiquitous Learning (CJ, EL), pp. 364–373.
HIMI-IIE-2007-TsengLH #learning #mobile: A Mobile Environment for Chinese Language Learning (CCT, CHL, WLH), pp. 485–489.
HIMI-MTT-2007-CornsML #approach #development #machine learning #optimisation #using: Development of an Approach for Optimizing the Accuracy of Classifying Claims Narratives Using a Machine Learning Tool (TEXTMINER[4]) (HLC, HRM, MRL), pp. 411–416.
HIMI-MTT-2007-MullerKDCB #human-computer #machine learning: Machine Learning and Applications for Brain-Computer Interfacing (KRM, MK, GD, GC, BB), pp. 705–714.
OCSC-2007-ChenY07a #collaboration #design #difference #industrial #learning: 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 #learning #self: Self-Awareness in a Computer Supported Collaborative Learning Environment (KC, MHC), pp. 284–291.
ICEIS-AIDSS-2007-BenschBRBSB #operating system #optimisation #predict #self: Self-Learning Prediction System for Optimisation of Workload Management in a Mainframe Operating System (MB, DB, WR, MB, WGS, PB), pp. 212–218.
ICEIS-AIDSS-2007-PessiotTUAG #collaboration #learning #rank: Learning to Rank for Collaborative Filtering (JFP, TVT, NU, MRA, PG), pp. 145–151.
ICEIS-AIDSS-2007-RamabadranG #approach #flexibility #learning: Intelligent E-Learning Systems — An Intelligent Approach to Flexible Learning Methodologies (SR, VG), pp. 107–112.
ICEIS-AIDSS-2007-YingboJJ #learning #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 #learning #modelling #process: A Modeling Language for Collaborative Learning Educational Units — Supporting the Coordination of Collaborative Activities (MCR), pp. 334–339.
ICEIS-HCI-2007-DeryckeC #flexibility #framework: A Flexible Infrastructure for P-Learning: A First Application in the Field of Professional Training (AD, VC), pp. 215–222.
ICEIS-J-2007-LuciaFPT07a #collaboration #distributed #learning: A Service Oriented Collaborative Distributed Learning Object Management System (ADL, RF, IP, GT), pp. 341–354.
ICEIS-SAIC-2007-LuciaFPT #collaboration #distributed #learning #named: CD-LOMAS: A Collaborative Distributed Learning Object Management System (ADL, RF, IP, GT), pp. 34–44.
ICEIS-SAIC-2007-MorgadoRP #evaluation #learning: Key Issues for Learning Objects Evaluation (EMM, ÁBR, FJGP), pp. 149–154.
ICEIS-SAIC-2007-PetrieMKLZ #challenge #lessons learnt: SWS Challenge — Status, Perspectives, Lessons Learned So Far (CJP, TMS, UK, HL, MZ), pp. 447–452.
CIKM-2007-ErtekinHBG #classification #learning: Learning on the border: active learning in imbalanced data classification (SE, JH, LB, CLG), pp. 127–136.
CIKM-2007-LiuTZ #learning #network: Ensembling Bayesian network structure learning on limited data (FL, FT, QZ), pp. 927–930.
CIKM-2007-OuyangLL #learning #summary #topic: Developing learning strategies for topic-based summarization (OY, SL, WL), pp. 79–86.
CIKM-2007-Pereira #learning: Learning to join everything (FP0), pp. 9–10.
CIKM-2007-SongZYZD #distance #estimation #learning #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 #learning #summary #web: Learning query-biased web page summarization (CW, FJ, LZ, HJZ), pp. 555–562.
ECIR-2007-DavyL #categorisation #learning #query: Active Learning with History-Based Query Selection for Text Categorisation (MD, SL), pp. 695–698.
ECIR-2007-Gori #learning: Learning in Hyperlinked Environments (MG), p. 3.
ECIR-2007-Monz #learning #query: Model Tree Learning for Query Term Weighting in Question Answering (CM), pp. 589–596.
ECIR-2007-MoreauCS #automation #machine learning #query #using: Automatic Morphological Query Expansion Using Analogy-Based Machine Learning (FM, VC, PS), pp. 222–233.
ECIR-2007-XuAZ #feedback #learning: Incorporating Diversity and Density in Active Learning for Relevance Feedback (ZX, RA, YZ), pp. 246–257.
ECIR-2007-YeungBCK #approach #documentation #learning: A Bayesian Approach for Learning Document Type Relevance (PCKY, SB, CLAC, MK), pp. 753–756.
ICML-2007-AgarwalC #graph #learning #random #rank: Learning random walks to rank nodes in graphs (AA, SC), pp. 9–16.
ICML-2007-AndoZ #generative #learning: Two-view feature generation model for semi-supervised learning (RKA, TZ), pp. 25–32.
ICML-2007-Azran #algorithm #learning #markov #multi #random: The rendezvous algorithm: multiclass semi-supervised learning with Markov random walks (AA), pp. 49–56.
ICML-2007-Bar-HillelW #distance #learning #similarity: Learning distance function by coding similarity (ABH, DW), pp. 65–72.
ICML-2007-BickelBS #learning: Discriminative learning for differing training and test distributions (SB, MB, TS), pp. 81–88.
ICML-2007-BunescuM #learning #multi: Multiple instance learning for sparse positive bags (RCB, RJM), pp. 105–112.
ICML-2007-CaoQLTL #approach #learning #rank: Learning to rank: from pairwise approach to listwise approach (ZC, TQ, TYL, MFT, HL), pp. 129–136.
ICML-2007-ChengV #image #learning: Learning to compress images and videos (LC, SVNV), pp. 161–168.
ICML-2007-DaiYXY #learning: Boosting for transfer learning (WD, QY, GRX, YY), pp. 193–200.
ICML-2007-DavisKJSD #learning #metric: Information-theoretic metric learning (JVD, BK, PJ, SS, ISD), pp. 209–216.
ICML-2007-DollarRB #algorithm #analysis #learning: Non-isometric manifold learning: analysis and an algorithm (PD, VR, SJB), pp. 241–248.
ICML-2007-Hanneke #bound #complexity #learning: A bound on the label complexity of agnostic active learning (SH), pp. 353–360.
ICML-2007-HoiJL #constraints #kernel #learning #matrix #parametricity: Learning nonparametric kernel matrices from pairwise constraints (SCHH, RJ, MRL), pp. 361–368.
ICML-2007-HulseKN #learning: Experimental perspectives on learning from imbalanced data (JVH, TMK, AN), pp. 935–942.
ICML-2007-Jaeger #learning #network #parametricity #relational: Parameter learning for relational Bayesian networks (MJ), pp. 369–376.
ICML-2007-KimP #learning #recursion: A recursive method for discriminative mixture learning (MK, VP), pp. 409–416.
ICML-2007-KrauseG #approach #learning #process: Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach (AK, CG), pp. 449–456.
ICML-2007-KropotovV #learning #on the: On one method of non-diagonal regularization in sparse Bayesian learning (DK, DV), pp. 457–464.
ICML-2007-LeeCVK #learning #multi: Learning a meta-level prior for feature relevance from multiple related tasks (SIL, VC, DV, DK), pp. 489–496.
ICML-2007-LiLL #learning #scalability: Large-scale RLSC learning without agony (WL, KHL, KSL), pp. 529–536.
ICML-2007-LiYW #distance #framework #learning #metric #reduction: A transductive framework of distance metric learning by spectral dimensionality reduction (FL, JY, JW), pp. 513–520.
ICML-2007-Mahadevan #3d #adaptation #learning #multi #using: Adaptive mesh compression in 3D computer graphics using multiscale manifold learning (SM), pp. 585–592.
ICML-2007-MannM #learning #robust #scalability: Simple, robust, scalable semi-supervised learning via expectation regularization (GSM, AM), pp. 593–600.
ICML-2007-MihalkovaM #bottom-up #learning #logic #markov #network: Bottom-up learning of Markov logic network structure (LM, RJM), pp. 625–632.
ICML-2007-MoschittiZ #effectiveness #kernel #learning #performance #relational: Fast and effective kernels for relational learning from texts (AM, FMZ), pp. 649–656.
ICML-2007-NiCD #learning #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: Learning state-action basis functions for hierarchical MDPs (SO, SM), pp. 705–712.
ICML-2007-ParkerFT #learning #performance #query #retrieval: Learning for efficient retrieval of structured data with noisy queries (CP, AF, PT), pp. 729–736.
ICML-2007-PetersS #learning: Reinforcement learning by reward-weighted regression for operational space control (JP, SS), pp. 745–750.
ICML-2007-PhuaF #approximate #learning #linear: Tracking value function dynamics to improve reinforcement learning with piecewise linear function approximation (CWP, RF), pp. 751–758.
ICML-2007-RainaBLPN #learning #self: Self-taught learning: transfer learning from unlabeled data (RR, AB, HL, BP, AYN), pp. 759–766.
ICML-2007-RakotomamonjyBCG #kernel #learning #multi #performance: More efficiency in multiple kernel learning (AR, FRB, SC, YG), pp. 775–782.
ICML-2007-SternHG #game studies #learning: Learning to solve game trees (DHS, RH, TG), pp. 839–846.
ICML-2007-SunJSF #algorithm #kernel #learning: A kernel-based causal learning algorithm (XS, DJ, BS, KF), pp. 855–862.
ICML-2007-TaylorS #learning: Cross-domain transfer for reinforcement learning (MET, PS), pp. 879–886.
ICML-2007-WachmanK #kernel #learning #order: Learning from interpretations: a rooted kernel for ordered hypergraphs (GW, RK), pp. 943–950.
ICML-2007-WangYF #difference #learning #on the: On learning with dissimilarity functions (LW, CY, JF), pp. 991–998.
ICML-2007-WangZQ #learning #metric #towards: Dirichlet aggregation: unsupervised learning towards an optimal metric for proportional data (HYW, HZ, HQ), pp. 959–966.
ICML-2007-WilsonFRT #approach #learning #multi: Multi-task reinforcement learning: a hierarchical Bayesian approach (AW, AF, SR, PT), pp. 1015–1022.
ICML-2007-WoznicaKH #learning: Learning to combine distances for complex representations (AW, AK, MH), pp. 1031–1038.
ICML-2007-WuYYS #learning: Local learning projections (MW, KY, SY, BS), pp. 1039–1046.
ICML-2007-XueDC #flexibility #learning #matrix #multi #process: The matrix stick-breaking process for flexible multi-task learning (YX, DBD, LC), pp. 1063–1070.
ICML-2007-XuF #learning #linear #on the #ranking: On learning linear ranking functions for beam search (YX, AF), pp. 1047–1054.
ICML-2007-YeCJ #kernel #learning #parametricity #programming: Discriminant kernel and regularization parameter learning via semidefinite programming (JY, JC, SJ), pp. 1095–1102.
ICML-2007-YuTY #learning #multi #robust: Robust multi-task learning with t-processes (SY, VT, KY), pp. 1103–1110.
ICML-2007-ZhangAV #learning #multi #random: Conditional random fields for multi-agent reinforcement learning (XZ, DA, SVNV), pp. 1143–1150.
ICML-2007-ZhaoL #feature model #learning: Spectral feature selection for supervised and unsupervised learning (ZZ, HL), pp. 1151–1157.
ICML-2007-ZhouB #clustering #learning #multi: Spectral clustering and transductive learning with multiple views (DZ, CJCB), pp. 1159–1166.
ICML-2007-ZhouX #learning #multi #on the: On the relation between multi-instance learning and semi-supervised learning (ZHZ, JMX), pp. 1167–1174.
ICML-2007-ZienO #kernel #learning #multi: Multiclass multiple kernel learning (AZ, CSO), pp. 1191–1198.
KDD-2007-ChenZYL #adaptation #clustering #distance #learning #metric: Nonlinear adaptive distance metric learning for clustering (JC, ZZ, JY, HL), pp. 123–132.
KDD-2007-DeodharG #clustering #framework #learning: A framework for simultaneous co-clustering and learning from complex data (MD, JG), pp. 250–259.
KDD-2007-DingSJL #framework #kernel #learning #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 #learning #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 #learning #mining: Data mining at the crossroads: successes, failures and learning from them (SP), pp. 1053–1055.
KDD-2007-RadlinskiJ #learning #ranking: Active exploration for learning rankings from clickthrough data (FR, TJ), pp. 570–579.
KDD-2007-RaoBFSON #detection #machine learning #named: LungCAD: a clinically approved, machine learning system for lung cancer detection (RBR, JB, GF, MS, NO, DPN), pp. 1033–1037.
KDD-2007-Schickel-ZuberF #clustering #learning #recommendation #using: Using hierarchical clustering for learning theontologies used in recommendation systems (VSZ, BF), pp. 599–608.
KDD-2007-Sculley #feedback #learning: Practical learning from one-sided feedback (DS), pp. 609–618.
KDD-2007-ShengL #learning: Partial example acquisition in cost-sensitive learning (VSS, CXL), pp. 638–646.
KDD-2007-YanL #machine learning: Machine learning for stock selection (RJY, CXL), pp. 1038–1042.
KDD-2007-YeJC #analysis #kernel #learning #matrix #polynomial #programming: Learning the kernel matrix in discriminant analysis via quadratically constrained quadratic programming (JY, SJ, JC), pp. 854–863.
KDD-2007-ZhaoB #web: Corroborate and learn facts from the web (SZ, JB), pp. 995–1003.
MLDM-2007-CeciABM #learning #relational: Transductive Learning from Relational Data (MC, AA, NB, DM), pp. 324–338.
MLDM-2007-ChristiansenD #approach #case study #evaluation #generative #machine learning #testing: A Machine Learning Approach to Test Data Generation: A Case Study in Evaluation of Gene Finders (HC, CMD), pp. 742–755.
MLDM-2007-EkdahlK #classification #learning #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 #learning: 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-Holness #network: A Direct Measure for the Efficacy of Bayesian Network Structures Learned from Data (GH), pp. 601–615.
MLDM-2007-JiangI #learning: Dynamic Distance-Based Active Learning with SVM (JJ, HHSI), pp. 296–309.
MLDM-2007-Kertesz-FarkasKP #classification #equivalence #learning: Equivalence Learning in Protein Classification (AKF, AK, SP), pp. 824–837.
MLDM-2007-Lehmann #hybrid #learning #ontology: Hybrid Learning of Ontology Classes (JL), pp. 883–898.
MLDM-2007-NgaiY: Fast-Maneuvering Target Seeking Based on Double-Action Q-Learning (DCKN, NHCY), pp. 653–666.
MLDM-2007-SadoddinG #case study #comparative #data mining #detection #machine learning #mining: A Comparative Study of Unsupervised Machine Learning and Data Mining Techniques for Intrusion Detection (RS, AAG), pp. 404–418.
MLDM-2007-VanderlooyMS #empirical #evaluation #learning: Off-Line Learning with Transductive Confidence Machines: An Empirical Evaluation (SV, LvdM, IGSK), pp. 310–323.
MLDM-2007-YuL #approach #classification #graph #named: PE-PUC: A Graph Based PU-Learning Approach for Text Classification (SY, CL), pp. 574–584.
RecSys-2007-RubensS #collaboration #learning: Influence-based collaborative active learning (NR, MS), pp. 145–148.
RecSys-2007-TaghipourKG #approach #learning #recommendation #web: Usage-based web recommendations: a reinforcement learning approach (NT, AAK, SSG), pp. 113–120.
RecSys-2007-TiemannP #hybrid #learning #music #recommendation #towards: Towards ensemble learning for hybrid music recommendation (MT, SP), pp. 177–178.
SEKE-2007-FarEHA #approach #concept #learning #named #ontology #statistics: Adjudicator: A Statistical Approach for Learning Ontology Concepts from Peer Agents (BHF, AHE, NH, MA), p. 654–?.
SEKE-2007-FollecoKHS #learning #quality: Learning from Software Quality Data with Class Imbalance and Noise (AF, TMK, JVH, CS), p. 487–?.
SEKE-2007-MurphyKA #approach #machine learning #testing: An Approach to Software Testing of Machine Learning Applications (CM, GEK, MA), p. 167–?.
SIGIR-2007-EfthimiadisF #education #information retrieval #learning #named: IR-Toolbox: an experiential learning tool for teaching IR (ENE, NGF), p. 914.
SIGIR-2007-ErtekinHG #learning #problem: Active learning for class imbalance problem (SE, JH, CLG), pp. 823–824.
SIGIR-2007-JansenSB #learning #online #paradigm: Viewing online searching within a learning paradigm (BJJ, BKS, DLB), pp. 859–860.
SIGIR-2007-VelipasaogluSP #constraints #learning: Improving active learning recall via disjunctive boolean constraints (EV, HS, JOP), pp. 893–894.
SIGIR-2007-WangZ #web: Learn from web search logs to organize search results (XW, CZ), pp. 87–94.
SIGIR-2007-XuL07a #learning #rank: Learning to rank collections (JX, XL), pp. 765–766.
SIGIR-2007-ZhangHRJ #learning #query #using: Query rewriting using active learning for sponsored search (WVZ, XH, BR, RJ), pp. 853–854.
SIGIR-2007-ZhengCSZ #framework #learning #ranking #using: A regression framework for learning ranking functions using relative relevance judgments (ZZ, KC, GS, HZ), pp. 287–294.
RE-2007-EgyedGHB #lessons learnt #requirements #traceability: Value-Based Requirements Traceability: Lessons Learned (AE, PG, MH, SB), pp. 115–118.
SAC-2007-BarratT #learning #recognition: A progressive learning method for symbols recognition (SB, ST), pp. 627–631.
SAC-2007-RulloCP #categorisation #learning: Learning rules with negation for text categorization (PR, CC, VLP), pp. 409–416.
SAC-2007-YingboJJ #approach #machine learning #workflow: A machine learning approach to semi-automating workflow staff assignment (YL, JW, JS), pp. 340–345.
ICSE-2007-Staron #education #evaluation #learning #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 #learning #programming #using: Using Soloman-Felder Learning Style Index to Evaluate Pedagogical Resources for Introductory Programming Classes (IAZ), pp. 723–726.
LCTES-2007-AbouGhazalehFRXLCMM #cpu #machine learning #scalability #using: Integrated CPU and l2 cache voltage scaling using machine learning (NA, APF, CR, RX, FL, BRC, DM, RGM), pp. 41–50.
PPoPP-2007-LeeBSSSM #learning #modelling #parallel #performance: Methods of inference and learning for performance modeling of parallel applications (BCL, DMB, BRdS, MS, KS, SAM), pp. 249–258.
CAV-2007-SinhaC #composition #lazy evaluation #learning #satisfiability #using #verification: SAT-Based Compositional Verification Using Lazy Learning (NS, EMC), pp. 39–54.
SAT-2007-ArgelichM #learning #satisfiability: Partial Max-SAT Solvers with Clause Learning (JA, FM), pp. 28–40.
TestCom-FATES-2007-ShahbazLG #component #integration #learning #testing: Learning and Integration of Parameterized Components Through Testing (MS, KL, RG), pp. 319–334.
VMCAI-2007-Madhusudan #algorithm #learning #tutorial #verification: Learning Algorithms and Formal Verification (Invited Tutorial) (PM), p. 214.
ASE-2006-NeumullerG #automation #case study #lessons learnt #traceability: Automating Software Traceability in Very Small Companies: A Case Study and Lessons Learned (CN, PG), pp. 145–156.
CASE-2006-ReveliotisB #algorithm #learning #performance: Efficient learning algorithms for episodic tasks with acyclic state spaces (SR, TB), pp. 411–418.
CASE-2006-ZhouD #game studies #learning: An evolutionary game model on supply chains learning through imitation (MZ, FD), pp. 645–648.
DAC-2006-WangGG #deduction #difference #learning #logic: Predicate learning and selective theory deduction for a difference logic solver (CW, AG, MKG), pp. 235–240.
DocEng-2006-ChidlovskiiFL #documentation #interface #learning #named: ALDAI: active learning documents annotation interface (BC, JF, LL), pp. 184–185.
DocEng-2006-LecerfC #documentation #learning: Document annotation by active learning techniques (LL, BC), pp. 125–127.
HT-2006-Al-KhalifaD #evolution #metadata #semantics #standard: The evolution of metadata from standards to semantics in E-learning applications (HSAK, HCD), pp. 69–72.
VLDB-2006-ShivamBC #cost analysis #learning #modelling #optimisation: Active and Accelerated Learning of Cost Models for Optimizing Scientific Applications (PS, SB, JSC), pp. 535–546.
CSEET-2006-Kontio #education #industrial #lessons learnt #named #re-engineering: Panel: Industrial Impact through Education — Lessons Learned from Barry Boehm’s Contributions to Software Engineering (JK), pp. 237–240.
CSEET-2006-WangS #learning #re-engineering: Writing as a Tool for Learning Software Engineering (AIW, CFS), pp. 35–42.
ITiCSE-2006-AmzadO #learning #modelling: Model based project centered team learning (IA, AJO), p. 328.
ITiCSE-2006-BerglundW #empirical #student: Students learn CS in different ways: insights from an empirical study (AB, MW), pp. 265–269.
ITiCSE-2006-BiancoL #game studies #named: PlayToLearn: a game adventure in the realm of Si Piuh (GMB, IL), p. 331.
ITiCSE-2006-BirdC #learning #problem: Building a search engine to drive problem-based learning (SB, JRC), pp. 153–157.
ITiCSE-2006-Ellis06a #approach #learning #named #self: Self-grading: an approach to supporting self-directed learning (HJCE), p. 349.
ITiCSE-2006-GiangrandiM #quote: “Numeri e Macchine”: a virtual museum to learn the history of computing (PG, CM), pp. 78–82.
ITiCSE-2006-GriswoldS #learning #performance #scalability #ubiquitous: Ubiquitous presenter: fast, scalable active learning for the whole classroom (WGG, BS), p. 358.
ITiCSE-2006-HielscherW #automaton #education #formal method #learning #named: AtoCC: learning environment for teaching theory of automata and formal languages (MH, CW), p. 306.
ITiCSE-2006-HughesP #learning #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: Learning project planning the agile way (FK, SP, GC, KM), p. 324.
ITiCSE-2006-OKellyG #approach #education #learning #problem #programming: RoboCode & problem-based learning: a non-prescriptive approach to teaching programming (JO, JPG), pp. 217–221.
ITiCSE-2006-PlimmerA #education #human-computer #learning: Peer teaching extends HCI learning (BP, RA), pp. 53–57.
ITiCSE-2006-Quade #hybrid #learning #re-engineering: Developing a hybrid software engineering curse that promotes project-based active learning (AMQ), p. 308.
ITiCSE-2006-Rodger #automaton #formal method #learning: Learning automata and formal languages interactively with JFLAP (SHR), p. 360.
ITiCSE-2006-RussellMN #education #machine learning: Teaching AI through machine learning projects (IR, ZM, TWN), p. 323.
FASE-2006-RaffeltS #automaton #learning #library #named: LearnLib: A Library for Automata Learning and Experimentation (HR, BS), pp. 377–380.
ICPC-2006-Tilley #challenge #documentation #lessons learnt: Program Redocumentation: Lessons Learned & Future Challenges (SRT), p. xiv.
STOC-2006-AngluinACW #injection #learning: Learning a circuit by injecting values (DA, JA, JC, YW), pp. 584–593.
STOC-2006-Feldman #approximate #learning #logic #query: Hardness of approximate two-level logic minimization and PAC learning with membership queries (VF), pp. 363–372.
CHI-2006-GweonRCZ #adaptation #collaboration #learning #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 #learning #simulation: Embedded phenomena: supporting science learning with classroom-sized distributed simulations (TM), pp. 691–700.
CHI-2006-RoblesSRN #how: Being watched or being special: how I learned to stop worrying and love being monitored, surveilled, and assessed (ER, AS, KR, CN), pp. 831–839.
CHI-2006-SiekCR #how #learning #people: Pride and prejudice: learning how chronically ill people think about food (KAS, KHC, YR), pp. 947–950.
CSCW-2006-Danis #collaboration #learning #performance: Forms of collaboration in high performance computing: exploring implications for learning (CD), pp. 501–504.
CSCW-2006-RazaviI #behaviour #information management #learning: A grounded theory of information sharing behavior in a personal learning space (MNR, LI), pp. 459–468.
ICEIS-AIDSS-2006-Fornells-HerreraRMB #approach #evolution: Decision Support System for Breast Cancer Diagnosis by a Meta-Learning Approach Based on Grammar Evolution (AFH, EGiR, EBiM, JMB), pp. 222–229.
ICEIS-HCI-2006-Patokorpi #learning: Constructivist Instructional Principles, Learner Psychology and Technological Enablers of Learning (EP), pp. 103–109.
ICEIS-SAIC-2006-LuciaFGPT #learning #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 #learning #process: Supporting Complex Collaborative Learning Activities — The Libresource Approach (OM, HSM, PM, FAR, CG), pp. 59–65.
ICEIS-SAIC-2006-OliveiraGSBC #adaptation #automation #framework #learning #multi: A Multi-Agent Based Framework for Supporting Learning in Adaptive Automated Negotiation (RSdO, HG, AS, IIB, EdBC), pp. 153–158.
CIKM-2006-Flake #how #internet: How I learned to stop worrying and love the imminent internet singularity (GWF), p. 2.
CIKM-2006-LuPLA #feature model #identification #machine learning #query: Coupling feature selection and machine learning methods for navigational query identification (YL, FP, XL, NA), pp. 682–689.
CIKM-2006-ZhaZFS #difference #learning #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: Learning Links Between a User’s Calendar and Information Needs (EV, VK), pp. 557–560.
ECIR-2006-VittautG #information retrieval #machine learning #ranking: Machine Learning Ranking for Structured Information Retrieval (JNV, PG), pp. 338–349.
ICML-2006-AbbeelQN #learning #modelling #using: Using inaccurate models in reinforcement learning (PA, MQ, AYN), pp. 1–8.
ICML-2006-AgarwalBB #graph #higher-order #learning: Higher order learning with graphs (SA, KB, SB), pp. 17–24.
ICML-2006-AsgharbeygiSL #difference #learning #relational: Relational temporal difference learning (NA, DJS, PL), pp. 49–56.
ICML-2006-BalcanB #formal method #learning #on the #similarity: On a theory of learning with similarity functions (MFB, AB), pp. 73–80.
ICML-2006-BalcanBL #learning: Agnostic active learning (MFB, AB, JL), pp. 65–72.
ICML-2006-BowlingMJNW #learning #policy #predict #using: Learning predictive state representations using non-blind policies (MHB, PM, MJ, JN, DFW), pp. 129–136.
ICML-2006-BrefeldS #learning: Semi-supervised learning for structured output variables (UB, TS), pp. 145–152.
ICML-2006-CaruanaN #algorithm #comparison #empirical #learning: An empirical comparison of supervised learning algorithms (RC, ANM), pp. 161–168.
ICML-2006-CheungK #framework #learning #multi: A regularization framework for multiple-instance learning (PMC, JTK), pp. 193–200.
ICML-2006-ConitzerG #algorithm #learning #online #problem: Learning algorithms for online principal-agent problems (and selling goods online) (VC, NG), pp. 209–216.
ICML-2006-DegrisSW #learning #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 #learning #naive bayes #performance: Efficient learning of Naive Bayes classifiers under class-conditional classification noise (FD, CNM, LR), pp. 265–272.
ICML-2006-desJardinsEW #learning #set: Learning user preferences for sets of objects (Md, EE, KW), pp. 273–280.
ICML-2006-EpshteynD #learning: Qualitative reinforcement learning (AE, GD), pp. 305–312.
ICML-2006-FinkSSU #learning #multi #online: Online multiclass learning by interclass hypothesis sharing (MF, SSS, YS, SU), pp. 313–320.
ICML-2006-GlobersonR #learning #robust: Nightmare at test time: robust learning by feature deletion (AG, STR), pp. 353–360.
ICML-2006-Haffner #kernel #learning #performance: Fast transpose methods for kernel learning on sparse data (PH), pp. 385–392.
ICML-2006-Hanneke #analysis #graph #learning: An analysis of graph cut size for transductive learning (SH), pp. 393–399.
ICML-2006-HertzBW #classification #kernel #learning: Learning a kernel function for classification with small training samples (TH, ABH, DW), pp. 401–408.
ICML-2006-HoiJZL #classification #image #learning: Batch mode active learning and its application to medical image classification (SCHH, RJ, JZ, MRL), pp. 417–424.
ICML-2006-KellerMP #approximate #automation #learning #programming: Automatic basis function construction for approximate dynamic programming and reinforcement learning (PWK, SM, DP), pp. 449–456.
ICML-2006-KonidarisB #information management #learning: Autonomous shaping: knowledge transfer in reinforcement learning (GK, AGB), pp. 489–496.
ICML-2006-KulisSD #kernel #learning #matrix #rank: Learning low-rank kernel matrices (BK, MAS, ISD), pp. 505–512.
ICML-2006-McAuleyCSF #higher-order #image #learning: Learning high-order MRF priors of color images (JJM, TSC, AJS, MOF), pp. 617–624.
ICML-2006-NaorR #learning: Learning to impersonate (MN, GNR), pp. 649–656.
ICML-2006-NejatiLK #learning #network: Learning hierarchical task networks by observation (NN, PL, TK), pp. 665–672.
ICML-2006-NevmyvakaFK #execution #learning: Reinforcement learning for optimized trade execution (YN, YF, MK), pp. 673–680.
ICML-2006-PoupartVHR #learning: An analytic solution to discrete Bayesian reinforcement learning (PP, NAV, JH, KR), pp. 697–704.
ICML-2006-RahmaniG #learning #multi #named: MISSL: multiple-instance semi-supervised learning (RR, SAG), pp. 705–712.
ICML-2006-RainaNK #learning #using: Constructing informative priors using transfer learning (RR, AYN, DK), pp. 713–720.
ICML-2006-RuckertK #approach #learning #statistics: A statistical approach to rule learning (UR, SK), pp. 785–792.
ICML-2006-SenG #learning #markov #network: Cost-sensitive learning with conditional Markov networks (PS, LG), pp. 801–808.
ICML-2006-SilvaS #learning #metric #modelling: Bayesian learning of measurement and structural models (RBdAeS, RS), pp. 825–832.
ICML-2006-SinghiL #bias #classification #learning #set: Feature subset selection bias for classification learning (SKS, HL), pp. 849–856.
ICML-2006-SongE #human-computer #interface #learning: Classifying EEG for brain-computer interfaces: learning optimal filters for dynamical system features (LS, JE), pp. 857–864.
ICML-2006-StrehlLWLL #learning: PAC model-free reinforcement learning (ALS, LL, EW, JL, MLL), pp. 881–888.
ICML-2006-StrehlMLH #learning #problem: Experience-efficient learning in associative bandit problems (ALS, CM, MLL, HH), pp. 889–896.
ICML-2006-XuWSS #learning #predict: Discriminative unsupervised learning of structured predictors (LX, DFW, FS, DS), pp. 1057–1064.
ICML-2006-YuBT #design #learning: Active learning via transductive experimental design (KY, JB, VT), pp. 1081–1088.
ICPR-v1-2006-Al-ZubiS #adaptation #learning: Learning to Imitate Human Movement to Adapt to Environmental Changes (SAZ, GS), pp. 191–194.
ICPR-v1-2006-FredJ #clustering #learning #similarity: Learning Pairwise Similarity for Data Clustering (ALNF, AKJ), pp. 925–928.
ICPR-v1-2006-IshidaTIMM #generative #identification #learning: Identification of degraded traffic sign symbols by a generative learning method (HI, TT, II, YM, HM), pp. 531–534.
ICPR-v1-2006-JiangXT #learning: Shape Alignment by Learning a Landmark-PDM Coupled Model (YJ, JX, HTT), pp. 959–962.
ICPR-v1-2006-KoTSH #image #learning #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-Lampert #machine learning #video: Machine Learning for Video Compression: Macroblock Mode Decision (CHL), pp. 936–940.
ICPR-v1-2006-LiHS #approach #bound #image #machine learning: A Machine Learning Approach for Locating Boundaries of Liver Tumors in CT Images (YL, SH, KS), pp. 400–403.
ICPR-v1-2006-OngB #clustering #learning: Learning Wormholes for Sparsely Labelled Clustering (EJO, RB), pp. 916–919.
ICPR-v1-2006-TavakkoliNB #detection #learning #recursion #robust: Robust Recursive Learning for Foreground Region Detection in Videos with Quasi-Stationary Backgrounds (AT, MN, GB), pp. 315–318.
ICPR-v1-2006-TrujilloO #detection #evolution #how #using: Using Evolution to Learn How to Perform Interest Point Detection (LT, GO), pp. 211–214.
ICPR-v1-2006-YousfiACC #database #image #learning: Supervised Learning for Guiding Hierarchy Construction: Application to Osteo-Articular Medical Images Database (KY, CA, JPC, JC), pp. 484–487.
ICPR-v2-2006-AutioL #learning #online #sequence: Online Learning of Discriminative Patterns from Unlimited Sequences of Candidates (IA, JTL), pp. 437–440.
ICPR-v2-2006-CamastraSV #algorithm #benchmark #challenge #machine learning #metric #pattern matching #pattern recognition #recognition: Offline Cursive Character Challenge: a New Benchmark for Machine Learning and Pattern Recognition Algorithms. (FC, MS, AV), pp. 913–916.
ICPR-v2-2006-ChenJY #learning #reduction #robust: Robust Nonlinear Dimensionality Reduction for Manifold Learning (HC, GJ, KY), pp. 447–450.
ICPR-v2-2006-DagliRH #information management: Utilizing Information Theoretic Diversity for SVM Active Learn (CKD, SR, TSH), pp. 506–511.
ICPR-v2-2006-GaoLL #approach #classification #learning #optimisation: An ensemble classifier learning approach to ROC optimization (SG, CHL, JHL), pp. 679–682.
ICPR-v2-2006-GuoQ #3d #learning: Learning and Inference of 3D Human Poses from Gaussian Mixture Modeled Silhouettes (FG, GQ), pp. 43–47.
ICPR-v2-2006-HarpazH #geometry #learning: Exploiting the Geometry of Gene Expression Patterns for Unsupervised Learning (RH, RMH), pp. 670–674.
ICPR-v2-2006-JinM #learning #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 #learning: Correspondence-free Associative Learning (EJ, MF), pp. 441–446.
ICPR-v2-2006-KelmPM #classification #generative #learning #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 #learning #network: Learning Bayesian Networks for Cytogenetic Image Classification (BL, RM), pp. 772–775.
ICPR-v2-2006-PungprasertyingCK #analysis #approach #learning #migration #performance: Migration Analysis: An Alternative Approach for Analyzing Learning Performance (PP, RC, BK), pp. 837–840.
ICPR-v2-2006-ScalzoP #learning: Unsupervised Learning of Dense Hierarchical Appearance Represe (FS, JHP), pp. 395–398.
ICPR-v2-2006-StefanoDMF #learning: Improving Dynamic Learning Vector Quantization (CDS, CD, AM, ASdF), pp. 804–807.
ICPR-v2-2006-SungZL #learning #scalability #set: Accelerating the SVM Learning for Very Large Data Sets (ES, YZ, XL), pp. 484–489.
ICPR-v2-2006-WuLZH #learning: A Semi-supervised SVM for Manifold Learning (ZW, ChL, JZ, JH), pp. 490–493.
ICPR-v2-2006-XuWH #algorithm #learning: A maximum margin discriminative learning algorithm for temporal signals (WX, JW, ZH), pp. 460–463.
ICPR-v2-2006-ZhangJHW #detection #using: Learning-Based License Plate Detection Using Global and Local Features (HZ, WJ, XH, QW), pp. 1102–1105.
ICPR-v2-2006-ZhangPB #classification #learning #representation: Learning Optimal Filter Representation for Texture Classification (PZ, JP, BPB), pp. 1138–1141.
ICPR-v2-2006-ZhangR #incremental #learning: A New Data Selection Principle for Semi-Supervised Incremental Learning (RZ, AIR), pp. 780–783.
ICPR-v2-2006-ZhengL #analysis #component #kernel #learning #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 #learning #problem: Weakly Supervised Learning on Pre-image Problem in Kernel Methods (WSZ, JHL, PCY), pp. 711–715.
ICPR-v2-2006-ZouL #learning #performance #sequence: The Generalization Performance of Learning Machine Based on Phi-mixing Sequence (BZ, LL), pp. 548–551.
ICPR-v3-2006-AlahariPJ #learning #online #recognition: Learning Mixtures of Offline and Online features for Handwritten Stroke Recognition (KA, SLP, CVJ), pp. 379–382.
ICPR-v3-2006-GunselK #learning: Perceptual Audio Watermarking by Learning in Wavelet Domain (BG, SK), pp. 383–386.
ICPR-v3-2006-IsukapalliE #identification #learning #policy: Learning Policies for Efficiently Identifying Objects of Many Classes (RI, AME, RG), pp. 356–361.
ICPR-v3-2006-Martinez-ArroyoS #classification #learning #naive bayes: Learning an Optimal Naive Bayes Classifier (MMA, LES), pp. 1236–1239.
ICPR-v3-2006-TangelderS #image #learning #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 #learning #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 #learning #naive bayes: Learning an Optimal Naive Bayes Classifier (MMA, LES), p. 958.
ICPR-v4-2006-YangLPZZ #detection #learning: Active Learning Based Pedestrian Detection in Real Scenes (TY, JL, QP, CZ, YZ), pp. 904–907.
ICPR-v4-2006-ZhengLL #learning #network: Control Double Inverted Pendulum by Reinforcement Learning with Double CMAC Network (YZ, SL, ZL), pp. 639–642.
KDD-2006-AbeZL #detection #learning: Outlier detection by active learning (NA, BZ, JL), pp. 504–509.
KDD-2006-AgarwalCA #learning #rank: Learning to rank networked entities (AA, SC, SA), pp. 14–23.
KDD-2006-CarvalhoC #feature model #learning #online #performance: Single-pass online learning: performance, voting schemes and online feature selection (VRC, WWC), pp. 548–553.
KDD-2006-HettichP #lessons learnt #mining: Mining for proposal reviewers: lessons learned at the national science foundation (SH, MJP), pp. 862–871.
KDD-2006-HoiLC #classification #kernel #learning: Learning the unified kernel machines for classification (SCHH, MRL, EYC), pp. 187–196.
KDD-2006-LongWZY #graph #learning: Unsupervised learning on k-partite graphs (BL, XW, Z(Z, PSY), pp. 317–326.
KDD-2006-RosalesF #learning #linear #metric #programming: Learning sparse metrics via linear programming (RR, GF), pp. 367–373.
SIGIR-2006-AgichteinBDR #interactive #learning #modelling #predict #web: Learning user interaction models for predicting web search result preferences (EA, EB, STD, RR), pp. 3–10.
SIGIR-2006-AngelovaW #classification #graph: Graph-based text classification: learn from your neighbors (RA, GW), pp. 485–492.
SIGIR-2006-CarteretteP #learning #ranking: Learning a ranking from pairwise preferences (BC, DP), pp. 629–630.
SIGIR-2006-HuangZL #learning #taxonomy: Refining hierarchical taxonomy structure via semi-supervised learning (RH, ZZ, WL), pp. 653–654.
SIGIR-2006-LacerdaCGFZR #learning: Learning to advertise (AL, MC, MAG, WF, NZ, BARN), pp. 549–556.
SIGIR-2006-MaoPH #information management #named #ontology: DiLight: an ontology-based information access system for e-learning environments (MM, YP, DH), p. 733.
SIGIR-2006-WuJ #framework #graph #learning #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 #learning #query: Incorporating query difference for learning retrieval functions in information retrieval (HZ, ZZ, HF, GS), pp. 721–722.
SAC-2006-CraigL #classification #learning #using: Protein classification using transductive learning on phylogenetic profiles (RAC, LL), pp. 161–166.
SAC-2006-Ferrer-TroyanoAS #classification #data type #incremental #learning: Data streams classification by incremental rule learning with parameterized generalization (FJFT, JSAR, JCRS), pp. 657–661.
SAC-2006-PechenizkiyPT #feature model #learning #reduction: The impact of sample reduction on PCA-based feature extraction for supervised learning (MP, SP, AT), pp. 553–558.
SAC-2006-SoaresB #kernel #parametricity #using: Selecting parameters of SVM using meta-learning and kernel matrix-based meta-features (CS, PB), pp. 564–568.
ICSE-2006-Venkatagiri #approach #requirements: Engineering the software requirements of nonprofits: a service-learning approach (SV), pp. 643–648.
CGO-2006-AgakovBCFFOTTW #machine learning #optimisation #using: Using Machine Learning to Focus Iterative Optimization (FVA, EVB, JC, BF, GF, MFPO, JT, MT, CKIW), pp. 295–305.
CAV-2006-VardhanV #learning #named #verification: LEVER: A Tool for Learning Based Verification (AV, MV), pp. 471–474.
FATES-RV-2006-VeanesRC #learning #online #testing: Online Testing with Reinforcement Learning (MV, PR, CC), pp. 240–253.
ICLP-2006-Aguilar-Solis #approach #constraints #learning #parsing #semantics: Learning Semantic Parsers: A Constraint Handling Rule Approach (DAS), pp. 447–448.
SAT-2006-YuM #constraints #learning #linear #smt: Lemma Learning in SMT on Linear Constraints (YY, SM), pp. 142–155.
ASE-2005-Swartout #lessons learnt #scalability: Virtual humans: lessons learned in integrating a large-scale AI project (WRS), p. 2.
ASE-2005-VardhanV #branch #learning #verification: Learning to verify branching time properties (AV, MV), pp. 325–328.
DAC-2005-ParthasarathyICB #learning: Structural search for RTL with predicate learning (GP, MKI, KTC, FB), pp. 451–456.
DATE-2005-ChandrasekarH #fault #generative #incremental #integration #learning #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 #learning #performance #theorem proving: Efficient Conflict-Based Learning in an RTL Circuit Constraint Solver (MKI, GP, KTC), pp. 666–671.
HT-2005-BerlangaG #adaptation #design #learning #modelling #navigation #specification #using: Modelling adaptive navigation support techniques using the IMS learning design specification (AJB, FJG), pp. 148–150.
ICDAR-2005-BargeronVS #detection #learning: Boosting-based Transductive Learning for Text Detection (DB, PAV, PYS), pp. 1166–1171.
ICDAR-2005-CeciBM #comprehension #documentation #image #learning #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 #learning #semantics #web: A Learning Approach to Discovering Web Page Semantic Structures (JF, PH, MG), pp. 1055–1059.
ICDAR-2005-LavenLR #analysis #approach #documentation #image #learning #statistics: A Statistical Learning Approach To Document Image Analysis (KL, SL, STR), pp. 357–361.
ICDAR-2005-LiuCL #identification #image #machine learning #using: Language Identification of Character Images Using Machine Learning Techniques (YHL, FC, CCL), pp. 630–634.
ICDAR-2005-RaghavendraNSRS #learning #online #prototype #recognition: Prototype Learning Methods for Online Handwriting Recognition (BSR, CKN, GS, AGR, MS), pp. 287–291.
ICDAR-2005-SteinkrauSB #algorithm #machine learning #using: Using GPUs for Machine Learning Algorithms (DS, PYS, IB), pp. 1115–1119.
ICDAR-2005-Szummer #diagrams #learning #random: Learning Diagram Parts with Hidden Random Fields (MS), pp. 1188–1193.
SIGMOD-2005-BragaCCR #learning #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 #learning #query #statistics #xml: Statistical Learning Techniques for Costing XML Queries (NZ, PJH, VJ, GML, CZ), pp. 289–300.
CSEET-2005-BunseGOPS #education #learning #re-engineering: xd Software Engineering Education Applying a Blended Learning Strategy for (CB, IG, MO, CP, SSN), pp. 95–102.
CSEET-2005-Ellis #learning #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 #learning #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.
CSEET-2005-Selic #developer #what: What I Wish I Had Learned in School: Reflections on 30+ Years as a Software Developer (BS), p. 5.
ITiCSE-2005-AmershiACCMMP #design #learning #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: Learning styles across the curriculum (ATC, RES), pp. 241–245.
ITiCSE-2005-DavisW #convergence #education #learning #multi: A research-led curriculum in multimedia: learning about convergence (HCD, SW), pp. 29–33.
ITiCSE-2005-Dick #analysis #assessment #design #learning #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: Learning technical concepts with collaboration and communication skills (MJG), p. 391.
ITiCSE-2005-HurtadoV #learning: Learning UNIX in first year of computer engineering (MASH, CVP), p. 392.
ITiCSE-2005-LiccardiW #comprehension #difference #effectiveness: Understanding disciplinary differences: an insight into selecting effective e-learning approaches (IL, SW), p. 411.
ITiCSE-2005-LoftusR #learning #programming #question: Extreme programming promotes extreme learning? (CWL, MR), pp. 311–315.
ITiCSE-2005-Ludi #process #re-engineering #student: Active-learning activities that introduce students to software engineering fundamentals (SL), pp. 128–132.
ITiCSE-2005-Marcelino #learning #programming: Learning repetition structures in programming (MJM), p. 351.
ITiCSE-2005-NugentSSPL #design #development #learning #validation: Design, development, and validation of a learning object for CS1 (GN, LKS, AS, SP, JL), p. 370.
ITiCSE-2005-Olsevicova #topic: Application of topic maps in e-learning environment (KO), p. 363.
ITiCSE-2005-Truong #learning: The environment for learning to program (NT), p. 383.
ITiCSE-2005-TruongBR #learning #web: Learning to program through the web (NT, PB, PR), pp. 9–13.
ITiCSE-2005-Vinha #learning #reuse #theory and practice: Reusable learning objects: theory to practice (AV), p. 413.
ICSM-2005-FerencBFL #design pattern #machine learning #mining: Design Pattern Mining Enhanced by Machine Learning (RF, ÁB, LJF, JL), pp. 295–304.
ICSM-2005-ZvegintzovP #lessons learnt #maintenance: Sixty Years of Software Maintenance: Lessons Learned (NZ, GP), pp. 726–727.
MSR-2005-HuangL #learning #mining #process #verification #version control: Mining version histories to verify the learning process of Legitimate Peripheral Participants (SKH, KmL), pp. 21–25.
STOC-2005-KaplanKM #learning: Learning with attribute costs (HK, EK, YM), pp. 356–365.
STOC-2005-MosselR #learning #markov #modelling: Learning nonsingular phylogenies and hidden Markov models (EM, SR), pp. 366–375.
STOC-2005-Regev #encryption #fault #learning #linear #on the #random: On lattices, learning with errors, random linear codes, and cryptography (OR), pp. 84–93.
CIAA-2005-GarciaRCA #learning #question: Is Learning RFSAs Better Than Learning DFAs? (PG, JR, AC, GIA), pp. 343–344.
CIAA-2005-HigueraPT #automaton #finite #learning #probability #recognition: Learning Stochastic Finite Automata for Musical Style Recognition (CdlH, FP, FT), pp. 345–346.
CHI-2005-BondarenkoJ #learning: Dcuments at Hand: Learning from Paper to Improve Digital Technologies (OB, RJ), pp. 121–130.
CHI-2005-XieLGM #image #learning: Learning user interest for image browsing on small-form-factor devices (XX, HL, SG, WYM), pp. 671–680.
CHI-2005-YeeP #learning #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 #learning #lifecycle #workflow: Learning, planning, and the life cycle of workflow management (DRF, HMF), pp. 39–46.
ICEIS-v2-2005-ColaceSVF #algorithm #approach #learning #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 #learning #multi: Handling Multiple Events in Hybrid BDI Agents with Reinforcement Learning: A Container Application (PL, DA), pp. 83–90.
ICEIS-v2-2005-MashechkinPR #anti #approach #enterprise #machine learning: Enterprise Anti-Spam Solution Based on Machine Learning Approach (IM, MP, AR), pp. 188–193.
ICEIS-v5-2005-DexterP #assurance #quality: Cross-Domain Mapping: Quality Assurance and E-Learning Provision (HD, JP), pp. 199–205.
ICEIS-v5-2005-DixitM #classification #documentation #using: Electronic Document Classification Using Support Vector Machine — An Application for E-Learning (SD, LKM), pp. 191–198.
ICEIS-v5-2005-Fernandez-CaballeroGBL #adaptation #architecture #distance #learning: 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 #learning #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 #learning #student: Student’s Evaluation of Web-Based Learning Technologies in a Humancomputer Interaction Course (DGB), pp. 206–212.
ICEIS-v5-2005-IslamARR #distance #learning #mobile: Mobile Telephone Technology as a Distance Learning Tool (YMI, MA, ZR, MR), pp. 226–232.
ICEIS-v5-2005-LeR #learning #named: LINC: A Web-Based Learning Tool for Mixed-Mode Learning (THL, JR), pp. 154–160.
ICEIS-v5-2005-MahdaouiA #information management #workflow: A Cooperative Information System for E-Learning — A System Based on Workflows and Agents (LM, ZA), pp. 213–225.
CIKM-2005-AminiTULG #documentation #learning #using #xml: Learning to summarise XML documents using content and structure (MRA, AT, NU, ML, PG), pp. 297–298.
CIKM-2005-CarinoJLWY #machine learning #mining #web: Mining officially unrecognized side effects of drugs by combining web search and machine learning (CC, YJ, BL, PMW, CTY), pp. 365–372.
CIKM-2005-NottelmannS #information retrieval #machine learning #probability: Information retrieval and machine learning for probabilistic schema matching (HN, US), pp. 295–296.
CIKM-2005-RoussinovFN05a #approach #information retrieval #learning: Discretization based learning approach to information retrieval (DR, WF, FADN), pp. 321–322.
CIKM-2005-XiongSK #database #learning #multi #privacy: Privacy leakage in multi-relational databases via pattern based semi-supervised learning (HX, MS, VK), pp. 355–356.
ICML-2005-AbbeelN #learning: Exploration and apprenticeship learning in reinforcement learning (PA, AYN), pp. 1–8.
ICML-2005-AndersonM #algorithm #learning #markov #modelling: Active learning for Hidden Markov Models: objective functions and algorithms (BA, AM), pp. 9–16.
ICML-2005-BlockeelPS #learning #multi: Multi-instance tree learning (HB, DP, AS), pp. 57–64.
ICML-2005-BurgeL #learning #network: Learning class-discriminative dynamic Bayesian networks (JB, TL), pp. 97–104.
ICML-2005-BurgesSRLDHH #learning #rank #using: Learning to rank using gradient descent (CJCB, TS, ER, AL, MD, NH, GNH), pp. 89–96.
ICML-2005-ChangK #learning: Hedged learning: regret-minimization with learning experts (YHC, LPK), pp. 121–128.
ICML-2005-ChuG #learning #process: Preference learning with Gaussian processes (WC, ZG), pp. 137–144.
ICML-2005-CortesMW #learning: A general regression technique for learning transductions (CC, MM, JW), pp. 153–160.
ICML-2005-CrandallG #game studies #learning: Learning to compete, compromise, and cooperate in repeated general-sum games (JWC, MAG), pp. 161–168.
ICML-2005-DaumeM #approximate #learning #optimisation #predict #scalability: Learning as search optimization: approximate large margin methods for structured prediction (HDI, DM), pp. 169–176.
ICML-2005-DrakeV #learning: A practical generalization of Fourier-based learning (AD, DV), pp. 185–192.
ICML-2005-DriessensD #first-order #learning #modelling: Combining model-based and instance-based learning for first order regression (KD, SD), pp. 193–200.
ICML-2005-EngelMM #learning #process: Reinforcement learning with Gaussian processes (YE, SM, RM), pp. 201–208.
ICML-2005-GirolamiR #kernel #learning #modelling: Hierarchic Bayesian models for kernel learning (MG, SR), pp. 241–248.
ICML-2005-GroisW #approach #comprehension #learning: Learning strategies for story comprehension: a reinforcement learning approach (EG, DCW), pp. 257–264.
ICML-2005-HerbsterPW #graph #learning #online: Online learning over graphs (MH, MP, LW), pp. 305–312.
ICML-2005-IlghamiMNA #approximate #learning: Learning approximate preconditions for methods in hierarchical plans (OI, HMA, DSN, DWA), pp. 337–344.
ICML-2005-IresonCCFKL #information management #machine learning: Evaluating machine learning for information extraction (NI, FC, MEC, DF, NK, AL), pp. 345–352.
ICML-2005-JinCS #information retrieval #using: Learn to weight terms in information retrieval using category information (RJ, JYC, LS), pp. 353–360.
ICML-2005-JingPR #classification #learning #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 #learning #visual notation: Interactive learning of mappings from visual percepts to actions (SJ, JHP), pp. 393–400.
ICML-2005-KokD #learning #logic #markov #network: Learning the structure of Markov logic networks (SK, PMD), pp. 441–448.
ICML-2005-LangfordZ #classification #learning #performance: Relating reinforcement learning performance to classification performance (JL, BZ), pp. 473–480.
ICML-2005-Mahadevan #learning: Proto-value functions: developmental reinforcement learning (SM), pp. 553–560.
ICML-2005-MichelsSN #learning #using: High speed obstacle avoidance using monocular vision and reinforcement learning (JM, AS, AYN), pp. 593–600.
ICML-2005-NatarajanT #learning #multi: Dynamic preferences in multi-criteria reinforcement learning (SN, PT), pp. 601–608.
ICML-2005-NatarajanTADFR #first-order #learning #modelling #probability: Learning first-order probabilistic models with combining rules (SN, PT, EA, TGD, AF, ACR), pp. 609–616.
ICML-2005-Niculescu-MizilC #learning #predict: Predicting good probabilities with supervised learning (ANM, RC), pp. 625–632.
ICML-2005-OntanonP #learning #multi: Recycling data for multi-agent learning (SO, EP), pp. 633–640.
ICML-2005-PalettaFS #recognition #visual notation: Q-learning of sequential attention for visual object recognition from informative local descriptors (LP, GF, CS), pp. 649–656.
ICML-2005-PernkopfB #classification #generative #learning #network #parametricity: Discriminative versus generative parameter and structure learning of Bayesian network classifiers (FP, JAB), pp. 657–664.
ICML-2005-RayC #comparison #empirical #learning #multi: Supervised versus multiple instance learning: an empirical comparison (SR, MC), pp. 697–704.
ICML-2005-RosellHRP #learning #why: Why skewing works: learning difficult Boolean functions with greedy tree learners (BR, LH, SR, DP), pp. 728–735.
ICML-2005-RousuSSS #classification #learning #modelling #multi: Learning hierarchical multi-category text classification models (JR, CS, SS, JST), pp. 744–751.
ICML-2005-ScholkopfSB #machine learning #problem: Object correspondence as a machine learning problem (BS, FS, VB), pp. 776–783.
ICML-2005-SiddiqiM #learning #performance: Fast inference and learning in large-state-space HMMs (SMS, AWM), pp. 800–807.
ICML-2005-SilvaS #identification #learning #modelling: New d-separation identification results for learning continuous latent variable models (RBdAeS, RS), pp. 808–815.
ICML-2005-SimsekWB #clustering #graph #identification #learning: Identifying useful subgoals in reinforcement learning by local graph partitioning (ÖS, APW, AGB), pp. 816–823.
ICML-2005-SindhwaniNB #learning: Beyond the point cloud: from transductive to semi-supervised learning (VS, PN, MB), pp. 824–831.
ICML-2005-SinghPGBB #analysis #learning: 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 #learning: Explanation-Augmented SVM: an approach to incorporating domain knowledge into SVM learning (QS, GD), pp. 864–871.
ICML-2005-TaskarCKG #approach #learning #modelling #predict #scalability: Learning structured prediction models: a large margin approach (BT, VC, DK, CG), pp. 896–903.
ICML-2005-ToussaintV #learning #modelling: Learning discontinuities with products-of-sigmoids for switching between local models (MT, SV), pp. 904–911.
ICML-2005-Wiewiora #learning #predict: Learning predictive representations from a history (EW), pp. 964–971.
ICML-2005-WolfeJS #learning #predict: Learning predictive state representations in dynamical systems without reset (BW, MRJ, SPS), pp. 980–987.
ICML-2005-XuTYYK #learning #relational: Dirichlet enhanced relational learning (ZX, VT, KY, SY, HPK), pp. 1004–1011.
ICML-2005-YuTS #learning #multi #process: Learning Gaussian processes from multiple tasks (KY, VT, AS), pp. 1012–1019.
ICML-2005-ZhouHS #graph #learning: Learning from labeled and unlabeled data on a directed graph (DZ, JH, BS), pp. 1036–1043.
ICML-2005-ZhuL #graph #induction #learning #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 #learning #mining #semantics #statistics: Mining images on semantics via statistical learning (JF, HL, MSH), pp. 22–31.
KDD-2005-LowdM #learning: Adversarial learning (DL, CM), pp. 641–647.
KDD-2005-MeruguG #data flow #distributed #framework #learning #semistructured data: A distributed learning framework for heterogeneous data sources (SM, JG), pp. 208–217.
KDD-2005-PhanNHH #learning: Improving discriminative sequential learning with rare--but--important associations (XHP, MLN, TBH, SH), pp. 304–313.
KDD-2005-RadlinskiJ #feedback #learning #query #rank: Query chains: learning to rank from implicit feedback (FR, TJ), pp. 239–248.
KDD-2005-YangL #learning #predict: Learning to predict train wheel failures (CY, SL), pp. 516–525.
LSO-2005-DedeneSBL #generative #web #web service: New generation E-Learning technology by Web Services (GD, MS, MDB, WL), pp. 77–81.
LSO-2005-Fajtak #learning: Kick-off Workshops and Project Retrospectives: A Good Learning Software Organization Practice (FFF), pp. 112–114.
LSO-2005-Salo #agile #development #learning #validation: Systematical Validation of Learning in Agile Software Development Environment (OS), pp. 92–96.
MLDM-2005-BunkeDIK #analysis #graph #learning #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-EickRBV #assessment #clustering #distance #similarity #using: Using Clustering to Learn Distance Functions for Supervised Similarity Assessment (CFE, AR, AB, RV), pp. 120–131.
MLDM-2005-GhoshGYB05a #learning #parametricity: Determining Regularization Parameters for Derivative Free Neural Learning (RG, MG, JY, AMB), pp. 71–79.
MLDM-2005-KuhnertK #feedback #learning: Autonomous Vehicle Steering Based on Evaluative Feedback by Reinforcement Learning (KDK, MK), pp. 405–414.
MLDM-2005-ScalzoP #learning #visual notation: Unsupervised Learning of Visual Feature Hierarchies (FS, JHP), pp. 243–252.
MLDM-2005-SilvaJNP #geometry #learning #metric #using: Diagnosis of Lung Nodule Using Reinforcement Learning and Geometric Measures (ACS, VRdSJ, AdAN, ACdP), pp. 295–304.
SEKE-2005-GaoCMYB #learning #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 #learning #performance: Learning Efficiency Improvement of Fuzzy CMAC by Aitken Acceleration Method (CMH, CMC, HYC), pp. 556–595.
SEKE-2005-KinjoH #learning #modelling #object-oriented: An Object-Oriented Modeling Learning Support System With Inspection Comments (TK, AH), pp. 223–228.
SEKE-2005-SiciliaCR #learning #ontology #process: Ontologies of Software Artifacts and Activities: Resource Annotation and Application to Learning Technologies (MÁS, JJC, DR), pp. 145–150.
SIGIR-2005-JensenBGFC #learning #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 #learning #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 #learning #query: Learning to estimate query difficulty: including applications to missing content detection and distributed information retrieval (EYT, SF, DC, AD), pp. 512–519.
MoDELS-2005-ChengSB #analysis #automation #case study #experience #industrial #lessons learnt #modelling #uml: Lessons Learned from Automated Analysis of Industrial UML Class Models (An Experience Report) (BHCC, RS, BB), pp. 324–338.
MoDELS-2005-MarichK #development #lessons learnt #migration #modelling #scalability: Invited Presentation I: Lessons Learned, New Directions, and Migration Plans for Model-Driven Development of Large Scale Software Based Systems (MJM, HFK), p. 397.
MoDELS-2005-ChengSB #analysis #automation #case study #experience #industrial #lessons learnt #modelling #uml: Lessons Learned from Automated Analysis of Industrial UML Class Models (An Experience Report) (BHCC, RS, BB), pp. 324–338.
MoDELS-2005-MarichK #development #lessons learnt #migration #modelling #scalability: Invited Presentation I: Lessons Learned, New Directions, and Migration Plans for Model-Driven Development of Large Scale Software Based Systems (MJM, HFK), p. 397.
RE-2005-AvesaniBPS #machine learning #requirements #scalability: Facing Scalability Issues in Requirements Prioritization with Machine Learning Techniques (PA, CB, AP, AS), pp. 297–306.
RE-2005-Nesland #framework #implementation #lessons learnt #process #requirements: Initial Lessons Learned from the Definition and Implementation of a Platform Requirements Engineering Process at Intel Corporation (SN), pp. 429–433.
SAC-2005-BoninoCP #automation #concept #learning #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 #learning: Incremental rule learning based on example nearness from numerical data streams (FJFT, JSAR, JCRS), pp. 568–572.
SAC-2005-FradkinK #classification #learning: Methods for learning classifier combinations: no clear winner (DF, PBK), pp. 1038–1043.
SAC-2005-GamaMR #data type #learning: Learning decision trees from dynamic data streams (JG, PM, PPR), pp. 573–577.
SAC-2005-KatayamaKN #learning #process: Reinforcement learning agents with primary knowledge designed by analytic hierarchy process (KK, TK, HN), pp. 14–21.
SAC-2005-LunaLSHHB #learning: Learning system to introduce GIS to civil engineers (RL, WTL, JMS, RHH, MGH, MB), pp. 1737–1738.
SAC-2005-PandeyGM #algorithm #learning #probability #scheduling: Stochastic scheduling of active support vector learning algorithms (GP, HG, PM), pp. 38–42.
SAC-2005-TebriBC #incremental #learning: Incremental profile learning based on a reinforcement method (HT, MB, CC), pp. 1096–1101.
SAC-2005-ZhangM #learning #privacy: Privacy preserving learning in negotiation (SZ, FM), pp. 821–825.
ESEC-FSE-2005-ChatleyT #eclipse #learning #named: KenyaEclipse: learning to program in eclipse (RC, TT), pp. 245–248.
ICSE-2005-BernerWK #automation #lessons learnt #testing: Observations and lessons learned from automated testing (SB, RW, RKK), pp. 571–579.
ICSE-2005-Fox #dependence #machine learning #statistics: Addressing software dependability with statistical and machine learning techniques (AF), p. 8.
ICSE-2005-Liu #approach #open source #re-engineering: Enriching software engineering courses with service-learning projects and the open-source approach (CL), pp. 613–614.
CGO-2005-Hind #architecture #machine learning #virtual machine: Virtual Machine Learning: Thinking like a Computer Architect (MH), p. 11.
CAV-2005-AlurMN #composition #learning #verification: Symbolic Compositional Verification by Learning Assumptions (RA, PM, WN), pp. 548–562.
CAV-2005-LoginovRS #abstraction #induction #learning #refinement: Abstraction Refinement via Inductive Learning (AL, TWR, SS), pp. 519–533.
SAT-2005-GentR #learning: Local and Global Complete Solution Learning Methods for QBF (IPG, AGDR), pp. 91–106.
WICSA-2004-BardramCH #approach #architecture #design #learning #prototype: Architectural Prototyping: An Approach for Grounding Architectural Design and Learning (JB, HBC, KMH), pp. 15–24.
DAC-2004-WangMCA #learning #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 #learning #simulation #validation: Regression Simulation: Applying Path-Based Learning In Delay Test and Post-Silicon Validation (LCW), pp. 692–695.
DocEng-2004-ChidlovskiiF #documentation #learning #legacy: Supervised learning for the legacy document conversion (BC, JF), pp. 220–228.
HT-2004-DavisB #case study #experience #learning #migration: Experiences migrating microcosm learning materials (HCD, RAB), pp. 141–142.
CSEET-2004-HazzanT #aspect-oriented #education #learning #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 #learning: Software Engineers and HCI Practitioners Learning to Work Together: A Preliminary Look at Expectations (AEM), pp. 45–49.
ITiCSE-2004-ArgolloHMBFBLMR #collaboration #learning #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 #learning: Didactic strategies for promoting significant learning in formal languages and automata theory (CIC, MPG, AGM), pp. 7–11.
ITiCSE-2004-Dixon #automation #education #learning: A single CASE environment for teaching and learning (MD), p. 271.
ITiCSE-2004-Ford04a #generative #learning #programming: A learning object generator for programming (LF), p. 268.
ITiCSE-2004-Garner #learning #programming: The use of a code restructuring tool in the learning of programming (SG), p. 277.
ITiCSE-2004-Kerren #education #generative #learning: Generation as method for explorative learning in computer science education (AK), pp. 77–81.
ITiCSE-2004-Kumar #java #learning #programming: Web-based tutors for learning programming in C++/Java (AK), p. 266.
ITiCSE-2004-LeskaR #concept #game studies #learning #using: Learning O-O concepts in CS I using game projects (CL, JRR), p. 237.
ITiCSE-2004-McKennaL #concept #learning: Constructivist or instructivist: pedagogical concepts practically applied to a computer learning environment (PM, BL), pp. 166–170.
ITiCSE-2004-MelinC #learning #student: Project oriented student work: learning & examination (UM, SC), pp. 87–91.
ITiCSE-2004-MintonBD #case study #problem: If diversity is a problem could e-learning be part of the solution?: a case study (LM, RDB, VD), pp. 42–46.
ITiCSE-2004-PaciniFF #database #learning #problem #spreadsheet #tool support: Learning problem solving with spreadsheet and database tools (GP, GF, AF), p. 267.
ITiCSE-2004-PahlBK #database #interactive #learning #multi: Supporting active database learning and training through interactive multimedia (CP, RB, CK), pp. 27–31.
ITiCSE-2004-PowellMGFR #learning #programming: Dyslexia and learning computer programming (NP, DJM, JG, JF, JR), p. 242.
ITiCSE-2004-RamalingamLW #learning #modelling #self: Self-efficacy and mental models in learning to program (VR, DL, SW), pp. 171–175.
ITiCSE-2004-RatcliffeHE #collaboration #learning #student: Enhancing student learning through collaboration (MR, JH, WE), p. 272.
ITiCSE-2004-SadiqOSL #learning #named #online #sql: SQLator: an online SQL learning workbench (SWS, MEO, WS, JYCL), pp. 223–227.
ITiCSE-2004-Sheard #community #learning: Electronic learning communities: strategies for establishment and management (JS), pp. 37–41.
ITiCSE-2004-SimonAHS #case study #experience #learning #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 #learning #programming: Effective peer assessment for learning computer programming (JS, MJ), pp. 122–126.
ITiCSE-2004-WangC #assessment #learning #online #performance: Extending e-books with annotation, online support and assessment mechanisms to increase efficiency of learning (CYW, GDC), pp. 132–136.
CSMR-2004-Kajko-MattssonJKW: Lesson Learned from Attempts to Implement Daily Build (MKM, MJ, SK, FW), pp. 137–146.
CSMR-2004-Rosso #architecture #lessons learnt #mobile #performance #process #product line: The Process of and the Lessons Learned from Performance Tuning of a Product Family Software Architecture for Mobile Phones (CDR), pp. 270–278.
IWPC-2004-HammoudaGKS #diagrams #learning #modelling #uml: Tool-Supported Customization of UML Class Diagrams for Learning Complex System Models (IH, OG, KK, TS), pp. 24–33.
STOC-2004-AwerbuchK #adaptation #distributed #feedback #geometry #learning: Adaptive routing with end-to-end feedback: distributed learning and geometric approaches (BA, RDK), pp. 45–53.
ICALP-2004-AlonA #learning: Learning a Hidden Subgraph (NA, VA), pp. 110–121.
CHI-2004-KierasS #lessons learnt #modelling: Computational GOMS modeling of a complex team task: lessons learned (DEK, TPS), pp. 97–104.
CSCW-2004-CubranicMSB #case study #development #learning: Learning from project history: a case study for software development (DC, GCM, JS, KSB), pp. 82–91.
ICEIS-v2-2004-BendouM #graph #learning #network: Learning Bayesian Networks with Largest Chain Graphs (MB, PM), pp. 184–190.
ICEIS-v2-2004-ColaceSVF #algorithm #automation #learning #ontology: A Semi-Automatic Bayesian Algorithm for Ontology Learning (FC, MDS, MV, PF), pp. 191–196.
ICEIS-v2-2004-ColaceSVF04a #algorithm #comparison #learning #network: Bayesian Network Structural Learning from Data: An Algorithms Comparison (FC, MDS, MV, PF), pp. 527–530.
ICEIS-v2-2004-Kabiri #approximate #comparison #learning #network: A Comparison Between the Proportional Keen Approximator and the Neural Networks Learning Methods (PK), pp. 159–164.
ICEIS-v3-2004-Nobre #complexity #design #learning: Organisational Learning — Foundational Roots for Design for Complexity (ÂLN), pp. 85–93.
ICEIS-v4-2004-Carneiro #challenge #learning #network #process: Learning Processes and the Role of Technological Networks as an Innovative Challenge (AC), pp. 497–501.
ICEIS-v4-2004-DunkelBO #semantics #web: Semantic E-Learning Agents — Supporting Elearning by Semantic Web and Agents Technologies (JD, RB, SO), pp. 271–278.
ICEIS-v4-2004-FloresGVS #learning: Amplia Learning Environment: A Proposal for Pedagogical Negotiation (CDF, JCG, RMV, LJS), pp. 279–286.
ICEIS-v5-2004-ChenLK #assessment #perspective: Assessment of E-Learning Satisfaction from Critical Incidents Perspective (NSC, KML, K), pp. 27–34.
ICEIS-v5-2004-JantkeLGGTT #data mining #learning #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 #learning #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 #learning: Accessing Hypermedia Systems Efectiveness in Learning Contexts (SS, AS), pp. 250–253.
ICEIS-v5-2004-SoferM #requirements: An Investigation into the Requirements for an E-Learning System (YYS, SBM), pp. 233–237.
CIKM-2004-LiO #identification #learning #music: Semi-supervised learning for music artists style identification (TL, MO), pp. 152–153.
CIKM-2004-LiuZYYYCBM #learning #metric #similarity: Learning similarity measures in non-orthogonal space (NL, BZ, JY, QY, SY, ZC, FB, WYM), pp. 334–341.
CIKM-2004-MaZMS #framework #learning #query #similarity #using: A framework for refining similarity queries using learning techniques (YM, QZ, SM, DYS), pp. 158–159.
ICML-2004-AgarwalT #3d #learning: Learning to track 3D human motion from silhouettes (AA, BT).
ICML-2004-BachLJ #algorithm #kernel #learning #multi: Multiple kernel learning, conic duality, and the SMO algorithm (FRB, GRGL, MIJ).
ICML-2004-BahamondeBDQLCAG #case study #learning #set: Feature subset selection for learning preferences: a case study (AB, GFB, JD, JRQ, OL, JJdC, JA, FG).
ICML-2004-BilenkoBM #clustering #constraints #learning #metric: Integrating constraints and metric learning in semi-supervised clustering (MB, SB, RJM).
ICML-2004-BlumLRR #learning #random #using: Semi-supervised learning using randomized mincuts (AB, JDL, MRR, RR).
ICML-2004-Bouckaert #classification #learning: Estimating replicability of classifier learning experiments (RRB).
ICML-2004-BrefeldS #learning: Co-EM support vector learning (UB, TS).
ICML-2004-Brinker #learning #ranking: Active learning of label ranking functions (KB).
ICML-2004-CastilloW #case study #comparative #learning #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 #learning: Communication complexity as a lower bound for learning in games (VC, TS).
ICML-2004-EliazarP #learning #mobile #modelling #probability: Learning probabilistic motion models for mobile robots (AIE, RP).
ICML-2004-GaoWLC #approach #categorisation #learning #multi #robust: A MFoM learning approach to robust multiclass multi-label text categorization (SG, WW, CHL, TSC).
ICML-2004-GoldenbergM #learning #scalability: Tractable learning of large Bayes net structures from sparse data (AG, AWM).
ICML-2004-GrossmanD #classification #learning #network: Learning Bayesian network classifiers by maximizing conditional likelihood (DG, PMD).
ICML-2004-HuangYKL #classification #learning #scalability: Learning large margin classifiers locally and globally (KH, HY, IK, MRL).
ICML-2004-JamesS #learning #predict: Learning and discovery of predictive state representations in dynamical systems with reset (MRJ, SPS).
ICML-2004-KashimaT #algorithm #graph #kernel #learning #sequence: Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs (HK, YT).
ICML-2004-KokV: Sparse cooperative Q-learning (JRK, NAV).
ICML-2004-LawrenceP #learning: Learning to learn with the informative vector machine (NDL, JCP).
ICML-2004-MannorMHK #abstraction #clustering #learning: Dynamic abstraction in reinforcement learning via clustering (SM, IM, AH, UK).
ICML-2004-MelvilleM #learning: Diverse ensembles for active learning (PM, RJM).
ICML-2004-MerkeS #approximate #convergence #learning #linear: Convergence of synchronous reinforcement learning with linear function approximation (AM, RS).
ICML-2004-MoralesS #behaviour #learning: Learning to fly by combining reinforcement learning with behavioural cloning (EFM, CS).
ICML-2004-NatteeSNO #first-order #learning #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 #learning #using: Active learning using pre-clustering (HTN, AWMS).
ICML-2004-OngMCS #kernel #learning: Learning with non-positive kernels (CSO, XM, SC, AJS).
ICML-2004-PieterN #learning: Apprenticeship learning via inverse reinforcement learning (PA, AYN).
ICML-2004-Potts #incremental #learning #linear: Incremental learning of linear model trees (DP).
ICML-2004-RosalesAF #clustering #learning #using: Learning to cluster using local neighborhood structure (RR, KA, BJF).
ICML-2004-RosencrantzGT #learning #predict: Learning low dimensional predictive representations (MR, GJG, ST).
ICML-2004-RuckertK #bound #learning #towards: Towards tight bounds for rule learning (UR, SK).
ICML-2004-RudarySP #adaptation #constraints #learning #reasoning: Adaptive cognitive orthotics: combining reinforcement learning and constraint-based temporal reasoning (MRR, SPS, MEP).
ICML-2004-Ryabko #learning #online: Online learning of conditionally I.I.D. data (DR).
ICML-2004-Shalev-ShwartzSN #learning #online #pseudo: Online and batch learning of pseudo-metrics (SSS, YS, AYN).
ICML-2004-SimsekB #abstraction #identification #learning #using: Using relative novelty to identify useful temporal abstractions in reinforcement learning (ÖS, AGB).
ICML-2004-SzepesvariS: Interpolation-based Q-learning (CS, WDS).
ICML-2004-TaoSVO #approximate #learning #multi: SVM-based generalized multiple-instance learning via approximate box counting (QT, SDS, NVV, TTO).
ICML-2004-TaskarCK #learning #markov #network: Learning associative Markov networks (BT, VC, DK).
ICML-2004-ToutanovaMN #dependence #learning #modelling #random #word: Learning random walk models for inducing word dependency distributions (KT, CDM, AYN).
ICML-2004-TsochantaridisHJA #machine learning: Support vector machine learning for interdependent and structured output spaces (IT, TH, TJ, YA).
ICML-2004-WeinbergerSS #kernel #learning #matrix #reduction: Learning a kernel matrix for nonlinear dimensionality reduction (KQW, FS, LKS).
ICML-2004-Zadrozny #bias #classification #learning: Learning and evaluating classifiers under sample selection bias (BZ).
ICML-2004-ZhangYK #algorithm #kernel #learning #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 #learning #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 #learning #recognition: Learning Optimal Classifier Through Fuzzy Recognition Rate Maximization (MG, CS, SGD), pp. 204–207.
ICPR-v1-2004-GokcenJD #bound #learning: Comparing Optimal Bounding Ellipsoid and Support Vector Machine Active Learning (IG, DJ, JRD), pp. 172–175.
ICPR-v1-2004-LeangB #learning: Learning Integrated Perception-Based Speed Control (PL, BB), pp. 813–816.
ICPR-v1-2004-YiKZ #classification #learning: Classifier Combination based on Active Learning (XY, ZK, CZ), pp. 184–187.
ICPR-v2-2004-BeginF #approach #using: Blind Super-Resolution Using a Learning-Based Approach (IB, FPF), pp. 85–89.
ICPR-v2-2004-FangQ #detection #learning: Learning Sample Subspace with Application to Face Detection (JF, GQ), pp. 423–426.
ICPR-v2-2004-JingZLZZ #image #learning #retrieval: Learning in Hidden Annotation-Based Image Retrieval (FJ, BZ, ML, HZ, JZ), pp. 1001–1004.
ICPR-v2-2004-KaneS #classification #image #learning #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 #learning #representation: Learning High-level Independent Components of Images through a Spectral Representation (JTL, AH), pp. 72–75.
ICPR-v2-2004-LiuS #learning: Reinforcement Learning-Based Feature Learning for Object Tracking (FL, JS), pp. 748–751.
ICPR-v2-2004-SageB #learning: Joint Spatial and Temporal Structure Learning for Task based Control (KS, HB), pp. 48–51.
ICPR-v2-2004-ZiouB #analysis #finite #image #learning #using: Unsupervised Learning of a Finite Gamma Mixture Using MML: Application to SAR Image Analysis (DZ, NB), pp. 68–71.
ICPR-v3-2004-FanG #learning: Hierarchical Object Indexing and Sequential Learning (XF, DG), pp. 65–68.
ICPR-v3-2004-KoKB04a #learning #multi #problem: Improved N-Division Output Coding for Multiclass Learning Problems (JK, EK, HB), pp. 470–473.
ICPR-v3-2004-LuoKGHSRH #learning #multi: Active Learning to Recognize Multiple Types of Plankton (TL, KK, DBG, LOH, SS, AR, TH), pp. 478–481.
ICPR-v3-2004-MakiharaSS #interactive #learning #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 #learning #probability: A Probabilistic Approach to Learning Costs for Graph Edit Distance (MN, HB), pp. 389–393.
ICPR-v3-2004-ParedesV #fault #learning #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 #learning: Critical Vector Learning to Construct RBF Classifiers (DS, GSN, JG, DSY), pp. 359–362.
ICPR-v4-2004-Cardenas #classification #learning #multi #prototype #string: A Learning Model for Multiple-Prototype Classification of Strings (RAM), pp. 420–423.
ICPR-v4-2004-ChenC04a #bidirectional #dependence #learning #network: Improvement of Bidirectional Recurrent Neural Network for Learning Long-Term Dependencies (JC, NSC), pp. 593–596.
ICPR-v4-2004-FabletJB #automation #estimation #image #learning #statistics #using: Automatic Fish Age Estimation from Otolith Images using Statistical Learning (RF, NLJ, AB), pp. 503–506.
ICPR-v4-2004-McKennaN #learning #using: Learning Spatial Context from Tracking using Penalised Likelihoods (SJM, HNC), pp. 138–141.
ICPR-v4-2004-PeternelL #learning #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-PiriouBY #detection #image #modelling #probability: Learned Probabilistic Image Motion Models for Event Detection in Videos (GP, PB, JFY), pp. 207–210.
ICPR-v4-2004-QinandS04a #algorithm #kernel #learning #novel #prototype: A Novel Kernel Prototype-Based Learning Algorithm (AKQ, PNS), pp. 621–624.
ICPR-v4-2004-RaytchevYS #estimation #learning: Head Pose Estimation by Nonlinear Manifold Learning (BR, IY, KS), pp. 462–466.
ICPR-v4-2004-SamsonB #clustering #learning #parallel #robust #video: Learning Classes for Video Interpretation with a Robust Parallel Clustering Method (VS, PB), pp. 569–572.
ICPR-v4-2004-StefanoDM #approach #learning: A Dynamic Approach to Learning Vector Quantization (CDS, CD, AM), pp. 601–604.
ICPR-v4-2004-WuCW04a #learning #recognition: Face Recognition Based on Discriminative Manifold Learning (YW, KLC, LW), pp. 171–174.
KDD-2004-AbeVAS #learning: Cross channel optimized marketing by reinforcement learning (NA, NKV, CA, RS), pp. 767–772.
KDD-2004-AbeZL #learning #multi: An iterative method for multi-class cost-sensitive learning (NA, BZ, JL), pp. 3–11.
KDD-2004-CaruanaN #analysis #data mining #empirical #learning #metric #mining #performance: Data mining in metric space: an empirical analysis of supervised learning performance criteria (RC, ANM), pp. 69–78.
KDD-2004-EvgeniouP #learning #multi: Regularized multi--task learning (TE, MP), pp. 109–117.
KDD-2004-KolterM #bytecode #detection #learning: Learning to detect malicious executables in the wild (JZK, MAM), pp. 470–478.
KDD-2004-KummamuruKA #difference #learning #metric: Learning spatially variant dissimilarity (SVaD) measures (KK, RK, RA), pp. 611–616.
KDD-2004-Muslea #machine learning #online #query: Machine learning for online query relaxation (IM), pp. 246–255.
KDD-2004-PopesculU #clustering #concept #learning #relational #statistics: Cluster-based concept invention for statistical relational learning (AP, LHU), pp. 665–670.
KDD-2004-TruongLB #dataset #learning #random #using: Learning a complex metabolomic dataset using random forests and support vector machines (YT, XL, CB), pp. 835–840.
KR-2004-PasulaZK #learning #probability #relational: Learning Probabilistic Relational Planning Rules (HP, LSZ, LPK), pp. 683–691.
LSO-2004-ChauM #agile #learning #tool support: Tool Support for Inter-team Learning in Agile Software Organizations (TC, FM), pp. 98–109.
LSO-2004-FalboRBT #how #learning #risk management #using: Learning How to Manage Risks Using Organizational Knowledge (RdAF, FBR, GB, DFT), pp. 7–18.
LSO-2004-HolzM #learning #past present future #research: Research on Learning Software Organizations — Past, Present, and Future (HH, GM), pp. 1–6.
LSO-2004-MelnikR #learning: Impreciseness and Its Value from the Perspective of Software Organizations and Learning (GM, MMR), pp. 122–130.
LSO-2004-Roth-Berghofer #learning: Learning from HOMER, a Case-Based Help Desk Support System (TRB), pp. 88–97.
LSO-2004-SousaAO #learning #maintenance: Learning Software Maintenance Organizations (KDdS, NA, KMdO), pp. 67–77.
SEKE-2004-AvesaniBPS #approach #machine learning #process #requirements: Supporting the Requirements Prioritization Process. A Machine Learning approach (PA, CB, AP, AS), pp. 306–311.
SEKE-2004-DantasBW #game studies #learning #project management: A Simulation-Based Game for Project Management Experiential Learning (ARD, MdOB, CMLW), pp. 19–24.
SEKE-2004-MaxvilleLA #component #learning: Learning to Select Software Components (VM, CPL, JA), pp. 421–426.
SIGIR-2004-LamHC #learning #mining #similarity: Learning phonetic similarity for matching named entity translations and mining new translations (WL, RH, PSC), pp. 289–296.
SIGIR-2004-RoussinovR #learning #web: Learning patterns to answer open domain questions on the web (DR, JARF), pp. 500–501.
SIGIR-2004-XiLB #effectiveness #learning #ranking: Learning effective ranking functions for newsgroup search (WX, JL, EB), pp. 394–401.
SIGIR-2004-ZengHCMM #clustering #learning #web: Learning to cluster web search results (HJZ, QCH, ZC, WYM, JM), pp. 210–217.
SIGIR-2004-ZhangPZ #machine learning #recognition #using: Focused named entity recognition using machine learning (LZ, YP, TZ), pp. 281–288.
RE-2004-HaleyNST #categorisation #learning #requirements: The Conundrum of Categorising Requirements: Managing Requirements for Learning on the Move (DTH, BN, HCS, JT), pp. 309–314.
SAC-2004-BergholzC #interface #learning #query #web: Learning query languages of Web interfaces (AB, BC), pp. 1114–1121.
SAC-2004-Binemann-Zdanowicz #named #towards: SiteLang: : Edu: towards a context-driven e-learning content utilization model (ABZ), pp. 924–928.
SAC-2004-CesariniMT #process #workflow: Carrying on the e-learning process with a workflow management engine (MC, MM, RT), pp. 940–945.
SAC-2004-ChakravarthySJP #approach: A learning-based approach for fetching pages in WebVigiL (SC, AS, JJ, NP), pp. 1725–1731.
SAC-2004-DerntlM #case study #concept #evaluation #experience #learning: Patterns for blended, Person-Centered learning: strategy, concepts, experiences, and evaluation (MD, RMP), pp. 916–923.
SAC-2004-HatalaREW #communication #implementation #learning #network #repository: The eduSource Communication Language: implementing open network for learning repositories and services (MH, GR, TE, JW), pp. 957–962.
SAC-2004-LischkaK #execution #modelling: Modeling and execution of E-Learning resources (JL, DK), pp. 971–972.
SAC-2004-NeelyLEBNG #architecture #distributed #learning: An architecture for supporting vicarious learning in a distributed environment (SN, HL, DME, JB, JN, XG), pp. 963–970.
SAC-2004-OBrienH #analysis #authoring: Training Needs Analysis: the first step in authoring e-learning content (EO, TH), pp. 935–939.
SAC-2004-Vrasidas #design: Issues of pedagogy and design in e-learning systems (CV), pp. 911–915.
SAC-2004-ZaneroS #detection #learning: Unsupervised learning techniques for an intrusion detection system (SZ, SMS), pp. 412–419.
ICSE-2004-BrunE #fault #machine learning: Finding Latent Code Errors via Machine Learning over Program Executions (YB, MDE), pp. 480–490.
SAT-2004-SangBBKP #component #effectiveness #learning: Combining Component Caching and Clause Learning for Effective Model Counting (TS, FB, PB, HAK, TP), pp. 20–28.
DAC-2003-GuptaGWYA #bound #learning #model checking #satisfiability: Learning from BDDs in SAT-based bounded model checking (AG, MKG, CW, ZY, PA), pp. 824–829.
DATE-2003-LuWCH #correlation #learning #satisfiability: A Circuit SAT Solver With Signal Correlation Guided Learning (FL, LCW, KTC, RCYH), pp. 10892–10897.
ICDAR-2003-Legal-AyalaF #approach #image #learning #segmentation: Image Segmentation By Learning Approach (HALA, JF), pp. 819–823.
ICDAR-2003-MalerbaEACB #approach #documentation #layout #machine learning: Correcting the Document Layout: A Machine Learning Approach (DM, FE, OA, MC, MB), p. 97–?.
ICDAR-2003-RyuK #learning #recognition #word: Learning the lexicon from raw texts for open-vocabulary Korean word recognition (SR, JHK), pp. 202–206.
ICDAR-2003-ShimizuOWK #image #learning #network: Mirror Image Learning for Autoassociative Neural Networks (SS, WO, TW, FK), pp. 804–808.
ICDAR-2003-TakahashiN #learning #recognition: A class-modular GLVQ ensemble with outlier learning for handwritten digit recognition (KT, DN), pp. 268–272.
CSEET-2003-AlfonsoM #learning #re-engineering: Learning Software Engineering with Group Work (MIA, FM), p. 309–?.
ITiCSE-2003-ChalkBP #design #education #learning #programming: Designing and evaluating learning objects for introductory programming education (PC, CB, PP), p. 240.
ITiCSE-2003-DemetriadisTP #learning #multi #student #towards: A phenomenographic study of students’ attitudes toward the use of multiple media for learning (SND, ET, ASP), pp. 183–187.
ITiCSE-2003-EkateriniSP #education #learning #problem: Teaching IT in secondary education through problem-based learning could be really beneficial (GE, BS, GP), p. 243.
ITiCSE-2003-Garvin-DoxasB #interactive #learning: Creating learning environments that support interaction (KGD, LJB), p. 276.
ITiCSE-2003-GeorgiopoulosCWDGGKM #case study #experience #machine learning: CRCD in machine learning at the University of Central Florida preliminary experiences (MG, JC, ASW, RFD, EG, AJG, MKK, MM), p. 249.
ITiCSE-2003-GunawardenaA #approach #education #learning #programming: A customized learning objects approach to teaching programming (AG, VA), p. 264.
ITiCSE-2003-KurhilaMNFT #learning #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 #learning #user interface #using: Learning to develop GUIs in Java using closed labs (CL), p. 228.
ITiCSE-2003-LinosHL #re-engineering: A service-learning program for computer science and software engineering (PKL, SH, JL), pp. 30–34.
ITiCSE-2003-LynchM #learning #student: The winds of change: students’ comfort level in different learning environments (KL, SM), pp. 70–73.
ITiCSE-2003-MirmotahariHK #architecture #learning: Difficulties learning computer architecture (OM, CH, JK), p. 247.
ITiCSE-2003-Nodelman #learning #programming #theory and practice: Learning computer graphics by programming: linking theory and practice (VN), p. 261.
ITiCSE-2003-PearsPE #learning #online: Enriching online learning resources with “explanograms” (ANP, LP, CE), p. 237.
ITiCSE-2003-RagonisH #distance #multi: A multi-level distance learning-based course for high-school computer science leading-teachers (NR, BH), p. 224.
ITiCSE-2003-Trakhtenbrot #analysis #concept #how: Analysis of typical misconceptions in a theoretical CS course, and how to address them in e-learning (MBT), p. 241.
TACAS-2003-CobleighGP #composition #learning #verification: Learning Assumptions for Compositional Verification (JMC, DG, CSP), pp. 331–346.
CSMR-2003-Lanza #lessons learnt #named #visualisation: CodeCrawler — Lessons Learned in Building a Software Visualization Tool (ML), pp. 409–418.
ICSM-2003-LinosB #learning #maintenance #re-engineering: Service Learning in Software Engineering and Maintenance (PKL, CBK), p. 336–?.
WCRE-2003-Murphy #learning: Learning from the Past (GCM), pp. 2–3.
PLDI-2003-StephensonAMO #compilation #heuristic #machine learning #optimisation: Meta optimization: improving compiler heuristics with machine learning (MS, SPA, MCM, UMO), pp. 77–90.
STOC-2003-MosselOS #learning: Learning juntas (EM, RO, RAS), pp. 206–212.
DLT-2003-DrewesH #education #learning: Learning a Regular Tree Language from a Teacher (FD, JH), pp. 279–291.
FME-2003-WassyngL #formal method #implementation #industrial #lessons learnt: Lessons Learned from a Successful Implementation of Formal Methods in an Industrial Project (AW, ML), pp. 133–153.
CHI-2003-KitamuraYHKK #tool support: Things happening in the brain while humans learn to use new tools (YK, YY, HI, FK, MK), pp. 417–424.
ICEIS-v2-2003-BendouM #learning #network #semistructured data: Learning Bayesian Networks From Noisy Data (MB, PM), pp. 26–33.
ICEIS-v2-2003-ColaceSFV #learning #network #ontology: Ontology Learning Through Bayesian Networks (FC, MDS, PF, MV), pp. 430–433.
ICEIS-v2-2003-KeeniGS #learning #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 #learning #network: Tool for Automatic Learning of Bayesian Networks From Database: An Application in the Health Area (CK), pp. 474–481.
ICEIS-v4-2003-BrunsDH #smarttech: Secure Smart Card-Based Access to an E-Learning Portal (RB, JD, JvH), pp. 167–172.
ICEIS-v4-2003-LiuWS #design: Knowledge Construction in E-Learning — Designing an E-Learning Environment (KL, SW, LS), pp. 111–118.
ICEIS-v4-2003-SemeraroLDL #learning: Learning User Profiles for Intelligent Search (GS, PL, MD, OL), pp. 426–429.
ICEIS-v4-2003-TyrvainenJS #case study #learning #on the: On Estimating the Amount of Learning Materials a Case Study (PT, MJ, AS), pp. 127–135.
CIKM-2003-ZhangOR #learning #using: Learning cross-document structural relationships using boosting (ZZ, JO, DRR), pp. 124–130.
ECIR-2003-ShiEMSLLKO #approach #machine learning: A Machine Learning Approach for the Curation of Biomedical Literature (MS, DSE, RM, LS, JYKL, HTL, SSK, CJO), pp. 597–604.
ECIR-2003-TianC #collaboration #learning #rating #recommendation #similarity: Learning User Similarity and Rating Style for Collaborative Recommendation (LFT, KWC), pp. 135–145.
ICML-2003-Bar-HillelHSW #distance #equivalence #learning #using: Learning Distance Functions using Equivalence Relations (ABH, TH, NS, DW), pp. 11–18.
ICML-2003-BaramEL #algorithm #learning #online: Online Choice of Active Learning Algorithms (YB, REY, KL), pp. 19–26.
ICML-2003-BerardiCEM #analysis #layout #learning #logic programming #source code: Learning Logic Programs for Layout Analysis Correction (MB, MC, FE, DM), pp. 27–34.
ICML-2003-Bouckaert #algorithm #learning #testing: Choosing Between Two Learning Algorithms Based on Calibrated Tests (RRB), pp. 51–58.
ICML-2003-Brinker #learning: Incorporating Diversity in Active Learning with Support Vector Machines (KB), pp. 59–66.
ICML-2003-BrownW #ambiguity #composition #learning #network: The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods (GB, JLW), pp. 67–74.
ICML-2003-CerquidesM #learning #modelling #naive bayes: Tractable Bayesian Learning of Tree Augmented Naive Bayes Models (JC, RLdM), pp. 75–82.
ICML-2003-ConitzerS #algorithm #learning #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 #learning #modelling: Semi-Supervised Learning of Mixture Models (FGC, IC, MCC), pp. 99–106.
ICML-2003-CumbyR #kernel #learning #on the #relational: On Kernel Methods for Relational Learning (CMC, DR), pp. 107–114.
ICML-2003-DriessensR #learning #relational: Relational Instance Based Regression for Relational Reinforcement Learning (KD, JR), pp. 123–130.
ICML-2003-EngelMM #approach #difference #learning #process: Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning (YE, SM, RM), pp. 154–161.
ICML-2003-Even-DarMM #learning: Action Elimination and Stopping Conditions for Reinforcement Learning (EED, SM, YM), pp. 162–169.
ICML-2003-Flach #comprehension #geometry #machine learning #metric: The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics (PAF), pp. 194–201.
ICML-2003-GargR #learning: Margin Distribution and Learning (AG, DR), pp. 210–217.
ICML-2003-GeibelW #learning: Perceptron Based Learning with Example Dependent and Noisy Costs (PG, FW), pp. 218–225.
ICML-2003-GreenwaldH #correlation: Correlated Q-Learning (AG, KH), pp. 242–249.
ICML-2003-IsaacS #learning: Goal-directed Learning to Fly (AI, CS), pp. 258–265.
ICML-2003-Joachims #clustering #graph #learning: Transductive Learning via Spectral Graph Partitioning (TJ), pp. 290–297.
ICML-2003-KalousisH: Representational Issues in Meta-Learning (AK, MH), pp. 313–320.
ICML-2003-KennedyJ #learning #problem: Characteristics of Long-term Learning in Soar and its Application to the Utility Problem (WGK, KADJ), pp. 337–344.
ICML-2003-KirshnerPS #learning #permutation: Unsupervised Learning with Permuted Data (SK, SP, PS), pp. 345–352.
ICML-2003-KotnikK #learning #self: The Significance of Temporal-Difference Learning in Self-Play Training TD-Rummy versus EVO-rummy (CK, JKK), pp. 369–375.
ICML-2003-KrawiecB #learning #synthesis #visual notation: Visual Learning by Evolutionary Feature Synthesis (KK, BB), pp. 376–383.
ICML-2003-KwokT #kernel #learning: Learning with Idealized Kernels (JTK, IWT), pp. 400–407.
ICML-2003-LagoudakisP #classification #learning: Reinforcement Learning as Classification: Leveraging Modern Classifiers (MGL, RP), pp. 424–431.
ICML-2003-LaudD #analysis #learning: The Influence of Reward on the Speed of Reinforcement Learning: An Analysis of Shaping (AL, GD), pp. 440–447.
ICML-2003-LeeL #learning #using: Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression (WSL, BL), pp. 448–455.
ICML-2003-McGovernJ #identification #learning #multi #predict #relational #using: Identifying Predictive Structures in Relational Data Using Multiple Instance Learning (AM, DJ), pp. 528–535.
ICML-2003-MooreW #learning #network: Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure Learning (AWM, WKW), pp. 552–559.
ICML-2003-OngS #kernel #machine learning: Machine Learning with Hyperkernels (CSO, AJS), pp. 568–575.
ICML-2003-OntanonP #learning #multi: Justification-based Multiagent Learning (SO, EP), pp. 576–583.
ICML-2003-RichardsonD #learning #multi: Learning with Knowledge from Multiple Experts (MR, PMD), pp. 624–631.
ICML-2003-RivestP #network: Combining TD-learning with Cascade-correlation Networks (FR, DP), pp. 632–639.
ICML-2003-RuckertK #learning #probability: Stochastic Local Search in k-Term DNF Learning (UR, SK), pp. 648–655.
ICML-2003-RussellZ #learning: Q-Decomposition for Reinforcement Learning Agents (SJR, AZ), pp. 656–663.
ICML-2003-SinghLJPS #learning #predict: Learning Predictive State Representations (SPS, MLL, NKJ, DP, PS), pp. 712–719.
ICML-2003-StimpsonG #approach #learning #social: Learning To Cooperate in a Social Dilemma: A Satisficing Approach to Bargaining (JLS, MAG), pp. 728–735.
ICML-2003-TaskarWK #learning #testing: Learning on the Test Data: Leveraging Unseen Features (BT, MFW, DK), pp. 744–751.
ICML-2003-WangD #learning #modelling #policy: Model-based Policy Gradient Reinforcement Learning (XW, TGD), pp. 776–783.
ICML-2003-WangSPZ #learning #modelling #principle: Learning Mixture Models with the Latent Maximum Entropy Principle (SW, DS, FP, YZ), pp. 784–791.
ICML-2003-WiewioraCE #learning: Principled Methods for Advising Reinforcement Learning Agents (EW, GWC, CE), pp. 792–799.
ICML-2003-WinnerV #learning #named: DISTILL: Learning Domain-Specific Planners by Example (EW, MMV), pp. 800–807.
ICML-2003-WuC #adaptation #learning: Adaptive Feature-Space Conformal Transformation for Imbalanced-Data Learning (GW, EYC), pp. 816–823.
ICML-2003-Zhang #kernel #learning #metric #multi #representation #scalability #towards: Learning Metrics via Discriminant Kernels and Multidimensional Scaling: Toward Expected Euclidean Representation (ZZ), pp. 872–879.
ICML-2003-ZhangH #learning #taxonomy: Learning from Attribute Value Taxonomies and Partially Specified Instances (JZ, VH), pp. 880–887.
ICML-2003-ZhangXC #adaptation #learning: Exploration and Exploitation in Adaptive Filtering Based on Bayesian Active Learning (YZ, WX, JPC), pp. 896–903.
ICML-2003-ZhuGL #learning #using: Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions (XZ, ZG, JDL), pp. 912–919.
KDD-2003-FradkinM #machine learning #random: Experiments with random projections for machine learning (DF, DM), pp. 517–522.
KDD-2003-Koller #learning #relational #statistics: Statistical learning from relational data (DK), p. 4.
KDD-2003-NevilleJFH #learning #probability #relational: Learning relational probability trees (JN, DJ, LF, MH), pp. 625–630.
KDD-2003-SarawagiCG #learning #named #probability #topic: Cross-training: learning probabilistic mappings between topics (SS, SC, SG), pp. 177–186.
MLDM-2003-Bunke #data mining #graph #machine learning #mining #tool support: Graph-Based Tools for Data Mining and Machine Learning (HB), pp. 7–19.
MLDM-2003-ComiteGT #learning #multi: Learning Multi-label Alternating Decision Trees from Texts and Data (FDC, RG, MT), pp. 35–49.
MLDM-2003-Craw #learning #reasoning: Introspective Learning to Build Case-Based Reasoning (CBR) Knowledge Containers (SC), pp. 1–6.
MLDM-2003-KrawiecB #learning #recognition: Coevolutionary Feature Learning for Object Recognition (KK, BB), pp. 224–238.
MLDM-2003-KuhnertK #classification #image #learning: A Learning Autonomous Driver System on the Basis of Image Classification and Evolutional Learning (KDK, MK), pp. 400–412.
MLDM-2003-PiwowarskiG #documentation #information retrieval #machine learning: A Machine Learning Model for Information Retrieval with Structured Documents (BP, PG), pp. 425–438.
SEKE-2003-ChenJ #fuzzy #induction #information management #learning #multi #named: MFILM: a multi-dimensional fuzzy inductive learning method for knowledge acquisition (YTC, BJ), pp. 445–449.
SEKE-2003-SpanoudakisGZ #approach #machine learning #requirements #traceability: Revising Rules to Capture Requirements Traceability Relations: A Machine Learning Approach (GS, ASdG, AZ), pp. 570–577.
SIGIR-2003-GaoWLC #approach #categorisation #learning: A maximal figure-of-merit learning approach to text categorization (SG, WW, CHL, TSC), pp. 174–181.
SAC-2003-LiZLO #classification #functional #learning #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 #learning: An Approach for Adaptable Learning Systems with Respect to Psychological Aspects (HR, WW), pp. 558–563.
PPoPP-2003-Puppin #adaptation #convergence #machine learning #scheduling #using: Adapting convergent scheduling using machine learning (DP), p. 1.
CAV-2003-HungarNS #automaton #learning #optimisation: Domain-Specific Optimization in Automata Learning (HH, ON, BS), pp. 315–327.
SAT-2003-SabharwalBK #learning #performance #problem #using: Using Problem Structure for Efficient Clause Learning (AS, PB, HAK), pp. 242–256.
SIGMOD-2002-MarklL #learning: Learning table access cardinalities with LEO (VM, GML), p. 613.
VLDB-2002-SarawagiBKM #alias #interactive #learning #named: ALIAS: An Active Learning led Interactive Deduplication System (SS, AB, AK, CM), pp. 1103–1106.
CSEET-2002-Armarego #design #learning #problem: Advanced Software Design: A Case in Problem-Based Learning (JA), pp. 44–54.
CSEET-2002-JovanovicMMSM #lessons learnt #re-engineering #source code: Panel 3: Software Engineering Masters Programs — Lessons Learned (VMJ, KLM, DM, DS, PEM), pp. 253–255.
CSEET-2002-UmphressH #education #learning #process: Software Process as a Foundation for Teaching, Learning and Accrediting (DAU, JAHJ), pp. 160–169.
ITiCSE-2002-CarboneS #education #learning #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 #learning #network: Very active learning of network routing (LNC), p. 195.
ITiCSE-2002-Chalk #aspect-oriented #education #human-computer #learning: Evaluating the use of a virtual learning environment for teaching aspects of HCI (PC), pp. 125–129.
ITiCSE-2002-FabregaMJM #learning #network: A virtual network laboratory for learning IP networking (LF, JM, TJ, DM), pp. 161–164.
ITiCSE-2002-GarciaM #how #learning #using: Learning how to develop software using the toy LEGO mindstorms (MAG, HPM), p. 239.
ITiCSE-2002-HansenR #collaboration #education #learning #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 #learning: Reducing abstraction level when learning computability theory concepts (OH), pp. 156–160.
ITiCSE-2002-KasyanovK #education #learning: Web-based systems for supporting computer-science teaching and learning (VNK, EVK), p. 238.
ITiCSE-2002-Lapidot #experience #learning #self: Self-assessment as a powerful learning experience (TL), p. 198.
ITiCSE-2002-LastDHW #collaboration #learning #student: Learning from students: continuous improvement in international collaboration (MZL, MD, MLH, MW), pp. 136–140.
ITiCSE-2002-Nygaard #learning #object-oriented: COOL (comprehensive object-oriented learning) (KN), p. 218.
ITiCSE-2002-ParkinsonR #learning #performance #question: Do cognitive styles affect learning performance in different computer media? (AP, JAR), pp. 39–43.
ITiCSE-2002-PlekhanovaM #learning #process #re-engineering: Learning processes in software engineering projects (VP, WM), p. 230.
ITiCSE-2002-StewartKM #authoring #named: MediaMime: after-the-fact authoring annotation system for an e-learning environment (AS, PK, MM), p. 243.
ITiCSE-2002-VanDeGriftA #assessment #framework #learning #tool support: Learning to support the instructor: classroom assessment tools as discussion frameworks in CS 1 (TV, RJA), pp. 19–23.
ITiCSE-2002-WaltersASBK #learning: Increasing learning and decreasing costs in a computer fluency course (DW, CA, BS, DTB, HK), pp. 208–212.
STOC-2002-HellersteinR #learning #using: Exact learning of DNF formulas using DNF hypotheses (LH, VR), pp. 465–473.
CHI-2002-Ehret #learning #user interface #visual notation: Learning where to look: location learning in graphical user interfaces (BDE), pp. 211–218.
CHI-2002-SnowdonG #experience #learning: Diffusing information in organizational settings: learning from experience (DS, AG), pp. 331–338.
CHI-2002-ZhaiSA #learning: Movement model, hits distribution and learning in virtual keyboarding (SZ, AES, JA), pp. 17–24.
CAiSE-2002-BerlinM #database #feature model #machine learning #using: Database Schema Matching Using Machine Learning with Feature Selection (JB, AM), pp. 452–466.
ICEIS-2002-FloresG #algorithm #case study #estimation #fuzzy #learning #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: Learning to Teach Database Design by Trial and Error (AI, PM, DC, EC, FF), pp. 500–505.
ICEIS-2002-Oliver #automation: A Training Environment for Automated Sales Agents to Learn Negotiation Strategies (JRO), pp. 410–417.
ICEIS-2002-SantosNASR #classification #data mining #database #learning #mining #using: Augmented Data Mining over Clinical Databases Using Learning Classifier Systems (MFS, JN, AA, ÁMS, FR), pp. 512–516.
CIKM-2002-HuangCA #comparison #learning #web: Comparison of interestingness functions for learning web usage patterns (XH, NC, AA), pp. 617–620.
ICML-2002-BianchettiRS #concept #constraints #learning #relational: Constraint-based Learning of Long Relational Concepts (JAB, CR, MS), pp. 35–42.
ICML-2002-ChisholmT #learning #random: Learning Decision Rules by Randomized Iterative Local Search (MC, PT), pp. 75–82.
ICML-2002-DietterichBMS #learning #probability #refinement: Action Refinement in Reinforcement Learning by Probability Smoothing (TGD, DB, RLdM, CS), pp. 107–114.
ICML-2002-DriessensD #learning #relational: Integrating Experimentation and Guidance in Relational Reinforcement Learning (KD, SD), pp. 115–122.
ICML-2002-FerriFH #learning #using: Learning Decision Trees Using the Area Under the ROC Curve (CF, PAF, JHO), pp. 139–146.
ICML-2002-GhavamzadehM #learning: Hierarchically Optimal Average Reward Reinforcement Learning (MG, SM), pp. 195–202.
ICML-2002-GonzalezHC #concept #graph #learning #relational: Graph-Based Relational Concept Learning (JAG, LBH, DJC), pp. 219–226.
ICML-2002-GuestrinLP #coordination #learning: Coordinated Reinforcement Learning (CG, MGL, RP), pp. 227–234.
ICML-2002-GuestrinPS #learning #modelling: Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs (CG, RP, DS), pp. 235–242.
ICML-2002-Hengst #learning: Discovering Hierarchy in Reinforcement Learning with HEXQ (BH), pp. 243–250.
ICML-2002-JensenN #bias #feature model #learning #relational: Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning (DJ, JN), pp. 259–266.
ICML-2002-KakadeL #approximate #learning: Approximately Optimal Approximate Reinforcement Learning (SK, JL), pp. 267–274.
ICML-2002-LanckrietCBGJ #kernel #learning #matrix #programming: Learning the Kernel Matrix with Semi-Definite Programming (GRGL, NC, PLB, LEG, MIJ), pp. 323–330.
ICML-2002-LaudD #behaviour #learning: Reinforcement Learning and Shaping: Encouraging Intended Behaviors (AL, GD), pp. 355–362.
ICML-2002-LeckieR #distributed #learning #probability: Learning to Share Distributed Probabilistic Beliefs (CL, KR), pp. 371–378.
ICML-2002-MerkeS #approximate #convergence #learning: A Necessary Condition of Convergence for Reinforcement Learning with Function Approximation (AM, RS), pp. 411–418.
ICML-2002-Mladenic #learning #normalisation #using #word: Learning word normalization using word suffix and context from unlabeled data (DM), pp. 427–434.
ICML-2002-MusleaMK #learning #multi #robust: Active + Semi-supervised Learning = Robust Multi-View Learning (IM, SM, CAK), pp. 435–442.
ICML-2002-OatesDB #context-free grammar #learning: Learning k-Reversible Context-Free Grammars from Positive Structural Examples (TO, DD, VB), pp. 459–465.
ICML-2002-OLZ #learning #using: Stock Trading System Using Reinforcement Learning with Cooperative Agents (JO, JWL, BTZ), pp. 451–458.
ICML-2002-PanangadanD #2d #correlation #learning #navigation: Learning Spatial and Temporal Correlation for Navigation in a 2-Dimensional Continuous World (AP, MGD), pp. 474–481.
ICML-2002-ParkZ #learning: A Boosted Maximum Entropy Model for Learning Text Chunking (SBP, BTZ), pp. 482–489.
ICML-2002-PerkinsP #fixpoint #on the: On the Existence of Fixed Points for Q-Learning and Sarsa in Partially Observable Domains (TJP, MDP), pp. 490–497.
ICML-2002-PeshkinS #experience #learning: Learning from Scarce Experience (LP, CRS), pp. 498–505.
ICML-2002-PickettB #algorithm #learning #named: PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning (MP, AGB), pp. 506–513.
ICML-2002-Ryan #automation #behaviour #learning #modelling #using: Using Abstract Models of Behaviours to Automatically Generate Reinforcement Learning Hierarchies (MRKR), pp. 522–529.
ICML-2002-SeriT #learning #modelling: Model-based Hierarchical Average-reward Reinforcement Learning (SS, PT), pp. 562–569.
ICML-2002-ShapiroL #learning #using: Separating Skills from Preference: Using Learning to Program by Reward (DGS, PL), pp. 570–577.
ICML-2002-Stirling #learning: Learning to Fly by Controlling Dynamic Instabilities (DS), pp. 586–593.
ICML-2002-ThamDR #classification #learning #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 #learning #multi #retrieval #using: Content-Based Image Retrieval Using Multiple-Instance Learning (QZ, SAG, WY, JEF), pp. 682–689.
ICML-2002-ZubekD #heuristic #learning: Pruning Improves Heuristic Search for Cost-Sensitive Learning (VBZ, TGD), pp. 19–26.
ICPR-v1-2002-HadidKP #analysis #learning #linear #using: Unsupervised Learning Using Locally Linear Embedding: Experiments with Face Pose Analysis (AH, OK, MP), pp. 111–114.
ICPR-v1-2002-HaroE #learning #video: Learning Video Processing by Example (AH, IAE), pp. 487–491.
ICPR-v1-2002-RobertsMR #3d #learning #online: Online Appearance Learning or 3D Articulated Human Tracking (TJR, SJM, IWR), pp. 425–428.
ICPR-v2-2002-Al-ShaherH #learning #modelling #online #performance: Fast On-Line learning of Point Distribution Models (AAAS, ERH), pp. 208–211.
ICPR-v2-2002-Amin #learning #prototype #using: Prototyping Structural Description Using Decision Tree Learning Techniques (AA), pp. 76–79.
ICPR-v2-2002-ChiuLY #learning #personalisation: Learning User Preference in a Personalized CBIR Systeml (CYC, HCL, SNY), p. 532–?.
ICPR-v2-2002-ChoCWS #adaptation #classification #data type #image #learning #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 #learning #retrieval: Learning from Negative Example in Relevance Feedback for Content-Based Image Retrieval (MLK, DZ, AB), pp. 933–936.
ICPR-v2-2002-Lashkia #learning: Learning with Relevant Features and Examples (GVL), pp. 68–71.
ICPR-v2-2002-LiuB #concept #learning #semantics #video #visual notation: Learning Semantic Visual Concepts from Video (JL, BB), pp. 1061–1064.
ICPR-v2-2002-Maloof #analysis #machine learning #on the #statistics #testing: On Machine Learning, ROC Analysis, and Statistical Tests of Significance (MAM), pp. 204–207.
ICPR-v2-2002-PhungDV #analysis #education: Narrative Structure Analysis with Education and Training Videos for E-Learning (DQP, CD, SV), p. 835–?.
ICPR-v2-2002-RiviereMMTPF #graph #learning #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 #learning #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 #learning: Comparative Study on Mirror Image Learning (MIL) and GLVQ (MS, TW, WO, FK), p. 248–?.
ICPR-v2-2002-TohM #approach #learning #network: A Global Transformation Approach to RBF Neural Network Learning (KAT, KZM), pp. 96–99.
ICPR-v2-2002-Torkkola02a #feature model #learning #problem: Learning Feature Transforms Is an Easier Problem Than Feature Selection (KT), pp. 104–107.
ICPR-v2-2002-WechslerDL #learning #process #using: Hierarchical Interpretation of Human Activities Using Competitive Learning (HW, ZD, FL), pp. 338–341.
ICPR-v3-2002-ArtacJL #incremental #learning #online #recognition #visual notation: Incremental PCA or On-Line Visual Learning and Recognition (MA, MJ, AL), pp. 781–784.
ICPR-v3-2002-BaesensECV #classification #learning #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 #learning #network: Learning and Extracting Edges from Images by a Modified Hopfield Neural Network (SC, RL), pp. 431–434.
ICPR-v3-2002-ChoudhuryRPP #detection #learning #network: Boosting and Structure Learning in Dynamic Bayesian Networks for Audio-Visual Speaker Detection (TC, JMR, VP, AP), p. 789–?.
ICPR-v3-2002-CooperWABCHKKLOVVJKLM #geometry #problem: Bayesian Pot-Assembly from Fragments as Problems in Perceptual-Grouping and Geometric-Learning (DBC, ARW, SA, JB, YC, DH, KK, WK, FFL, XO, SV, EV, MSJ, BBK, DHL, DM), pp. 297–302.
ICPR-v3-2002-HoqueFG #classification #learning #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 #learning: Graph Spectral Approach for Learning View Structure (BL, RCW, ERH), pp. 785–788.
ICPR-v3-2002-Sakano #how #learning #query #search-based: Genetic Translator: How to Apply Query Learning to Practical OCR (HS), pp. 184–187.
ICPR-v3-2002-SinghR #learning #recognition #robust: Background Learning for Robust Face Recognition (RKS, ANR), pp. 525–528.
ICPR-v3-2002-SuW #identification #learning #process: A Learning Process to the Identification of Feature Points on Chinese Characters (YMS, JFW), pp. 93–97.
ICPR-v4-2002-KubotaMK #fault #learning #optimisation: A Discriminative Learning Criterion for the Overall Optimization of Error and Reject (SK, HM, YK), pp. 98–102.
ICPR-v4-2002-LiuSF #classification #learning #polynomial: Learning Quadratic Discriminant Function for Handwritten Character Classification (CLL, HS, HF), pp. 44–47.
KDD-2002-AntalGF #clustering #learning #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 #learning: A theoretical framework for learning from a pool of disparate data sources (SBD, JG, RS), pp. 443–449.
KDD-2002-CohenR #clustering #integration #learning #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 #learning #parallel: A parallel learning algorithm for text classification (CK, CJ), pp. 201–206.
KDD-2002-MahoneyC #detection #learning #modelling #network #novel: Learning nonstationary models of normal network traffic for detecting novel attacks (MVM, PKC), pp. 376–385.
KDD-2002-PednaultAZ #learning: Sequential cost-sensitive decision making with reinforcement learning (EPDP, NA, BZ), pp. 259–268.
KDD-2002-SarawagiB #interactive #learning #using: Interactive deduplication using active learning (SS, AB), pp. 269–278.
KDD-2002-TejadaKM #identification #independence #learning #string: Learning domain-independent string transformation weights for high accuracy object identification (ST, CAK, SM), pp. 350–359.
KDD-2002-YuHC #classification #learning #named #using #web: PEBL: positive example based learning for Web page classification using SVM (HY, JH, KCCC), pp. 239–248.
KR-2002-BeygelzimerR #complexity #learning #network: Inference Complexity as a Model-Selection Criterion for Learning Bayesian Networks (AB, IR), pp. 558–567.
LSO-2002-AngkasaputraPRT #collaboration #implementation #learning: The Collaborative Learning Methodology CORONET-Train: Implementation and Guidance (NA, DP, ER, ST), pp. 13–24.
LSO-2002-HenningerM #agile #concept #development #learning #question: Learning Software Organizations and Agile Software Development: Complementary or Contradictory Concepts? (SH, FM), pp. 1–3.
LSO-2002-HofmannW #approach #community #learning: Building Communities among Software Engineers: The ViSEK Approach to Intra- and Inter-Organizational Learning (BH, VW), pp. 25–33.
LSO-2002-NeuB #comprehension #learning #process #simulation: Learning and Understanding a Software Process through Simulation of Its Underlying Model (HN, UBK), pp. 81–93.
LSO-2002-Ruhe #learning #paradigm #re-engineering: Software Engineering Decision Support ? A New Paradigm for Learning Software Organizations (GR), pp. 104–113.
SEKE-2002-ArndtCGM #distance #learning #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 #learning: A systematic approach to produce small courseware modules for combined learning and knowledge management environements (IG, NA, DP), pp. 533–539.
SEKE-2002-LoiaSS #deduction #named #web: LearnMiner: deductive, tolerant agents for discovering didactic resources on the web (VL, SS, MIS), pp. 109–115.
SEKE-2002-MaidantchikMS #learning #requirements: Learning organizational knowledge: an evolutionary proposal for requirements engineering (CM, MM, GS), pp. 151–157.
SEKE-2002-TortoraSVD #learning #multi: A multilevel learning management system (GT, MS, GV, PD), pp. 541–547.
SIGIR-2002-AminiG #learning #summary: The use of unlabeled data to improve supervised learning for text summarization (MRA, PG), pp. 105–112.
UML-2002-AnidoCRS #concept #corba: Applying MDA Concepts to Develop a Domain CORBA Facility for E-learning (LEAR, MC, JSR, JMS), pp. 321–335.
SAC-2002-BoughanemT #adaptation #incremental #learning: Incremental adaptive filtering: profile learning and threshold calibration (MB, MT), pp. 640–644.
SAC-2002-ElishRF #collaboration #learning #network: Evaluating collaborative software in supporting organizational learning with Bayesian Networks (MOE, DCR, JEF), pp. 992–996.
SAC-2002-NevesBR #classification #game studies #learning: Learning the risk board game with classifier systems (AN, OB, ACR), pp. 585–589.
SAC-2002-SeleznyovM #detection #learning: Learning temporal patterns for anomaly intrusion detection (AS, OM), pp. 209–213.
ICSE-2002-BasiliMPZ #lessons learnt #process #re-engineering: Lessons learned from 25 years of process improvement: the rise and fall of the NASA software engineering laboratory (VRB, FEM, RP, MVZ), pp. 69–79.
HPCA-2002-CintraT #learning #parallel #thread: Speculative Multithreading Eliminating Squashes through Learning Cross-Thread Violations in Speculative Parallelization for Multiprocessors (MHC, JT), pp. 43–54.
CADE-2002-JamnikKP: Learn Omega-matic: System Description (MJ, MK, MP), pp. 150–155.
CAV-2002-ClarkeGKS #abstraction #machine learning #satisfiability #using: SAT Based Abstraction-Refinement Using ILP and Machine Learning Techniques (EMC, AG, JHK, OS), pp. 265–279.
ICLP-2002-MartinNSS #learning #logic #prolog: Learning in Logic with RichProlog (EM, PMN, AS, FS), pp. 239–254.
DAC-2001-GizdarskiF #complexity #framework #learning: A Framework for Low Complexity Static Learning (EG, HF), pp. 546–549.
DATE-2001-NovikovG #learning #multi #performance: An efficient learning procedure for multiple implication checks (YN, EIG), pp. 127–135.
HT-2001-ConlanHLWA #adaptation #learning #metadata: Extending eductional metadata schemas to describe adaptive learning resources (OC, CH, PL, VPW, DA), pp. 161–162.
ICDAR-2001-DongKS #framework #learning #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 #learning: An Improved Learning Scheme for the Moving Window Classifier (SH, MCF), pp. 607–611.
ICDAR-2001-KobayashiNMSA #flexibility #learning #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-NatteeN #classification #comprehension #documentation #geometry #machine learning #online #using: Geometric Method for Document Understanding and Classification Using On-line Machine Learning (CN, MN), pp. 602–606.
ICDAR-2001-ValvenyM #learning #using: Learning of Structural Descriptions of Graphic Symbols Using Deformable Template Matching (EV, EM), pp. 455–459.
ICDAR-2001-WakabayashiSOK #image #learning #recognition: Accuracy Improvement of Handwritten Numeral Recognition by Mirror Image Learning (TW, MS, WO, FK), pp. 338–343.
CSEET-2001-ArmaregoFR #development #learning #online #re-engineering: Constructing Software Engineering Knowledge: Development of an Online Learning Environment (JA, LF, GGR), pp. 258–267.
CSEET-2001-RatcliffeTW #learning: A Learning Environment for First Year Software Engineers (MR, LT, JW), pp. 268–275.
ITiCSE-2001-BlankPKHJR #collaboration #multi #named: CIMEL: constructive, collaborative inquiry-based multimedia E-learning (GDB, WMP, GDK, MH, HJ, SR), p. 179.
ITiCSE-2001-CarboneHMG #learning #programming: Characteristics of programming exercises that lead to poor learning tendencies: Part II (AC, JH, IM, DG), pp. 93–96.
ITiCSE-2001-Chalk #learning: Scaffolding learning in virtual environments (PC), pp. 85–88.
ITiCSE-2001-ChoiC #design #education #interactive #learning #multi #object-oriented #using: Using interactive multimedia for teaching and learning object oriented software design (SHC, SC), p. 176.
ITiCSE-2001-CiesielskiM #algorithm #animation #learning #student #using: Using animation of state space algorithms to overcome student learning difficulties (VC, PM), pp. 97–100.
ITiCSE-2001-Ginat #algorithm #learning #problem: Metacognitive awareness utilized for learning control elements in algorithmic problem solving (DG), pp. 81–84.
ITiCSE-2001-Kumar #c++ #interactive #learning #pointer: Learning the interaction between pointers and scope in C++ (ANK), pp. 45–48.
ITiCSE-2001-McCaugheyA #community #education #learning #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 #learning #on the: On integration of learning and technology (ZP), p. 185.
ITiCSE-2001-Rosbottom #distance #education #hybrid #learning: Hybrid learning — a safe route into web-based open and distance learning for the computer science teacher (JR), pp. 89–92.
ITiCSE-2001-Thomas #student: The coach supporting students as they learn to program (PT), p. 177.
ITiCSE-2001-ThomasL #distance #fault #learning #student #using: Observational studies of student errors in a distance learning environment using a remote recording and replay tool (PT, KL), pp. 117–120.
CSMR-2001-Wendorff #assessment #design pattern #lessons learnt #re-engineering #scalability: Assessment of Design Patterns during Software Reengineering: Lessons Learned from a Large Commercial Project (PW), pp. 77–84.
WCRE-2001-Davis #lessons learnt #reverse engineering: Lessons Learned in Data Reverse Engineering (KHD), pp. 323–327.
STOC-2001-KlivansS01a #learning: Learning DNF in time 2Õ(n1/3) (AK, RAS), pp. 258–265.
STOC-2001-SanjeevK #learning: Learning mixtures of arbitrary gaussians (SA, RK), pp. 247–257.
FLOPS-2001-Ferri-RamirezHR #functional #incremental #learning #logic programming #source code: Incremental Learning of Functional Logic Programs (CF, JHO, MJRQ), pp. 233–247.
FLOPS-2001-Sato #learning #logic programming #source code: Parameterized Logic Programs where Computing Meets Learning (TS), pp. 40–60.
ICALP-2001-Servedio #learning #quantum: Separating Quantum and Classical Learning (RAS), pp. 1065–1080.
CHI-2001-CorbettA #feedback #learning: Locus of feedback control in computer-based tutoring: impact on learning rate, achievement and attitudes (ATC, JRA), pp. 245–252.
CHI-2001-RossonS #education #learning #reuse #simulation: Teachers as simulation programmers: minimalist learning and reuse (MBR, CDS), pp. 237–244.
SVIS-2001-Faltin #algorithm #constraints #interactive #learning: Structure and Constraints in Interactive Exploratory Algorithm Learning (NF), pp. 213–226.
SVIS-2001-RossG #education #learning #named #web: Hypertextbooks: Animated, Active Learning, Comprehensive Teaching and Learning Resources for the Web (RJR, MTG), pp. 269–284.
ICEIS-v1-2001-DiazTO #machine learning #using: A Knowledge-Acquisition Methodology for a Blast Furnace Expert System Using Machine Learning Techniques (ED, JT, FO), pp. 336–339.
ICEIS-v1-2001-SierraRLG #analysis #image #machine learning #mobile #order #recognition: Machine Learning Approaches for Image Analysis: Recognition of Hand Orders by a Mobile Robot (BS, IR, EL, UG), pp. 330–335.
ICEIS-v2-2001-AudyBF #information management #learning: Information Systems Planning: Contributions from Organizational Learning (JLNA, JLB, HF), pp. 873–879.
ICEIS-v2-2001-BressanAAG #3d #learning #multi #web: Multiuser 3D Learning Environments in the Web (CMB, SdA, RBdA, CG), pp. 1170–1173.
CIKM-2001-NottelmannF #classification #datalog #learning #probability: Learning Probabilistic Datalog Rules for Information Classification and Transformation (HN, NF), pp. 387–394.
ICML-2001-AmarDGZ #learning #multi: Multiple-Instance Learning of Real-Valued Data (RAA, DRD, SAG, QZ), pp. 3–10.
ICML-2001-BlumC #graph #learning #using: Learning from Labeled and Unlabeled Data using Graph Mincuts (AB, SC), pp. 19–26.
ICML-2001-BowlingV #convergence #learning: Convergence of Gradient Dynamics with a Variable Learning Rate (MHB, MMV), pp. 27–34.
ICML-2001-ChajewskaKO #behaviour #learning: Learning an Agent’s Utility Function by Observing Behavior (UC, DK, DO), pp. 35–42.
ICML-2001-ChoiR #approximate #difference #fixpoint #learning #performance: A Generalized Kalman Filter for Fixed Point Approximation and Efficient Temporal Difference Learning (DC, BVR), pp. 43–50.
ICML-2001-DomingosH #algorithm #clustering #machine learning #scalability: A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering (PMD, GH), pp. 106–113.
ICML-2001-EngelM #embedded #learning #markov #process: Learning Embedded Maps of Markov Processes (YE, SM), pp. 138–145.
ICML-2001-Furnkranz #learning: Round Robin Rule Learning (JF), pp. 146–153.
ICML-2001-Geibel #bound #learning: Reinforcement Learning with Bounded Risk (PG), pp. 162–169.
ICML-2001-GetoorFKT #learning #modelling #probability #relational: Learning Probabilistic Models of Relational Structure (LG, NF, DK, BT), pp. 170–177.
ICML-2001-GhavamzadehM #learning: Continuous-Time Hierarchical Reinforcement Learning (MG, SM), pp. 186–193.
ICML-2001-GlickmanS #learning #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 #learning #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 #learning #word: Learning to Select Good Title Words: An New Approach based on Reverse Information Retrieval (RJ, AGH), pp. 242–249.
ICML-2001-Krawiec #comparison #learning: Pairwise Comparison of Hypotheses in Evolutionary Learning (KK), pp. 266–273.
ICML-2001-Lee #collaboration #learning #recommendation: Collaborative Learning and Recommender Systems (WSL), pp. 314–321.
ICML-2001-Littman #game studies: Friend-or-Foe Q-learning in General-Sum Games (MLL), pp. 322–328.
ICML-2001-LiuECBT #3d #mobile #modelling #using: Using EM to Learn 3D Models of Indoor Environments with Mobile Robots (YL, RE, DC, WB, ST), pp. 329–336.
ICML-2001-MarchandS #learning #set: Learning with the Set Covering Machine (MM, JST), pp. 345–352.
ICML-2001-McGovernB #automation #learning #using: Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density (AM, AGB), pp. 361–368.
ICML-2001-PerkinsB #learning #set: Lyapunov-Constrained Action Sets for Reinforcement Learning (TJP, AGB), pp. 409–416.
ICML-2001-PrecupSD #approximate #difference #learning: Off-Policy Temporal Difference Learning with Function Approximation (DP, RSS, SD), pp. 417–424.
ICML-2001-RoyM #estimation #fault #learning #reduction #towards: Toward Optimal Active Learning through Sampling Estimation of Error Reduction (NR, AM), pp. 441–448.
ICML-2001-SatoK #learning #markov #problem: Average-Reward Reinforcement Learning for Variance Penalized Markov Decision Problems (MS, SK), pp. 473–480.
ICML-2001-SingerV #implementation #learning #performance: Learning to Generate Fast Signal Processing Implementations (BS, MMV), pp. 529–536.
ICML-2001-StoneS #learning #scalability #towards: Scaling Reinforcement Learning toward RoboCup Soccer (PS, RSS), pp. 537–544.
ICML-2001-Venkataraman #learning: A procedure for unsupervised lexicon learning (AV), pp. 569–576.
ICML-2001-Wiering #learning #using: Reinforcement Learning in Dynamic Environments using Instantiated Information (MW), pp. 585–592.
ICML-2001-Wyatt #learning #using: Exploration Control in Reinforcement Learning using Optimistic Model Selection (JLW), pp. 593–600.
ICML-2001-ZinkevichB #learning #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 #learning: Generalized clustering, supervised learning, and data assignment (AK, PL, KW, JPY), pp. 299–304.
KDD-2001-ZadroznyE #learning: Learning and making decisions when costs and probabilities are both unknown (BZ, CE), pp. 204–213.
LSO-2001-FeldmannA #learning #on the: On the Status of Learning Software Organizations in the Year 2001 (RLF, KDA), pp. 2–7.
LSO-2001-Henninger #learning: Organizational Learning in Dynamic Domains (SH), pp. 8–16.
LSO-2001-Lehner #how: Keynote Address: How do Companies Learn? Selected Applications from the IT Sector (FL), p. 17.
LSO-2001-LindvallFCT #experience #lessons learnt: Lessons Learned about Structuring and Describing Experience for Three Experience Bases (ML, MF, PC, RT), pp. 106–119.
LSO-2001-PfahlADR #collaboration #learning #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 #learning #process: Organisational Learning and Software Process Improvement: A Case Study (JS), pp. 68–82.
LSO-2001-StarkloffP #approach #development #learning: Process-Integrated Learning: The ADVISOR Approach for Corporate Development (PS, KP), pp. 152–162.
MLDM-2001-BhanuD #clustering #concept #feedback #fuzzy #learning: Concepts Learning with Fuzzy Clustering and Relevance Feedback (BB, AD), pp. 102–116.
MLDM-2001-DongKS #framework #learning #recognition: Local Learning Framework for Recognition of Lowercase Handwritten Characters (JxD, AK, CYS), pp. 226–238.
MLDM-2001-Fernau #learning #xml: Learning XML Grammars (HF), pp. 73–87.
MLDM-2001-KollmarH #feature model #learning: Feature Selection for a Real-World Learning Task (DK, DHH), pp. 157–172.
MLDM-2001-Krawiec #comparison #learning #on the #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 #learning #network #using: Nonlinear Function Learning and Classification Using Optimal Radial Basis Function Networks (AK), pp. 217–225.
MLDM-2001-LinderP #how #learning: How to Automate Neural Net Based Learning (RL, SJP), pp. 206–216.
MLDM-2001-ShiWOK #image #learning #recognition: Mirror Image Learning for Handwritten Numeral Recognition (MS, TW, WO, FK), pp. 239–248.
SEKE-2001-NavarroH #adaptation #game studies #learning: Adapting Game Technology to Support Individual and Organizational Learning (EON, AvdH), pp. 347–354.
SEKE-2001-PfahlR #learning: System Dynamics as an Enabling Technology for Learning in Software Organizations (DP, GR), pp. 355–362.
SEKE-2001-VincenziNMDR #guidelines: Bayesian-Learning Based Guidelines to determine Equivalente Mutants (AMRV, EYN, JCM, MED, RAFR), pp. 180–187.
SIGIR-2001-Joachims #classification #learning #statistics: A Statistical Learning Model of Text Classification for Support Vector Machines (TJ), pp. 128–136.
SIGIR-2001-Kauwell #internet #question #visualisation: Does Visualization Improve Our Ability to Find and Learn from Internet Based Information? (DAK, JL, HJY, YJL, JE, AB), p. 453.
SIGIR-2001-LamL #approach #categorisation: A Meta-Learning Approach for Text Categorization (WL, KYL), pp. 303–309.
SIGIR-2001-LeeS #clustering #image #learning #retrieval #using: Intelligent Object-based Image Retrieval Using Cluster-driven Personal Preference Learning (KML, WNS), pp. 436–437.
RE-2001-Kovitz #backtracking #development #learning: Is Backtracking so Bad? The Role of Learning in Software Development (BK), p. 272.
SAC-2001-DeermanLP #algorithm #predict #problem #search-based: Linkage-learning genetic algorithm application to the protein structure prediction problem (KRD, GBL, RP), pp. 333–339.
SAC-2001-KallesK #design #game studies #learning #on the #using #verification: On verifying game designs and playing strategies using reinforcement learning (DK, PK), pp. 6–11.
SAC-2001-LeeGA #learning #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 #learning #relational #retrieval: Interactive document retrieval with relational learning (MO, SY), pp. 27–31.
SAT-2001-LagoudakisL #branch #learning #satisfiability: Learning to Select Branching Rules in the DPLL Procedure for Satisfiability (MGL, MLL), pp. 344–359.
HT-2000-FischerS #adaptation #automation #hypermedia #learning: Automatic creation of exercises in adaptive hypermedia learning systems (SF, RS), pp. 49–55.
HT-2000-Larsen #flexibility #hypermedia #what: Providing flexibility within hypertext systems: what we’ve learned at HT workshops, CyberMountain, and elsewhere (DL), pp. 268–269.
HT-2000-SpalterS #distance #hypermedia #jit #learning #reuse: Reusable hypertext structures for distance and JIT learning (AMS, RMS), pp. 29–38.
SIGMOD-2000-ChenDLT #learning #named #query #web: Fact: A Learning Based Web Query Processing System (SC, YD, HL, ZT), p. 587.
SIGMOD-2000-WattezCBFF #benchmark #learning #metric #query: Benchmarking Queries over Trees: Learning the Hard Truth the Hard Way (FW, SC, VB, GF, CF), pp. 510–511.
VLDB-2000-DiaoLCT #learning #query #towards #web: Toward Learning Based Web Query Processing (YD, HL, SC, ZT), pp. 317–328.
CSEET-2000-Cusick #education #lessons learnt #re-engineering #student: Lessons Learned from Teaching Software Engineering to Adult Students (JJC), p. 39–?.
CSEET-2000-DuleyM #education #student: Did We Really Teach That?: A Glimpse of Things Students (Don’t) Learn from Traditional CS1 (RD, SPM), pp. 237–245.
CSEET-2000-KorneckiZE #concept #learning #programming #realtime: Learning Real-Time Programming Concepts through VxWorks Lab Experiments (AJK, JZ, DE), p. 294–?.
CSEET-2000-WilliamsK #education #re-engineering: The Effects of “Pair-Pressure” and “Pair-Learning” on Software Engineering Education (LAW, RRK), pp. 59–65.
ITiCSE-2000-BlandL #learning: Agents, profiles, learning styles and tutors (poster session) (CGB, PBL), p. 185.
ITiCSE-2000-Chalk #learning #re-engineering #using: Apprenticeship learning of software engineering using Webworlds (PC), pp. 112–115.
ITiCSE-2000-Chang #analysis #concept #learning #web: Discovering learning patterns from Web logs by concept transformation analysis (poster session) (CKC), pp. 186–187.
ITiCSE-2000-Eremin: Software system to learn objects (poster session) (EE), p. 188.
ITiCSE-2000-Hobbs #assessment #email #learning: Email groups for learning and assessment (MH), p. 183.
ITiCSE-2000-KhuriH #algorithm #image #interactive #learning: Interactive packages for learning image compression algorithms (SK, HCH), pp. 73–76.
ITiCSE-2000-OuCLL #learning #web: Instructional instruments for Web group learning systems: the grouping, intervention, and strategy (KLO, GDC, CCL, BJL), pp. 69–72.
ITiCSE-2000-RosbottomCF #learning #online: A generic model for on-line learning (JR, JC, DF), pp. 108–111.
ITiCSE-2000-ShinYLL #database #education #learning: Plan of teaching & learning for database software through situated learning (poster session) (SBS, IHY, CHL, TWL), pp. 193–194.
ITiCSE-2000-SpalterS #case study #education #experience #interactive #learning: Integrating interactive computer-based learning experiences into established curricula: a case study (AMS, RMS), pp. 116–119.
ITiCSE-2000-Thompson #learning #maturity #process: Learning process maturity (poster session) (ET), p. 195.
FASE-2000-Hernandez-OralloR #learning #lifecycle #quality: Software as Learning: Quality Factors and Life-Cycle Revised (JHO, MJRQ), pp. 147–162.
STOC-2000-BlumKW #learning #problem #query #statistics: Noise-tolerant learning, the parity problem, and the statistical query model (AB, AK, HW), pp. 435–440.
CHI-2000-ConwayABCC #3d #lessons learnt #named: Alice: lessons learned from building a 3D system for novices (MC, SA, TB, DC, KC), pp. 486–493.
CHI-2000-CorbettT #difference #learning: Instructional interventions in computer-based tutoring: differential impact on learning time and accuracy (ATC, HJT), pp. 97–104.
CSCW-2000-CadizBSGGJ #collaboration #distance #distributed #learning #video: Distance learning through distributed collaborative video viewing (JJC, AB, ES, AG, JG, GJ), pp. 135–144.
CSCW-2000-SingleySFFS #algebra #collaboration #learning: Algebra jam: supporting teamwork and managing roles in a collaborative learning environment (MKS, MS, PGF, RGF, SS), pp. 145–154.
ICEIS-2000-KleinerSB #estimation #learning: Self Organizing Maps for Value Estimation to Solve Reinforcement Learning Tasks (AK, BS, OB), pp. 149–156.
ICEIS-2000-NobreC #information management #learning: Information Systems and Learning Organisations (ALN, MPeC), pp. 327–332.
ICEIS-2000-PetersHW #database #design #distributed #learning: Action Learning in a Decentralized Organization-The Case of Designing a Distributed Database (SCAP, MSHH, CEW), pp. 519–520.
CIKM-2000-GhaniJ #database #learning #multi: Learning a Monolingual Language Model from a Multilingual Text Database (RG, RJ), pp. 187–193.
CIKM-2000-LamL #documentation #learning: Learning to Extract Hierarchical Information from Semi-structured Documents (WL, WYL), pp. 250–257.
ICML-2000-AlerBI #information management #learning #representation: Knowledge Representation Issues in Control Knowledge Learning (RA, DB, PI), pp. 1–8.
ICML-2000-AllenG #comparison #empirical #learning: Model Selection Criteria for Learning Belief Nets: An Empirical Comparison (TVA, RG), pp. 1047–1054.
ICML-2000-BaxterB #learning: Reinforcement Learning in POMDP’s via Direct Gradient Ascent (JB, PLB), pp. 41–48.
ICML-2000-BoschZ #in memory #learning #multi: Unpacking Multi-valued Symbolic Features and Classes in Memory-Based Language Learning (AvdB, JZ), pp. 1055–1062.
ICML-2000-Bowling #convergence #learning #multi #problem: Convergence Problems of General-Sum Multiagent Reinforcement Learning (MHB), pp. 89–94.
ICML-2000-CampbellCS #classification #learning #query #scalability: Query Learning with Large Margin Classifiers (CC, NC, AJS), pp. 111–118.
ICML-2000-ChangCM #learning: Learning to Create Customized Authority Lists (HC, DC, AM), pp. 127–134.
ICML-2000-ChoiY #database #learning: Learning to Select Text Databases with Neural Nets (YSC, SIY), pp. 135–142.
ICML-2000-ChownD #approach #divide and conquer #information management #learning: A Divide and Conquer Approach to Learning from Prior Knowledge (EC, TGD), pp. 143–150.
ICML-2000-CoelhoG #approach #learning: Learning in Non-stationary Conditions: A Control Theoretic Approach (JACJ, RAG), pp. 151–158.
ICML-2000-Cohen #automation #concept #learning #web: Automatically Extracting Features for Concept Learning from the Web (WWC), pp. 159–166.
ICML-2000-CohnC #documentation #identification #learning: Learning to Probabilistically Identify Authoritative Documents (DC, HC), pp. 167–174.
ICML-2000-ConradtTVS #learning #online: On-line Learning for Humanoid Robot Systems (JC, GT, SV, SS), pp. 191–198.
ICML-2000-CravenPSBG #coordination #learning #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 #learning: Hidden Strengths and Limitations: An Empirical Investigation of Reinforcement Learning (GD), pp. 215–222.
ICML-2000-DyB #identification #learning #order #set: Feature Subset Selection and Order Identification for Unsupervised Learning (JGD, CEB), pp. 247–254.
ICML-2000-Eskin #detection #probability #semistructured data #using: Anomaly Detection over Noisy Data using Learned Probability Distributions (EE), pp. 255–262.
ICML-2000-FariasR #approximate #fixpoint #learning: Fixed Points of Approximate Value Iteration and Temporal-Difference Learning (DPdF, BVR), pp. 207–214.
ICML-2000-FernG #empirical #learning #online: Online Ensemble Learning: An Empirical Study (AF, RG), pp. 279–286.
ICML-2000-FiechterR #learning #scalability: Learning Subjective Functions with Large Margins (CNF, SR), pp. 287–294.
ICML-2000-ForsterW #bound #learning: Relative Loss Bounds for Temporal-Difference Learning (JF, MKW), pp. 295–302.
ICML-2000-GiordanaSSB #framework #learning #relational: Analyzing Relational Learning in the Phase Transition Framework (AG, LS, MS, MB), pp. 311–318.
ICML-2000-GoldbergM #learning #modelling #multi: Learning Multiple Models for Reward Maximization (DG, MJM), pp. 319–326.
ICML-2000-GoldmanZ #learning: Enhancing Supervised Learning with Unlabeled Data (SAG, YZ), pp. 327–334.
ICML-2000-GordonM #learning: Learning Filaments (GJG, AM), pp. 335–342.
ICML-2000-Hall #feature model #machine learning: Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning (MAH), pp. 359–366.
ICML-2000-HallH #information retrieval #learning #multi #natural language: Learning Curved Multinomial Subfamilies for Natural Language Processing and Information Retrieval (KBH, TH), pp. 351–358.
ICML-2000-Heskes #empirical #learning: Empirical Bayes for Learning to Learn (TH), pp. 367–374.
ICML-2000-HosteDSG #corpus: Meta-Learning for Phonemic Annotation of Corpora (VH, WD, EFTKS, SG), pp. 375–382.
ICML-2000-HougenGS #approach #learning: An Integrated Connectionist Approach to Reinforcement Learning for Robotic Control (DFH, MLG, JRS), pp. 383–390.
ICML-2000-HuangSK #constraints #declarative #learning: Learning Declarative Control Rules for Constraint-BAsed Planning (YCH, BS, HAK), pp. 415–422.
ICML-2000-HuW #game studies #probability: Experimental Results on Q-Learning for General-Sum Stochastic Games (JH, MPW), pp. 407–414.
ICML-2000-KatayamaKK #learning #using: A Universal Generalization for Temporal-Difference Learning Using Haar Basis Functions (SK, HK, SK), pp. 447–454.
ICML-2000-KephartT #pseudo: Pseudo-convergent Q-Learning by Competitive Pricebots (JOK, GT), pp. 463–470.
ICML-2000-Khardon #learning: Learning Horn Expressions with LogAn-H (RK), pp. 471–478.
ICML-2000-KimN #learning #network #set: Learning Bayesian Networks for Diverse and Varying numbers of Evidence Sets (ZWK, RN), pp. 479–486.
ICML-2000-KomarekM #adaptation #machine learning #performance #scalability #set: A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets (PK, AWM), pp. 495–502.
ICML-2000-LagoudakisL #algorithm #learning #using: Algorithm Selection using Reinforcement Learning (MGL, MLL), pp. 511–518.
ICML-2000-LaneB #interface #learning #reduction: Data Reduction Techniques for Instance-Based Learning from Human/Computer Interface Data (TL, CEB), pp. 519–526.
ICML-2000-Langley #machine learning: Crafting Papers on Machine Learning (PL), pp. 1207–1216.
ICML-2000-LauerR #algorithm #distributed #learning #multi: An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems (ML, MAR), pp. 535–542.
ICML-2000-Li #learning #online: Selective Voting for Perception-like Online Learning (YL), pp. 559–566.
ICML-2000-MamitsukaA #database #learning #mining #performance #query #scalability: Efficient Mining from Large Databases by Query Learning (HM, NA), pp. 575–582.
ICML-2000-MollPB #machine learning #problem: Machine Learning for Subproblem Selection (RM, TJP, AGB), pp. 615–622.
ICML-2000-MorimotoD #behaviour #learning #using: Acquisition of Stand-up Behavior by a Real Robot using Hierarchical Reinforcement Learning (JM, KD), pp. 623–630.
ICML-2000-MuggletonBS #biology #learning #product line #sequence: Learning Chomsky-like Grammars for Biological Sequence Families (SM, CHB, AS), pp. 631–638.
ICML-2000-NgR #algorithm #learning: Algorithms for Inverse Reinforcement Learning (AYN, SJR), pp. 663–670.
ICML-2000-NikovskiN #learning #mobile #modelling #navigation #probability: Learning Probabilistic Models for Decision-Theoretic Navigation of Mobile Robots (DN, IRN), pp. 671–678.
ICML-2000-OSullivanLCB #algorithm #named #robust: FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness (JO, JL, RC, AB), pp. 703–710.
ICML-2000-PaccanaroH #concept #distributed #learning #linear: Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space (AP, GEH), pp. 711–718.
ICML-2000-PennockMGH #algorithm #learning: A Normative Examination of Ensemble Learning Algorithms (DMP, PMRI, CLG, EH), pp. 735–742.
ICML-2000-PfahringerBG #algorithm #learning: Meta-Learning by Landmarking Various Learning Algorithms (BP, HB, CGGC), pp. 743–750.
ICML-2000-PiaterG #development #learning #visual notation: Constructive Feature Learning and the Development of Visual Expertise (JHP, RAG), pp. 751–758.
ICML-2000-Randlov #learning #physics #problem: Shaping in Reinforcement Learning by Changing the Physics of the Problem (JR), pp. 767–774.
ICML-2000-RandlovBR #algorithm #learning: Combining Reinforcement Learning with a Local Control Algorithm (JR, AGB, MTR), pp. 775–782.
ICML-2000-Reynolds #adaptation #bound #clustering #learning: Adaptive Resolution Model-Free Reinforcement Learning: Decision Boundary Partitioning (SIR), pp. 783–790.
ICML-2000-RichterS #learning #modelling: Knowledge Propagation in Model-based Reinforcement Learning Tasks (CR, JS), pp. 791–798.
ICML-2000-RyanR #learning: Learning to Fly: An Application of Hierarchical Reinforcement Learning (MRKR, MDR), pp. 807–814.
ICML-2000-SannerALL #learning #performance: Achieving Efficient and Cognitively Plausible Learning in Backgammon (SS, JRA, CL, MCL), pp. 823–830.
ICML-2000-SchohnC #learning #less is more: Less is More: Active Learning with Support Vector Machines (GS, DC), pp. 839–846.
ICML-2000-SchuurmansS #adaptation #learning: An Adaptive Regularization Criterion for Supervised Learning (DS, FS), pp. 847–854.
ICML-2000-SegalK #incremental #learning: Incremental Learning in SwiftFile (RS, JOK), pp. 863–870.
ICML-2000-ShultzR #comparison #knowledge-based #learning #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 #learning: Obtaining Simplified Rule Bases by Hybrid Learning (RBdAeS, TBL), pp. 879–886.
ICML-2000-SingerV #learning #modelling #performance #predict: Learning to Predict Performance from Formula Modeling and Training Data (BS, MMV), pp. 887–894.
ICML-2000-SmartK #learning: Practical Reinforcement Learning in Continuous Spaces (WDS, LPK), pp. 903–910.
ICML-2000-SmolaS #approximate #machine learning #matrix: Sparse Greedy Matrix Approximation for Machine Learning (AJS, BS), pp. 911–918.
ICML-2000-SohT #image #learning #using: Using Learning by Discovery to Segment Remotely Sensed Images (LKS, CT), pp. 919–926.
ICML-2000-SridharanT #automation #multi: Multi-agent Q-learning and Regression Trees for Automated Pricing Decisions (MS, GT), pp. 927–934.
ICML-2000-Strens #framework #learning: A Bayesian Framework for Reinforcement Learning (MJAS), pp. 943–950.
ICML-2000-Talavera #concept #feature model #incremental #learning #probability: Feature Selection and Incremental Learning of Probabilistic Concept Hierarchies (LT), pp. 951–958.
ICML-2000-TellerV #evolution #learning #performance #programming: Efficient Learning Through Evolution: Neural Programming and Internal Reinforcement (AT, MMV), pp. 959–966.
ICML-2000-TongK #classification #learning: Support Vector Machine Active Learning with Application sto Text Classification (ST, DK), pp. 999–1006.
ICML-2000-TorkkolaC #learning: Mutual Information in Learning Feature Transformations (KT, WMC), pp. 1015–1022.
ICML-2000-TowellPM #learning: Learning Priorities From Noisy Examples (GGT, TP, MRM), pp. 1031–1038.
ICML-2000-VaithyanathanD #learning: Hierarchical Unsupervised Learning (SV, BD), pp. 1039–1046.
ICML-2000-Veeser #approach #automaton #finite #learning: An Evolutionary Approach to Evidence-Based Learning of Deterministic Finite Automata (SV), pp. 1071–1078.
ICML-2000-VijayakumarS #incremental #learning #realtime: Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space (SV, SS), pp. 1079–1086.
ICML-2000-WnagZ #approach #lazy evaluation #learning #multi #problem: Solving the Multiple-Instance Problem: A Lazy Learning Approach (JW, JDZ), pp. 1119–1126.
ICML-2000-YangAP #effectiveness #learning #multi #validation: Combining Multiple Learning Strategies for Effective Cross Validation (YY, TA, TP), pp. 1167–1174.
ICML-2000-Zaanen #learning #recursion #syntax #using: Bootstrapping Syntax and Recursion using Alginment-Based Learning (MvZ), pp. 1063–1070.
ICPR-v1-2000-BhanuF #image #interactive #learning #segmentation: Learning Based Interactive Image Segmentation (BB, SF), pp. 1299–1302.
ICPR-v1-2000-LiuW #learning #recognition #representation: Learning the Face Space — Representation and Recognition (CL, HW), pp. 1249–1256.
ICPR-v1-2000-NelsonS #3d #empirical #learning #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 #learning #recognition #using: Learning Temporal Context in Active Object Recognition Using Bayesian Analysis (LP, MP, AP), pp. 1695–1699.
ICPR-v1-2000-PiaterG #learning #network #recognition: Feature Learning for Recognition with Bayesian Networks (JHP, RAG), pp. 1017–1020.
ICPR-v2-2000-BuhmannZ #clustering #learning: Active Learning for Hierarchical Pairwise Data Clustering (JMB, TZ), pp. 2186–2189.
ICPR-v2-2000-BurrellP #algorithm #detection #learning #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 #learning #modelling #performance #predict: Learning Image Feature Extraction: Modeling, Tracking and Predicting Human Performance (TC), pp. 2215–2218.
ICPR-v2-2000-ChouS #algorithm #classification #learning #multi: A Hierarchical Multiple Classifier Learning Algorithm (YYC, LGS), pp. 2152–2155.
ICPR-v2-2000-Figueiredo #approximate #learning #on the: On Gaussian Radial Basis Function Approximations: Interpretation, Extensions, and Learning Strategies (MATF), pp. 2618–2621.
ICPR-v2-2000-HiraokaHHMMY #algorithm #analysis #learning #linear: Successive Learning of Linear Discriminant Analysis: Sanger-Type Algorithm (KH, KiH, MH, HM, TM, SY), pp. 2664–2667.
ICPR-v2-2000-HongH #learning #sequence: Learning to Extract Temporal Signal Patterns from Temporal Signal Sequence (PH, TSH), pp. 2648–2651.
ICPR-v2-2000-KavallieratouSFK #learning #segmentation #using: Handwritten Character Segmentation Using Transformation-Based Learning (EK, ES, NF, GKK), pp. 2634–2637.
ICPR-v2-2000-KeglKN #classification #complexity #learning #network: Radial Basis Function Networks and Complexity Regularization in Function Learning and Classification (BK, AK, HN), pp. 2081–2086.
ICPR-v2-2000-LawK #clustering #learning #modelling #sequence: Rival Penalized Competitive Learning for Model-Based Sequence Clustering (MHCL, JTK), pp. 2195–2198.
ICPR-v2-2000-LohRW #incremental #learning #named #network: IFOSART: A Noise Resistant Neural Network Capable of Incremental Learning (AWKL, MCR, GAWW), pp. 2985–2988.
ICPR-v2-2000-MitraMP #database #incremental #learning #scalability: Data Condensation in Large Databases by Incremental Learning with Support Vector Machines (PM, CAM, SKP), pp. 2708–2711.
ICPR-v2-2000-MugurelVW #incremental #learning #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 #learning #modelling #multi: Learning Sparse Multiple Cause Models (MRN, LSC, TSH, BJF), pp. 2642–2647.
ICPR-v2-2000-Sato #classification #fault #learning: A Learning Method for Definite Canonicalization Based on Minimum Classification Error (AS), pp. 2199–2202.
ICPR-v4-2000-HeisterkampPD #image #learning #query #retrieval: Feature Relevance Learning with Query Shifting for Content-Based Image Retrieval (DRH, JP, HKD), pp. 4250–4253.
ICPR-v4-2000-IskeRMS #behaviour #learning #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 #learning #using: Active learning using adaptive resampling (VSI, CA, TZ), pp. 91–98.
KDD-2000-KimSM #feature model #learning #search-based: Feature selection in unsupervised learning via evolutionary search (YK, WNS, FM), pp. 365–369.
KDD-2000-YamanishiTWM #algorithm #detection #finite #learning #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 #learning: Experimental Results on Learning Soft Constraints (AB, FR, AS), pp. 435–444.
KR-2000-CumbyR #learning #relational: Relational Representations that Facilitate Learning (CMC, DR), pp. 425–434.
KR-2000-MartinG #concept #learning #policy #using: Learning Generalized Policies in Planning Using Concept Languages (MM, HG), pp. 667–677.
SIGIR-2000-AsadovS #documentation #learning #navigation #semantics: Semantic Explorer — navigation in documents collections, Proxima Daily — learning personal newspaper (VA, SS), p. 388.
SIGIR-2000-ChuangY #approach #machine learning #summary: Extracting sentence segments for text summarization: a machine learning approach (WTC, JY), pp. 152–159.
SIGIR-2000-Hofmann #learning #modelling #probability #web: Learning probabilistic models of the Web (TH), pp. 369–371.
SIGIR-2000-PetasisCVPKS #adaptation #automation #machine learning #probability: Automatic adaptation of proper noun dictionaries through cooperation of machine learning and probabilistic methods (GP, AC, PV, GP, VK, CDS), pp. 128–135.
SIGIR-2000-ZhaiJE #adaptation #approach #heuristic #learning: Exploration of a heuristic approach to threshold learning in adaptive filtering (CZ, PJ, DAE), pp. 360–362.
OOPSLA-2000-BastidePSN #corba #experience #lessons learnt #specification: Formal specification of CORBA services: experience and lessons learned (RB, PAP, OS, DN), pp. 105–117.
TOOLS-EUROPE-2000-NobleW #game studies #learning: GOF Pursuit — Learning Patterns by Playing (JN, CW), p. 462.
SAC-2000-BarraCPGRS #distance #education #learning: Teach++: A Cooperative Distance Learning and Teaching Environment (MB, GC, UFP, VG, CR, VS), pp. 124–130.
SAC-2000-PereiraC #adaptation #behaviour #information retrieval #learning: The Influence of Learning in the Behaviour of Information Retrieval Adaptive Agents (FBP, EC), pp. 452–457.
SAC-2000-RoselliCLPS #learning: WWW-Based Cooperative Learning (TR, CC, SL, MVP, GS), pp. 1014–1020.
ICSE-2000-Curtis00a #lessons learnt #process #tutorial: Software process improvement (tutorial session): best practices and lessons learned (BC), p. 828.
ICSE-2000-Moore #education #lessons learnt #re-engineering #tool support #using: Lessons learned from teaching reflective software engineering using the Leap toolkit (CAM), pp. 672–675.
ICSE-2000-Ramakrishnan #interactive #internet #learning #named #object-oriented #testing #visual notation: LIGHTVIEWS — visual interactive Internet environment for learning OO software testing (SR), pp. 692–695.
CL-2000-KameyaS #learning #logic programming #performance #source code: Efficient EM Learning with Tabulation for Parameterized Logic Programs (YK, TS), pp. 269–284.
DATE-1999-Marques-SilvaG #equivalence #learning #recursion #satisfiability #using: Combinational Equivalence Checking Using Satisfiability and Recursive Learning (JPMS, TG), pp. 145–149.
HT-1999-SeebergSRFS #learning: Individual Tables of Contents in Web-Based Learning Systems (CS, AS, KR, SF, RS), pp. 167–168.
ICDAR-1999-HebertPG #detection #incremental #learning #using: Cursive Character Detection using Incremental Learning (JFH, MP, NG), pp. 808–811.
ICDAR-1999-Ho #identification #keyword #learning #performance #word: Fast Identification of Stop Words for Font Learning and Keyword Spotting (TKH), pp. 333–336.
ICDAR-1999-LebourgeoisBE #learning #using: Structure Relation between Classes for Supervised Learning using Pretopology (FL, MB, HE), pp. 33–36.
ICDAR-1999-LiN #classification #documentation #learning: A Document Classification and Extraction System with Learning Ability (XL, PAN), pp. 197–200.
ICDAR-1999-LiuN99a #algorithm #classification #learning #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 #learning: Continuous Learning Systems: Postal Address Readers with Built-In Learning Capability (UM, TB, HS), pp. 329–332.
ICDAR-1999-Walischewski #automation #learning: Learning Regions of Interest in Postal Automation (HW), pp. 317–320.
ITiCSE-1999-Ben-AriK #concurrent #learning #parallel #process: Thinking parallel: the process of learning concurrency (MBA, YBDK), pp. 13–16.
ITiCSE-1999-Clear #collaboration #concept #education #interactive #learning: 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 #learning #programming: Research-led innovation in teaching and learning programming (JD, TJ), pp. 5–8.
ITiCSE-1999-DeeR #approach #education #learning: 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 #learning: Designing courseware on algorithms for active learning with virtual board games (NF), pp. 135–138.
ITiCSE-1999-HabermanG #distance #education #learning: Distance learning model with local workshop sessions applied to in-service teacher training (BH, DG), pp. 64–67.
ITiCSE-1999-LowderH #feedback #learning #student: Web-based student feedback to improve learning (JL, DH), pp. 151–154.
ITiCSE-1999-MiaoPW #collaboration #learning: Combining the metaphors of an institute and of networked computers for building collaborative learning environments (YM, HRP, MW), p. 188.
ITiCSE-1999-ScherzP #learning: An organizer for project-based learning and instruction in computer science (ZS, SP), pp. 88–90.
ITiCSE-1999-SheardH #learning #student: A special learning environment for repeat students (JS, DH), pp. 56–59.
ITiCSE-1999-Taylor99a #education #learning: Math link: linking curriculum, instructional strategies, and technology to enhance teaching and learning (HGT), p. 201.
ITiCSE-1999-Utting #education #learning: Gathering and disseminating good practice at teaching and learning conferences (IU), p. 202.
ITiCSE-1999-YoungDM #online #question: Who wants to learn online? (SY, RD, MM), p. 207.
STOC-1999-Servedio #complexity #learning: Computational Sample Complexity and Attribute-Efficient Learning (RAS), pp. 701–710.
ICALP-1999-Watanabe #learning: From Computational Learning Theory to Discovery Science (OW0), pp. 134–148.
WIA-1999-BrauneDKW #animation #automaton #finite #generative #learning: 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: Learning and Rewriting in Fuzzy Rule Graphs (IF, MK, MRB), pp. 263–270.
CHI-1999-MoherJOG #learning: Bridging Strategies for VR-Based Learning (TGM, AEJ, SO, MG), pp. 536–543.
CHI-1999-PlowmanKLST #design #learning #multi: Designing Multimedia for Learning: Narrative Guidance and Narrative Construction (LP, RL, DL, MS, JT), pp. 310–317.
CHI-1999-Soto #analysis #learning #quality #semantics: Learning and Performing by Exploration: Label Quality Measured by Latent Semantic Analysis (RS), pp. 418–425.
HCI-CCAD-1999-BrownS #development #education #learning #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 #learning: Teaching tasks in an adaptive learning environment (RMC, RM, EP, PR), pp. 740–744.
HCI-CCAD-1999-Chiu #algorithm #approach #learning #search-based #using: Learning path planning using genetic algorithm approach (CC), pp. 71–75.
HCI-CCAD-1999-Danielsson #learning #network: Learning in networks (UD), pp. 407–411.
HCI-CCAD-1999-EngelKM #lessons learnt: Conventions for cooperation — lessons learned from videoconferencing (AE, SK, AM), pp. 382–386.
HCI-CCAD-1999-FachB #adaptation #design #learning: Training wheels: an “old” method for designing modern and adaptable learning environments (PWF, MB), pp. 725–729.
HCI-CCAD-1999-HartmannSMGS #learning #tool support: Tools for computer-supported learning in organisations (EAH, DS, KM, MG, HS), pp. 377–381.
HCI-CCAD-1999-JohnsonO #learning #multi #problem #using: Innovative mathematical learning environments — Using multimedia to solve real world problems (LFJ, POJ), pp. 677–681.
HCI-CCAD-1999-KashiharaUT #learning #visualisation: Visualizing knowledge structure for exploratory learning in hyperspace (AK, HU, JT), pp. 667–671.
HCI-CCAD-1999-KasviKVPR #learning: Supporting a learning operative organization (JJJK, IK, MV, AP, LR), pp. 197–201.
HCI-CCAD-1999-KutayHW #human-computer #learning: Achieving learning outcomes in HCI for computing — an experiential testbed (CK, PH, GW), pp. 626–631.
HCI-CCAD-1999-MatsumotoNMK #human-computer #interactive #learning #process: Learning human-computer interactive process of learning with intelligence tutoring systems (TM, HN, EM, KK), pp. 1216–1220.
HCI-CCAD-1999-McNeese #analysis #learning #metric #performance #process #protocol: 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 #learning: Asynchronous distance learning for corporate education: experiences with Lotus LearningSpace (LN, DI), pp. 750–754.
HCI-CCAD-1999-OppermannS #adaptation #learning #mobile: Adaptive mobile museum guide for information and learning on demand (RO, MS), pp. 642–646.
HCI-CCAD-1999-ParamythisSSS #case study #lessons learnt #web: Non-visual web browsing: lessons learned from the AVANTI case study (AP, MS, AS, CS), pp. 812–817.
HCI-CCAD-1999-PatelKR #learning: Cognitive apprenticeship based learning environment in numeric domains (AP, K, DR), pp. 637–641.
HCI-CCAD-1999-Rebstock #case study #complexity #industrial #lessons learnt: Adding complexity to the electronic market model: lessons learned from an oil industry case study (MR), pp. 1147–1151.
HCI-CCAD-1999-Seufert #learning #named #network: PLATO — “electronic cookbook” for Internet-based learning networks (SS), pp. 707–711.
HCI-CCAD-1999-Siemer-Matravers #collaboration #learning: Collaborative learning — a cure for intelligent tutoring systems (JSM), pp. 652–656.
HCI-CCAD-1999-SinitsaM #interactive #learning #taxonomy: Interactive dictionary in a context of learning (KMS, AM), pp. 662–666.
HCI-CCAD-1999-YenWNL #case study #design #education #information management #learning: 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 #learning: Psychological Aspects of the Organization of the Distance Learning (SSA, OVM), pp. 124–128.
HCI-EI-1999-ChengYH #design #distributed #human-computer #interface #learning: Cognition and Learning in Distributed Design Environments: Experimental Studies and Human-Computer Interfaces (FC, YHY, HH), pp. 631–635.
HCI-EI-1999-HuangWC #learning #programming: A Flow-chart Based Learning System for Computer Programming (KHH, KW, SYC), pp. 1298–1302.
HCI-EI-1999-Nyssen #learning #towards: Training Simulators in Anesthesia: Towards a Hierarchy of Learning Situations (ASN), pp. 890–894.
HCI-EI-1999-PentlandRW #adaptation #gesture #interface #learning #word: Perceptual Intelligence: learning gestures and words for individualized, adaptive interfaces (AP, DR, CRW), pp. 286–290.
HCI-EI-1999-ScharKK #concept #learning #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 #learning #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 #learning #student: A New Concept for Designing Internet Learning Applications for Students of Electrical Engineering (DT, BS), pp. 590–594.
ICEIS-1999-Habrant #database #learning #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 #learning #parallel: A Learning Approach to Processor Allocation in Parallel Systems (AWA, TDW, LWD), pp. 531–537.
CIKM-1999-WidyantoroIY #adaptation #algorithm #learning: An Adaptive Algorithm for Learning Changes in User Interests (DHW, TRI, JY), pp. 405–412.
ICML-1999-AbeL #concept #learning #linear #probability #using: Associative Reinforcement Learning using Linear Probabilistic Concepts (NA, PML), pp. 3–11.
ICML-1999-AbeN #internet #learning: Learning to Optimally Schedule Internet Banner Advertisements (NA, AN), pp. 12–21.
ICML-1999-BontempiBB #learning #predict: Local Learning for Iterated Time-Series Prediction (GB, MB, HB), pp. 32–38.
ICML-1999-Bosch #abstraction #in memory #learning: Instance-Family Abstraction in Memory-Based Language Learning (AvdB), pp. 39–48.
ICML-1999-Boyan #difference #learning: Least-Squares Temporal Difference Learning (JAB), pp. 49–56.
ICML-1999-BrodieD #induction #learning #using: Learning to Ride a Bicycle using Iterated Phantom Induction (MB, GD), pp. 57–66.
ICML-1999-FreundM #algorithm #learning: The Alternating Decision Tree Learning Algorithm (YF, LM), pp. 124–133.
ICML-1999-GervasioIL #adaptation #evaluation #learning #scheduling: Learning User Evaluation Functions for Adaptive Scheduling Assistance (MTG, WI, PL), pp. 152–161.
ICML-1999-IijimaYYK #adaptation #behaviour #distributed #learning: Distributed Robotic Learning: Adaptive Behavior Acquisition for Distributed Autonomous Swimming Robot in Real World (DI, WY, HY, YK), pp. 191–199.
ICML-1999-Kadous #learning #multi: Learning Comprehensible Descriptions of Multivariate Time Series (MWK), pp. 454–463.
ICML-1999-KimuraK #linear #performance: Efficient Non-Linear Control by Combining Q-learning with Local Linear Controllers (HK, SK), pp. 210–219.
ICML-1999-LentL #learning #performance: Learning Hierarchical Performance Knowledge by Observation (MvL, JEL), pp. 229–238.
ICML-1999-MorikBJ #approach #case study #knowledge-based #learning #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 #learning #logic programming: Learning Discriminatory and Descriptive Rules by an Inductive Logic Programming System (MP, AS), pp. 288–297.
ICML-1999-PeshkinMK #learning #memory management #policy: Learning Policies with External Memory (LP, NM, LPK), pp. 307–314.
ICML-1999-PriceB #learning #multi: Implicit Imitation in Multiagent Reinforcement Learning (BP, CB), pp. 325–334.
ICML-1999-RennieM #learning #using #web: Using Reinforcement Learning to Spider the Web Efficiently (JR, AM), pp. 335–343.
ICML-1999-SakakibaraK #context-free grammar #learning #using: GA-based Learning of Context-Free Grammars using Tabular Representations (YS, MK), pp. 354–360.
ICML-1999-ThompsonCM #information management #learning #natural language #parsing: Active Learning for Natural Language Parsing and Information Extraction (CAT, MEC, RJM), pp. 406–414.
ICML-1999-ThrunLF #learning #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 #learning: Model Selection in Unsupervised Learning with Applications To Document Clustering (SV, BD), pp. 433–443.
ICML-1999-VovkGS #algorithm: Machine-Learning Applications of Algorithmic Randomness (VV, AG, CS), pp. 444–453.
ICML-1999-Zhang #approach #learning: An Region-Based Learning Approach to Discovering Temporal Structures in Data (WZ), pp. 484–492.
ICML-1999-ZhengWT #lazy evaluation #learning #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 #learning #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 #learning #online #scalability: The Application of AdaBoost for Distributed, Scalable and On-Line Learning (WF, SJS, JZ), pp. 362–366.
KDD-1999-SyedLS99a #concept #incremental #learning: Handling Concept Drifts in Incremental Learning with Support Vector Machines (NAS, HL, KKS), pp. 317–321.
MLDM-1999-AizenbergAK #algorithm #image #learning #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 #learning: Symbolic Learning Techniques in Paper Document Processing (OA, FE, FAL, DM), pp. 159–173.
MLDM-1999-GiacintoR #automation #classification #design #learning #multi: Automatic Design of Multiple Classifier Systems by Unsupervised Learning (GG, FR), pp. 131–143.
MLDM-1999-Jahn #image #learning #preprocessor: Unsupervised Learning of Local Mean Gray Values for Image Pre-processing (HJ), pp. 64–74.
MLDM-1999-KingL #clustering #information retrieval #learning: Non-hierarchical Clustering with Rival Penalized Competitive Learning for Information Retrieval (IK, TKL), pp. 116–130.
MLDM-1999-Petrou #learning #pattern matching #pattern recognition #recognition: Learning in Pattern Recognition (MP), pp. 1–12.
SIGIR-1999-LamY #adaptation #learning #online: An Intelligent Adaptive Filtering Agent Based on an On-Line Learning Model (poster abstract) (WL, KLY), pp. 287–288.
OOPSLA-1999-KerstenM #aspect-oriented #case study #learning #named #programming #using: Atlas: A Case Study in Building a Web-Based Learning Environment using Aspect-oriented Programming (MK, GCM), pp. 340–352.
TOOLS-EUROPE-1999-Ishaq #industrial #lessons learnt #object-oriented: Lessons Learned Introducing an Object-Oriented Databse in the Telecom Industry (AI), pp. 214–223.
TOOLS-USA-1999-Ramakrishnan #community #distributed #education #learning #testing #visualisation: Visualizing O-O Testing in Virtual Communities — Distributed Teaching and Learning (SR), p. 300–?.
TOOLS-USA-1999-YannakopoulosFS #framework #lessons learnt: Object Lessons Learned from an Intelligent Agents Framework for Telephony-Based Applications (DY, MF, MS), pp. 222–236.
SAC-1999-VenkataramanaR #automaton #framework #learning: A Learning Automata Based Framework for Task Assignment in Heterogeneous Computing Systems (RDV, NR), pp. 541–547.
ESEC-FSE-1999-LevesonHR #design #lessons learnt #process #specification: Designing Specification Languages for Process Control Systems: Lessons Learned and Steps to the Future (NGL, MPEH, JDR), pp. 127–145.
ICSE-1999-WoodmanGMH #programming #smalltalk: OU LearningWorks: A Customized Programming Environment for Smalltalk Modules (MW, RG, MM, SH), pp. 638–641.
CSL-1999-Balcazar #consistency #learning #query: The Consistency Dimension, Compactness, and Query Learning (JLB), pp. 2–13.
ICLP-1999-SatoF #learning #logic programming: Reactive Logic Programming by Reinforcement Learning (TS, SF), p. 617.
ASE-1998-MaoSL #case study #machine learning #reuse #using #verification: Reusability Hypothesis Verification using Machine Learning Techniques: A Case Study (YM, HAS, HL), pp. 84–93.
DAC-1998-El-MalehKR #learning #performance: A Fast Sequential Learning Technique for Real Circuits with Application to Enhancing ATPG Performance (AHEM, MK, JR), pp. 625–631.
CSEET-1998-Hislop #education #learning #network: Teaching Via Asynchronous Learning Networks (GWH), pp. 16–35.
ITiCSE-1998-AbunawassMN #design #distance #education #learning: 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 #learning #modelling #web: Learning “from” or “through” the Web: models of Web based education (DC), pp. 51–54.
ITiCSE-1998-Daly #approach #learning: A proposed structure for a computer based learning environment — a pragmatic approach (poster) (CD), p. 276.
ITiCSE-1998-DavidovicT #learning: Open learning environment and instruction system (OLEIS) (AD, ET), pp. 69–73.
ITiCSE-1998-Ellis #development #internet #learning #multi #problem: Group 1 (working group): development and use of multimedia and Internet resources for a problem based learning environment (AE), p. 269.
ITiCSE-1998-Goldberg #artificial reality: Building a system in virtual reality with LearningWorks (AG), pp. 5–9.
ITiCSE-1998-GrayBS #java #learning: A constructivist learning environment implemented in Java (JG, TB, CS), pp. 94–97.
ITiCSE-1998-LeungN #case study #learning #library #web: Does World Wide Web provide better resources than library for learning — a case study (poster) (RMWL, EMWN), p. 290.
ITiCSE-1998-LewisM #comparison #compilation #learning: A comparison between novice and experienced compiler users in a learning environment (SL, GM), pp. 157–161.
ITiCSE-1998-MooreS #c #learning #multi #programming: A multimedia C programming course that supports different learning situations (poster) (SM, MS), p. 295.
ITiCSE-1998-Richardson #information management #learning #optimisation: First year information systems papers — optimising learning — minimising administration (poster) (ASR), p. 301.
ITiCSE-1998-Thomas98a #student: Observing students electronically as they learn (poster) (PGT), p. 307.
ITiCSE-1998-TiwariH #collaboration #learning #student #using: Learning groupware through using groupware-computer supported collaborative learning with face to face students (AT, CH), pp. 236–238.
ITiCSE-1998-Wans #interactive #learning #multi: An interactive multimedia learning system for the postlingually deaf (poster) (CW), p. 309.
ITiCSE-1998-WhitehurstPI #distance #learning #student: Utilising the student model in distance learning (RAW, CLP, JSI), pp. 254–256.
ITiCSE-1998-Zagursky #flexibility #learning: Information technology for flexible and learning and training (poster) (VZ), p. 312.
FASE-1998-Jones #what: Some Mistakes I Have and What I Have Learned from Them (CBJ), pp. 7–20.
STOC-1998-Bshouty #algorithm #composition #learning #theorem: A New Composition Theorem for Learning Algorithms (NHB), pp. 583–589.
STOC-1998-Damaschke #adaptation #learning: Adaptive versus Nonadaptive Attribute-Efficient Learning (PD), pp. 590–596.
CHI-1998-ChinR #collaboration #design #evolution #learning #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 #learning: The Design of Guided Learner-Adaptable Scaffolding in Interactive Learning Environments (SLJ, JK, ES), pp. 187–194.
CHI-1998-RoseDMBN #community #design #implementation #learning: Building an Electronic Learning Community: From Design to Implementation (AR, WD, GM, JBJ, VN), pp. 203–210.
CHI-1998-Strommen #interface #learning: When the Interface is a Talking Dinosaur: Learning Across Media with ActiMates Barney (ES), pp. 288–295.
CHI-1998-SumnerT #case study #design #experience #learning: New Media, New Practices: Experiences in Open Learning Course Design (TS, JT), pp. 432–439.
CIKM-1998-DumaisPHS #algorithm #categorisation #induction #learning: Inductive Learning Algorithms and Representations for Text Categorization (STD, JCP, DH, MS), pp. 148–155.
CIKM-1998-HongL #fuzzy #learning: Learning Fuzzy Knowledge from Training Examples (TPH, CYL), pp. 161–166.
CIKM-1998-YuL #adaptation #algorithm #learning #online: A New On-Line Learning Algorithm for Adaptive Text Filtering (KLY, WL), pp. 156–160.
ICML-1998-AbeM #learning #query #using: Query Learning Strategies Using Boosting and Bagging (NA, HM), pp. 1–9.
ICML-1998-AlerBI #approach #learning #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 #learning: An Experimental Evaluation of Coevolutive Concept Learning (CA, AG, GLB, LS), pp. 19–27.
ICML-1998-BaxterTW #named: KnightCap: A Chess Programm That Learns by Combining TD(λ) with Game-Tree Search (JB, AT, LW), pp. 28–36.
ICML-1998-BillsusP #collaboration #learning: Learning Collaborative Information Filters (DB, MJP), pp. 46–54.
ICML-1998-BonetG #learning #sorting: Learning Sorting and Decision Trees with POMDPs (BB, HG), pp. 73–81.
ICML-1998-Dietterich #learning: The MAXQ Method for Hierarchical Reinforcement Learning (TGD), pp. 118–126.
ICML-1998-DzeroskiRB #learning #relational: Relational Reinforcement Learning (SD, LDR, HB), pp. 136–143.
ICML-1998-Freitag #information management #learning #multi: Multistrategy Learning for Information Extraction (DF), pp. 161–169.
ICML-1998-FriessCC #algorithm #kernel #learning #performance: The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines (TTF, NC, CC), pp. 188–196.
ICML-1998-GaborKS #learning #multi: Multi-criteria Reinforcement Learning (ZG, ZK, CS), pp. 197–205.
ICML-1998-GarciaN #algorithm #analysis #learning: A Learning Rate Analysis of Reinforcement Learning Algorithms in Finite-Horizon (FG, SMN), pp. 215–223.
ICML-1998-Heskes #approach #learning #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 #learning #multi: Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm (JH, MPW), pp. 242–250.
ICML-1998-JuilleP #case study #learning: Coevolutionary Learning: A Case Study (HJ, JBP), pp. 251–259.
ICML-1998-KearnsS #learning: Near-Optimal Reinforcement Learning in Polynominal Time (MJK, SPS), pp. 260–268.
ICML-1998-KimuraK #algorithm #analysis #learning #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 #learning #probability #process #using: Using Learning for Approximation in Stochastic Processes (DK, RF), pp. 287–295.
ICML-1998-LiquiereS #graph #machine learning: Structural Machine Learning with Galois Lattice and Graphs (ML, JS), pp. 305–313.
ICML-1998-LittmanJK #corpus #independence #learning #representation: Learning a Language-Independent Representation for Terms from a Partially Aligned Corpus (MLL, FJ, GAK), pp. 314–322.
ICML-1998-MargaritisT #3d #image #learning #sequence: Learning to Locate an Object in 3D Space from a Sequence of Camera Images (DM, ST), pp. 332–340.
ICML-1998-MaronR #classification #learning #multi: Multiple-Instance Learning for Natural Scene Classification (OM, ALR), pp. 341–349.
ICML-1998-McCallumN #classification #learning: Employing EM and Pool-Based Active Learning for Text Classification (AM, KN), pp. 350–358.
ICML-1998-MooreSBL #learning #named #optimisation: Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions (AWM, JGS, JAB, MSL), pp. 386–394.
ICML-1998-Ng #feature model #learning #on the: On Feature Selection: Learning with Exponentially Many Irrelevant Features as Training Examples (AYN), pp. 404–412.
ICML-1998-PendrithM #analysis #learning #markov: An Analysis of Direct Reinforcement Learning in Non-Markovian Domains (MDP, MM), pp. 421–429.
ICML-1998-RandlovA #learning #using: Learning to Drive a Bicycle Using Reinforcement Learning and Shaping (JR, PA), pp. 463–471.
ICML-1998-ReddyT #first-order #learning #source code: Learning First-Order Acyclic Horn Programs from Entailment (CR, PT), pp. 472–480.
ICML-1998-RyanP #architecture #composition #learning #named: RL-TOPS: An Architecture for Modularity and Re-Use in Reinforcement Learning (MRKR, MDP), pp. 481–487.
ICML-1998-SamuelCV #learning: An Investigation of Transformation-Based Learning in Discourse (KS, SC, KVS), pp. 497–505.
ICML-1998-SaundersGV #algorithm #learning: Ridge Regression Learning Algorithm in Dual Variables (CS, AG, VV), pp. 515–521.
ICML-1998-StuartB #learning: Learning the Grammar of Dance (JMS, EB), pp. 547–555.
ICML-1998-SuttonPS #learning: Intra-Option Learning about Temporally Abstract Actions (RSS, DP, SPS), pp. 556–564.
ICPR-1998-BukerK #hybrid #learning: Learning in an active hybrid vision system (UB, BK), pp. 178–181.
ICPR-1998-ConnellJ #learning #online #prototype: Learning prototypes for online handwritten digits (SDC, AKJ), pp. 182–184.
ICPR-1998-DayP #learning #modelling: A projection filter for use with parameterised learning models (MJSD, JSP), pp. 867–869.
ICPR-1998-DutaJ #concept #image #learning: Learning the human face concept in black and white images (ND, AKJ), pp. 1365–1367.
ICPR-1998-Gimelfarb #interactive #modelling #question #segmentation #what: Supervised segmentation by pairwise interactions: do Gibbs models learn what we expect? (GLG), pp. 817–819.
ICPR-1998-HickinbothamHA #learning: Learning feature characteristics (SJH, ERH, JA), pp. 1160–1164.
ICPR-1998-KeglKN #classification #learning #network #parametricity: Radial basis function networks in nonparametric classification and function learning (BK, AK, HN), pp. 565–570.
ICPR-1998-KnutssonBL #learning #multi: Learning multidimensional signal processing (HK, MB, TL), pp. 1416–1420.
ICPR-1998-LamOX #classification #learning: 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 #learning: Discriminative learning for minimum error and minimum reject classification (HM), pp. 136–140.
ICPR-1998-MorookaZH #approach #modelling: Next best viewpoint (NBV) planning for active object modeling based on a learning-by-showing approach (KM, HZ, TH), pp. 677–681.
ICPR-1998-Nagy #estimation #learning #persistent: Persistent issues in learning and estimation (GN), pp. 561–564.
ICPR-1998-OrnesDS #network #visual notation: A visual neural network that learns perceptual relationships (CO, AD, JS), pp. 873–875.
ICPR-1998-PengB #learning #recognition: Local reinforcement learning for object recognition (JP, BB), pp. 272–274.
ICPR-1998-SatoY #classification #learning #using: A formulation of learning vector quantization using a new misclassification measure (AS, KY), pp. 322–325.
ICPR-1998-WengH #learning #recognition #sequence: Sensorimotor action sequence learning with application to face recognition under discourse (J(W, WSH), pp. 252–254.
KDD-1998-AndersonM #learning #performance: ADtrees for Fast Counting and for Fast Learning of Association Rules (BSA, AWM), pp. 134–138.
KDD-1998-ChanS #case study #detection #learning #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 #learning #mining: Coactive Learning for Distributed Data Mining (DLG, LAB), pp. 209–213.
KDD-1998-HandleyLR #learning #predict: Learning to Predict the Duration of an Automobile Trip (SH, PL, FAR), pp. 219–223.
KDD-1998-LaneB #concept #identification #learning #online #security: Approaches to Online Learning and Concept Drift for User Identification in Computer Security (TL, CEB), pp. 259–263.
KDD-1998-MoodyS #learning: Reinforcement Learning for Trading Systems and Portfolios (JEM, MS), pp. 279–283.
KDD-1998-WeissH #learning #predict #sequence: Learning to Predict Rare Events in Event Sequences (GMW, HH), pp. 359–363.
SIGIR-1998-Callan #learning: Learning While Filtering Focuments (JPC), pp. 224–231.
OOPSLA-1998-OlkenJMPA #distributed #lessons learnt #monitoring: Object Lessons Learned from a Distributed System for Remote Building Monitoring and Operation (FO, HAJ, CM, MAP, MFA), pp. 284–295.
ICRE-1998-SongHMS #lessons learnt #requirements: Lessons Learned from Building a Web-Based Requirements Tracing System (XS, WMH, GM, WS), pp. 41–50.
SAC-1998-BillardL #automaton #behaviour #distributed #learning #simulation: Simulation of period-doubling behavior in distributed learning automata (EB, SL), pp. 690–695.
SAC-1998-ChungC #interactive #learning #multi: A multimedia system for interactive learning of organ literature (SC, SC), pp. 117–121.
FSE-1998-MasudaSU #design pattern #learning: Applying Design Patterns to Decision Tree Learning System (GM, NS, KU), pp. 111–120.
ICSE-1998-AlmeidaLM #modelling: An Investigation on the Use of Machine Learned Models for Estimating Correction Costs (MAdA, HL, WLM), pp. 473–476.
ICSE-1998-AprilAM #assurance #lessons learnt #process: Process Assurance Audits: Lessons Learned (AA, AA, EM), pp. 482–485.
ICSE-1998-BoehmE #lessons learnt #requirements: Software Requirements Negotiation: Some Lessons Learned (BWB, AE), pp. 503–506.
ICSE-1998-HanakawaMM #development #learning #simulation: A Learning Curve Based Simulation Model for Software Development (NH, SM, KiM), pp. 350–359.
ISSTA-1998-Hamlet #question #testing #what: What Can We Learn by Testing a Program? (RGH), pp. 50–52.
ICDAR-1997-AminKS #machine learning #recognition: Hand Printed Chinese Character Recognition via Machine Learning (AA, SGK, CS), pp. 190–194.
ICDAR-1997-EspositoMSAG #library #machine learning #semantics: Information Capture and Semantic Indexing of Digital Libraries through Machine Learning Techniques (FE, DM, GS, CDA, GdG), pp. 722–727.
ICDAR-1997-JunkerH #classification #documentation #learning: Evaluating OCR and Non-OCR Text Representations for Learning Document Classifiers (MJ, RH), pp. 1060–1066.
ICDAR-1997-WaizumiKSN #classification #learning #using: High speed rough classification for handwritten characters using hierarchical learning vector quantization (YW, NK, KS, YN), pp. 23–27.
ICDAR-1997-YamauchiIT #learning #multi #recognition: Shape based Learning for a Multi-Template Method, and its Application to Handprinted Numeral Recognition (TY, YI, JT), pp. 495–498.
PODS-1997-GunopulosKMT #data mining #machine learning #mining: Data mining, Hypergraph Transversals, and Machine Learning (DG, RK, HM, HT), pp. 209–216.
ITiCSE-1997-BerghelNSTT #design #education: You learned all you need to design educational software design in kindergarten (panel) (HB, CAN, ES, HGT, JT), p. 139.
ITiCSE-1997-Boulet #distance #learning: Distance learning of the management of software projects (MMB), pp. 136–138.
ITiCSE-1997-Carswell #communication #distance #education #internet #learning #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 #learning: The use of the WWW to support distance learning through NTU (DDDI, JH), pp. 8–10.
ITiCSE-1997-Janser #algorithm #interactive #learning #visualisation: An interactive learning system visualizing computer graphics algorithms (AWJ), pp. 21–23.
ITiCSE-1997-Lawhead97a #distance #learning #web #what: The Web and distance learning (panel): what is appropriate and what is not (PBL), p. 144.
ITiCSE-1997-Makkonen #collaboration #hypermedia #learning #question: Does collaborative hypertext support better engagement in learning of the basics in informatics? (PM), pp. 130–132.
ITiCSE-1997-Moser #game studies #learning #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 #learning #multi #using: Using multimedia communication technologies in distance learning (TR, DF, EP, SA), pp. 6–7.
ITiCSE-WGR-1997-Barikzai #collaboration #learning: Integrating courseware into collaborative learning enviroments (demonstration) (SB), p. 145.
ITiCSE-WGR-1997-CarlssonKO #education #flexibility #learning: Networked PBL teaching the teacher on flexible learning (poster) (RC, GK, BO), p. 147.
ITiCSE-WGR-1997-GavrilovaSU #distance #internet #learning: Teletutor workbench for Internet distance learning environment (poster) (TG, TS, SU), p. 149.
ITiCSE-WGR-1997-Goldberg97a #learning: WebCT, a tool for the creation of sophisticated web-based learning environments (demonstration) (MWG), p. 149.
ITiCSE-WGR-1997-LawheadABCCDDFS #distance #learning #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 #learning: The emergence of sophisticated distributed teaching and learning environments (HM), pp. 112–113.
STOC-1997-AuerLS #approximate #learning #pseudo #set: Approximating Hyper-Rectangles: Learning and Pseudo-Random Sets (PA, PML, AS), pp. 314–323.
STOC-1997-Ben-DavidBK #algorithm #composition #concept #geometry #learning #theorem: A Composition Theorem for Learning Algorithms with Applications to Geometric Concept Classes (SBD, NHB, EK), pp. 324–333.
DLT-1997-DavidES #learning #string: Learning String Adjunct and Tree Adjunct Languages (NGD, JDE, KGS), pp. 411–427.
CHI-1997-RappinGRL #interface #learning #usability: Balancing Usability and Learning in an Interface (NR, MG, MR, PL), pp. 479–486.
CHI-1997-ScaifeRAD #design #interactive #learning: Designing For or Designing With? Informant Design For Interactive Learning Environments (MS, YR, FA, MD), pp. 343–350.
HCI-CC-1997-Brodner #process: The Process of Organisational Learning-Experiences from a Joint Project (PB), pp. 253–256.
HCI-CC-1997-MajchrzakB #design #lessons learnt #using: Lessons Learned from Using a Computer-Based Tool to Support Sociotechnical Systems Design (AM, BB), pp. 221–224.
HCI-CC-1997-Nishimura #empirical: Building Cyber-Community-Learning from CyberCampus[TM] Experiment (TN), pp. 35–39.
HCI-SEC-1997-DasaiKY #collaboration #distance #learning: A Collaborative Distance Learning System and its Experimental Results (TD, HK, KY), pp. 165–168.
HCI-SEC-1997-EnyedyVG #design #interactive #learning: Designing Interactions for Guided Inquiry Learning Environments (NE, PV, BG), pp. 157–160.
HCI-SEC-1997-HollnagelH #communication #lessons learnt #problem #question: Twenty-Five Years of Operator-Process Communication: Lessons Learned and Problems Solved? (EH, JØH), pp. 221–224.
HCI-SEC-1997-Keating #learning: Computer Based Learning: GroupSystems[R] in the Wireless Classroom (CCK), pp. 119–122.
HCI-SEC-1997-Moustakis #human-computer #machine learning #people #question: Do People in HCI Use Machine Learning? (VM), pp. 95–98.
HCI-SEC-1997-MurphyKG #interface #learning: Enhancing the Interface to Provide Intelligent Computer Aided Language Learning (MM, AK, AG), pp. 149–152.
HCI-SEC-1997-Neal #distance #learning #multi #using: Using Multiple Technologies for Distance Learning (LN), pp. 111–114.
HCI-SEC-1997-Nguifo #interactive #machine learning: An Interactive Environment for Dynamic Control of Machine Learning Systems (EMN), pp. 31–34.
HCI-SEC-1997-PatelK #design #interactive #interface #learning: Granular Interface Design: Decomposing Learning Tasks and Enhancing Tutoring Interaction (AP, K), pp. 161–164.
HCI-SEC-1997-Pohl #machine learning #modelling #named: LaboUr — Machine Learning for User Modeling (WP), pp. 27–30.
HCI-SEC-1997-WilliamsFSTE #education #learning #named #student: PEBBLES: Providing Education by Bringing Learning Environments to Students (LAW, DIF, GS, JT, RE), pp. 115–118.
AdaEurope-1997-BakerO #ada #c #implementation #interface #lessons learnt: Ada Bindings for C Interfaces: Lessons Learned from the Florist Implementation (TPB, DIO), pp. 13–22.
CIKM-1997-ChengBL #approach #learning #network: Learning Belief Networks from Data: An Information Theory Based Approach (JC, DAB, WL), pp. 325–331.
ICML-1997-AtkesonS #learning: Robot Learning From Demonstration (CGA, SS), pp. 12–20.
ICML-1997-Auer #approach #empirical #evaluation #learning #multi #on the: On Learning From Multi-Instance Examples: Empirical Evaluation of a Theoretical Approach (PA), pp. 21–29.
ICML-1997-BottaGP #first-order #learning #logic #named: FONN: Combining First Order Logic with Connectionist Learning (MB, AG, RP), pp. 46–56.
ICML-1997-DattaK #learning #prototype: Learning Symbolic Prototypes (PD, DFK), pp. 75–82.
ICML-1997-Decatur #classification #induction #learning: PAC Learning with Constant-Partition Classification Noise and Applications to Decision Tree Induction (SED), pp. 83–91.
ICML-1997-Fiechter #bound #learning #online: Expected Mistake Bound Model for On-Line Reinforcement Learning (CNF), pp. 116–124.
ICML-1997-Friedman #learning #network: Learning Belief Networks in the Presence of Missing Values and Hidden Variables (NF), pp. 125–133.
ICML-1997-KimuraMK #approximate #learning: Reinforcement Learning in POMDPs with Function Approximation (HK, KM, SK), pp. 152–160.
ICML-1997-PrecupS #learning: Exponentiated Gradient Methods for Reinforcement Learning (DP, RSS), pp. 272–277.
ICML-1997-ReddyT #learning #using: Learning Goal-Decomposition Rules using Exercises (CR, PT), pp. 278–286.
ICML-1997-RistadY #distance #edit distance #learning #string: Learning String Edit Distance (ESR, PNY), pp. 287–295.
ICML-1997-SakrLCHG #data access #learning #memory management #modelling #multi #predict: Predicting Multiprocessor Memory Access Patterns with Learning Models (MFS, SPL, DMC, BGH, CLG), pp. 305–312.
ICML-1997-Schapire #learning #multi #problem #using: Using output codes to boost multiclass learning problems (RES), pp. 313–321.
ICML-1997-SuematsuHL #approach #learning #markov: A Bayesian Approach to Model Learning in Non-Markovian Environments (NS, AH, SL), pp. 349–357.
ICML-1997-TadepalliD #learning: Hierarchical Explanation-Based Reinforcement Learning (PT, TGD), pp. 358–366.
ICML-1997-ZupanBBD #composition #machine learning: Machine Learning by Function Decomposition (BZ, MB, IB, JD), pp. 421–429.
KDD-1997-BergstenSS #analysis #data mining #machine learning #mining: Applying Data Mining and Machine Learning Techniques to Submarine Intelligence Analysis (UB, JS, PS), pp. 127–130.
KDD-1997-Hekanaho #concept #learning: GA-Based Rule Enhancement in Concept Learning (JH), pp. 183–186.
KDD-1997-KramerPH #machine learning #mining: Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail (SK, BP, CH), pp. 223–226.
KDD-1997-PazzaniMS #learning: Beyond Concise and Colorful: Learning Intelligible Rules (MJP, SM, WRS), pp. 235–238.
KDD-1997-RubinsteinH #learning: Discriminative vs Informative Learning (YDR, TH), pp. 49–53.
KDD-1997-Soderland #learning #web: Learning to Extract Text-Based Information from the World Wide Web (SS), pp. 251–254.
KDD-1997-StolfoPTLFC #database #distributed #java #named: JAM: Java Agents for Meta-Learning over Distributed Databases (SJS, ALP, ST, WL, DWF, PKC), pp. 74–81.
KDD-1997-ZighedRF #learning #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 #learning #usability: Feature Selection, Perceptron Learning, and a Usability Case Study for Text Categorization (HTN, WBG, KLL), pp. 67–73.
SIGIR-1997-SinghalMB #learning #query: Learning Routing Queries in a Query Zone (AS, MM, CB), pp. 25–32.
PLILP-1997-WhittleBL #editing #ml #standard: An Editor for Helping Novices to Learn Standard ML (JW, AB, HL), pp. 389–405.
RE-1997-Viravan #lessons learnt: Lessons Learned from Applying the Spiral Model in the Software (CV), p. 40.
SAC-1997-Goldberg #learning: Virtual teams virtual projects = real learning (abstract only) (AG), p. 1.
SAC-1997-SolowayN #education #future of #learning #lessons learnt: The future of computers in education: learning 10 lessons from the past (abstract only) (ES, CAN), p. 2.
ICSE-1997-Curtis #lessons learnt #process #tutorial: Software Process Improvement: Methods and Lessons Learned (Tutorial) (BC), pp. 624–625.
ICSE-1997-Hefner #lessons learnt #maturity #security: Lessons Learned with the Systems Security Engineering Capability Maturity Model (RH), pp. 566–567.
CADE-1997-KolbeB #learning #named #proving: Plagiator — A Learning Prover (TK, JB), pp. 256–259.
CSEE-1996-Boehm #requirements #student: Helping Students Learn Requirements Engineering (BWB), pp. 96–99.
ITiCSE-1996-BrodlieWW #learning #novel #visualisation: Scientific visualization — some novel approaches to learning (KB, JDW, HW), pp. 28–32.
ITiCSE-1996-CaoLLPZ #education #information management #learning: Integrating CSCW in a cooperative learning environment to teach information systems (NVC, AL, ML, OP, CZ), pp. 125–129.
ITiCSE-1996-FinkelW #learning: Computer supported peer learning in an introductory computer science course (DF, CEW), pp. 55–56.
ITiCSE-1996-JohansenKB #interactive #learning: Interactive learning with gateway labs (MJ, JK, DB), p. 232.
ITiCSE-1996-LeesC #learning #natural language #operating system: Applying natural language technology to the learning of operating systems functions (BL, JC), pp. 11–13.
ITiCSE-1996-McConnell #learning: Active learning and its use in computer science (JJM), pp. 52–54.
ITiCSE-1996-Prey #education #learning: Cooperative learning and closed laboratories in an undergraduate computer science curriculum (JCP), pp. 23–24.
ITiCSE-1996-Tjaden #how #learning #student #visual notation: How visual software influences learning in college students (BJT), p. 229.
STOC-1996-BergadanoCV #learning #query: Learning Sat-k-DNF Formulas from Membership Queries (FB, DC, SV), pp. 126–130.
STOC-1996-BshoutyGMST #concept #geometry #learning: Noise-Tolerant Distribution-Free Learning of General Geometric Concepts (NHB, SAG, HDM, SS, HT), pp. 151–160.
STOC-1996-Cesa-BianchiDFS #bound #learning: Noise-Tolerant Learning Near the Information-Theoretic Bound (NCB, ED, PF, HUS), pp. 141–150.
STOC-1996-KearnsM #algorithm #learning #on the #top-down: On the Boosting Ability of Top-Down Decision Tree Learning Algorithms (MJK, YM), pp. 459–468.
CHI-1996-SolowayJKQRSSSES #case study #design #learning: 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 #learning #network #online #theory and practice #tutorial: Asynchronous Learning Networks: The Theory and Practice of Collaborative Learning Online (Tutorial) (SRH, MT), p. 5.
CSCW-1996-OlsonT #lessons learnt: Groupware in the Wild: Lessons Learned from a Year of Virtual Collocation (JSO, SDT), pp. 419–427.
TRI-Ada-1996-NebeshF #ada #component #html #learning #using: Learning to Use Ada 95 Components Using HTML Linking (BN, MBF), pp. 207–210.
TRI-Ada-1996-ParrishCLM #ada #assessment #learning #process #re-engineering: Active Learning and Process Assessment: Two Experiments in an Ada-Based Software Engineering Course (ASP, DC, CL, DM), pp. 157–161.
AKDDM-1996-HsuK #induction #learning #optimisation #query #semantics #using: Using Inductive Learning To Generate Rules for Semantic Query Optimization (CNH, CAK), pp. 425–445.
CIKM-1996-Huffman #learning: Learning to Extract Information From Text Based on User-Provided Examples (SBH), pp. 154–163.
ICML-1996-AbeL #learning #modelling #using #word: Learning Word Association Norms Using Tree Cut Pair Models (NA, HL), pp. 3–11.
ICML-1996-BanderaVBHB #visual notation: Residual Q-Learning Applied to Visual Attention (CB, FJV, JMB, MEH, LCBI), pp. 20–27.
ICML-1996-BlanzieriK #learning #network #online: Learning Radial Basis Function Networks On-line (EB, PK), pp. 37–45.
ICML-1996-BoyanM #evaluation #learning #scalability: Learning Evaluation Functions for Large Acyclic Domains (JAB, AWM), pp. 63–70.
ICML-1996-Caruana #algorithm #learning #multi: Algorithms and Applications for Multitask Learning (RC), pp. 87–95.
ICML-1996-DietterichKM #framework #learning: Applying the Waek Learning Framework to Understand and Improve C4.5 (TGD, MJK, YM), pp. 96–104.
ICML-1996-EmdeW #learning #relational: Relational Instance-Based Learning (WE, DW), pp. 122–130.
ICML-1996-EzawaSN #learning #network #risk management: Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management (KJE, MS, SWN), pp. 139–147.
ICML-1996-FriedmanG #learning #network: Discretizing Continuous Attributes While Learning Bayesian Networks (NF, MG), pp. 157–165.
ICML-1996-GeibelW #concept #learning #relational: Learning Relational Concepts with Decision Trees (PG, FW), pp. 166–174.
ICML-1996-GoetzKM #adaptation #learning #online: On-Line Adaptation of a Signal Predistorter through Dual Reinforcement Learning (PG, SK, RM), pp. 175–181.
ICML-1996-GoldmanS #algorithm #empirical: A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geormetric Patterns (SAG, SDS), pp. 191–199.
ICML-1996-GordonS #learning #parametricity #statistics: Nonparametric Statistical Methods for Experimental Evaluations of Speedup Learning (GJG, AMS), pp. 200–206.
ICML-1996-GreinerGR #classification #learning: Learning Active Classifiers (RG, AJG, DR), pp. 207–215.
ICML-1996-Hekanaho #concept #learning: Background Knowledge in GA-based Concept Learning (JH), pp. 234–242.
ICML-1996-JappyNG #horn clause #learning #robust #source code: Negative Robust Learning Results from Horn Clause Programs (PJ, RN, OG), pp. 258–265.
ICML-1996-KoenigS #distance #learning #navigation: Passive Distance Learning for Robot Navigation (SK, RGS), pp. 266–274.
ICML-1996-LittmanS #convergence: A Generalized Reinforcement-Learning Model: Convergence and Applications (MLL, CS), pp. 310–318.
ICML-1996-Mahadevan #learning: Sensitive Discount Optimality: Unifying Discounted and Average Reward Reinforcement Learning (SM), pp. 328–336.
ICML-1996-Mannila #data mining #machine learning #mining: Data Mining and Machine Learning (Abstract) (HM), p. 555.
ICML-1996-Moore #learning: Reinforcement Learning in Factories: The Auton Project (Abstract) (AWM0), p. 556.
ICML-1996-Munos #algorithm #convergence #learning: A Convergent Reinforcement Learning Algorithm in the Continuous Case: The Finite-Element Reinforcement Learning (RM), pp. 337–345.
ICML-1996-OliverBW #learning #using: Unsupervised Learning Using MML (JJO, RAB, CSW), pp. 364–372.
ICML-1996-PendrithR #difference #learning: Actual Return Reinforcement Learning versus Temporal Differences: Some Theoretical and Experimental Results (MDP, MRKR), pp. 373–381.
ICML-1996-Perez #learning #representation: Representing and Learning Quality-Improving Search Control Knowledge (MAP), pp. 382–390.
ICML-1996-PerezR #concept #learning: Learning Despite Concept Variation by Finding Structure in Attribute-based Data (EP, LAR), pp. 391–399.
ICML-1996-ReddyTR #composition #empirical #learning: Theory-guided Empirical Speedup Learning of Goal Decomposition Rules (CR, PT, SR), pp. 409–417.
ICML-1996-Saerens #fault #learning: Non Mean Square Error Criteria for the Training of Learning Machines (MS), pp. 427–434.
ICML-1996-SinghP #classification #learning #network #performance: Efficient Learning of Selective Bayesian Network Classifiers (MS, GMP), pp. 453–461.
ICML-1996-Suzuki #algorithm #learning #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 #learning #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 #learning #multi: Discovering Structure in Multiple Learning Tasks: The TC Algorithm (ST, JO), pp. 489–497.
ICML-1996-TirriKM #learning: Prababilistic Instance-Based Learning (HT, PK, PM), pp. 507–515.
ICML-1996-Widmer #incremental #recognition: Recognition and Exploitation of Contextual CLues via Incremental Meta-Learning (GW), pp. 525–533.
ICML-1996-ZuckerG #learning #performance #representation: Representation Changes for Efficient Learning in Structural Domains (JDZ, JGG), pp. 543–551.
ICPR-1996-AizenbergAK #image #learning #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 #learning #regular expression: Learning of context-sensitive languages described by augmented regular expressions (RA, AS), pp. 745–749.
ICPR-1996-BebisGLS #learning #modelling #recognition: Learning affine transformations of the plane for model-based object recognition (GB, MG, NdVL, MS), pp. 60–64.
ICPR-1996-Bobrowski #classification #learning #set: Piecewise-linear classifiers, formal neurons and separability of the learning sets (LB), pp. 224–228.
ICPR-1996-BurgeBM #component #learning #polymorphism #recognition: Recognition and learning with polymorphic structural components (MB, WB, WM), pp. 19–23.
ICPR-1996-DemsarS #image #machine learning #using: Using machine learning for content-based image retrieving (JD, FS), pp. 138–142.
ICPR-1996-FischlS #adaptation #approximate #image: Learned adaptive nonlinear filtering for anisotropic diffusion approximation in image processing (BF, ELS), pp. 276–280.
ICPR-1996-FrankH #approach #learning: Pretopological approach for supervised learning (FL, HE), pp. 256–260.
ICPR-1996-HoogsB #learning #modelling: Model-based learning of segmentations (AH, RB), pp. 494–499.
ICPR-1996-KositskyU #learning: Learning class regions by the union of ellipsoids (MK, SU), pp. 750–757.
ICPR-1996-LuettinTB96a #learning: Learning to recognise talking faces (JL, NAT, SWB), pp. 55–59.
ICPR-1996-Muraki #fault #learning #statistics: Error correction scheme augmented with statistical and lexical learning capability, for Japanese OCR (KM), pp. 560–564.
ICPR-1996-MuraseN #approach #generative #learning #recognition: Learning by a generation approach to appearance-based object recognition (HM, SKN), pp. 24–29.
ICPR-1996-PelilloF #learning #network: Autoassociative learning in relaxation labeling networks (MP, AMF), pp. 105–110.
ICPR-1996-PengB #learning #recognition: Delayed reinforcement learning for closed-loop object recognition (JP, BB), pp. 310–314.
ICPR-1996-SainzS #context-sensitive grammar #learning #modelling #using: Learning bidimensional context-dependent models using a context-sensitive language (MS, AS), pp. 565–569.
ICPR-1996-Stoyanov #learning #network: An improved backpropagation neural network learning (IPS), pp. 586–588.
ICPR-1996-WengC #incremental #learning #navigation: Incremental learning for vision-based navigation (JW, SC), pp. 45–49.
ICPR-1996-Yamakawa #feature model #learning #recognition: Matchability-oriented feature selection for recognition structure learning (HY), pp. 123–127.
ICPR-1996-ZanardiHC #interactive #learning #mobile: Mutual learning or unsupervised interactions between mobile robots (CZ, JYH, PC), pp. 40–44.
ICPR-1996-ZhengB #adaptation #detection #learning: Adaptive object detection based on modified Hebbian learning (YJZ, BB), pp. 164–168.
KDD-1996-ChanS #database #modelling: Sharing Learned Models among Remote Database Partitions by Local Meta-Learning (PKC, SJS), pp. 2–7.
KDD-1996-FawcettP #data mining #effectiveness #machine learning #mining #profiling: Combining Data Mining and Machine Learning for Effective User Profiling (TF, FJP), pp. 8–13.
KDD-1996-Feelders #learning #modelling #using: Learning from Biased Data Using Mixture Models (AJF), pp. 102–107.
KDD-1996-LakshminarayanHGS #machine learning #using: Imputation of Missing Data Using Machine Learning Techniques (KL, SAH, RPG, TS), pp. 140–145.
KDD-1996-Musick #learning #network: Rethinking the Learning of Belief Network Probabilities (RM), pp. 120–125.
KDD-1996-Sahami #classification #dependence #learning: Learning Limited Dependence Bayesian Classifiers (MS), pp. 335–338.
KDD-1996-StolorzC #learning #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 #learning #using: Interactive Knowledge Discovery from Marketing Questionnaire Using Simulated Breeding and Inductive Learning Methods (TT, YI), pp. 279–282.
KR-1996-Ghallab #learning #on the #online #recognition #representation: On Chronicles: Representation, On-line Recognition and Learning (MG), pp. 597–606.
SIGIR-1996-CohenS #categorisation #learning: Context-sensitive Learning Methods for Text Categorization (WWC, YS), pp. 307–315.
OOPSLA-1996-KleindienstPT #corba #implementation #lessons learnt #persistent: Lessons Learned from Implementing the CORBA Persistent Object Service (JK, FP, PT), pp. 150–167.
ICRE-1996-NobeW #lessons learnt #modelling #requirements #using: Lessons Learned from a Trial Application of Requirements Modeling Using Statecharts (CRN, WEW), pp. 86–93.
ICSE-1996-GodartCCMS #architecture #design #implementation #lessons learnt #process: Designing and Implementing COO: Design Process, Architectural Style, Lessons Learned (CG, GC, FC, PM, HS), pp. 342–352.
HPDC-1996-WangK #multi: A Broadband Multimedia TeleLearning System (RW, AK), pp. 132–139.
CADE-1996-DenzingerS #learning #proving #theorem proving: Learning Domain Knowledge to Improve Theorem Proving (JD, SS), pp. 62–76.
DAC-1995-JainMF #learning #verification: Advanced Verification Techniques Based on Learning (JJ, RM, MF), pp. 420–426.
ICDAR-v1-1995-TakasuSK #documentation #image #learning: A rule learning method for academic document image processing (AT, SS, EK), pp. 239–242.
ICDAR-v2-1995-DengelD #approach #classification #clustering #documentation #machine learning: Clustering and classification of document structure-a machine learning approach (AD, FD), pp. 587–591.
ICDAR-v2-1995-MatsunagaK #case study #classification #learning #statistics: An experimental study of learning curves for statistical pattern classifiers (TM, HK), pp. 1103–1106.
ICDAR-v2-1995-ZiinoAS #machine learning #recognition #using: Recognition of hand printed Latin characters using machine learning (DZ, AA, CS), pp. 1098–1102.
CSEE-1995-DickJ #education #industrial #learning: Industry Involvement in Undergraduate Curricula: Reinforcing Learning by Applying the Principles (GND, SFJ), pp. 51–63.
CSEE-1995-Mahy #learning #re-engineering: From TRAINING to LEARNING: The Reengineering of Training at DMR Group Inc. (IM), p. 433.
STOC-1995-HellersteinPRW #how #query #question: How many queries are needed to learn? (LH, KP, VR, DW), pp. 190–199.
ICALP-1995-FortnowFGKKSS #learning: Measure, Category and Learning Theory (LF, RF, WIG, MK, SAK, CHS, FS), pp. 558–569.
CHI-1995-AalstCM #analysis #design #framework #learning #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 #learning #modelling: Modeling Time-Constrained Learning in a Highly Interactive Task (MIB, BEJ), pp. 19–26.
CHI-1995-JohnP #approach #case study #learning #using: Learning and Using the Cognitive Walkthrough Method: A Case Study Approach (BEJ, HP), pp. 429–436.
CHI-1995-MitchellPB #learning #using: Learning to Write Together Using Groupware (AM, IP, RB), pp. 288–295.
CIKM-1995-ChenM #information management #learning: Learning Subjective Relevance to Facilitate Information Access (JRC, NM), pp. 218–225.
ICML-1995-AbeLN #2d #algorithm #learning #online #using: On-line Learning of Binary Lexical Relations Using Two-dimensional Weighted Majority Algorithms (NA, HL, AN), pp. 3–11.
ICML-1995-AlmuallimAK #learning #on the: On Handling Tree-Structured Attributed in Decision Tree Learning (HA, YA, SK), pp. 12–20.
ICML-1995-AuerHM #theory and practice: Theory and Applications of Agnostic PAC-Learning with Small Decision Trees (PA, RCH, WM), pp. 21–29.
ICML-1995-Baird #algorithm #approximate #learning: Residual Algorithms: Reinforcement Learning with Function Approximation (LCBI), pp. 30–37.
ICML-1995-Benson #induction #learning #modelling: Inductive Learning of Reactive Action Models (SB), pp. 47–54.
ICML-1995-ChanS #comparative #evaluation: A Comparative Evaluation of Voting and Meta-learning on Partitioned Data (PKC, SJS), pp. 90–98.
ICML-1995-CichoszM #difference #learning #performance: Fast and Efficient Reinforcement Learning with Truncated Temporal Differences (PC, JJM), pp. 99–107.
ICML-1995-Cohen95a #categorisation #learning #relational: Text Categorization and Relational Learning (WWC), pp. 124–132.
ICML-1995-Croft #information retrieval #machine learning: Machine Learning and Information Retrieval (Abstract) (WBC), p. 587.
ICML-1995-Cussens #algorithm #analysis #finite #learning: A Bayesian Analysis of Algorithms for Learning Finite Functions (JC), pp. 142–149.
ICML-1995-DattaK #concept #learning #prototype: Learning Prototypical Concept Descriptions (PD, DFK), pp. 158–166.
ICML-1995-DietterichF #learning #perspective: Explanation-Based Learning and Reinforcement Learning: A Unified View (TGD, NSF), pp. 176–184.
ICML-1995-Duff #problem: Q-Learning for Bandit Problems (MOD), pp. 209–217.
ICML-1995-Fuchs #adaptation #heuristic #learning #parametricity #proving: Learning Proof Heuristics by Adaptive Parameters (MF), pp. 235–243.
ICML-1995-GambardellaD #approach #learning #named #problem: Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem (LMG, MD), pp. 252–260.
ICML-1995-Heckerman #learning #network: Learning With Bayesian Networks (Abstract) (DH), p. 588.
ICML-1995-Hekanaho #concept #learning #multimodal: Symbiosis in Multimodal Concept Learning (JH), pp. 278–285.
ICML-1995-KimuraYK #learning #probability: Reinforcement Learning by Stochastic Hill Climbing on Discounted Reward (HK, MY, SK), pp. 295–303.
ICML-1995-KrishnanLV #learning: Learning to Make Rent-to-Buy Decisions with Systems Applications (PK, PML, JSV), pp. 233–330.
ICML-1995-Lang #learning #named: NewsWeeder: Learning to Filter Netnews (KL), pp. 331–339.
ICML-1995-Littlestone #algorithm #learning: Comparing Several Linear-threshold Learning Algorithms on Tasks Involving Superfluous Attributes (NL), pp. 353–361.
ICML-1995-LittmanCK #learning #policy #scalability: Learning Policies for Partially Observable Environments: Scaling Up (MLL, ARC, LPK), pp. 362–370.
ICML-1995-MaassW #learning #performance: Efficient Learning with Virtual Threshold Gates (WM, MKW), pp. 378–386.
ICML-1995-McCallum #learning: Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State (AM), pp. 387–395.
ICML-1995-MoriartyM #evolution #learning #performance: Efficient Learning from Delayed Rewards through Symbiotic Evolution (DEM, RM), pp. 396–404.
ICML-1995-Niyogi #complexity #learning: Free to Choose: Investigating the Sample Complexity of Active Learning of Real Valued Functions (PN), pp. 405–412.
ICML-1995-NockG #learning #on the: On Learning Decision Committees (RN, OG), pp. 413–420.
ICML-1995-Pomerleau #learning: Learning for Automotive Collision Avoidance and Autonomous Control (DP), p. 589.
ICML-1995-SalganicoffU #learning #multi #using: Active Exploration and Learning in real-Valued Spaces using Multi-Armed Bandit Allocation Indices (MS, LHU), pp. 480–487.
ICML-1995-SquiresS #automation #machine learning #recognition: Automatic Speaker Recognition: An Application of Machine Learning (BS, CS), pp. 515–521.
ICML-1995-StreetMW #approach #induction #learning #predict: An Inductive Learning Approach to Prognostic Prediction (WNS, OLM, WHW), pp. 522–530.
ICML-1995-TowellVGJ #information retrieval #learning: Learning Collection FUsion Strategies for Information Retrieval (GGT, EMV, NKG, BJL), pp. 540–548.
ICML-1995-Wang #approach #incremental #learning: Learning by Observation and Practice: An Incremental Approach for Planning Operator Acquisition (XW), pp. 549–557.
ICML-1995-Weiss #learning: Learning with Rare Cases and Small Disjuncts (GMW), pp. 558–565.
ICML-1995-YamazakiPM #ambiguity #learning #natural language: Learning Hierarchies from Ambiguous Natural Language Data (TY, MJP, CJM), pp. 575–583.
KDD-1995-AugierVK #algorithm #first-order #learning #logic #search-based: Learning First Order Logic Rules with a Genetic Algorithm (SA, GV, YK), pp. 21–26.
KDD-1995-ChanS #machine learning #scalability: Learning Arbiter and Combiner Trees from Partitioned Data for Scaling Machine Learning (PKC, SJS), pp. 39–44.
KDD-1995-CortesJC #learning #quality: Limits on Learning Machine Accuracy Imposed by Data Quality (CC, LDJ, WPC), pp. 57–62.
KDD-1995-HuC #database #learning #set #similarity: Rough Sets Similarity-Based Learning from Databases (XH, NC), pp. 162–167.
KDD-1995-SpirtesM #learning #network: Learning Bayesian Networks with Discrete Variables from Data (PS, CM), pp. 294–299.
SEKE-1995-LiangT #domain model #learning #modelling: Apprenticeship Learning of Domain Models (YL, GT), pp. 54–62.
SEKE-1995-PanY #database #named #object-oriented #query: EQL: A Learn-Easy and Use-Easy Query Language for Object-Oriented Databases (WWP, WPY), pp. 366–373.
SIGIR-1995-VoorheesGJ #learning: Learning Collection Fusion Strategies (EMV, NKG, BJL), pp. 172–179.
SAC-1995-GuzdialRC #collaboration #education #interactive #learning #multi: Collaborative and multimedia interactive learning environment for engineering education (MG, NR, DC), pp. 5–9.
SAC-1995-StearnsC #concept #machine learning #rule-based: Rule-based machine learning of spatial data concepts (SS, DCSC), pp. 242–247.
SAC-1995-Tschichold-Gurman #classification #fuzzy #generative #incremental #learning #using: Generation and improvement of fuzzy classifiers with incremental learning using fuzzy RuleNet (NNTG), pp. 466–470.
ICSE-1995-HenningerLR #analysis #approach #learning: An Organizational Learning Approach to Domain Analysis (SH, KL, AR), pp. 95–104.
ICLP-1995-Sato #learning #logic programming #semantics #source code #statistics: A Statistical Learning Method for Logic Programs with Distribution Semantics (TS), pp. 715–729.
KBSE-1994-MintonW #machine learning #source code #using: Using Machine Learning to Synthesize Search Programs (SM, SRW), pp. 31–38.
SIGMOD-1994-HanFHCC #database #information management #named #prototype #relational: DBLearn: A System Prototype for Knowledge Discovery in Relational Databases (JH, YF, YH, YC, NC), p. 516.
CSEE-1994-MooreP #experience #learning #re-engineering: Learning by Doing: Goals & Experience of Two Software Engineering Project Courses (MMM, CP), pp. 151–164.
STOC-1994-ApsitisFS #approach #learning: Choosing a learning team: a topological approach (KA, RF, CHS), pp. 283–289.
STOC-1994-AuerL #learning #simulation: Simulating access to hidden information while learning (PA, PML), pp. 263–272.
STOC-1994-BlumFJKMR #analysis #fourier #learning #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 #learning #pseudo: Pseudorandom generators and learning algorithms for AC (MS), pp. 478–486.
CHI-1994-KurtenbachB94a #learning #performance: User learning and performance with marking menus (GK, WB), pp. 258–264.
CSCW-1994-WanJ #approach #collaboration #learning #using: Computer Supported Collaborative Learning Using CLARE: The Approach and Experimental Findings (DW, PMJ), pp. 187–198.
TRI-Ada-1994-Pena #bibliography #implementation #lessons learnt #process: Lessons Learned in Implementing a Team Review Process (RP), pp. 24–28.
CIKM-1994-LamirelC #approach #database #design #interactive #learning #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 #learning #recursion #set: Learning Recursive Relations with Randomly Selected Small Training Sets (DWA, SL, CXL, SM), pp. 12–18.
ICML-1994-DruckerCJCV #algorithm #machine learning: Boosting and Other Machine Learning Algorithms (HD, CC, LDJ, YL, VV), pp. 53–61.
ICML-1994-Elomaa #learning: In Defense of C4.5: Notes Learning One-Level Decision Trees (TE), pp. 62–69.
ICML-1994-GervasioD #approach #incremental #learning: An Incremental Learning Approach for Completable Planning (MTG, GD), pp. 78–86.
ICML-1994-Gil #incremental #learning #refinement: Learning by Experimentation: Incremental Refinement of Incomplete Planning Domains (YG), pp. 87–95.
ICML-1994-GiordanaSZ #algorithm #concept #learning #search-based: Learning Disjunctive Concepts by Means of Genetic Algorithms (AG, LS, FZ), pp. 96–104.
ICML-1994-Heger #learning: Consideration of Risk in Reinformance Learning (MH), pp. 105–111.
ICML-1994-LewisC #learning #nondeterminism: Heterogenous Uncertainty Sampling for Supervised Learning (DDL, JC), pp. 148–156.
ICML-1994-Littman #framework #game studies #learning #markov #multi: Markov Games as a Framework for Multi-Agent Reinforcement Learning (MLL), pp. 157–163.
ICML-1994-Mahadevan #case study #learning: 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 #learning: Reward Functions for Accelerated Learning (MJM), pp. 181–189.
ICML-1994-PengW #incremental #multi: Incremental Multi-Step Q-Learning (JP, RJW), pp. 226–232.
ICML-1994-Pereira #bias #machine learning #natural language #problem: Frequencies vs. Biases: Machine Learning Problems in Natural Language Processing — Abstract (FCNP), p. 380.
ICML-1994-SchapireW #algorithm #analysis #learning #on the #worst-case: On the Worst-Case Analysis of Temporal-Difference Learning Algorithms (RES, MKW), pp. 266–274.
ICML-1994-SinghJJ #learning #markov #process: Learning Without State-Estimation in Partially Observable Markovian Decision Processes (SPS, TSJ, MIJ), pp. 284–292.
ICML-1994-TchoumatchenkoG #framework #learning: A Baysian Framework to Integrate Symbolic and Neural Learning (IT, JGG), pp. 302–308.
ICML-1994-ThamP #architecture #composition: A Modular Q-Learning Architecture for Manipulator Task Decomposition (CKT, RWP), pp. 309–317.
ICML-1994-ZuckerG #concept #learning: Selective Reformulation of Examples in Concept Learning (JDZ, JGG), pp. 352–360.
KDD-1994-AronisP #induction #machine learning #relational: Efficiently Constructing Relational Features from Background Knowledge for Inductive Machine Learning (JMA, FJP), pp. 347–358.
KDD-1994-Furnkranz #comparison #concept #learning #relational: A Comparison of Pruning Methods for Relational Concept Learning (JF), pp. 371–382.
KDD-1994-HeckermanGC #learning #network #statistics: Learning Bayesian Networks: The Combination of Knowledge and Statistical Data (DH, DG, DMC), pp. 85–96.
KDD-1994-HuCX #database #learning: Learning Data Trend Regularities From Databases in a Dynamic Environment (XH, NC, JX), pp. 323–334.
KDD-1994-Kaufman #development #learning #multi #tool support #using: Comparing International Development Patterns Using Multi-Operator Learning and Discovery Tools (KAK), pp. 431–440.
KDD-1994-SasisekharanSW #machine learning #maintenance #network #using: Proactive Network Maintenance Using Machine Learning (RS, VS, SMW), pp. 453–462.
KDD-1994-ShenMOZ #database #deduction #induction #learning #using: Using Metagueries to Integrate Inductive Learning and Deductive Database Technology (WMS, BGM, KO, CZ), pp. 335–346.
KR-1994-Carbonell #information management #learning #representation: Knowledge Representation Issues in Integrated Planning and Learning Systems (Abstract) (JGC), p. 633.
KR-1994-CohenH #learning #logic: Learning the Classic Description Logic: Theoretical and Experimental Results (WWC, HH), pp. 121–133.
SEKE-1994-AbranDMMS #analysis #hypermedia #learning #using: Structured hypertext for using and learning function point analysis (AA, JMD, DM, MM, DSP), pp. 164–171.
SEKE-1994-ReynoldsZ #algorithm #learning #using: Learning to understand software from examples using cultural algorithms (RGR, EZ), pp. 188–192.
SIGIR-1994-Allen #information retrieval #learning #performance: Perceptual Speed, Learning and Information Retrieval Performance (BA), pp. 71–80.
SIGIR-1994-ApteDW #automation #categorisation #independence #learning #modelling #towards: Towards Language Independent Automated Learning of Text Categorisation Models (CA, FD, SMW), pp. 23–30.
SIGIR-1994-Yang #categorisation #effectiveness #learning #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 #learning #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 #learning #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 #learning: Application of Boolean expression minimization to learning via hierarchical generalization (JC), pp. 303–307.
SAC-1994-HughesWK #learning: Virtual space learning: creating text-based learning environments (BH, JW, BK), pp. 578–582.
SAC-1994-Janikow #algorithm #fuzzy #learning #search-based: A genetic algorithm for learning fuzzy controllers (CZJ), pp. 232–236.
SAC-1994-RosenG #network #using: Training hard to learn networks using advanced simulated annealing methods (BER, JMG), pp. 256–260.
SAC-1994-RothermelT #bibliography #learning #logic: Test Review: a new method of computer-assisted learning to promote careful reading and logical skills (DR, GT), pp. 573–577.
SAC-1994-WongM #learning #specification #verification: Specification and verification of learning (KWW, RAM), pp. 6–9.
ICDAR-1993-Dengel #documentation #learning: Initial learning of document structure (AD), pp. 86–90.
ICDAR-1993-Ho #independence #learning #recognition: Recognition of handwritten digits by combining independent learning vector quantizations (TKH), pp. 818–821.
ICDAR-1993-Kawatani #learning #polynomial #recognition: Handprinted numeral recognition with the learning quadratic discriminant function (TK), pp. 14–17.
ICDAR-1993-KuritaK #database #image #learning #visual notation: Learning of personal visual impression for image database systems (TK, TK), pp. 547–552.
ICDAR-1993-LebourgeoisH #learning: A contextual processing for an OCR system, based on pattern learning (FL, JLH), pp. 862–865.
ICDAR-1993-SatohMS #comprehension #image #learning: Drawing image understanding system with capability of rule learning (SS, HM, MS), pp. 119–124.
STOC-1993-FreundKRRSS #automaton #finite #learning #performance #random: Efficient learning of typical finite automata from random walks (YF, MJK, DR, RR, RES, LS), pp. 315–324.
STOC-1993-Kearns #learning #performance #query #statistics: Efficient noise-tolerant learning from statistical queries (MJK), pp. 392–401.
STOC-1993-Kharitonov #encryption #learning: Cryptographic hardness of distribution-specific learning (MK), pp. 372–381.
STOC-1993-Maass #bound #complexity #learning: Bounds for the computational power and learning complexity of analog neural nets (WM), pp. 335–344.
FME-1993-OwreRSH #architecture #fault tolerance #lessons learnt #verification: Formal Verification for Fault-Tolerant Architectures: Some Lessons Learned (SO, JMR, NS, FWvH), pp. 482–500.
HCI-SHI-1993-HutchingsHC #hypermedia #learning: A Model of Learning with Hypermedia Systems (GH, WH, CJC), pp. 494–499.
HCI-SHI-1993-LeclercM #learning #natural language: Natural Language as Object and Medium in Computer-Based Learning (SL, SdM), pp. 373–378.
HCI-SHI-1993-NogamiYYM #development #learning: Development of a Simulation-Based Intelligent Tutoring System for Assisting PID Control Learning (TN, YY, IY, SM), pp. 814–818.
HCI-SHI-1993-RizzoPCB #learning: 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 #learning #problem: A Situated Cognition Approach to Problem Solving with Implications for Computer-Based Learning and Assessment (MFY, MDM), pp. 825–830.
INTERCHI-1993-NilsenJOBRM #learning #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 #learning: Do algorithm animations assist learning?: an empirical study and analysis (JTS, ANB, CL), pp. 61–66.
CIKM-1993-ChanS #learning #multi: Experiments on Multi-Strategy Learning by Meta-Learning (PKC, SJS), pp. 314–323.
CIKM-1993-EickJ #algorithm #classification #learning #search-based: Learning Bayesian Classification Rules through Genetic Algorithms (CFE, DJ), pp. 305–313.
ICML-1993-BrezellecS #bottom-up #learning #named: ÉLÉNA: A Bottom-Up Learning Method (PB, HS), pp. 9–16.
ICML-1993-Cardie #learning #using: Using Decision Trees to Improve Case-Based Learning (CC), pp. 25–32.
ICML-1993-Caruana #bias #induction #knowledge-based #learning #multi: Multitask Learning: A Knowledge-Based Source of Inductive Bias (RC), pp. 41–48.
ICML-1993-ClarkM #induction #learning #modelling #using: Using Qualitative Models to Guide Inductive Learning (PC, SM), pp. 49–56.
ICML-1993-CravenS #learning #network #using: Learning Symbolic Rules Using Artificial Neural Networks (MC, JWS), pp. 73–80.
ICML-1993-DanylukP #fault #learning #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 #learning #multi: Concept Sharing: A Means to Improve Multi-Concept Learning (PD, DFK), pp. 89–96.
ICML-1993-FayyadWD #automation #machine learning #named #scalability: SKICAT: A Machine Learning System for Automated Cataloging of Large Scale Sky Surveys (UMF, NW, SGD), pp. 112–119.
ICML-1993-FrazierP #learning: Learning From Entailment: An Application to Propositional Horn Sentences (MF, LP), pp. 120–127.
ICML-1993-GratchCD #learning #network #scheduling: Learning Search Control Knowledge for Deep Space Network Scheduling (JG, SAC, GD), pp. 135–142.
ICML-1993-HuffmanL #interactive #learning #natural language: Learning Procedures from Interactive Natural Language Instructions (SBH, JEL), pp. 143–150.
ICML-1993-JordanJ #approach #divide and conquer #learning #statistics: Supervised Learning and Divide-and-Conquer: A Statistical Approach (MIJ, RAJ), pp. 159–166.
ICML-1993-Kaelbling #learning #probability: Hierarchical Learning in Stochastic Domains: Preliminary Results (LPK), pp. 167–173.
ICML-1993-KimR #learning: Constraining Learning with Search Control (JK, PSR), pp. 174–181.
ICML-1993-Lin #learning #scalability: Scaling Up Reinforcement Learning for Robot Control (LJL), pp. 182–189.
ICML-1993-MitchellT #comparison #learning #network: Explanation Based Learning: A Comparison of Symbolic and Neural Network Approaches (TMM, ST), pp. 197–204.
ICML-1993-Mladenic #combinator #concept #induction #learning #optimisation: Combinatorial Optimization in Inductive Concept Learning (DM), pp. 205–211.
ICML-1993-NortonH #learning #probability: Learning DNF Via Probabilistic Evidence Combination (SWN, HH), pp. 220–227.
ICML-1993-Quinlan #learning #modelling: Combining Instance-Based and Model-Based Learning (JRQ), pp. 236–243.
ICML-1993-RagavanR #concept #learning #lookahead: Lookahead Feature Construction for Learning Hard Concepts (HR, LAR), pp. 252–259.
ICML-1993-Salganicoff #adaptation #learning: Density-Adaptive Learning and Forgetting (MS), pp. 276–283.
ICML-1993-Schwartz #learning: A Reinforcement Learning Method for Maximizing Undiscounted Rewards (AS), pp. 298–305.
ICML-1993-SuttonW #learning #online #random: Online Learning with Random Representations (RSS, SDW), pp. 314–321.
ICML-1993-Tadepalli #bias #learning #query: Learning from Queries and Examples with Tree-structured Bias (PT), pp. 322–329.
ICML-1993-Tan #independence #learning #multi: Multi-Agent Reinforcement Learning: Independent versus Cooperative Agents (MT), pp. 330–337.
SEKE-1993-EspositoMS #information management #machine learning #refinement: Machine Learning Techniques for Knowledge Acquisition and Refinement (FE, DM, GS), pp. 319–323.
SEKE-1993-WillisP #machine learning #program transformation #reuse: Machine Learning for Program Transformations in Software Reuse (CPW, DJP), pp. 275–277.
SAC-1993-GallionSCB #algorithm #learning: Dynamic ID3: A Symbolic Learning Algorithm for Many-Valued Attribute Domains (RG, CLS, DCSC, WEB), pp. 14–20.
SAC-1993-KountanisS #concept #graph #learning: Graphs as a Language to Describe Learning System Concepts (DIK, ES), pp. 469–475.
SAC-1993-VaidyanathanL #analysis #bound #learning: Analysis of Upper Bound in Valiant’s Model for Learning Bounded CNF Expressions (SV, SL), pp. 754–761.
FSE-1993-Bergadano #generative #learning #testing: Test Case Generation by Means of Learning Techniques (FB), pp. 149–162.
HPDC-1993-FletcherO #distributed #learning #network #parallel: Parallel and Distributed Systems for Constructive Neural Network Learning (JF, ZO), pp. 174–178.
HT-ECHT-1992-Colorni #hypermedia #learning #research: A Hypertext for Learning Operational Research (Demonstration) (AC), p. 291.
HT-ECHT-1992-Eco #education #hypermedia #learning #multi: Hypermedia for Teaching and Learning: A Multimedia Guide to the History of European Civilization (MuG) (UE), p. 288.
PODS-1992-Greiner #learning #performance #query: Learning Efficient Query Processing Strategies (RG), pp. 33–46.
STOC-1992-Angluin #bibliography #learning: Computational Learning Theory: Survey and Selected Bibliography (DA), pp. 351–369.
STOC-1992-BlumR #learning #performance #query: Fast Learning of k-Term DNF Formulas with Queries (AB, SR), pp. 382–389.
STOC-1992-BshoutyHH #learning: Learning Arithmetic Read-Once Formulas (NHB, TRH, LH), pp. 370–381.
CHI-1992-Clancey #bibliography #learning #research: Overview of the Institute for Research on Learning (WJC), pp. 571–572.
CHI-1992-Spohrer #case study #experience #learning #prototype: Simulation-based learning systems: prototypes and experiences (AJ, JCS), pp. 523–524.
CSCW-1992-BerlinJ #collaboration #learning #problem: Consultants and Apprentices: Observations about Learning and Collaborative Problem Solving (LMB, RJ), pp. 130–137.
CSCW-1992-Orlikowski #implementation #learning: Learning from Notes: Organizational Issues in Groupware Implementation (WJO), pp. 362–369.
TRI-Ada-C-1992-Beidler #component #education #tool support #what: Building on the Booch Components: What Can Be Learned When Modifying Real World Software Tools for Educational Use (JB), pp. 157–164.
CAiSE-1992-FouqueV #analysis #approach #machine learning: Building a Tool for Software Code Analysis: A Machine Learning Approach (GF, CV), pp. 278–289.
KR-1992-GreinerS #approximate #learning: Learning Useful Horn Approximations (RG, DS), pp. 383–392.
ML-1992-AlmuallimD #concept #learning #on the: On Learning More Concepts (HA, TGD), pp. 11–19.
ML-1992-Bhatnagar #learning: Learning by Incomplete Explanation-Based Learning (NB), pp. 37–42.
ML-1992-Chen #learning: Improving Path Planning with Learning (PCC), pp. 55–61.
ML-1992-Christiansen #learning #nondeterminism #predict: Learning to Predict in Uncertain Continuous Tasks (ADC), pp. 72–81.
ML-1992-ClouseU #education #learning: A Teaching Method for Reinforcement Learning (JAC, PEU), pp. 92–110.
ML-1992-ConverseH #learning: Learning to Satisfy Conjunctive Goals (TMC, KJH), pp. 117–122.
ML-1992-CoxR #learning #multi: Multistrategy Learning with Introspective Meta-Explanations (MTC, AR), pp. 123–128.
ML-1992-Etzioni #analysis #learning: An Asymptotic Analysis of Speedup Learning (OE), pp. 129–136.
ML-1992-GiordanaS #algorithm #concept #learning #search-based #using: Learning Structured Concepts Using Genetic Algorithms (AG, CS), pp. 169–178.
ML-1992-GratchD #analysis #learning #problem: An Analysis of Learning to Plan as a Search Problem (JG, GD), pp. 179–188.
ML-1992-GrefenstetteR #approach #learning: An Approach to Anytime Learning (JJG, CLR), pp. 189–195.
ML-1992-Hickey #algorithm #approach #evaluation #towards: Artificial Universes — Towards a Systematic Approach to Evaluation Algorithms which Learn form Examples (RJH), pp. 196–205.
ML-1992-HirschbergP #analysis #concept #learning: Average Case Analysis of Learning κ-CNF Concepts (DSH, MJP), pp. 206–211.
ML-1992-HoggerB #approach #heuristic #learning #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 #learning: Combining Competition and Cooperation in Supervised Inductive Learning (CZJ), pp. 241–248.
ML-1992-KononenkoK #generative #learning #multi #optimisation #probability: Learning as Optimization: Stochastic Generation of Multiple Knowledge (IK, MK), pp. 257–262.
ML-1992-Mahadevan #learning #modelling #probability: Enhancing Transfer in Reinforcement Learning by Building Stochastic Models of Robot Actions (SM), pp. 290–299.
ML-1992-Mao #learning #named: THOUGHT: An Integrated Learning System for Acquiring Knowledge Structure (CM), pp. 300–309.
ML-1992-Markov #approach #concept #learning: An Approach to Concept Learning Based on Term Generalization (ZM), pp. 310–315.
ML-1992-McCallum #learning #performance #proximity #using: Using Transitional Proximity for Faster Reinforcement Learning (AM), pp. 316–321.
ML-1992-Merckt #concept #flexibility #named: NFDT: A System that Learns Flexible Concepts Based on Decision Trees for Numerical Attributes (TVdM), pp. 322–331.
ML-1992-RubyK #learning #optimisation: Learning Episodes for Optimization (DR, DFK), pp. 379–384.
ML-1992-SammutHKM #learning: Learning to Fly (CS, SH, DK, DM), pp. 385–393.
ML-1992-Singh #algorithm #learning #modelling #scalability: Scaling Reinforcement Learning Algorithms by Learning Variable Temporal Resolution Models (SPS), pp. 406–415.
ML-1992-Tesauro #difference #learning: Temporal Difference Learning of Backgammon Strategy (GT), pp. 451–457.
ML-1992-Zhang #learning: Selecting Typical Instances in Instance-Based Learning (JZ), pp. 470–479.
OOPSLA-1992-LiuGG #learning #object-oriented #question #what: What Contributes to Successful Object-Oriented Learning? (CL, SG, BG), pp. 77–86.
KBSE-1991-BailinGT #re-engineering: A Learning-Based Software Engineering Environment (SCB, RHG, WT), pp. 198–206.
KBSE-1991-HarandiL #design #machine learning #perspective: Acquiring Software Design Schemas: A Machine Learning Perspective (MTH, HYL), pp. 188–197.
VLDB-1991-PalmerZ #named: Fido: A Cache That Learns to Fetch (MP, SBZ), pp. 255–264.
SEI-1991-RiedlWFKM #re-engineering #what: What We Have Learned About Software Engineering Expertise (TRR, JSW, JTF, GAK, JDM), pp. 261–270.
STOC-1991-KushilevitzM #fourier #learning #using: Learning Decision Trees Using the Fourier Sprectrum (Extended Abstract) (EK, YM), pp. 455–464.
STOC-1991-LittlestoneLW #learning #linear #online: On-Line Learning of Linear Functions (NL, PML, MKW), pp. 465–475.
WSA-1991-Breuer #analysis #learning #synthesis: An Analysis/Synthesis Language with Learning Strategies (PTB), pp. 202–209.
CHI-1991-PalmiterE #evaluation #learning: An evaluation of animated demonstrations of learning computer-based tasks (SP, JE), pp. 257–263.
KDD-1991-BergadanoGSBM #learning: Integrated Learning in a Real Domain (FB, AG, LS, FB, DDM), pp. 277–288.
KDD-1991-UthurusamyFS #learning: Learning Useful Rules from Inconclusive Data (RU, UMF, WSS), pp. 141–158.
KR-1991-ChalasaniEM #algorithm #performance #permutation #problem: Integrating Efficient Model-Learning and Problem-Solving Algorithms in Permutation Environments (PC, OE, JM), pp. 89–98.
ML-1991-Bain #learning: Experiments in Non-Monotonic Learning (MB), pp. 380–384.
ML-1991-Berenji #approximate #learning #refinement: Refinement of Approximate Reasoning-based Controllers by Reinforcement Learning (HRB), pp. 475–479.
ML-1991-BottaRSS #abduction #learning #using: Improving Learning Using Causality and Abduction (MB, SR, LS, SBS), pp. 480–484.
ML-1991-Brand #learning: Decision-Theoretic Learning in an Action System (MB), pp. 283–287.
ML-1991-BratkoMV #learning #modelling: Learning Qualitative Models of Dynamic Systems (IB, SM, AV), pp. 385–388.
ML-1991-BrunkP #algorithm #concept #learning #relational: An Investigation of Noise-Tolerant Relational Concept Learning Algorithms (CB, MJP), pp. 389–393.
ML-1991-ChienGD #learning #on the: On Becoming Decreasingly Reactive: Learning to Deliberate Minimally (SAC, MTG, GD), pp. 288–292.
ML-1991-ChienWDDFGL #automation #machine learning: Machine Learning in Engineering Automation (SAC, BLW, TGD, RJD, BF, JG, SCYL), pp. 577–580.
ML-1991-CobbG #learning #persistent: Learning the Persistence of Actions in Reactive Control Rules (HGC, JJG), pp. 292–297.
ML-1991-Day #csp #heuristic #learning #problem: Learning Variable Descriptors for Applying Heuristics Across CSP Problems (DSD), pp. 127–131.
ML-1991-desJardins #bias #learning #probability: Probabilistic Evaluating of Bias for Learning Systems (Md), pp. 495–499.
ML-1991-DzeroskiL #comparison #empirical #learning: Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL (SD, NL), pp. 399–402.
ML-1991-Goel #formal method #incremental #learning: Model Revision: A Theory of Incremental Model Learning (AKG), pp. 605–609.
ML-1991-GokerM #incremental #information retrieval #learning: Incremental Learning in a Probalistic Information Retrieval System (AG, TLM), pp. 255–259.
ML-1991-HastingsLL #learning #word: Learning Words From Context (PMH, SLL, RKL), pp. 55–59.
ML-1991-Herrmann #learning: Learning Analytical Knowledge About VLSI-Design from Observation (JH), pp. 610–614.
ML-1991-HirakiGYA #image #learning: Learning Spatial Relations from Images (KH, JHG, YY, YA), pp. 407–411.
ML-1991-HsuS #evaluation #learning: Learning Football Evaluation for a Walking Robot (GTH, RGS), pp. 303–307.
ML-1991-HummeS #using: Using Inverse Resolution to Learn Relations from Experiments (DH, CS), pp. 412–416.
ML-1991-JordanR #learning #modelling: Internal World Models and Supervised Learning (MIJ, DER), pp. 70–74.
ML-1991-Kadie #induction #learning: Quantifying the Value of Constructive Induction, Knowledge, and Noise Filtering on Inductive Learning (CMK), pp. 153–157.
ML-1991-Kadie91a #concept #learning #set: Continous Conceptual Set Covering: Learning Robot Operators From Examples (CMK), pp. 615–619.
ML-1991-KijsirikulNS #learning #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: Learning to Select a Model in a Changing World (MMK, SAR), pp. 313–317.
ML-1991-Krulwich #learning: Learning from Deliberated Reactivity (BK), pp. 318–322.
ML-1991-Kwok #adaptation #architecture #learning #query #using: Query Learning Using an ANN with Adaptive Architecture (KLK), pp. 260–264.
ML-1991-LeckieZ #approach #induction #learning: Learning Search Control Rules for Planning: An Inductive Approach (CL, IZ), pp. 422–426.
ML-1991-Lewis #information retrieval #learning: Learning in Intelligent Information Retrieval (DDL), pp. 235–239.
ML-1991-Lin #education #learning #self: Self-improvement Based on Reinforcement Learning, Planning and Teaching (LJL), pp. 323–327.
ML-1991-MahadevanC #architecture #learning #scalability: Scaling Reinforcement Learning to Robotics by Exploiting the Subsumption Architecture (SM, JC), pp. 328–332.
ML-1991-MartinB #bias #learning #variability: Variability Bias and Category Learning (JDM, DB), pp. 90–94.
ML-1991-Maza #concept #learning #prototype: A Prototype Based Symbolic Concept Learning System (MdlM), pp. 41–45.
ML-1991-MillanT #learning: Learning to Avoid Obstacles Through Reinforcement (JdRM, CT), pp. 298–302.
ML-1991-OliveiraS #concept #learning #network: Learning Concepts by Synthesizing Minimal Threshold Gate Networks (ALO, ALSV), pp. 193–197.
ML-1991-ORorkeMABC #evaluation #machine learning: Machine Learning for Nondestructive Evaluation (PO, SM, MA, WB, DCSC), pp. 620–624.
ML-1991-PageF #learning: Learning Constrained Atoms (CDPJ, AMF), pp. 427–431.
ML-1991-PazzaniBS #approach #concept #learning #relational: A Knowledge-intensive Approach to Learning Relational Concepts (MJP, CB, GS), pp. 432–436.
ML-1991-Pierce #learning #set: Learning a Set of Primitive Actions with an Uninterpreted Sensorimotor Apparatus (DRP), pp. 338–342.
ML-1991-RaedtBM #concept #constraints #interactive: Integrity Constraints and Interactive Concept-Learning (LDR, MB, BM), pp. 394–398.
ML-1991-RagavanR #empirical #learning: Relations, Knowledge and Empirical Learning (HR, LAR), pp. 188–192.
ML-1991-Reich #design #learning: Design Integrated Learning Systems for Engineering Design (YR), pp. 635–639.
ML-1991-Schlimmer #consistency #database #induction #learning: Database Consistency via Inductive Learning (JCS), pp. 640–644.
ML-1991-SilversteinP #induction #learning #relational: Relational Clichés: Constraining Induction During Relational Learning (GS, MJP), pp. 203–207.
ML-1991-Singh #composition #learning: Transfer of Learning Across Compositions of Sequentail Tasks (SPS), pp. 348–352.
ML-1991-SuttonM #learning #polynomial: Learning Polynomial Functions by Feature Construction (RSS, CJM), pp. 208–212.
ML-1991-Tadepalli #learning: Learning with Incrutable Theories (PT), pp. 544–548.
ML-1991-Tan #learning #representation: Learning a Cost-Sensitive Internal Representation for Reinforcement Learning (MT), pp. 358–362.
ML-1991-TecuciM #adaptation #learning #multi: A Method for Multistrategy Task-Adaptive Learning Based on Plausible Justifications (GT, RSM), pp. 549–553.
ML-1991-Thompson #approach #information retrieval #machine learning: Machine Learning in the Combination of Expert Opinion Approach to IR (PT), pp. 270–274.
ML-1991-VanLehnJ #correctness #learning #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 #learning: A Study of How Domain Knowledge Improves Knowledge-Based Learning Systems (BLW, SCYL), pp. 559–563.
ML-1991-Whitehead #complexity: Complexity and Cooperation in Q-Learning (SDW), pp. 363–367.
ML-1991-Wixson #composition #learning #scalability: Scaling Reinforcement Learning Techniques via Modularity (LEW), pp. 3368–372.
ML-1991-YamanishiK #learning #probability #search-based #sequence: Learning Stochastic Motifs from Genetic Sequences (KY, AK), pp. 467–471.
ECOOP-1991-BergsteinL #incremental #learning #optimisation #taxonomy: Incremental Class Dictionary Learning and Optimization (PLB, KJL), pp. 377–396.
LOPSTR-1991-Eusterbrock #abstraction #learning #logic programming #source code: Speed-up Transformations of Logic Programs by Abstraction and Learning (JE), pp. 167–182.
STOC-1990-Blum #infinity #learning: Learning Boolean Functions in an Infinite Atribute Space (Extended Abstract) (AB), pp. 64–72.
ICALP-1990-JainS #learning: Language Learning by a “Team” (Extended Abstract) (SJ, AS), pp. 153–166.
ICALP-1990-Watanabe #formal method #learning #query: A Formal Study of Learning via Queries (OW0), pp. 139–152.
CHI-1990-CarrollSBA #learning #smalltalk: A view matcher for learning Smalltalk (JMC, JAS, RKEB, SRA), pp. 431–437.
CHI-1990-HowesP #analysis #learning #semantics: Semantic analysis during exploratory learning (AH, SJP), pp. 399–406.
CSCW-1990-BullenB #experience #learning #user interface: Learning from User Experience with Groupware (CVB, JLB), pp. 291–302.
ML-1990-ArunkumarY #information management #learning #representation #using: Knowledge Acquisition from Examples using Maximal Representation Learning (SA, SY), pp. 2–8.
ML-1990-BergadanoGSMB #learning: Integrated Learning in a real Domain (FB, AG, LS, DDM, FB), pp. 322–329.
ML-1990-ChanW #analysis #induction #learning #performance #probability: Performance Analysis of a Probabilistic Inductive Learning System (KCCC, AKCW), pp. 16–23.
ML-1990-Cohen #analysis #concept #learning #representation: An Analysis of Representation Shift in Concept Learning (WWC), pp. 104–112.
ML-1990-Cohen90a #approximate #learning: Learning Approximate Control Rules of High Utility (WWC), pp. 268–276.
ML-1990-Epstein #learning: Learning Plans for Competitive Domains (SLE), pp. 190–197.
ML-1990-Flann #abstraction: Applying Abstraction and Simplification to Learn in Intractable Domains (NSF), pp. 277–285.
ML-1990-GenestMP #approach #learning: Explanation-Based Learning with Incomplete Theories: A Three-step Approach (JG, SM, BP), pp. 286–294.
ML-1990-Hammond #learning #process: Learning and Enforcement: Stabilizing Environments to Facilitate Activity (KJH), pp. 204–210.
ML-1990-Hirsh #bound #consistency #learning #nondeterminism: Learning from Data with Bounded Inconsistency (HH), pp. 32–39.
ML-1990-Holder #machine learning #problem: The General Utility Problem in Machine Learning (LBH), pp. 402–410.
ML-1990-Hume #induction #learning: Learning Procedures by Environment-Driven Constructive Induction (DVH), pp. 113–121.
ML-1990-Kaelbling #learning: Learning Functions in k-DNF from Reinforcement (LPK), pp. 162–169.
ML-1990-KoMT #learning #string: Learning String Patterns and Tree Patterns from Examples (KIK, AM, WGT), pp. 384–391.
ML-1990-Lehman #learning: A General Method for Learning Idiosyncratic Grammars (JFL), pp. 368–376.
ML-1990-LytinenM #comparison #learning: A Comparison of Learning Techniques in Second Language Learning (SLL, CEM), pp. 377–383.
ML-1990-McCallumS #algorithm #search-based #using: Using Genetic Algorithms to Learn Disjunctive Rules from Examples (AM, KAS), pp. 149–152.
ML-1990-ObradovicP #learning #multi: Learning with Discrete Multi-Valued Neurons (ZO, IP), pp. 392–399.
ML-1990-PazzaniS #algorithm #analysis #learning: Average Case Analysis of Conjunctive Learning Algorithms (MJP, WS), pp. 339–347.
ML-1990-Ram #incremental #learning: Incremental Learning of Explanation Patterns and Their Indices (AR), pp. 313–320.
ML-1990-RamseyGS #contest #difference #learning: Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment (CLR, JJG, ACS), pp. 211–215.
ML-1990-SammutC #learning #performance #question: Is Learning Rate a Good Performance Criterion for Learning? (CS, JC), pp. 170–178.
ML-1990-SchoenauerS #incremental #learning: Incremental Learning of Rules and Meta-rules (MS, MS), pp. 49–57.
ML-1990-Segen #clustering #graph #learning: Graph Clustering and Model Learning by Data Compression (JS), pp. 93–101.
ML-1990-SilverFIVB #framework #learning #multi: A Framework for Multi-Paradigmatic Learning (BS, WJF, GAI, JV, KB), pp. 348–356.
ML-1990-Sutton #approximate #architecture #learning #programming: Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming (RSS), pp. 216–224.
ML-1990-WhiteheadB #learning: Active Perception and Reinforcement Learning (SDW, DHB), pp. 179–188.
SEKE-1990-EstevaR #induction #learning #reuse: Learning to Recognize Reusable Software by Induction (JCE, RGR), pp. 19–24.
SEKE-1990-Mazurov #learning #parallel #process: Parallel Processes of Decision Making and Multivalued Interpretation of Contradictory Data by Learning Neuron Machines (VDM), p. 165.
SEKE-1990-VolovikMT #re-engineering #what: What Software Engineering Can Learn From Practitioners (DV, RM, WTT), pp. 216–221.
SIGIR-1990-HalinCK #image #machine learning #retrieval: Machine Learning and Vectorial Matching for an Image Retrieval Model: EXPRIM and the System RIVAGE (GH, MC, PK), pp. 99–114.
HT-1989-RousSYY #hypermedia #lessons learnt: Lessons Learned from the ACM Hypertext on Hypertext Project (BR, BS, NY, EAY), pp. 385–386.
STOC-1989-KearnsV #automaton #encryption #finite #learning: Cryptographic Limitations on Learning Boolean Formulae and Finite Automata (MJK, LGV), pp. 433–444.
CHI-1989-BlackBMC #effectiveness #learning #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 #learning #metric: Learning and transfer of measurement tasks (AYL, PGP, WAB), pp. 115–120.
ML-1989-Aha #concept #incremental #independence #learning: Incremental, Instance-Based Learning of Independent and Graded Concept Descriptions (DWA), pp. 387–391.
ML-1989-Anderson #learning #network: Tower of Hanoi with Connectionist Networks: Learning New Features (CWA), pp. 345–349.
ML-1989-BarlettaK #empirical #learning: Improving Explanation-Based Indexing with Empirical Learning (RB, RK), pp. 84–86.
ML-1989-BergadanoGP #deduction #induction #learning #top-down: Deduction in Top-Down Inductive Learning (FB, AG, SP), pp. 23–25.
ML-1989-Buntine #classification #learning #using: Learning Classification Rules Using Bayes (WLB), pp. 94–98.
ML-1989-Chan #induction #learning: Inductive Learning with BCT (PKC), pp. 104–108.
ML-1989-ChaseZPBMH #approximate: Approximating Learned Search Control Knowledge (MPC, MZ, RLP, JDB, PPM, HH), pp. 218–220.
ML-1989-Chien #learning: Learning by Analyzing Fortuitous Occurrences (SAC), pp. 249–251.
ML-1989-Chrisman #bias: Evaluating Bias During Pac-Learning (LC), pp. 469–471.
ML-1989-ClearwaterCHB #incremental #learning: Incremental Batch Learning (SHC, TPC, HH, BGB), pp. 366–370.
ML-1989-ConverseHM #learning: Learning from Opportunity (TMC, KJH, MM), pp. 246–248.
ML-1989-Cornuejols #incremental #learning: An Exploration Into Incremental Learning: the INFLUENCE System (AC), pp. 383–386.
ML-1989-Diederich #learning: “Learning by Instruction” in connectionist Systems (JD), pp. 66–68.
ML-1989-Dietterich #induction #learning: Limitations on Inductive Learning (TGD), pp. 124–128.
ML-1989-Fawcett #learning: Learning from Plausible Explanations (TF), pp. 37–39.
ML-1989-FisherMMST #learning: Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems (DHF, KBM, RJM, JWS, GGT), pp. 169–173.
ML-1989-Flann #abstraction #learning #problem: Learning Appropriate Abstractions for Planning in Formation Problems (NSF), pp. 235–239.
ML-1989-Fogarty #algorithm #incremental #learning #realtime #search-based: An Incremental Genetic Algorithm for Real-Time Learning (TCF), pp. 416–419.
ML-1989-FriedrichN #algorithm #induction #learning #using: Using Domain Knowledge to Improve Inductive Learning Algorithms for Diagnosis (GF, WN), pp. 75–77.
ML-1989-GamsK #empirical #learning: New Empirical Learning Mechanisms Perform Significantly Better in Real Life Domains (MG, AK), pp. 99–103.
ML-1989-GervasioD #learning: Explanation-Based Learning of Reactive Operations (MTG, GD), pp. 252–254.
ML-1989-Grefenstette #algorithm #incremental #learning #search-based: Incremental Learning of Control Strategies with Genetic algorithms (JJG), pp. 340–344.
ML-1989-Haines #learning: Explanation Based Learning as Constrained Search (DH), pp. 43–45.
ML-1989-HilliardLRP #approach #classification #hybrid #learning #problem #scheduling: Learning Decision Rules for scheduling Problems: A Classifier Hybrid Approach (MRH, GEL, GR, MRP), pp. 188–190.
ML-1989-Hirsh #empirical #learning: Combining Empirical and Analytical Learning with Version Spaces (HH), pp. 29–33.
ML-1989-Jones #learning #problem: Learning to Retrieve Useful Information for Problem Solving (RMJ), pp. 212–214.
ML-1989-Kaelbling #embedded #framework #learning: A Formal Framework for Learning in Embedded Systems (LPK), pp. 350–353.
ML-1989-Katz #learning #network: Integrating Learning in a Neural Network (BFK), pp. 69–71.
ML-1989-Keller #compilation #learning #performance: Compiling Learning Vocabulary from a Performance System Description (RMK), pp. 482–495.
ML-1989-Knoblock #abstraction #learning: Learning Hierarchies of Abstraction Spaces (CAK), pp. 241–245.
ML-1989-LambertTL #algorithm #concept #hybrid #learning #recursion: Generalized Recursive Splitting Algorithms for Learning Hybrid Concepts (BLL, DKT, SCYL), pp. 496–498.
ML-1989-Langley #empirical #learning: Unifying Themes in Empirical and Explanation-Based Learning (PL), pp. 2–4.
ML-1989-LeviPS #learning: Learning Tactical Plans for Pilot Aiding (KRL, DLP, VLS), pp. 191–193.
ML-1989-Marie #bias #dependence #learning: Building A Learning Bias from Perceived Dependencies (CdSM), pp. 501–502.
ML-1989-Martin #learning: Reducing Redundant Learning (JDM), pp. 396–399.
ML-1989-MasonCM #learning: Experiments in Robot Learning (MTM, ADC, TMM), pp. 141–145.
ML-1989-MatwinM #learning: Learning Procedural Knowledge in the EBG Context (SM, JM), pp. 197–199.
ML-1989-MooneyO #aspect-oriented #concept #induction #learning: Induction Over the Unexplained: Integrated Learning of Concepts with Both Explainable and Conventional Aspects (RJM, DO), pp. 5–7.
ML-1989-Morris #learning: Reducing Search and Learning Goal Preferences (SM), pp. 46–48.
ML-1989-MuggletonBMM #comparison #machine learning: An Experimental Comparison of Human and Machine Learning Formalisms (SM, MB, JHM, DM), pp. 113–118.
ML-1989-NumaoS #learning #similarity: Explanation-Based Acceleration of Similarity-Based Learning (MN, MS), pp. 58–60.
ML-1989-ORorkeCO #learning: Learning to Recognize Plans Involving Affect (PO, TC, AO), pp. 209–211.
ML-1989-PagalloH #algorithm: Two Algorithms That Learn DNF by Discovering Relevant Features (GP, DH), pp. 119–123.
ML-1989-Paredis #behaviour #learning: Learning the Behavior of Dynamical Systems form Examples (JP), pp. 137–140.
ML-1989-Pazzani #learning: Explanation-Based Learning with Week Domain Theories (MJP), pp. 72–74.
ML-1989-Puget #invariant #learning: Learning Invariants from Explanations (JFP), pp. 200–204.
ML-1989-RasZ #concept #learning: Imprecise Concept Learning within a Growing Language (ZWR, MZ), pp. 314–319.
ML-1989-Redmond #learning #reasoning: Combining Case-Based Reasoning, Explanation-Based Learning, and Learning form Instruction (MR), pp. 20–22.
ML-1989-RudyK #learning: Learning to Plan in Complex Domains (DR, DFK), pp. 180–182.
ML-1989-SarrettP #algorithm #empirical #learning: One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning (WS, MJP), pp. 26–28.
ML-1989-ScottM #case study #experience #learning #nondeterminism: Uncertainty Based Selection of Learning Experiences (PDS, SM), pp. 358–361.
ML-1989-Selfridge #adaptation #case study #contest #learning: Atoms of Learning II: Adaptive Strategies A Study of Two-Person Zero-Sum Competition (OGS), pp. 412–415.
ML-1989-Shavlik #analysis #empirical #learning: An Empirical Analysis of EBL Approaches for Learning Plan Schemata (JWS), pp. 183–187.
ML-1989-ShavlikT #learning #network: Combining Explanation-Based Learning and Artificial Neural Networks (JWS, GGT), pp. 90–93.
ML-1989-SobekL #learning #using: Using Learning to Recover Side-Effects of Operators in Robotics (RPS, JPL), pp. 205–208.
ML-1989-Spackman #detection #induction #learning #tool support: Signal Detection Theory: Valuable Tools for Evaluating Inductive Learning (KAS), pp. 160–163.
ML-1989-Subramanian #machine learning: Representational Issues in Machine Learning (DS), pp. 426–429.
ML-1989-TanS #approach #concept #learning #recognition: Cost-Sensitive Concept Learning of Sensor Use in Approach ad Recognition (MT, JCS), pp. 392–395.
ML-1989-TecuciK #learning #multi: Multi-Strategy Learning in Nonhomongeneous Domain Theories (GT, YK), pp. 14–16.
ML-1989-Utgoff #incremental #learning: Improved Training Via Incremental Learning (PEU), pp. 362–365.
ML-1989-WefaldR #adaptation #learning: Adaptive Learning of Decision-Theoretic Search Control Knowledge (EW, SJR), pp. 408–411.
ML-1989-WhiteheadB: A Role for Anticipation in Reactive Systems that Learn (SDW, DHB), pp. 354–357.
ML-1989-Widmer #deduction #integration #learning: A Tight Integration of Deductive Learning (GW), pp. 11–13.
ML-1989-Wollowski #learning: A Schema for an Integrated Learning System (MW), pp. 87–89.
ML-1989-YagerF #learning: Participatory Learning: A Constructivist Model (RRY, KMF), pp. 420–425.
ML-1989-ZhangM #learning: A Description of Preference Criterion in Constructive Learning: A Discussion of Basis Issues (JZ, RSM), pp. 17–19.
SIGIR-1989-Belew #adaptation #documentation #information retrieval #representation #using: Adaptive Information Retrieval: Using a Connectionist Representation to Retrieve and Learn About Documents (RKB), pp. 11–20.
NACLP-1989-MarkovitchS #approach #automation #machine learning: Automatic Ordering of Subgoals — A Machine Learning Approach (SM, PDS), pp. 224–240.
DAC-1988-MaoC #algorithm #generative #metric #named #self #testing #using: Dytest: A Self-Learning Algorithm Using Dynamic Testability Measures to Accelerate Test Generation (WM, MDC), pp. 591–596.
SEI-1988-Stevens #learning: SEI Demonstration: Advanced Learning Technologies Project (SS), p. 120.
STOC-1988-KearnsL #fault #learning: Learning in the Presence of Malicious Errors (Extended Abstract) (MJK, ML), pp. 267–280.
Best-of-PLDI-1988-Shivers88a #analysis #control flow #higher-order #lessons learnt: Higher-order control-flow analysis in retrospect: lessons learned, lessons abandoned (with retrospective) (OS), pp. 257–269.
CSCW-1988-Hiltz #collaboration #learning: Collaborative Learning in a Virtual Classroom: Highlights of Findings (SRH), pp. 282–290.
ML-1988-Amsterdam #learning: Extending the Valiant Learning Model (JA), pp. 381–394.
ML-1988-Carpineto #approach #generative #learning: An Approach Based on Integrated Learning to Generating Stories (CC), pp. 298–304.
ML-1988-Cohen #learning #multi: Generalizing Number and Learning from Multiple Examples in Explanation Based Learning (WWC), pp. 256–269.
ML-1988-Etzioni #approach #learning #reliability: Hypothesis Filtering: A Practical Approach to Reliable Learning (OE), pp. 416–429.
ML-1988-Gross #concept #incremental #learning #multi #using: Incremental Multiple Concept Learning Using Experiments (KPG), pp. 65–72.
ML-1988-Helft #first-order #learning: Learning Systems of First-Order Rules (NH), pp. 395–401.
ML-1988-Hirsh #learning #reasoning: Reasoning about Operationality for Explanation-Based Learning (HH), pp. 214–220.
ML-1988-IbaWL #concept #incremental #learning: Trading Off Simplicity and Coverage in Incremental concept Learning (WI, JW, PL), pp. 73–79.
ML-1988-JongS #game studies #learning #using: Using Experience-Based Learning in Game Playing (KADJ, ACS), pp. 284–290.
ML-1988-Kadie #learning #named: Diffy-S: Learning Robot Operator Schemata from Examples (CMK), pp. 430–436.
ML-1988-Kerber #using: Using a Generalization Hierarchy to Learn from Examples (RK), pp. 1–7.
ML-1988-Lebowitz: Deferred Commitment in UNIMEM: Waiting to Learn (ML), pp. 80–86.
ML-1988-Lynne #learning: Competitive Reinforcement Learning (KJL), pp. 188–199.
ML-1988-MahadevanT #learning #on the: On the Tractability of Learning from Incomplete Theories (SM, PT), pp. 235–241.
ML-1988-MarkovitchS #learning: The Role of Forgetting in Learning (SM, PDS), pp. 459–465.
ML-1988-NatarajanT #framework #learning: Two New Frameworks for Learning (BKN, PT), pp. 402–415.
ML-1988-Pazzani #learning: Integrated Learning with Incorrect and Incomplete Theories (MJP), pp. 291–297.
ML-1988-Sammut #algorithm #evaluation: Experimental Results from an Evaluation of Algorithms that Learn to Control Dynamic Systems (CS), pp. 437–443.
ML-1988-Segen #graph #learning #modelling: Learning Graph Models of Shape (JS), pp. 29–35.
ML-1988-Spackman #category theory #learning: Learning Categorical Decision Criteria in Biomedical Domains (KAS), pp. 36–46.
ML-1988-Tesauro #learning: Connectionist Learning of Expert Backgammon Evaluations (GT), pp. 200–206.
ML-1988-Williams #learning: Learning to Program by Examining and Modifying Cases (RSW), pp. 318–324.
ML-1988-WisniewskiA #induction #learning: Some Interesting Properties of a Connectionist Inductive Learning System (EJW, JAA), pp. 181–187.
SIGIR-1988-YuM #information retrieval #learning: Two Learning Schemes in Information Retrieval (CTY, HM), pp. 201–218.
PPEALS-1988-TambeKGFMN #learning #named #parallel: Soar/PSM-E: Investigating Match Parallelism in a Learning Production System (MT, DK, AG, CF, BM, AN), pp. 146–160.
CADE-1988-DonatW #higher-order #learning #using: Learning and Applying Generalised Solutions using Higher Order Resolution (MRD, LAW), pp. 41–60.
STOC-1987-Natarajan #learning #on the: On Learning Boolean Functions (BKN), pp. 296–304.
ICALP-1987-PittS #learning #probability: Probability and Plurality for Aggregations of Learning Machines (LP, CHS), pp. 1–10.
ICALP-1987-Valiant #formal method #learning: Recent Developments in the Theory of Learning (Abstract) (LGV), p. 563.
HCI-CE-1987-Bosser #evaluation #learning: The Evaluation of Learning Requirement of IT Systems (TB), pp. 45–52.
SIGIR-1987-OommenM #automaton #clustering #learning #performance #probability #using: Fast Object Partitioning Using Stochastic Learning Automata (BJO, DCYM), pp. 111–122.
ICSE-1987-Boehm #lessons learnt #process: Software Process Management: Lessons Learned from History (BWB), pp. 296–298.
CSL-1987-RinnS #fault #learning: Learning by Teams from Examples with Errors (RR, BS), pp. 223–234.
SIGIR-1986-DeogunR #clustering #documentation #framework #information retrieval #learning: User-Oriented Document Clustering: A Framework for Learning in Information Retrieval (JSD, VVR), pp. 157–163.
SIGIR-1986-WongZ #approach #information retrieval #machine learning: A Machine Learning Approach to Information Retrieval (SKMW, WZ), pp. 228–233.
VLDB-1985-BorgidaW #database #exception #learning: Accommodating Exceptions in Databases, and Refining the Schema by Learning from them (AB, KEW), pp. 72–81.
SIGIR-1985-Gordon #algorithm #documentation #learning: A Learning Algorithm Applied to Document Description (MG), pp. 179–186.
SIGIR-1984-Allan #information retrieval #learning: Computerised Information Retrieval Systems for Open Learning (BA), pp. 325–341.