BibSLEIGH
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Stem learn$ (all stems)

4419 papers:

ECSAECSA-2015-KiwelekarW #architecture #learning
Learning Objectives for a Course on Software Architecture (AWK, HSW), pp. 169–180.
CASECASE-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.
CASECASE-2015-FarhanPWL #algorithm #machine learning #predict #using
Predicting individual thermal comfort using machine learning algorithms (AAF, KRP, BW, PBL), pp. 708–713.
CASECASE-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.
CASECASE-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.
CASECASE-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.
CASECASE-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.
CASECASE-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.
CASECASE-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.
CASECASE-2015-WatteyneAV #lessons learnt #scalability
Lessons learned from large-scale dense IEEE802.15.4 connectivity traces (TW, CA, XV), pp. 145–150.
CASECASE-2015-ZhangWZZ #automaton #learning #optimisation #performance
Incorporation of ordinal optimization into learning automata for high learning efficiency (JZ, CW, DZ, MZ), pp. 1206–1211.
DACDAC-2015-SztipanovitsBNK #cyber-physical #design #lessons learnt
Design tool chain for cyber-physical systems: lessons learned (JS, TB, SN, XDK, EKJ), p. 6.
DACDAC-2015-VenkataramaniRL #classification #energy #machine learning
Scalable-effort classifiers for energy-efficient machine learning (SV, AR, JL, MS), p. 6.
DATEDATE-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.
DATEDATE-2015-ChenM #distributed #learning #manycore #optimisation #performance
Distributed reinforcement learning for power limited many-core system performance optimization (ZC, DM), pp. 1521–1526.
DATEDATE-2015-KanounS #big data #concept #data type #detection #learning #online #scheduling #streaming
Big-data streaming applications scheduling with online learning and concept drift detection (KK, MvdS), pp. 1547–1550.
DATEDATE-2015-RenTB #detection #learning #statistics
Detection of illegitimate access to JTAG via statistical learning in chip (XR, VGT, RD(B), pp. 109–114.
DATEDATE-2015-ZhuM #linear #machine learning #optimisation #programming #using
Optimizing dynamic trace signal selection using machine learning and linear programming (CSZ, SM), pp. 1289–1292.
DocEngDocEng-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.
DocEngDocEng-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.
DRRDRR-2015-FuLLQT #diagrams #learning #multi #retrieval
A diagram retrieval method with multi-label learning (SF, XL, LL, JQ, ZT).
HTHT-2015-KirchnerR #collaboration #in the cloud #learning
Collaborative Learning in the Cloud: A Cross-Cultural Perspective of Collaboration (KK, LR), pp. 333–336.
HTHT-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.
SIGMODSIGMOD-2015-HuangBTRTR #machine learning #scalability
Resource Elasticity for Large-Scale Machine Learning (BH, MB, YT, BR, ST, FRR), pp. 137–152.
SIGMODSIGMOD-2015-KumarNP #learning #linear #modelling #normalisation
Learning Generalized Linear Models Over Normalized Data (AK, JFN, JMP), pp. 1969–1984.
SIGMODSIGMOD-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.
VLDBVLDB-2015-KumarJYNP #machine learning #normalisation #optimisation
Demonstration of Santoku: Optimizing Machine Learning over Normalized Data (AK, MJ, BY, JFN, JMP), pp. 1864–1875.
VLDBVLDB-2015-QianGJ #adaptation #comparison #learning
Learning User Preferences By Adaptive Pairwise Comparison (LQ, JG, HVJ), pp. 1322–1333.
ITiCSEITiCSE-2015-AlshammariAH #adaptation #education #learning #security
The Impact of Learning Style Adaptivity in Teaching Computer Security (MA, RA, RJH), pp. 135–140.
ITiCSEITiCSE-2015-AndersonNM #programming
Facilitating Programming Success in Data Science Courses through Gamified Scaffolding and Learn2Mine (PEA, TN, RAM), pp. 99–104.
ITiCSEITiCSE-2015-Annamaa #ide #learning #programming #python
Thonny, : a Python IDE for Learning Programming (AA), p. 343.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2015-Hamilton #education #learning
Learning and Teaching Computing Sustainability (MH), p. 338.
ITiCSEITiCSE-2015-Harms #community #learning #source code
Department Programs to Encourage and Support Service Learning and Community Engagement (DEH), p. 330.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2015-ParreiraPC #c #named #student
PCRS-C: Helping Students Learn C (DMP, AP, MC), p. 347.
ITiCSEITiCSE-2015-QuinsonO #education #learning #programming
A Teaching System to Learn Programming: the Programmer’s Learning Machine (MQ, GO), pp. 260–265.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2015-SettleLS #community #learning
A Computer Science Linked-courses Learning Community (AS, JL, TS), pp. 123–128.
ITiCSEITiCSE-2015-TarmazdiVSFF #learning #using #visualisation
Using Learning Analytics to Visualise Computer Science Teamwork (HT, RV, CS, KEF, NJGF), pp. 165–170.
ITiCSEITiCSE-2015-Tudor #learning #optimisation #query #xml
Virtual Learning Laboratory about Query Optimization against XML Data (LNT), p. 348.
ICSMEICSME-2015-CorleyDK #feature model #learning
Exploring the use of deep learning for feature location (CSC, KD, NAK), pp. 556–560.
ICSMEICSME-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.
MSRMSR-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.
MSRMSR-2015-WhiteVVP #learning #repository #towards
Toward Deep Learning Software Repositories (MW, CV, MLV, DP), pp. 334–345.
STOCSTOC-2015-BarakKS #composition #learning #taxonomy
Dictionary Learning and Tensor Decomposition via the Sum-of-Squares Method (BB, JAK, DS), pp. 143–151.
STOCSTOC-2015-Bresler #graph #learning #modelling
Efficiently Learning Ising Models on Arbitrary Graphs (GB), pp. 771–782.
STOCSTOC-2015-GeHK #learning
Learning Mixtures of Gaussians in High Dimensions (RG, QH, SMK), pp. 761–770.
STOCSTOC-2015-HardtP #bound #learning
Tight Bounds for Learning a Mixture of Two Gaussians (MH, EP), pp. 753–760.
STOCSTOC-2015-LiRSS #learning #statistics
Learning Arbitrary Statistical Mixtures of Discrete Distributions (JL, YR, LJS, CS), pp. 743–752.
LATALATA-2015-Yoshinaka #boolean grammar #grammar inference #learning
Learning Conjunctive Grammars and Contextual Binary Feature Grammars (RY), pp. 623–635.
FMFM-2015-Damm #analysis #automation #lessons learnt #named #verification
AVACS: Automatic Verification and Analysis of Complex Systems Highlights and Lessons Learned (WD), pp. 18–19.
SEFMSEFM-2015-Muhlberg0DLP #learning #source code #verification
Learning Assertions to Verify Linked-List Programs (JTM, DHW, MD, GL, FP), pp. 37–52.
ICFPICFP-2015-ZhuNJ #learning #refinement
Learning refinement types (HZ, AVN, SJ), pp. 400–411.
CHICHI-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.
CHICHI-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.
CHICHI-2015-CaiGGM #education #named
Wait-Learning: Leveraging Wait Time for Second Language Education (CJC, PJG, JRG, RCM), pp. 3701–3710.
CHICHI-2015-DavisK #learning #student
Investigating High School Students’ Perceptions of Digital Badges in Afterschool Learning (KD, EK), pp. 4043–4046.
CHICHI-2015-KardanC #adaptation #evaluation #interactive #learning #simulation
Providing Adaptive Support in an Interactive Simulation for Learning: An Experimental Evaluation (SK, CC), pp. 3671–3680.
CHICHI-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.
CHICHI-2015-Noble #learning #self
Resilience Ex Machina: Learning a Complex Medical Device for Haemodialysis Self-Treatment (PJN), pp. 4147–4150.
CHICHI-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.
CHICHI-2015-ShovmanBSS #3d #interface #learning
Twist and Learn: Interface Learning in 3DOF Exploration of 3D Scatterplots (MMS, JLB, AS, KCSB), pp. 313–316.
CHICHI-2015-StrohmayerCB #learning #people
Exploring Learning Ecologies among People Experiencing Homelessness (AS, RC, MB), pp. 2275–2284.
CHICHI-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.
CHICHI-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.
CSCWCSCW-2015-Anya #design #exclamation #question #what
Bridge the Gap!: What Can Work Design in Crowdwork Learn from Work Design Theories? (OA), pp. 612–627.
CSCWCSCW-2015-ChengB #classification #hybrid #machine learning #named
Flock: Hybrid Crowd-Machine Learning Classifiers (JC, MSB), pp. 600–611.
CSCWCSCW-2015-CoetzeeLFHH #interactive #learning #scalability
Structuring Interactions for Large-Scale Synchronous Peer Learning (DC, SL, AF, BH, MAH), pp. 1139–1152.
CSCWCSCW-2015-DornSS #collaboration #learning
Piloting TrACE: Exploring Spatiotemporal Anchored Collaboration in Asynchronous Learning (BD, LBS, AS), pp. 393–403.
CSCWCSCW-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.
HCIDHM-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.
HCIDHM-HM-2015-NishimuraK #case study #learning
A Study on Learning Effects of Marking with Highlighter Pen (HN, NK), pp. 357–367.
HCIDUXU-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.
HCIDUXU-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.
HCIDUXU-DD-2015-Schneidermeier #lessons learnt #usability
Lessons Learned in Usability Consulting (TS), pp. 247–255.
HCIDUXU-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.
HCIDUXU-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.
HCIDUXU-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.
HCIDUXU-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.
HCIDUXU-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.
HCIHCI-DE-2015-BakkeB #developer #learning #proximity
The Closer the Better: Effects of Developer-User Proximity for Mutual Learning (SB, TB), pp. 14–26.
HCIHCI-IT-2015-TadayonMGRZLGP #case study #interactive #learning
Interactive Motor Learning with the Autonomous Training Assistant: A Case Study (RT, TLM, MG, PMRF, JZ, ML, MG, SP), pp. 495–506.
HCIHIMI-IKC-2015-AraiTA #development #learning
Development of a Learning Support System for Class Structure Mapping Based on Viewpoint (TA, TT, TA), pp. 285–293.
HCIHIMI-IKC-2015-HasegawaD #approach #framework #learning #ubiquitous
A Ubiquitous Lecture Archive Learning Platform with Note-Centered Approach (SH, JD), pp. 294–303.
HCIHIMI-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.
HCIHIMI-IKC-2015-Iwata #difference #learning
Method to Generate an Operation Learning Support System by Shortcut Key Differences in Similar Software (HI), pp. 332–340.
HCIHIMI-IKC-2015-KimitaMMNIS #education #learning
Learning State Model for Value Co-Creative Education Services (KK, KM, SM, YN, TI, YS), pp. 341–349.
HCIHIMI-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.
HCIHIMI-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.
HCIHIMI-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.
HCILCT-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.
HCILCT-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.
HCILCT-2015-DirinN #design #development #framework
Assessments of User Centered Design Framework for M-learning Application Development (AD, MN), pp. 62–74.
HCILCT-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.
HCILCT-2015-FardounAC #education #evaluation
Creation of Meaningful-Learning and Continuous Evaluation Education System (HMF, AA, APC), pp. 218–226.
HCILCT-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.
HCILCT-2015-FonsecaRVG #3d #education #learning
From Formal to Informal 3D Learning. Assesment of Users in the Education (DF, ER, FV, ODG), pp. 460–469.
HCILCT-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.
HCILCT-2015-GonzalezHGS #interactive #learning #student #tool support
Exploring Student Interactions: Learning Analytics Tools for Student Tracking (MÁCG, ÁHG, FJGP, MLSE), pp. 50–61.
HCILCT-2015-HoffmannPLSMJ #learning #student
Enhancing the Learning Success of Engineering Students by Virtual Experiments (MH, LP, LL, KS, TM, SJ), pp. 394–405.
HCILCT-2015-KimAKW #game studies #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.
HCILCT-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.
HCILCT-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.
HCILCT-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.
HCILCT-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.
HCILCT-2015-OrehovackiB #game studies #learning #programming #quality
Inspecting Quality of Games Designed for Learning Programming (TO, SB), pp. 620–631.
HCILCT-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.
HCILCT-2015-ShimizuO #design #implementation #learning #novel #word
Design and Implementation of Novel Word Learning System “Überall” (RS, KO), pp. 148–159.
HCILCT-2015-TamuraTHN #generative #learning #wiki
Generating Quizzes for History Learning Based on Wikipedia Articles (YT, YT, YH, YIN), pp. 337–346.
HCILCT-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.
HCILCT-2015-VielRTP #design #interactive #learning #multi
Design Solutions for Interactive Multi-video Multimedia Learning Objects (CCV, KRHR, CACT, MdGCP), pp. 160–171.
HCILCT-2015-YusoffK #design #game studies #interactive #learning #persuasion
Game Rhetoric: Interaction Design Model of Persuasive Learning for Serious Games (ZY, AK), pp. 644–654.
ICEISICEIS-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.
ICEISICEIS-v1-2015-RibeiroTWBE #learning
A Learning Model for Intelligent Agents Applied to Poultry Farming (RR, MT, ALW, APB, FE), pp. 495–503.
ICEISICEIS-v1-2015-SouzaBGBE #learning #online
Applying Ensemble-based Online Learning Techniques on Crime Forecasting (AJdS, APB, HMG, JPB, FE), pp. 17–24.
ICEISICEIS-v2-2015-Judrups #analysis #information management #integration
Analysis of Knowledge Management and E-Learning Integration Approaches (JJ), pp. 451–456.
ECIRECIR-2015-HuynhHR #analysis #learning #sentiment #strict
Learning Higher-Level Features with Convolutional Restricted Boltzmann Machines for Sentiment Analysis (TH, YH, SMR), pp. 447–452.
ECIRECIR-2015-KingI #clustering #generative #music
Generating Music Playlists with Hierarchical Clustering and Q-Learning (JK, VI), pp. 315–326.
ECIRECIR-2015-LiHLZ #twitter
Selecting Training Data for Learning-Based Twitter Search (DL, BH, TL, XZ), pp. 501–506.
ECIRECIR-2015-NicosiaBM #learning #rank
Learning to Rank Aggregated Answers for Crossword Puzzles (MN, GB, AM), pp. 556–561.
ECIRECIR-2015-PasinatoMZ #elicitation #learning #rating
Active Learning Applied to Rating Elicitation for Incentive Purposes (MBP, CEM, GZ), pp. 291–302.
ECIRECIR-2015-PelejaM #learning #retrieval #sentiment
Learning Sentiment Based Ranked-Lexicons for Opinion Retrieval (FP, JM), pp. 435–440.
ICMLICML-2015-AmidU #learning #multi
Multiview Triplet Embedding: Learning Attributes in Multiple Maps (EA, AU), pp. 1472–1480.
ICMLICML-2015-BachHBG #learning #performance
Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs (SHB, BH, JLBG, LG), pp. 381–390.
ICMLICML-2015-BlumH #contest #machine learning #reliability
The Ladder: A Reliable Leaderboard for Machine Learning Competitions (AB, MH), pp. 1006–1014.
ICMLICML-2015-Bou-AmmarTE #learning #policy #sublinear
Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret (HBA, RT, EE), pp. 2361–2369.
ICMLICML-2015-ChangKADL #education #learning
Learning to Search Better than Your Teacher (KWC, AK, AA, HDI, JL), pp. 2058–2066.
ICMLICML-2015-ChenSYU #learning #modelling
Learning Deep Structured Models (LCC, AGS, ALY, RU), pp. 1785–1794.
ICMLICML-2015-CilibertoMPR #learning #multi
Convex Learning of Multiple Tasks and their Structure (CC, YM, TAP, LR), pp. 1548–1557.
ICMLICML-2015-CohenH #learning #online
Following the Perturbed Leader for Online Structured Learning (AC, TH), pp. 1034–1042.
ICMLICML-2015-DanielyGS #adaptation #learning #online
Strongly Adaptive Online Learning (AD, AG, SSS), pp. 1405–1411.
ICMLICML-2015-FetayaU #invariant #learning
Learning Local Invariant Mahalanobis Distances (EF, SU), pp. 162–168.
ICMLICML-2015-GarberHM #learning #online
Online Learning of Eigenvectors (DG, EH, TM), pp. 560–568.
ICMLICML-2015-GuptaAGN #learning #precise
Deep Learning with Limited Numerical Precision (SG, AA, KG, PN), pp. 1737–1746.
ICMLICML-2015-HallakSMM #learning #modelling
Off-policy Model-based Learning under Unknown Factored Dynamics (AH, FS, TAM, SM), pp. 711–719.
ICMLICML-2015-Hernandez-Lobato15b #learning #network #probability #scalability
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks (JMHL, RA), pp. 1861–1869.
ICMLICML-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.
ICMLICML-2015-HongYKH #learning #network #online
Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network (SH, TY, SK, BH), pp. 597–606.
ICMLICML-2015-HsiehND #learning #matrix
PU Learning for Matrix Completion (CJH, NN, ISD), pp. 2445–2453.
ICMLICML-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.
ICMLICML-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.
ICMLICML-2015-JiangKS #abstraction #learning #modelling
Abstraction Selection in Model-based Reinforcement Learning (NJ, AK, SS), pp. 179–188.
ICMLICML-2015-Kandemir #learning #process #symmetry
Asymmetric Transfer Learning with Deep Gaussian Processes (MK), pp. 730–738.
ICMLICML-2015-KvetonSWA #learning #rank
Cascading Bandits: Learning to Rank in the Cascade Model (BK, CS, ZW, AA), pp. 767–776.
ICMLICML-2015-LakshmananOR #bound #learning
Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning (KL, RO, DR), pp. 524–532.
ICMLICML-2015-LeC #learning #metric #using
Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations (TL, MC), pp. 2002–2011.
ICMLICML-2015-LiuY #graph #learning #predict
Bipartite Edge Prediction via Transductive Learning over Product Graphs (HL, YY), pp. 1880–1888.
ICMLICML-2015-LondonHG #approximate #learning
The Benefits of Learning with Strongly Convex Approximate Inference (BL, BH, LG), pp. 410–418.
ICMLICML-2015-LongC0J #adaptation #learning #network
Learning Transferable Features with Deep Adaptation Networks (ML, YC, JW, MJ), pp. 97–105.
ICMLICML-2015-Lopez-PazMST #learning #towards
Towards a Learning Theory of Cause-Effect Inference (DLP, KM, BS, IT), pp. 1452–1461.
ICMLICML-2015-MaclaurinDA #learning #optimisation
Gradient-based Hyperparameter Optimization through Reversible Learning (DM, DKD, RPA), pp. 2113–2122.
ICMLICML-2015-MarietS #algorithm #fixpoint #learning #process
Fixed-point algorithms for learning determinantal point processes (ZM, SS), pp. 2389–2397.
ICMLICML-2015-MenonROW #estimation #learning
Learning from Corrupted Binary Labels via Class-Probability Estimation (AKM, BvR, CSO, BW), pp. 125–134.
ICMLICML-2015-PerrotH #analysis #learning #metric
A Theoretical Analysis of Metric Hypothesis Transfer Learning (MP, AH), pp. 1708–1717.
ICMLICML-2015-PhamRFA #learning #multi #novel
Multi-instance multi-label learning in the presence of novel class instances (ATP, RR, XZF, JPA), pp. 2427–2435.
ICMLICML-2015-PiechHNPSG #feedback #learning #student
Learning Program Embeddings to Propagate Feedback on Student Code (CP, JH, AN, MP, MS, LJG), pp. 1093–1102.
ICMLICML-2015-PlessisNS #learning
Convex Formulation for Learning from Positive and Unlabeled Data (MCdP, GN, MS), pp. 1386–1394.
ICMLICML-2015-Romera-ParedesT #approach #learning
An embarrassingly simple approach to zero-shot learning (BRP, PHST), pp. 2152–2161.
ICMLICML-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.
ICMLICML-2015-SibonyCJ #learning #ranking #statistics
MRA-based Statistical Learning from Incomplete Rankings (ES, SC, JJ), pp. 1432–1441.
ICMLICML-2015-Sohl-DicksteinW #learning #using
Deep Unsupervised Learning using Nonequilibrium Thermodynamics (JSD, EAW, NM, SG), pp. 2256–2265.
ICMLICML-2015-SrivastavaMS #learning #using #video
Unsupervised Learning of Video Representations using LSTMs (NS, EM, RS), pp. 843–852.
ICMLICML-2015-SteinhardtL15a #learning #modelling #predict
Learning Fast-Mixing Models for Structured Prediction (JS, PL), pp. 1063–1072.
ICMLICML-2015-SwaminathanJ #feedback #learning
Counterfactual Risk Minimization: Learning from Logged Bandit Feedback (AS, TJ), pp. 814–823.
ICMLICML-2015-TangSX #learning #network
Learning Scale-Free Networks by Dynamic Node Specific Degree Prior (QT, SS, JX), pp. 2247–2255.
ICMLICML-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.
ICMLICML-2015-VanseijenS #learning
A Deeper Look at Planning as Learning from Replay (HV, RS), pp. 2314–2322.
ICMLICML-2015-WangALB #learning #multi #on the #representation
On Deep Multi-View Representation Learning (WW, RA, KL, JAB), pp. 1083–1092.
ICMLICML-2015-WangWLCW #learning #multi #segmentation
Multi-Task Learning for Subspace Segmentation (YW, DPW, QL, WC, IJW), pp. 1209–1217.
ICMLICML-2015-WangY #learning #matrix #multi
Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices (JW, JY), pp. 1747–1756.
ICMLICML-2015-WeiIB #learning #set
Submodularity in Data Subset Selection and Active Learning (KW, RKI, JAB), pp. 1954–1963.
ICMLICML-2015-WeissN #alias #learning
Learning Parametric-Output HMMs with Two Aliased States (RW, BN), pp. 635–644.
ICMLICML-2015-WenKA #combinator #learning #performance #scalability
Efficient Learning in Large-Scale Combinatorial Semi-Bandits (ZW, BK, AA), pp. 1113–1122.
ICMLICML-2015-WuS #algorithm #learning #modelling #online
An Online Learning Algorithm for Bilinear Models (YW, SS), pp. 890–898.
ICMLICML-2015-YogatamaFDS #learning #word
Learning Word Representations with Hierarchical Sparse Coding (DY, MF, CD, NAS), pp. 87–96.
ICMLICML-2015-YuB #learning
Learning Submodular Losses with the Lovasz Hinge (JY, MBB), pp. 1623–1631.
ICMLICML-2015-YuCL #learning #multi #online #rank
Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams (RY, DC, YL), pp. 238–247.
KDDKDD-2015-Agarwal #machine learning #scalability #statistics #web
Scaling Machine Learning and Statistics for Web Applications (DA), p. 1621.
KDDKDD-2015-Athey #evaluation #machine learning #policy
Machine Learning and Causal Inference for Policy Evaluation (SA), pp. 5–6.
KDDKDD-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.
KDDKDD-2015-Durrant-Whyte #machine learning
Data, Knowledge and Discovery: Machine Learning meets Natural Science (HDW), p. 7.
KDDKDD-2015-DuS #adaptation #feature model #learning
Unsupervised Feature Selection with Adaptive Structure Learning (LD, YDS), pp. 209–218.
KDDKDD-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.
KDDKDD-2015-GleichM #algorithm #graph #learning #using
Using Local Spectral Methods to Robustify Graph-Based Learning Algorithms (DFG, MWM), pp. 359–368.
KDDKDD-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.
KDDKDD-2015-HanZ #learning #multi
Learning Tree Structure in Multi-Task Learning (LH, YZ), pp. 397–406.
KDDKDD-2015-JohanssonD #geometry #graph #learning #similarity #using
Learning with Similarity Functions on Graphs using Matchings of Geometric Embeddings (FDJ, DPD), pp. 467–476.
KDDKDD-2015-Koller #named #question #what
MOOCS: What Have We Learned? (DK), p. 3.
KDDKDD-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.
KDDKDD-2015-LanH #complexity #learning #multi
Reducing the Unlabeled Sample Complexity of Semi-Supervised Multi-View Learning (CL, JH), pp. 627–634.
KDDKDD-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.
KDDKDD-2015-MaoWGS #graph #learning #reduction
Dimensionality Reduction Via Graph Structure Learning (QM, LW, SG, YS), pp. 765–774.
KDDKDD-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.
KDDKDD-2015-Papagiannopoulou #learning #multi
Discovering and Exploiting Deterministic Label Relationships in Multi-Label Learning (CP, GT, IT), pp. 915–924.
KDDKDD-2015-Pratt #machine learning #predict #protocol #proving
Proof Protocol for a Machine Learning Technique Making Longitudinal Predictions in Dynamic Contexts (KBP), pp. 2049–2058.
KDDKDD-2015-RiondatoU15a #algorithm #learning #statistics
VC-Dimension and Rademacher Averages: From Statistical Learning Theory to Sampling Algorithms (MR, EU), pp. 2321–2322.
KDDKDD-2015-Schleier-Smith #agile #architecture #machine learning #realtime
An Architecture for Agile Machine Learning in Real-Time Applications (JSS), pp. 2059–2068.
KDDKDD-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.
KDDKDD-2015-ShashidharPA #machine learning
Spoken English Grading: Machine Learning with Crowd Intelligence (VS, NP, VA), pp. 2089–2097.
KDDKDD-2015-SunAYMMBY #classification #learning
Transfer Learning for Bilingual Content Classification (QS, MSA, BY, CM, VM, AB, JY), pp. 2147–2156.
KDDKDD-2015-TanSZ0 #learning #transitive
Transitive Transfer Learning (BT, YS, EZ, QY), pp. 1155–1164.
KDDKDD-2015-VeeriahDQ #architecture #learning #predict
Deep Learning Architecture with Dynamically Programmed Layers for Brain Connectome Prediction (VV, RD, GJQ), pp. 1205–1214.
KDDKDD-2015-WangWY #collaboration #learning #recommendation
Collaborative Deep Learning for Recommender Systems (HW, NW, DYY), pp. 1235–1244.
KDDKDD-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.
KDDKDD-2015-XuSB #learning #predict
Longitudinal LASSO: Jointly Learning Features and Temporal Contingency for Outcome Prediction (TX, JS, JB), pp. 1345–1354.
KDDKDD-2015-YangH #learning #multi
Model Multiple Heterogeneity via Hierarchical Multi-Latent Space Learning (PY, JH), pp. 1375–1384.
KDDKDD-2015-YangSJWDY #learning #visual notation
Structural Graphical Lasso for Learning Mouse Brain Connectivity (SY, QS, SJ, PW, ID, JY), pp. 1385–1394.
KDDKDD-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.
KDDKDD-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.
KDDKDD-2015-ZhaoSYCLR #learning #multi
Multi-Task Learning for Spatio-Temporal Event Forecasting (LZ, QS, JY, FC, CTL, NR), pp. 1503–1512.
MLDMMLDM-2015-Chou #data-driven #geometry #learning
Data Driven Geometry for Learning (EPC), pp. 395–402.
MLDMMLDM-2015-DhulekarNOY #graph #learning #mining #predict
Seizure Prediction by Graph Mining, Transfer Learning, and Transformation Learning (ND, SN, BO, BY), pp. 32–52.
MLDMMLDM-2015-FerrerSR #approximate #distance #edit distance #graph #heuristic #learning
Learning Heuristics to Reduce the Overestimation of Bipartite Graph Edit Distance Approximation (MF, FS, KR), pp. 17–31.
MLDMMLDM-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.
MLDMMLDM-2015-KrasotkinaM #approach #optimisation #ranking
A Bayesian Approach to Sparse Learning-to-Rank for Search Engine Optimization (OK, VM), pp. 382–394.
MLDMMLDM-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.
RecSysRecSys-2015-AlmahairiKCC #collaboration #distributed #learning
Learning Distributed Representations from Reviews for Collaborative Filtering (AA, KK, KC, ACC), pp. 147–154.
RecSysRecSys-2015-HuD #machine learning #recommendation #scalability
Scalable Recommender Systems: Where Machine Learning Meets Search (SYDH, JD), pp. 365–366.
RecSysRecSys-2015-SongCL #incremental #matrix #recommendation
Incremental Matrix Factorization via Feature Space Re-learning for Recommender System (QS, JC, HL), pp. 277–280.
SEKESEKE-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.
SEKESEKE-2015-GoswamiWS #learning #performance #using
Using Learning Styles of Software Professionals to Improve their Inspection Team Performance (AG, GSW, AS), pp. 680–685.
SEKESEKE-2015-LiuXC #learning #recommendation
Context-aware Recommendation System with Anonymous User Profile Learning (YL, YX, MC), pp. 93–98.
SEKESEKE-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.
SEKESEKE-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.
SEKESEKE-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.
SEKESEKE-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.
SEKESEKE-2015-WanderleyP #detection #folksonomy #learning
Learning Folksonomies for Trend Detection in Task-Oriented Dialogues (GW, ECP), pp. 483–488.
SEKESEKE-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.
SIGIRSIGIR-2015-Arora #learning
Promoting User Engagement and Learning in Amorphous Search Tasks (PA), p. 1051.
SIGIRSIGIR-2015-CormackG #bibliography #learning #multi #perspective
Multi-Faceted Recall of Continuous Active Learning for Technology-Assisted Review (GVC, MRG), pp. 763–766.
SIGIRSIGIR-2015-FoleyBJ #learning #web
Learning to Extract Local Events from the Web (JF, MB, VJ), pp. 423–432.
SIGIRSIGIR-2015-HarveyHE #learning #query
Learning by Example: Training Users with High-quality Query Suggestions (MH, CH, DE), pp. 133–142.
SIGIRSIGIR-2015-Li15a #information retrieval #learning
Transfer Learning for Information Retrieval (PL), p. 1061.
SIGIRSIGIR-2015-LiuW #collaboration #learning
Learning Context-aware Latent Representations for Context-aware Collaborative Filtering (XL, WW), pp. 887–890.
SIGIRSIGIR-2015-MehrotraY #learning #query #rank #using
Representative & Informative Query Selection for Learning to Rank using Submodular Functions (RM, EY), pp. 545–554.
SIGIRSIGIR-2015-SeverynM #learning #network #rank
Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks (AS, AM), pp. 373–382.
SIGIRSIGIR-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.
SIGIRSIGIR-2015-SpinaPR #learning #microblog
Active Learning for Entity Filtering in Microblog Streams (DS, MHP, MdR), pp. 975–978.
SIGIRSIGIR-2015-WangGLXWC #learning #recommendation #representation
Learning Hierarchical Representation Model for NextBasket Recommendation (PW, JG, YL, JX, SW, XC), pp. 403–412.
SIGIRSIGIR-2015-WangLWZZ #learning #named
LBMCH: Learning Bridging Mapping for Cross-modal Hashing (YW, XL, LW, WZ, QZ), pp. 999–1002.
SIGIRSIGIR-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.
SIGIRSIGIR-2015-ZamaniMS #adaptation #evaluation #learning #multi
Adaptive User Engagement Evaluation via Multi-task Learning (HZ, PM, AS), pp. 1011–1014.
SIGIRSIGIR-2015-ZhengC #distributed #learning
Learning to Reweight Terms with Distributed Representations (GZ, JC), pp. 575–584.
MoDELSMoDELS-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.
MoDELSMoDELS-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.
OOPSLAOOPSLA-2015-OhYY #adaptation #learning #optimisation #program analysis
Learning a strategy for adapting a program analysis via bayesian optimisation (HO, HY, KY), pp. 572–588.
SACSAC-2015-BarrosCMP #education #learning #repository #reuse
Integrating educational repositories to improve the reuse of learning objects (HB, EC, JM, RP), pp. 270–272.
SACSAC-2015-Brefeld #learning #multi
Multi-view learning with dependent views (UB), pp. 865–870.
SACSAC-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.
SACSAC-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.
SACSAC-2015-GomesBE #classification #data type #learning
Pairwise combination of classifiers for ensemble learning on data streams (HMG, JPB, FE), pp. 941–946.
SACSAC-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.
SACSAC-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.
SACSAC-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.
SACSAC-2015-OmatuYI #classification #learning #smell
Smell classification of wines by the learning vector quantization method (SO, MY, YI), pp. 195–200.
SACSAC-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.
SACSAC-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.
SACSAC-2015-Pesare #learning #social
Smart learning environments for social learning (EP), pp. 273–274.
SACSAC-2015-ReadPB #data type #learning
Deep learning in partially-labeled data streams (JR, FPC, AB), pp. 954–959.
SACSAC-2015-ReddySC #approach #aspect-oriented #incremental #learning #performance #weaving
Incremental aspect weaving: an approach for faster AOP learning (YRR, AS, MC), pp. 1480–1485.
SACSAC-2015-RegoMP #approach #detection #folksonomy #learning
A supervised learning approach to detect subsumption relations between tags in folksonomies (ASdCR, LBM, CESP), pp. 409–415.
SACSAC-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.
SACSAC-2015-SugiyamaS #learning #multi
Meta-strategy for cooperative tasks with learning of environments in multi-agent continuous tasks (AS, TS), pp. 494–500.
SACSAC-2015-WanderleyP #folksonomy #learning
Learning folksonomies from task-oriented dialogues (GMPW, ECP), pp. 360–367.
ESEC-FSEESEC-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-FSEESEC-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-FSEESEC-FSE-2015-SunXLLQ #abstraction #learning #named #testing #validation
TLV: abstraction through testing, learning, and validation (JS, HX, YL, SWL, SQ), pp. 698–709.
ICSEICSE-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.
ICSEICSE-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.
ICSEICSE-v1-2015-ZhuHFZLZ #developer #learning
Learning to Log: Helping Developers Make Informed Logging Decisions (JZ, PH, QF, HZ, MRL, DZ), pp. 415–425.
ICSEICSE-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.
ICSEICSE-v2-2015-Honsel #evolution #learning #mining #simulation #statistics
Statistical Learning and Software Mining for Agent Based Simulation of Software Evolution (VH), pp. 863–866.
ICSEICSE-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.
ICSEICSE-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.
ICSEICSE-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.
ICSEICSE-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.
ICSEICSE-v2-2015-SedelmaierL #education #induction #learning #re-engineering
Active and Inductive Learning in Software Engineering Education (YS, DL), pp. 418–427.
ICSEICSE-v2-2015-SoundarajanJR #collaboration #re-engineering
Collaborative and Cooperative-Learning in Software Engineering Courses (NS, SJ, RR), pp. 319–322.
ICSEICSE-v2-2015-WilkinsG #design #learning #student
Drawing Insight from Student Perceptions of Reflective Design Learning (TVW, JCG), pp. 253–262.
ASPLOSASPLOS-2015-LiuCLZZTFZC #machine learning #named
PuDianNao: A Polyvalent Machine Learning Accelerator (DFL, TC, SL, JZ, SZ, OT, XF, XZ, YC), pp. 369–381.
CGOCGO-2015-McAfeeO #framework #generative #learning #multi #named
EMEURO: a framework for generating multi-purpose accelerators via deep learning (LCM, KO), pp. 125–135.
HPCAHPCA-2015-WuGLJC #estimation #machine learning #performance #using
GPGPU performance and power estimation using machine learning (GYW, JLG, AL, NJ, DC), pp. 564–576.
PPoPPPPoPP-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.
CAVCAV-2015-BrazdilCCFK #learning #markov #process
Counterexample Explanation by Learning Small Strategies in Markov Decision Processes (TB, KC, MC, AF, JK), pp. 158–177.
CAVCAV-2015-GehrDV #commutative #learning #specification
Learning Commutativity Specifications (TG, DD, MTV), pp. 307–323.
CAVCAV-2015-IsbernerHS #automaton #framework #learning #open source
The Open-Source LearnLib — A Framework for Active Automata Learning (MI, FH, BS), pp. 487–495.
CAVCAV-2015-Saha0M #learning #named
Alchemist: Learning Guarded Affine Functions (SS, PG, PM), pp. 440–446.
ICLPICLP-2015-MartinezRIAT #learning #modelling #probability
Learning Probabilistic Action Models from Interpretation Transitions (DM, TR, KI, GA, CT).
ICLPICLP-J-2015-LawRB #constraints #learning #programming #set
Learning weak constraints in answer set programming (ML, AR, KB), pp. 511–525.
ICSTSAT-2015-TuHJ #learning #named #reasoning #satisfiability
QELL: QBF Reasoning with Extended Clause Learning and Levelized SAT Solving (KHT, TCH, JHRJ), pp. 343–359.
WICSAWICSA-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.
ASEASE-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.
CASECASE-2014-HabibDBHP #android #learning
Learning human-like facial expressions for Android Phillip K. Dick (AH, SKD, ICB, DH, DOP), pp. 1159–1165.
CASECASE-2014-HwangLW #adaptation #learning
Adaptive reinforcement learning in box-pushing robots (KSH, JLL, WHW), pp. 1182–1187.
CASECASE-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.
CASECASE-2014-MaDLZ #learning #modelling #simulation
Modeling and simulation of product diffusion considering learning effect (KPM, XD, CFL, JZ), pp. 665–670.
CASECASE-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.
CASECASE-2014-MinakaisMW #learning
Groundhog Day: Iterative learning for building temperature control (MM, SM, JTW), pp. 948–953.
CASECASE-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.
CASECASE-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.
DACDAC-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.
DACDAC-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.
DACDAC-2014-FarkashHB #incremental #validation
Coverage Learned Targeted Validation for Incremental HW Changes (MF, BGH, MB), p. 6.
DATEDATE-2014-HanKNV #learning
A deep learning methodology to proliferate golden signoff timing (SSH, ABK, SN, ASV), pp. 1–6.
DATEDATE-2014-XuB #hybrid #question
Hybrid side-channel/machine-learning attacks on PUFs: A new threat? (XX, WB), pp. 1–6.
DRRDRR-2014-CartonLC #interactive #learning #named
LearnPos: a new tool for interactive learning positioning (CC, AL, BC), p. ?–12.
DRRDRR-2014-MaXA #algorithm #machine learning #segmentation #video
A machine learning based lecture video segmentation and indexing algorithm (DM, BX, GA), p. ?–8.
DRRDRR-2014-TaoTX #documentation #learning #random #using
Document page structure learning for fixed-layout e-books using conditional random fields (XT, ZT, CX), p. ?–9.
HTHT-2014-AbbasiTL #learning #scalability #using
Scalable learning of users’ preferences using networked data (MAA, JT, HL), pp. 4–12.
SIGMODSIGMOD-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.
SIGMODSIGMOD-2014-Sedlar #compilation #how
How i learned to stop worrying and love compilers (ES), pp. 1–2.
VLDBVLDB-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.
VLDBVLDB-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.
VLDBVLDB-2014-ZouJLGWX #framework #learning #named
Mariana: Tencent Deep Learning Platform and its Applications (YZ, XJ, YL, ZG, EW, BX), pp. 1772–1777.
VLDBVLDB-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.
CSEETCSEET-2014-Ackerman #learning #re-engineering
An active learning module for an introduction to software engineering course (AFA), pp. 190–191.
CSEETCSEET-2014-BoeschS #automation #learning
Automated mentor assignment in blended learning environments (CB, KS), pp. 94–98.
CSEETCSEET-2014-Ding #learning #re-engineering #self
Self-guided learning environment for undergraduate software engineering (JD), pp. 188–189.
CSEETCSEET-2014-FranklBK #development #learning
Learning and working together as prerequisites for the development of high-quality software (GF, SB, BK), pp. 154–157.
CSEETCSEET-2014-KroppMMZ #agile #collaboration #education #learning
Teaching and learning agile collaboration (MK, AM, MM, CGZ), pp. 139–148.
CSEETCSEET-2014-PotterSDW #game studies #learning #named
InspectorX: A game for software inspection training and learning (HP, MS, LD, VW), pp. 55–64.
CSEETCSEET-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.
CSEETCSEET-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.
ITiCSEITiCSE-2014-BainB #programming #question #why
Why is programming so hard to learn? (GB, IB), p. 356.
ITiCSEITiCSE-2014-BerryK #game studies #learning #programming
The state of play: a notional machine for learning programming (MB, MK), pp. 21–26.
ITiCSEITiCSE-2014-EckerdalKTNSM #education #learning
Teaching and learning with MOOCs: computing academics’ perspectives and engagement (AE, PK, NT, AN, JS, LM), pp. 9–14.
ITiCSEITiCSE-2014-EllisH #learning #open source #re-engineering
Structuring software engineering learning within open source software participation (HJCE, GWH), p. 326.
ITiCSEITiCSE-2014-EllisJBPHD #learning
Learning within a professional environment: shared ownership of an HFOSS project (HJCE, SJ, DB, LP, GWH, JD), p. 337.
ITiCSEITiCSE-2014-FalknerVF #identification #learning #self
Identifying computer science self-regulated learning strategies (KF, RV, NJGF), pp. 291–296.
ITiCSEITiCSE-2014-GroverCP #learning
Assessing computational learning in K-12 (SG, SC, RP), pp. 57–62.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2014-Hijon-NeiraVPC #experience #game studies #learning #programming
Game programming for improving learning experience (RBHN, JÁVI, CPR, LC), pp. 225–230.
ITiCSEITiCSE-2014-Jasute #education #geometry #interactive #learning #visualisation
An interactive visualization method of constructionist teaching and learning of geometry (EJ), p. 349.
ITiCSEITiCSE-2014-KothiyalMI #learning #question #scalability
Think-pair-share in a large CS1 class: does learning really happen? (AK, SM, SI), pp. 51–56.
ITiCSEITiCSE-2014-Marcos-Abed #case study #effectiveness #learning #programming
Learning computer programming: a study of the effectiveness of a COAC# (JMA), p. 333.
ITiCSEITiCSE-2014-MedinaSGG #learning #student #using
Learning outcomes using objectives with computer science students (JAM, JJS, EGL, AGC), p. 339.
ITiCSEITiCSE-2014-PirkerRG #education #learning #student
Motivational active learning: engaging university students in computer science education (JP, MRS, CG), pp. 297–302.
ITiCSEITiCSE-2014-PriorCL #case study #experience #learning
Things coming together: learning experiences in a software studio (JP, AC, JL), pp. 129–134.
ITiCSEITiCSE-2014-Rogers #learning #question
New technology, new learning? (YR), p. 1.
ITiCSEITiCSE-2014-TaubBA #learning #physics
The effect of computer science on the learning of computational physics (RT, MBA, MA), p. 352.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2014-Verwaal #learning
Team based learning in theoretical computer science (NV), p. 331.
ITiCSEITiCSE-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.
ITiCSEITiCSE-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.
TFPIETFPIE-2014-Walck #haskell #physics #programming
Learn Physics by Programming in Haskell (SNW), pp. 67–77.
TACASTACAS-2014-MalerM #learning #regular expression #scalability
Learning Regular Languages over Large Alphabets (OM, IEM), pp. 485–499.
SANERCSMR-WCRE-2014-XiaFLCW #behaviour #learning #multi #towards
Towards more accurate multi-label software behavior learning (XX, YF, DL, ZC, XW), pp. 134–143.
ICPCICPC-2014-KaulgudAMT #comprehension #learning
Comprehension support during knowledge transitions: learning from field (VSK, KMA, JM, GT), pp. 205–206.
ICSMEICSME-2014-BinkleyL #information retrieval #learning #rank
Learning to Rank Improves IR in SE (DB, DJL), pp. 441–445.
ICSMEICSME-2014-XuanM #fault #learning #locality #metric #multi #ranking
Learning to Combine Multiple Ranking Metrics for Fault Localization (JX, MM), pp. 191–200.
STOCSTOC-2014-AwasthiBL #learning #linear #locality #power of
The power of localization for efficiently learning linear separators with noise (PA, MFB, PML), pp. 449–458.
STOCSTOC-2014-Christiano #learning #online #programming
Online local learning via semidefinite programming (PC), pp. 468–474.
STOCSTOC-2014-DanielyLS #complexity #learning
From average case complexity to improper learning complexity (AD, NL, SSS), pp. 441–448.
ICALPICALP-v1-2014-Volkovich #bound #learning #on the
On Learning, Lower Bounds and (un)Keeping Promises (IV), pp. 1027–1038.
ICALPICALP-v2-2014-DamsHK #learning #network
Jamming-Resistant Learning in Wireless Networks (JD, MH, TK), pp. 447–458.
LATALATA-2014-LaurenceLNST #learning #transducer
Learning Sequential Tree-to-Word Transducers (GL, AL, JN, SS, MT), pp. 490–502.
FMFM-2014-LinH #composition #concurrent #learning #model checking #synthesis
Compositional Synthesis of Concurrent Systems through Causal Model Checking and Learning (SWL, PAH), pp. 416–431.
SEFMSEFM-2014-CasselHJS #finite #learning #state machine
Learning Extended Finite State Machines (SC, FH, BJ, BS), pp. 250–264.
CHICHI-2014-DontchevaMBG #crowdsourcing #learning #performance
Combining crowdsourcing and learning to improve engagement and performance (MD, RRM, JRB, EMG), pp. 3379–3388.
CHICHI-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.
CHICHI-2014-GreenbergG #learning #online
Learning to fail: experiencing public failure online through crowdfunding (MDG, EG), pp. 581–590.
CHICHI-2014-KovacsM #learning
Smart subtitles for vocabulary learning (GK, RCM), pp. 853–862.
CHICHI-2014-KuleszaACFC #concept #evolution #machine learning
Structured labeling for facilitating concept evolution in machine learning (TK, SA, RC, DF, DXC), pp. 3075–3084.
CHICHI-2014-MentisCS #learning
Learning to see the body: supporting instructional practices in laparoscopic surgical procedures (HMM, AC, SDS), pp. 2113–2122.
CHICHI-2014-MonserratLZC #interactive #learning
L.IVE: an integrated interactive video-based learning environment (TJKPM, YL, SZ, XC), pp. 3399–3402.
CHICHI-2014-PilliasRL #design #game studies #lessons learnt #video
Designing tangible video games: lessons learned from the sifteo cubes (CP, RRB, GL), pp. 3163–3166.
CHICHI-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.
CSCWCSCW-2014-MillerZGG #collaboration #learning #people #research
Pair research: matching people for collaboration, learning, and productivity (RCM, HZ, EG, EG), pp. 1043–1048.
CSCWCSCW-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.
CSCWCSCW-2014-ZhuDKK #assessment #learning #performance
Reviewing versus doing: learning and performance in crowd assessment (HZ, SPD, REK, AK), pp. 1445–1455.
HCIDHM-2014-GotoYTWS
Application of E-learning System Reality in Kyoto-style Earthen Wall Training (AG, HY, YT, ZW, HS), pp. 247–253.
HCIDUXU-DI-2014-ChangH
Effect of Perception-Compatibility, Learning-Factor, and Symbol-Carrier on Single LED Symbol System Recognizing (CCC, TKPH), pp. 417–424.
HCIDUXU-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.
HCIDUXU-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.
HCIDUXU-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.
HCIDUXU-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.
HCIDUXU-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.
HCIDUXU-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.
HCIDUXU-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.
HCIDUXU-ELAS-2014-Portugal #design
Design, User-Experience and Teaching-Learning (CP), pp. 230–241.
HCIDUXU-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.
HCIHCI-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.
HCIHCI-AIMT-2014-MikamiM #3d #effectiveness #learning
Effectiveness of Virtual Hands in 3D Learning Material (TM, SM), pp. 93–101.
HCIHCI-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.
HCIHCI-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.
HCIHCI-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.
HCIHCI-AS-2014-SchwallerKAL #feedback #gesture #learning #visual notation
Improving In-game Gesture Learning with Visual Feedback (MS, JK, LA, DL), pp. 643–653.
HCIHCI-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.
HCIHCI-TMT-2014-MorDHF #education #human-computer #learning #online
Teaching and Learning HCI Online (EM, MGD, EH, NF), pp. 230–241.
HCIHCI-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.
HCIHCI-TMT-2014-YajimaTS #collaboration #learning
Proposal of Collaborative Learning Support Method in Risk Communications (HY, NT, RS), pp. 457–465.
HCIHIMI-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.
HCIHIMI-AS-2014-HirashimaYH #learning #problem #word
Triplet Structure Model of Arithmetical Word Problems for Learning by Problem-Posing (TH, SY, YH), pp. 42–50.
HCIHIMI-AS-2014-HirokawaFSY #learning #mindmap
Learning Winespeak from Mind Map of Wine Blogs (SH, BF, TS, CY), pp. 383–393.
HCIHIMI-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.
HCIHIMI-AS-2014-MikamiT #learning #music #performance
A Music Search System for Expressive Music Performance Learning (TM, KT), pp. 80–89.
HCIHIMI-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.
HCIHIMI-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.
HCIHIMI-AS-2014-YamaguchiTT #learning #process #visualisation
Visualizing Mental Learning Processes with Invisible Mazes for Continuous Learning (TY, KT, KT), pp. 137–148.
HCIHIMI-DE-2014-LinKT #analysis #collaboration #design #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.
HCILCT-NLE-2014-Choffat-Durr #distance #process
Distance Exchange Projects at Elementary School: A Focus on a Co-learning Process (ACD), pp. 380–387.
HCILCT-NLE-2014-KaprosP #learning
Empowering L&D Managers through Customisation of Inline Learning Analytics (EK, NP), pp. 282–291.
HCILCT-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.
HCILCT-NLE-2014-LimongelliS #fuzzy #modelling #personalisation #student
Fuzzy Student Modeling for Personalization of e-Learning Courses (CL, FS), pp. 292–301.
HCILCT-NLE-2014-Milde #editing #html #learning #online
An HTML5-Based Online Editor for Creating Annotated Learning Videos (JTM), pp. 172–179.
HCILCT-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.
HCILCT-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.
HCILCT-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.
HCILCT-NLE-2014-MoriT #development #learning
Development of a Fieldwork Support System for Group Work in Project-Based Learning (MM, AT), pp. 429–440.
HCILCT-NLE-2014-Piki #collaboration #learning #process #question
Learner Engagement in Computer-Supported Collaborative Learning Activities: Natural or Nurtured? (AP), pp. 107–118.
HCILCT-NLE-2014-Said #sorting
Card Sorting Assessing User Attitude in E-Learning (GRES), pp. 261–272.
HCILCT-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.
HCILCT-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.
HCILCT-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.
HCILCT-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.
HCILCT-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.
HCILCT-TRE-2014-Bharali #learning #online #process
Enhancing Online Learning Activities for Groups in Flipped Classrooms (RB), pp. 269–276.
HCILCT-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.
HCILCT-TRE-2014-Castro #case study #collaboration #learning #named
Mosca — A Case Study on Collaborative Work — Combining Dimensions while Learning (SC), pp. 388–396.
HCILCT-TRE-2014-EradzeL #design #interactive #learning
Interrelation between Pedagogical Design and Learning Interaction Patterns in different Virtual Learning Environments (ME, ML), pp. 23–32.
HCILCT-TRE-2014-Hayes14a #approach #development #game studies #learning #simulation
An Approach to Holistic Development of Serious Games and Learning Simulations (ATH), pp. 42–49.
HCILCT-TRE-2014-HiramatsuIFS #development #learning #using
Development of the Learning System for Outdoor Study Using Zeigarnik Effect (YH, AI, MF, FS), pp. 127–137.
HCILCT-TRE-2014-IkedaS #learning
Dream Drill: A Bedtime Learning Application (AI, IS), pp. 138–145.
HCILCT-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.
HCILCT-TRE-2014-MartinezMLLC #3d #interactive #learning
Supporting Learning with 3D Interactive Applications in Early Years (ACM, MJMS, MLS, DCPL, MC), pp. 11–22.
HCILCT-TRE-2014-MartinWH #interactive #learning #mobile
Sensor Based Interaction Mechanisms in Mobile Learning (KUM, MW, WH), pp. 165–172.
HCILCT-TRE-2014-OliveiraM #learning #network #research
Digital Identity of Researchers and Their Personal Learning Network (NRO, LM), pp. 467–477.
HCILCT-TRE-2014-ShahoumianSZPH #education #learning #simulation
Blended Simulation Based Medical Education: A Complex Learning/Training Opportunity (AS, MS, MZ, GP, JH), pp. 478–485.
HCILCT-TRE-2014-ShimizuO #effectiveness #learning #question
Which Is More Effective for Learning German and Japanese Language, Paper or Digital? (RS, KO), pp. 309–318.
HCILCT-TRE-2014-SzklannyW #learning #prototype
Prototyping M-Learning Course on the Basis of Puzzle Learning Methodology (KS, MW), pp. 215–226.
HCILCT-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.
AdaEuropeAdaEurope-2014-Laine #lessons learnt
Lessons Learned and Easily Forgotten (RL), pp. 1–6.
HILTHILT-2014-BarnesT #ada #design #lessons learnt
Ada 83 to Ada 2012: lessons learned over 30 years of language design (JB, STT), pp. 3–4.
ICEISICEIS-v1-2014-ShakirIB #machine learning #topic
Machine Learning Techniques for Topic Spotting (NS, EI, ISB), pp. 450–455.
ICEISICEIS-v2-2014-MahmoudBAG #approach #learning
A New Approach Based on Learning Services to Generate Appropriate Learning Paths (CBM, FB, MHA, FG), pp. 643–646.
ICEISICEIS-v2-2014-OtonBGGB #learning #metadata #using
Description of Accessible Learning Resources by Using Metadata (SO, CB, EG, AGC, RB), pp. 620–626.
ICEISICEIS-v2-2014-ZhengJL #hybrid #learning #taxonomy #using
Cross-Sensor Iris Matching using Patch-based Hybrid Dictionary Learning (BRZ, DYJ, YHL), pp. 169–174.
ICEISICEIS-v3-2014-AzevedoF #case study #education #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.
ICEISICEIS-v3-2014-BalinaAMS #development #metamodelling
Meta Model of e-Learning Materials Development (SB, IA, IM, ES), pp. 150–155.
ICEISICEIS-v3-2014-PaulinsBA #visualisation
e-Learning Material Presentation and Visualization Types and Schemes (NP, SB, IA), pp. 138–143.
CIKMCIKM-2014-DeBBGC #learning #linear
Learning a Linear Influence Model from Transient Opinion Dynamics (AD, SB, PB, NG, SC), pp. 401–410.
CIKMCIKM-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.
CIKMCIKM-2014-GoncalvesDCSZB #learning #multi
Multi-task Sparse Structure Learning (ARG, PD, SC, VS, FJVZ, AB), pp. 451–460.
CIKMCIKM-2014-JinZXDLH #learning #multi
Multi-task Multi-view Learning for Heterogeneous Tasks (XJ, FZ, HX, CD, PL, QH), pp. 441–450.
CIKMCIKM-2014-MaoWHO #classification #learning #linear #multi
Nonlinear Classification via Linear SVMs and Multi-Task Learning (XM, OW, WH, PO), pp. 1955–1958.
CIKMCIKM-2014-PfeifferNB #learning #network #probability #using
Active Exploration in Networks: Using Probabilistic Relationships for Learning and Inference (JJPI, JN, PNB), pp. 639–648.
CIKMCIKM-2014-PimplikarGBP #learning
Learning to Propagate Rare Labels (RP, DG, DB, GRP), pp. 201–210.
CIKMCIKM-2014-ShiKBLH #learning #named #recommendation
CARS2: Learning Context-aware Representations for Context-aware Recommendations (YS, AK, LB, ML, AH), pp. 291–300.
CIKMCIKM-2014-VinzamuriLR #learning
Active Learning based Survival Regression for Censored Data (BV, YL, CKR), pp. 241–250.
CIKMCIKM-2014-WangMC #learning #parametricity
Structure Learning via Parameter Learning (WYW, KM, WWC), pp. 1199–1208.
CIKMCIKM-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.
CIKMCIKM-2014-XiePLW #framework #image #learning #multi
A Cross-modal Multi-task Learning Framework for Image Annotation (LX, PP, YL, SW), pp. 431–440.
CIKMCIKM-2014-YangTZ #learning #streaming
Active Learning for Streaming Networked Data (ZY, JT, YZ), pp. 1129–1138.
CIKMCIKM-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.
CIKMCIKM-2014-YuX #interactive #learning #network #predict #scalability #social
Learning Interactions for Social Prediction in Large-scale Networks (XY, JX), pp. 161–170.
CIKMCIKM-2014-ZhongPXYM #adaptation #collaboration #learning #recommendation
Adaptive Pairwise Preference Learning for Collaborative Recommendation with Implicit Feedbacks (HZ, WP, CX, ZY, ZM), pp. 1999–2002.
CIKMCIKM-2014-ZhuSY #information retrieval #learning #taxonomy
Cross-Modality Submodular Dictionary Learning for Information Retrieval (FZ, LS, MY), pp. 1479–1488.
ECIRECIR-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.
ECIRECIR-2014-BreussT #interactive #learning #recommendation #social #social media
Learning from User Interactions for Recommending Content in Social Media (MB, MT), pp. 598–604.
ECIRECIR-2014-FiliceCCB #effectiveness #kernel #learning #online
Effective Kernelized Online Learning in Language Processing Tasks (SF, GC, DC, RB), pp. 347–358.
ECIRECIR-2014-NainiA #feature model #learning #rank
Exploiting Result Diversification Methods for Feature Selection in Learning to Rank (KDN, ISA), pp. 455–461.
ECIRECIR-2014-QiDCW #information management #learning
Deep Learning for Character-Based Information Extraction (YQ, SGD, RC, JW), pp. 668–674.
ICMLICML-c1-2014-AroraBGM #bound #learning
Provable Bounds for Learning Some Deep Representations (SA, AB, RG, TM), pp. 584–592.
ICMLICML-c1-2014-DenisGH #bound #learning #matrix
Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning (FD, MG, AH), pp. 449–457.
ICMLICML-c1-2014-DickGS #learning #markov #online #process #sequence
Online Learning in Markov Decision Processes with Changing Cost Sequences (TD, AG, CS), pp. 512–520.
ICMLICML-c1-2014-JainT #bound #independence #learning
(Near) Dimension Independent Risk Bounds for Differentially Private Learning (PJ, AGT), pp. 476–484.
ICMLICML-c1-2014-LacosteMLL #learning
Agnostic Bayesian Learning of Ensembles (AL, MM, FL, HL), pp. 611–619.
ICMLICML-c1-2014-LajugieBA #clustering #learning #metric #problem
Large-Margin Metric Learning for Constrained Partitioning Problems (RL, FRB, SA), pp. 297–305.
ICMLICML-c1-2014-LuoS #learning #online #towards
Towards Minimax Online Learning with Unknown Time Horizon (HL, RES), pp. 226–234.
ICMLICML-c1-2014-MohriM #algorithm #learning #optimisation
Learning Theory and Algorithms for revenue optimization in second price auctions with reserve (MM, AMM), pp. 262–270.
ICMLICML-c1-2014-RooshenasL #interactive #learning #network
Learning Sum-Product Networks with Direct and Indirect Variable Interactions (AR, DL), pp. 710–718.
ICMLICML-c1-2014-ShalitC #coordination #learning #matrix #orthogonal
Coordinate-descent for learning orthogonal matrices through Givens rotations (US, GC), pp. 548–556.
ICMLICML-c1-2014-ShiZ #learning #online
Online Bayesian Passive-Aggressive Learning (TS, JZ), pp. 378–386.
ICMLICML-c1-2014-SolomonRGB #learning
Wasserstein Propagation for Semi-Supervised Learning (JS, RMR, LJG, AB), pp. 306–314.
ICMLICML-c1-2014-TandonR #graph #learning
Learning Graphs with a Few Hubs (RT, PDR), pp. 602–610.
ICMLICML-c1-2014-Yu0KD #learning #multi #scalability
Large-scale Multi-label Learning with Missing Labels (HFY, PJ, PK, ISD), pp. 593–601.
ICMLICML-c2-2014-AffandiFAT #kernel #learning #parametricity #process
Learning the Parameters of Determinantal Point Process Kernels (RHA, EBF, RPA, BT), pp. 1224–1232.
ICMLICML-c2-2014-AminHK #learning
Learning from Contagion (Without Timestamps) (KA, HH, MK), pp. 1845–1853.
ICMLICML-c2-2014-AndoniPV0 #learning #network
Learning Polynomials with Neural Networks (AA, RP, GV, LZ), pp. 1908–1916.
ICMLICML-c2-2014-AziziAG #composition #learning #network
Learning Modular Structures from Network Data and Node Variables (EA, EA, JEG), pp. 1440–1448.
ICMLICML-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.
ICMLICML-c2-2014-Bou-AmmarERT #learning #multi #online #policy
Online Multi-Task Learning for Policy Gradient Methods (HBA, EE, PR, MET), pp. 1206–1214.
ICMLICML-c2-2014-BrunskillL #learning
PAC-inspired Option Discovery in Lifelong Reinforcement Learning (EB, LL), pp. 316–324.
ICMLICML-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.
ICMLICML-c2-2014-CohenW #commutative #learning
Learning the Irreducible Representations of Commutative Lie Groups (TC, MW), pp. 1755–1763.
ICMLICML-c2-2014-DuLBS #information management #learning #network
Influence Function Learning in Information Diffusion Networks (ND, YL, MFB, LS), pp. 2016–2024.
ICMLICML-c2-2014-FangCL #graph #learning
Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness Probabilistically (YF, KCCC, HWL), pp. 406–414.
ICMLICML-c2-2014-GrandeWH #learning #performance #process
Sample Efficient Reinforcement Learning with Gaussian Processes (RCG, TJW, JPH), pp. 1332–1340.
ICMLICML-c2-2014-HoangLJK #learning #process
Nonmyopic ϵ-Bayes-Optimal Active Learning of Gaussian Processes (TNH, BKHL, PJ, MSK), pp. 739–747.
ICMLICML-c2-2014-HoulsbyHG #learning #matrix #robust
Cold-start Active Learning with Robust Ordinal Matrix Factorization (NH, JMHL, ZG), pp. 766–774.
ICMLICML-c2-2014-HuS #machine learning #multi #predict
Multi-period Trading Prediction Markets with Connections to Machine Learning (JH, AJS), pp. 1773–1781.
ICMLICML-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.
ICMLICML-c2-2014-KricheneDB #convergence #learning #on the
On the convergence of no-regret learning in selfish routing (WK, BD, AMB), pp. 163–171.
ICMLICML-c2-2014-LevineK #learning #network #optimisation #policy
Learning Complex Neural Network Policies with Trajectory Optimization (SL, VK), pp. 829–837.
ICMLICML-c2-2014-LiG #classification #learning #representation #semantics
Latent Semantic Representation Learning for Scene Classification (XL, YG), pp. 532–540.
ICMLICML-c2-2014-LimL #learning #metric #performance #ranking
Efficient Learning of Mahalanobis Metrics for Ranking (DL, GRGL), pp. 1980–1988.
ICMLICML-c2-2014-LinK #constraints #learning #performance #representation
Stable and Efficient Representation Learning with Nonnegativity Constraints (THL, HTK), pp. 1323–1331.
ICMLICML-c2-2014-LinYHY #distance #learning
Geodesic Distance Function Learning via Heat Flow on Vector Fields (BL, JY, XH, JY), pp. 145–153.
ICMLICML-c2-2014-LiuD #learning #problem #set
Learnability of the Superset Label Learning Problem (LPL, TGD), pp. 1629–1637.
ICMLICML-c2-2014-LiZ #higher-order #learning #problem
High Order Regularization for Semi-Supervised Learning of Structured Output Problems (YL, RSZ), pp. 1368–1376.
ICMLICML-c2-2014-LiZ0 #learning #multi
Bayesian Max-margin Multi-Task Learning with Data Augmentation (CL, JZ, JC), pp. 415–423.
ICMLICML-c2-2014-MengEH #learning #modelling #visual notation
Learning Latent Variable Gaussian Graphical Models (ZM, BE, AOHI), pp. 1269–1277.
ICMLICML-c2-2014-MizrahiDF #learning #linear #markov #parallel #random
Linear and Parallel Learning of Markov Random Fields (YDM, MD, NdF), pp. 199–207.
ICMLICML-c2-2014-MnihG #learning #network
Neural Variational Inference and Learning in Belief Networks (AM, KG), pp. 1791–1799.
ICMLICML-c2-2014-NiuDPS #approximate #learning #multi
Transductive Learning with Multi-class Volume Approximation (GN, BD, MCdP, MS), pp. 1377–1385.
ICMLICML-c2-2014-PandeyD #learning #network
Learning by Stretching Deep Networks (GP, AD), pp. 1719–1727.
ICMLICML-c2-2014-PentinaL #bound #learning
A PAC-Bayesian bound for Lifelong Learning (AP, CHL), pp. 991–999.
ICMLICML-c2-2014-QinLJ #learning #optimisation
Sparse Reinforcement Learning via Convex Optimization (ZQ, WL, FJ), pp. 424–432.
ICMLICML-c2-2014-ReedSZL #interactive #learning
Learning to Disentangle Factors of Variation with Manifold Interaction (SR, KS, YZ, HL), pp. 1431–1439.
ICMLICML-c2-2014-RippelGA #learning #order
Learning Ordered Representations with Nested Dropout (OR, MAG, RPA), pp. 1746–1754.
ICMLICML-c2-2014-RodriguesPR #classification #learning #multi #process
Gaussian Process Classification and Active Learning with Multiple Annotators (FR, FCP, BR), pp. 433–441.
ICMLICML-c2-2014-SantosZ #learning
Learning Character-level Representations for Part-of-Speech Tagging (CNdS, BZ), pp. 1818–1826.
ICMLICML-c2-2014-SilvaKB #learning
Active Learning of Parameterized Skills (BCdS, GK, AGB), pp. 1737–1745.
ICMLICML-c2-2014-SongGJMHD #learning #locality #on the
On learning to localize objects with minimal supervision (HOS, RBG, SJ, JM, ZH, TD), pp. 1611–1619.
ICMLICML-c2-2014-SunIM #classification #learning #linear
Learning Mixtures of Linear Classifiers (YS, SI, AM), pp. 721–729.
ICMLICML-c2-2014-SunM #geometry #learning #statistics
An Information Geometry of Statistical Manifold Learning (KS, SMM), pp. 1–9.
ICMLICML-c2-2014-TrigeorgisBZS #learning
A Deep Semi-NMF Model for Learning Hidden Representations (GT, KB, SZ, BWS), pp. 1692–1700.
ICMLICML-c2-2014-WangHS #learning
Active Transfer Learning under Model Shift (XW, TKH, JS), pp. 1305–1313.
ICMLICML-c2-2014-WangNH #distance #learning #metric #robust
Robust Distance Metric Learning via Simultaneous L1-Norm Minimization and Maximization (HW, FN, HH), pp. 1836–1844.
ICMLICML-c2-2014-WangSSMK #learning #metric
Two-Stage Metric Learning (JW, KS, FS, SMM, AK), pp. 370–378.
ICMLICML-c2-2014-WenYG #learning #nondeterminism #robust
Robust Learning under Uncertain Test Distributions: Relating Covariate Shift to Model Misspecification (JW, CNY, RG), pp. 631–639.
ICMLICML-c2-2014-WuCLY #behaviour #consistency #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.
ICPRICPR-2014-AkinM #detection #learning #online
Online Learning and Detection with Part-Based, Circulant Structure (OA, KM), pp. 4229–4233.
ICPRICPR-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.
ICPRICPR-2014-Alvarez-MezaMC #adaptation #learning #video
Correntropy-Based Adaptive Learning to Support Video Surveillance Systems (AMÁM, SMG, GCD), pp. 2590–2595.
ICPRICPR-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.
ICPRICPR-2014-ArvanitopoulosBT #analysis #learning
Laplacian Support Vector Analysis for Subspace Discriminative Learning (NA, DB, AT), pp. 1609–1614.
ICPRICPR-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.
ICPRICPR-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.
ICPRICPR-2014-BertonL #graph #learning
Graph Construction Based on Labeled Instances for Semi-supervised Learning (LB, AdAL), pp. 2477–2482.
ICPRICPR-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.
ICPRICPR-2014-CaiTF #learning #recognition #taxonomy
Learning Pose Dictionary for Human Action Recognition (JxC, XT, GCF), pp. 381–386.
ICPRICPR-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.
ICPRICPR-2014-ChengZHT #learning #recognition
Semi-supervised Learning for RGB-D Object Recognition (YC, XZ, KH, TT), pp. 2377–2382.
ICPRICPR-2014-ChenK14a #learning
Learning to Count with Back-propagated Information (KC, JKK), pp. 4672–4677.
ICPRICPR-2014-ChenZW #identification #learning #metric
Relevance Metric Learning for Person Re-identification by Exploiting Global Similarities (JC, ZZ, YW), pp. 1657–1662.
ICPRICPR-2014-CheplyginaSTPLB #classification #learning #multi
Classification of COPD with Multiple Instance Learning (VC, LS, DMJT, JJHP, ML, MdB), pp. 1508–1513.
ICPRICPR-2014-CruzSC #on the
On Meta-learning for Dynamic Ensemble Selection (RMOC, RS, GDCC), pp. 1230–1235.
ICPRICPR-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.
ICPRICPR-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.
ICPRICPR-2014-FangZ #classification #learning
Cross Domain Shared Subspace Learning for Unsupervised Transfer Classification (ZF, ZZ), pp. 3927–3932.
ICPRICPR-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.
ICPRICPR-2014-FiratCV #detection #learning #representation
Representation Learning for Contextual Object and Region Detection in Remote Sensing (OF, GC, FTYV), pp. 3708–3713.
ICPRICPR-2014-FornoniC #learning #naive bayes #recognition
Scene Recognition with Naive Bayes Non-linear Learning (MF, BC), pp. 3404–3409.
ICPRICPR-2014-GanSZ #learning
An Extended Isomap for Manifold Topology Learning with SOINN Landmarks (QG, FS, JZ), pp. 1579–1584.
ICPRICPR-2014-GeDGC #learning
Background Subtraction with Dynamic Noise Sampling and Complementary Learning (WG, YD, ZG, YC), pp. 2341–2346.
ICPRICPR-2014-GengWX #adaptation #estimation #learning
Facial Age Estimation by Adaptive Label Distribution Learning (XG, QW, YX), pp. 4465–4470.
ICPRICPR-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.
ICPRICPR-2014-GuoZLCZ #clustering #kernel #learning #multi
Multiple Kernel Learning Based Multi-view Spectral Clustering (DG, JZ, XL, YC, CZ), pp. 3774–3779.
ICPRICPR-2014-HooKPC #comprehension #image #learning #random
Enhanced Random Forest with Image/Patch-Level Learning for Image Understanding (WLH, TKK, YP, CSC), pp. 3434–3439.
ICPRICPR-2014-HouYW #adaptation #learning #recognition #self
Domain Adaptive Self-Taught Learning for Heterogeneous Face Recognition (CAH, MCY, YCFW), pp. 3068–3073.
ICPRICPR-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.
ICPRICPR-2014-JhuoL #detection #learning #multi #video
Video Event Detection via Multi-modality Deep Learning (IHJ, DTL), pp. 666–671.
ICPRICPR-2014-KhoshrouCT #learning #multi #video
Active Learning from Video Streams in a Multi-camera Scenario (SK, JSC, LFT), pp. 1248–1253.
ICPRICPR-2014-KrauseGDLF #fine-grained #learning #recognition
Learning Features and Parts for Fine-Grained Recognition (JK, TG, JD, LJL, FFL), pp. 26–33.
ICPRICPR-2014-KumarG #documentation #keyword #learning
Bayesian Active Learning for Keyword Spotting in Handwritten Documents (GK, VG), pp. 2041–2046.
ICPRICPR-2014-LeiSLCXP #learning #metric #similarity
Humanoid Robot Imitation with Pose Similarity Metric Learning (JL, MS, ZNL, CC, XX, SP), pp. 4240–4245.
ICPRICPR-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.
ICPRICPR-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.
ICPRICPR-2014-LiuYHTH #learning #recognition #visual notation
Semi-supervised Learning for Cross-Device Visual Location Recognition (PL, PY, KH, TT, HWH), pp. 2873–2878.
ICPRICPR-2014-LiuZC #identification #learning #metric #multi #parametricity
Parametric Local Multi-modal Metric Learning for Person Re-identification (KL, ZCZ, AC), pp. 2578–2583.
ICPRICPR-2014-LuoJ #encoding #image #learning #retrieval #semantics
Learning Semantic Binary Codes by Encoding Attributes for Image Retrieval (JL, ZJ), pp. 279–284.
ICPRICPR-2014-ManfrediGC #energy #graph #image #learning #segmentation
Learning Graph Cut Energy Functions for Image Segmentation (MM, CG, RC), pp. 960–965.
ICPRICPR-2014-MarcaciniDHR #approach #clustering #documentation #learning #metric
Privileged Information for Hierarchical Document Clustering: A Metric Learning Approach (RMM, MAD, ERH, SOR), pp. 3636–3641.
ICPRICPR-2014-MontagnerjH #machine learning
A Machine Learning Based Method for Staff Removal (IdSM, RHJ, NSTH), pp. 3162–3167.
ICPRICPR-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.
ICPRICPR-2014-NieJ #learning #linear #using
Feature Learning Using Bayesian Linear Regression Model (SN, QJ), pp. 1502–1507.
ICPRICPR-2014-NieKZ #learning #recognition #using
Periocular Recognition Using Unsupervised Convolutional RBM Feature Learning (LN, AK, SZ), pp. 399–404.
ICPRICPR-2014-NilufarP #detection #learning #programming
Learning to Detect Contours with Dynamic Programming Snakes (SN, TJP), pp. 984–989.
ICPRICPR-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.
ICPRICPR-2014-PatriciaTC #adaptation #learning #multi #performance
Multi-source Adaptive Learning for Fast Control of Prosthetics Hand (NP, TT, BC), pp. 2769–2774.
ICPRICPR-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.
ICPRICPR-2014-PhamKC #graph #image #learning
Semi-supervised Learning on Bi-relational Graph for Image Annotation (HDP, KHK, SC), pp. 2465–2470.
ICPRICPR-2014-PillaiFR #classification #learning #multi
Learning of Multilabel Classifiers (IP, GF, FR), pp. 3452–3456.
ICPRICPR-2014-RenYZH #classification #image #learning #nearest neighbour
Learning Convolutional Nonlinear Features for K Nearest Neighbor Image Classification (WR, YY, JZ, KH), pp. 4358–4363.
ICPRICPR-2014-RiabchenkoKC #generative #learning #modelling
Learning Generative Models of Object Parts from a Few Positive Examples (ER, JKK, KC), pp. 2287–2292.
ICPRICPR-2014-RozzaMP #graph #kernel #learning #novel
A Novel Graph-Based Fisher Kernel Method for Semi-supervised Learning (AR, MM, AP), pp. 3786–3791.
ICPRICPR-2014-SaitoAFRSGC #learning #using
Active Semi-supervised Learning Using Optimum-Path Forest (PTMS, WPA, AXF, PJdR, CTNS, JFG, MHdC), pp. 3798–3803.
ICPRICPR-2014-SatoKSK #classification #learning #multi
Learning Multiple Complex Features Based on Classification Results (YS, KK, YS, MK), pp. 3369–3373.
ICPRICPR-2014-SavakisRP #difference #gesture #learning #using
Gesture Control Using Active Difference Signatures and Sparse Learning (AES, RR, RWP), pp. 3969–3974.
ICPRICPR-2014-ShenHSGM #framework #interactive #learning
Interactive Framework for Insect Tracking with Active Learning (MS, WH, PS, CGG, DM), pp. 2733–2738.
ICPRICPR-2014-StraehleKKH #learning #multi #random
Multiple Instance Learning with Response-Optimized Random Forests (CNS, MK, UK, FAH), pp. 3768–3773.
ICPRICPR-2014-UmakanthanDFS #learning #multi #process #representation #taxonomy
Multiple Instance Dictionary Learning for Activity Representation (SU, SD, CF, SS), pp. 1377–1382.
ICPRICPR-2014-VellankiDVP #learning #parametricity
Nonparametric Discovery of Learning Patterns and Autism Subgroups from Therapeutic Data (PV, TVD, SV, DQP), pp. 1828–1833.
ICPRICPR-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.
ICPRICPR-2014-WangGJ #learning #using
Learning with Hidden Information Using a Max-Margin Latent Variable Model (ZW, TG, QJ), pp. 1389–1394.
ICPRICPR-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.
ICPRICPR-2014-WangWJ #learning
Learning with Hidden Information (ZW, XW, QJ), pp. 238–243.
ICPRICPR-2014-WangZWB #learning #modelling
Learning Room Occupancy Patterns from Sparsely Recovered Light Transport Models (QW, XZ, MW, KLB), pp. 1987–1992.
ICPRICPR-2014-WanHA #image #learning #recognition
Indoor Scene Recognition from RGB-D Images by Learning Scene Bases (SW, CH, JKA), pp. 3416–3421.
ICPRICPR-2014-WatanabeW #analysis #component #distance #learning #metric #performance
Logistic Component Analysis for Fast Distance Metric Learning (KW, TW), pp. 1278–1282.
ICPRICPR-2014-WuHYWT #image #network #segmentation
Early Hierarchical Contexts Learned by Convolutional Networks for Image Segmentation (ZW, YH, YY, LW, TT), pp. 1538–1543.
ICPRICPR-2014-WuJ #detection #learning
Learning the Deep Features for Eye Detection in Uncontrolled Conditions (YW, QJ), pp. 455–459.
ICPRICPR-2014-WuLWHJ #learning #multi
Multi-label Learning with Missing Labels (BW, ZL, SW, BGH, QJ), pp. 1964–1968.
ICPRICPR-2014-WuS #learning #multi #recognition
Regularized Multi-view Multi-metric Learning for Action Recognition (XW, SKS), pp. 471–476.
ICPRICPR-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.
ICPRICPR-2014-XieUKG #incremental #learning
Incremental Learning with Support Vector Data Description (WX, SU, SK, MG), pp. 3904–3909.
ICPRICPR-2014-XuS #learning #network #using
Bayesian Network Structure Learning Using Causality (ZX, SNS), pp. 3546–3551.
ICPRICPR-2014-YangN #integration #learning #multi
Semi-supervised Learning of Geospatial Objects through Multi-modal Data Integration (YY, SN), pp. 4062–4067.
ICPRICPR-2014-YangXWL #learning #realtime
Real-Time Tracking via Deformable Structure Regression Learning (XY, QX, SW, PL), pp. 2179–2184.
ICPRICPR-2014-YangYH #learning
Diversity-Based Ensemble with Sample Weight Learning (CY, XCY, HWH), pp. 1236–1241.
ICPRICPR-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.
ICPRICPR-2014-YiLLL #identification #learning #metric
Deep Metric Learning for Person Re-identification (DY, ZL, SL, SZL), pp. 34–39.
ICPRICPR-2014-YinYPH #case study #classification #learning
Shallow Classification or Deep Learning: An Experimental Study (XCY, CY, WYP, HWH), pp. 1904–1909.
ICPRICPR-2014-YooJKC #learning #optimisation
Transfer Learning of Motion Patterns in Traffic Scene via Convex Optimization (YJY, HJ, SWK, JYC), pp. 4158–4163.
ICPRICPR-2014-ZenRS #distance #learning #matrix #metric
Simultaneous Ground Metric Learning and Matrix Factorization with Earth Mover’s Distance (GZ, ER, NS), pp. 3690–3695.
ICPRICPR-2014-ZhangM14a #detection #learning #multi
Simultaneous Detection of Multiple Facial Action Units via Hierarchical Task Structure Learning (XZ, MHM), pp. 1863–1868.
ICPRICPR-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.
ICPRICPR-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.
ICPRICPR-2014-ZhuS #learning #recognition #taxonomy
Correspondence-Free Dictionary Learning for Cross-View Action Recognition (FZ, LS), pp. 4525–4530.
ICPRICPR-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.
KDDKDD-2014-Bengio #learning #scalability
Scaling up deep learning (YB), p. 1966.
KDDKDD-2014-BensonRS #learning #multi #network #scalability
Learning multifractal structure in large networks (ARB, CR, SS), pp. 1326–1335.
KDDKDD-2014-DalessandroCRPWP #learning #online #scalability
Scalable hands-free transfer learning for online advertising (BD, DC, TR, CP, MHW, FJP), pp. 1573–1582.
KDDKDD-2014-GaddeAO #graph #learning #using
Active semi-supervised learning using sampling theory for graph signals (AG, AA, AO), pp. 492–501.
KDDKDD-2014-GohR #learning
Box drawings for learning with imbalanced data (STG, CR), pp. 333–342.
KDDKDD-2014-GongZFY #learning #multi #performance
Efficient multi-task feature learning with calibration (PG, JZ, WF, JY), pp. 761–770.
KDDKDD-2014-GrabockaSWS #learning
Learning time-series shapelets (JG, NS, MW, LST), pp. 392–401.
KDDKDD-2014-Kushnir #adaptation #kernel #learning
Active-transductive learning with label-adapted kernels (DK), pp. 462–471.
KDDKDD-2014-LanSB #analysis #learning
Time-varying learning and content analytics via sparse factor analysis (ASL, CS, RGB), pp. 452–461.
KDDKDD-2014-LiangRR #learning #personalisation
Personalized search result diversification via structured learning (SL, ZR, MdR), pp. 751–760.
KDDKDD-2014-Mullainathan #machine learning #question #social
Bugbears or legitimate threats?: (social) scientists’ criticisms of machine learning? (SM), p. 4.
KDDKDD-2014-PerozziAS #learning #named #online #social
DeepWalk: online learning of social representations (BP, RAR, SS), pp. 701–710.
KDDKDD-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.
KDDKDD-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.
KDDKDD-2014-QianHJPZ #approach #distance #learning #metric #using
Distance metric learning using dropout: a structured regularization approach (QQ, JH, RJ, JP, SZ), pp. 323–332.
KDDKDD-2014-Rudin #algorithm #machine learning
Algorithms for interpretable machine learning (CR), p. 1519.
KDDKDD-2014-Salakhutdinov #learning
Deep learning (RS), p. 1973.
KDDKDD-2014-ShaoAK #concept #data type #learning #prototype
Prototype-based learning on concept-drifting data streams (JS, ZA, SK), pp. 412–421.
KDDKDD-2014-SrikantA #machine learning #programming #using
A system to grade computer programming skills using machine learning (SS, VA), pp. 1887–1896.
KDDKDD-2014-TayebiEGB #embedded #learning #predict #using
Spatially embedded co-offence prediction using supervised learning (MAT, ME, UG, PLB), pp. 1789–1798.
KDDKDD-2014-VasishtDVK #classification #learning #multi
Active learning for sparse bayesian multilabel classification (DV, ACD, MV, AK), pp. 472–481.
KDDKDD-2014-WangNH #adaptation #induction #learning #scalability
Large-scale adaptive semi-supervised learning via unified inductive and transductive model (DW, FN, HH), pp. 482–491.
KDDKDD-2014-WangSE #collaboration #learning #permutation
Active collaborative permutation learning (JW, NS, JE), pp. 502–511.
KDDKDD-2014-WangSW #learning #modelling
Unsupervised learning of disease progression models (XW, DS, FW), pp. 85–94.
KDDKDD-2014-XuL #behaviour #learning #problem
Product selection problem: improve market share by learning consumer behavior (SX, JCSL), pp. 851–860.
KDDKDD-2014-YangH #learning #parametricity
Learning with dual heterogeneity: a nonparametric bayes model (HY, JH), pp. 582–590.
KDDKDD-2014-ZhangTMF #learning #network
Supervised deep learning with auxiliary networks (JZ, GT, YM, WF), pp. 353–361.
KDDKDD-2014-ZhouC #adaptation #documentation #learning #rank
Unifying learning to rank and domain adaptation: enabling cross-task document scoring (MZ, KCCC), pp. 781–790.
KDIRKDIR-2014-Bleiweiss #execution #machine learning #using
SoC Processor Discovery for Program Execution Matching Using Unsupervised Machine Learning (AB), pp. 192–201.
KDIRKDIR-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.
KDIRKDIR-2014-SuciuICDP #learning #word
Learning Good Opinions from Just Two Words Is Not Bad (DAS, VVI, ACC, MD, RP), pp. 233–241.
KEODKEOD-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.
KMISKMIS-2014-AtrashAM #collaboration #learning
Supporting Organizational Learning with Collaborative Annotation (AA, MHA, CM), pp. 237–244.
KMISKMIS-2014-BartuskovaK #information management #learning
Knowledge Management and Sharing in E-Learning — Hierarchical System for Managing Learning Resources (AB, OK), pp. 179–185.
KMISKMIS-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.
KMISKMIS-2014-HisakaneS #learning #visualisation
A Visualization System of Discussion Structure in Case Method Learning (DH, MS), pp. 126–132.
KMISKMIS-2014-SmirnovS #implementation #information management #lessons learnt
Role-Driven Knowledge Management Implementation — Lessons Learned (AVS, NS), pp. 36–43.
KRKR-2014-KonevLOW #learning #lightweight #logic #ontology
Exact Learning of Lightweight Description Logic Ontologies (BK, CL, AO, FW).
KRKR-2014-Michael #learning #predict
Simultaneous Learning and Prediction (LM).
MLDMMLDM-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.
MLDMMLDM-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.
MLDMMLDM-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.
MLDMMLDM-2014-KuleshovB #data mining #learning #mining
Manifold Learning in Data Mining Tasks (APK, AVB), pp. 119–133.
MLDMMLDM-2014-NeumannHRL #case study #experience #learning
A Robot Waiter Learning from Experiences (BN, LH, PR, JL), pp. 285–299.
MLDMMLDM-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.
RecSysRecSys-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.
RecSysRecSys-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.
RecSysRecSys-2014-SaveskiM #learning #recommendation
Item cold-start recommendations: learning local collective embeddings (MS, AM), pp. 89–96.
SEKESEKE-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.
SEKESEKE-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.
SEKESEKE-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.
SEKESEKE-2014-SinghS #machine learning #requirements #using
Software Requirement Prioritization using Machine Learning (DS, AS), pp. 701–704.
SIGIRSIGIR-2014-CanCM #feedback #modelling #ranking
Incorporating query-specific feedback into learning-to-rank models (EFC, WBC, RM), pp. 1035–1038.
SIGIRSIGIR-2014-CormackG #bibliography #evaluation #protocol
Evaluation of machine-learning protocols for technology-assisted review in electronic discovery (GVC, MRG), pp. 153–162.
SIGIRSIGIR-2014-CostaCS #learning #modelling #ranking
Learning temporal-dependent ranking models (MC, FMC, MJS), pp. 757–766.
SIGIRSIGIR-2014-EfronWS #learning #query
Learning sufficient queries for entity filtering (ME, CW, GS), pp. 1091–1094.
SIGIRSIGIR-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.
SIGIRSIGIR-2014-JiangKCC #behaviour #learning #query
Learning user reformulation behavior for query auto-completion (JYJ, YYK, PYC, PJC), pp. 445–454.
SIGIRSIGIR-2014-LengCL #image #learning #random #retrieval #scalability
Random subspace for binary codes learning in large scale image retrieval (CL, JC, HL), pp. 1031–1034.
SIGIRSIGIR-2014-LiuL #learning #probability #segmentation #word
Probabilistic ensemble learning for vietnamese word segmentation (WL, LL), pp. 931–934.
SIGIRSIGIR-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.
SIGIRSIGIR-2014-PanYMLNR #image #learning
Click-through-based cross-view learning for image search (YP, TY, TM, HL, CWN, YR), pp. 717–726.
SIGIRSIGIR-2014-QiuCYLL #learning #personalisation #ranking
Item group based pairwise preference learning for personalized ranking (SQ, JC, TY, CL, HL), pp. 1219–1222.
SIGIRSIGIR-2014-SokolovHR #learning #query
Learning to translate queries for CLIR (AS, FH, SR), pp. 1179–1182.
SIGIRSIGIR-2014-SpinaGA #detection #learning #monitoring #online #similarity #topic
Learning similarity functions for topic detection in online reputation monitoring (DS, JG, EA), pp. 527–536.
SIGIRSIGIR-2014-UstaAVOU #analysis #education #how #learning #student
How k-12 students search for learning?: analysis of an educational search engine log (AU, ISA, IBV, RO, ÖU), pp. 1151–1154.
SIGIRSIGIR-2014-VulicZM #e-commerce #formal method #learning
Learning to bridge colloquial and formal language applied to linking and search of E-Commerce data (IV, SZ, MFM), pp. 1195–1198.
SIGIRSIGIR-2014-WuMHR #image #learning #personalisation
Learning to personalize trending image search suggestion (CCW, TM, WHH, YR), pp. 727–736.
SIGIRSIGIR-2014-YuWZTSZ #learning #rank
Hashing with List-Wise learning to rank (ZY, FW, YZ, ST, JS, YZ), pp. 999–1002.
SIGIRSIGIR-2014-ZhuLGCN #learning
Learning for search result diversification (YZ, YL, JG, XC, SN), pp. 293–302.
SIGIRSIGIR-2014-ZhuNG #adaptation #learning #random #social
An adaptive teleportation random walk model for learning social tag relevance (XZ, WN, MG), pp. 223–232.
MODELSMoDELS-2014-BakiSCMF #learning #model transformation
Learning Implicit and Explicit Control in Model Transformations by Example (IB, HAS, QC, PM, MF), pp. 636–652.
MODELSMoDELS-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.
MODELSMoDELS-2014-BakiSCMF #learning #model transformation
Learning Implicit and Explicit Control in Model Transformations by Example (IB, HAS, QC, PM, MF), pp. 636–652.
MODELSMoDELS-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.
RERE-2014-MaalejG #lessons learnt
Capturing and sharing domain knowledge with business rules lessons learned from a global software vendor (WM, SG), pp. 364–373.
SACSAC-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.
SACSAC-2014-DhanjalC #learning #network
Learning reputation in an authorship network (CD, SC), pp. 1724–1726.
SACSAC-2014-LiWL #learning #mobile #online #recognition
Online learning with mobile sensor data for user recognition (HGL, XW, ZL), pp. 64–70.
SACSAC-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.
SACSAC-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.
FSEFSE-2014-AllamanisBBS #learning
Learning natural coding conventions (MA, ETB, CB, CAS), pp. 281–293.
FSEFSE-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.
FSEFSE-2014-YeBL #debugging #learning #rank #using
Learning to rank relevant files for bug reports using domain knowledge (XY, RCB, CL), pp. 689–699.
ICSEICSE-2014-HeWYZ #learning #reasoning
Symbolic assume-guarantee reasoning through BDD learning (FH, BYW, LY, LZ), pp. 1071–1082.
ICSEICSE-2014-JingYZWL #fault #learning #predict #taxonomy
Dictionary learning based software defect prediction (XYJ, SY, ZWZ, SSW, JL), pp. 414–423.
ICSEICSE-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.
ICSEICSE-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.
ASPLOSASPLOS-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.
HPCAHPCA-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.
OSDIOSDI-2014-ChilimbiSAK #learning #performance #scalability
Project Adam: Building an Efficient and Scalable Deep Learning Training System (TMC, YS, JA, KK), pp. 571–582.
OSDIOSDI-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.
CAVCAV-2014-0001LMN #framework #invariant #learning #named #robust
ICE: A Robust Framework for Learning Invariants (PG, CL, PM, DN), pp. 69–87.
CAVCAV-2014-HeizmannHP #analysis #learning #source code #termination
Termination Analysis by Learning Terminating Programs (MH, JH, AP), pp. 797–813.
SMTSMT-2014-KorovinKS #learning #towards
Towards Conflict-Driven Learning for Virtual Substitution (KK, MK, TS), p. 71.
ASEASE-2013-DietrichCS #effectiveness #learning #query #requirements #retrieval
Learning effective query transformations for enhanced requirements trace retrieval (TD, JCH, YS), pp. 586–591.
ASEASE-2013-GuoCASW #approach #learning #performance #predict #statistics #variability
Variability-aware performance prediction: A statistical learning approach (JG, KC, SA, NS, AW), pp. 301–311.
ASEASE-2013-Xiao0LLS #learning #named #type system
TzuYu: Learning stateful typestates (HX, JS, YL, SWL, CS), pp. 432–442.
CASECASE-2013-LiX #adaptation #learning
Off-line learning based adaptive dispatching rule for semiconductor wafer fabrication facility (LL, HX), pp. 1028–1033.
CASECASE-2013-OFlahertyE #bound #learning #sequence
Learning to locomote: Action sequences and switching boundaries (RO, ME), pp. 7–12.
CASECASE-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.
DACDAC-2013-LiuC #on the #synthesis
On learning-based methods for design-space exploration with high-level synthesis (HYL, LPC), p. 7.
DACDAC-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.
DATEDATE-2013-DeOrioLBB #debugging #detection #machine learning
Machine learning-based anomaly detection for post-silicon bug diagnosis (AD, QL, MB, VB), pp. 491–496.
DATEDATE-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.
DocEngDocEng-2013-DoTT #documentation #taxonomy #using
Document noise removal using sparse representations over learned dictionary (THD, ST, ORT), pp. 161–168.
DocEngDocEng-2013-Esposito #documentation #machine learning
Symbolic machine learning methods for historical document processing (FE), pp. 1–2.
ICDARICDAR-2013-AgarwalGC #learning
Greedy Search for Active Learning of OCR (AA, RG, SC), pp. 837–841.
ICDARICDAR-2013-BougueliaBB #approach #classification #documentation #learning
A Stream-Based Semi-supervised Active Learning Approach for Document Classification (MRB, YB, AB), pp. 611–615.
ICDARICDAR-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.
ICDARICDAR-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.
ICDARICDAR-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.
ICDARICDAR-2013-NguyenCBO #image #interactive #learning
Interactive Knowledge Learning for Ancient Images (NVN, MC, AB, JMO), pp. 300–304.
ICDARICDAR-2013-PuriST #learning #network
Bayesian Network Structure Learning and Inference Methods for Handwriting (MP, SNS, YT), pp. 1320–1324.
ICDARICDAR-2013-SchambachR #learning #network #sequence
Stabilize Sequence Learning with Recurrent Neural Networks by Forced Alignment (MPS, SFR), pp. 1270–1274.
ICDARICDAR-2013-SuL #learning #recognition
Discriminative Weighting and Subspace Learning for Ensemble Symbol Recognition (FS, TL), pp. 1088–1092.
ICDARICDAR-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.
ICDARICDAR-2013-TeradaHFU #detection #on the
On the Possibility of Structure Learning-Based Scene Character Detector (YT, RH, YF, SU), pp. 472–476.
ICDARICDAR-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.
ICDARICDAR-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.
ICDARICDAR-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.
ICDARICDAR-2013-Zhu0N #learning #recognition
Sub-structure Learning Based Handwritten Chinese Text Recognition (YZ, JS, SN), pp. 295–299.
PODSPODS-2013-AbouziedAPHS #learning #quantifier #query #verification
Learning and verifying quantified boolean queries by example (AA, DA, CHP, JMH, AS), pp. 49–60.
SIGMODSIGMOD-2013-CondieMPW #big data #machine learning
Machine learning for big data (TC, PM, NP, MW), pp. 939–942.
SIGMODSIGMOD-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.
VLDBVLDB-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.
VLDBVLDB-2013-BrunatoB #learning #optimisation
Learning and Intelligent Optimization (LION): One Ring to Rule Them All (MB, RB), pp. 1176–1177.
VLDBVLDB-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.
VLDBVLDB-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.
CSEETCSEET-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.
CSEETCSEET-2013-Georgas #composition #design #education #learning #towards
Toward infusing modular and reflective design learning throughout the curriculum (JCG), pp. 274–278.
CSEETCSEET-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.
CSEETCSEET-2013-StejskalS #learning #testing
Test-driven learning in high school computer science (RS, HPS), pp. 289–293.
CSEETCSEET-2013-Vallino #question #re-engineering #student #what
What should students learn in their first (and often only) software engineering course? (JV), pp. 335–337.
ITiCSEITiCSE-2013-Alshaigy #development #education #interactive #learning #programming language #python
Development of an interactive learning tool to teach python programming language (BA), p. 344.
ITiCSEITiCSE-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.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2013-FernandesCB #learning
A pilot project on non-conventional learning (SF, AC, LSB), p. 346.
ITiCSEITiCSE-2013-German #learning
Jump-starting team-based learning in the computer science classroom (DAG), p. 323.
ITiCSEITiCSE-2013-GorlatovaSKKZ #learning #research #scalability
Project-based learning within a large-scale interdisciplinary research effort (MG, JS, PRK, IK, GZ), pp. 207–212.
ITiCSEITiCSE-2013-HawthorneC #learning #source code
ACM core IT learning outcomes for associate-degree programs (EKH, RDC), p. 357.
ITiCSEITiCSE-2013-JalilPWL #design #interactive #learning #taxonomy
Design eye: an interactive learning environment based on the solo taxonomy (SAJ, BP, IW, ALR), pp. 22–27.
ITiCSEITiCSE-2013-JohnsonCH #contest #development #game studies #learning
Learning elsewhere: tales from an extracurricular game development competition (CJ, AC, SH), pp. 70–75.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2013-MellodgeR #arduino #case study #experience #framework #learning #student #using
Using the arduino platform to enhance student learning experiences (PM, IR), p. 338.
ITiCSEITiCSE-2013-Paule-RuizGPG #evaluation #framework #interactive #learning
Voice interactive learning: a framework and evaluation (MPPR, VMÁG, JRPP, MRG), pp. 34–39.
ITiCSEITiCSE-2013-QianYGBT #authentication #learning #mobile #network #security
Mobile device based authentic learning for computer network and security (KQ, MY, MG, PB, LT), p. 335.
ITiCSEITiCSE-2013-ReedZ #framework #learning
A hierarchical framework for mapping and quantitatively assessing program and learning outcomes (JR, HZ), pp. 52–57.
ITiCSEITiCSE-2013-RowanD #bibliography #learning #mobile #using
A systematic literature review on using mobile computing as a learning intervention (MR, JD), p. 339.
ITiCSEITiCSE-2013-Sanchez-Nielsen #learning #multi #student
Producing multimedia pills to stimulate student learning and engagement (ESN), pp. 165–170.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2013-ShiQC #adaptation #design #personalisation #social
Designing social personalized adaptive e-learning (LS, DAQ, AIC), p. 341.
ITiCSEITiCSE-2013-VihavainenVLP #learning #student #using
Scaffolding students’ learning using test my code (AV, TV, ML, MP), pp. 117–122.
ITiCSEITiCSE-2013-Wildsmith #learning #named
Kinetic: a learning environment within business (CW), p. 3.
TACASTACAS-2013-ChenW #algorithm #learning #library #named
BULL: A Library for Learning Algorithms of Boolean Functions (YFC, BYW), pp. 537–542.
TACASTACAS-2013-WhiteL #data type #evolution #identification #in memory #learning #memory management
Identifying Dynamic Data Structures by Learning Evolving Patterns in Memory (DHW, GL), pp. 354–369.
CSMRCSMR-2013-MinelliL #lessons learnt #mobile
Software Analytics for Mobile Applications-Insights & Lessons Learned (RM, ML), pp. 144–153.
CSMRCSMR-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.
ICSMEICSM-2013-FontanaZMM #approach #detection #machine learning #smell #towards
Code Smell Detection: Towards a Machine Learning-Based Approach (FAF, MZ, AM, MM), pp. 396–399.
ICSMEICSM-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.
ICSMEICSM-2013-Perez #design #lessons learnt #refactoring #smell #summary
Refactoring Planning for Design Smell Correction: Summary, Opportunities and Lessons Learned (JP), pp. 572–577.
ICSMEICSM-2013-SemenenkoDS #image #machine learning #named #testing
Browserbite: Accurate Cross-Browser Testing via Machine Learning over Image Features (NS, MD, TS), pp. 528–531.
ICSMEICSM-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.
ICSMEICSM-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.
WCREWCRE-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.
WCREWCRE-2013-MontandonBFV #api #framework #lessons learnt
Documenting APIs with examples: Lessons learned with the APIMiner platform (JEM, HB, DF, MTV), pp. 401–408.
SASSAS-2013-0001GHAN #concept #geometry #learning #verification
Verification as Learning Geometric Concepts (RS, SG, BH, AA, AVN), pp. 388–411.
STOCSTOC-2013-BrakerskiLPRS #fault #learning
Classical hardness of learning with errors (ZB, AL, CP, OR, DS), pp. 575–584.
DLTDLT-2013-BolligHLM #approach #automaton #learning
A Fresh Approach to Learning Register Automata (BB, PH, ML, BM), pp. 118–130.
ICALPICALP-v2-2013-FuscoPP #learning #performance
Learning a Ring Cheaply and Fast (EGF, AP, RP), pp. 557–568.
LATALATA-2013-BjorklundFK #automaton #learning
MAT Learning of Universal Automata (JB, HF, AK), pp. 141–152.
GT-VMTGT-VMT-2013-AlshanqitiHK #graph transformation #learning
Learning Minimal and Maximal Rules from Observations of Graph Transformations (AMA, RH, TAK).
CHICHI-2013-AndersonB #gesture #learning #performance
Learning and performance with gesture guides (FA, WFB), pp. 1109–1118.
CHICHI-2013-EdgeCW #learning #named
SpatialEase: learning language through body motion (DE, KYC, MW), pp. 469–472.
CHICHI-2013-HarpsteadMA #data analysis #education #game studies #learning
In search of learning: facilitating data analysis in educational games (EH, BAM, VA), pp. 79–88.
CHICHI-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.
CHICHI-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.
CHICHI-2013-SzafirM #adaptation #bibliography #learning #named
ARTFul: adaptive review technology for flipped learning (DS, BM), pp. 1001–1010.
CSCWCSCW-2013-KowY #community #learning
Media technologies and learning in the starcraft esport community (YMK, TY), pp. 387–398.
CSCWCSCW-2013-LinF #learning #network
Opportunities via extended networks for teens’ informal learning (PL, SDF), pp. 1341–1352.
HCIDHM-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.
HCIDUXU-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.
HCIDUXU-CXC-2013-ChoensawatSKH #education #learning
Desirability of a Teaching and Learning Tool for Thai Dance Body Motion (WC, KS, CK, KH), pp. 171–179.
HCIDUXU-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.
HCIDUXU-CXC-2013-MarchettiB #game studies #learning
Setting Conditions for Learning: Mediated Play and Socio-material Dialogue (EM, EPB), pp. 238–246.
HCIDUXU-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.
HCIDUXU-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.
HCIDUXU-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.
HCIDUXU-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.
HCIDUXU-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.
HCIHCI-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.
HCIHCI-AS-2013-AndujarEGM #learning
Evaluating Engagement Physiologically and Knowledge Retention Subjectively through Two Different Learning Techniques (MA, JIE, JEG, PM), pp. 335–342.
HCIHCI-AS-2013-BitontoLRR #collaboration #process #recommendation
Recommendation of Collaborative Activities in E-learning Environments (PDB, ML, TR, VR), pp. 484–492.
HCIHCI-AS-2013-CharoenpitO #biology #using
A New E-learning System Focusing on Emotional Aspect Using Biological Signals (SC, MO), pp. 343–350.
HCIHCI-AS-2013-EskildsenR #challenge #integration #learning
Challenges for Contextualizing Language Learning — Supporting Cultural Integration (SE, MR), pp. 361–369.
HCIHCI-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.
HCIHCI-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.
HCIHCI-AS-2013-HarunBON #learning #using
Refining Rules Learning Using Evolutionary PD (AFH, SB, CO, NLMN), pp. 376–385.
HCIHCI-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.
HCIHCI-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.
HCIHCI-AS-2013-LimaRSBSO #learning
Innovation in Learning — The Use of Avatar for Sign Language (TL, MSR, TAS, AB, ES, HSdO), pp. 428–433.
HCIHCI-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.
HCIHCI-AS-2013-MarsicoST #framework #personalisation
A Framework to Support Social-Collaborative Personalized e-Learning (MDM, AS, MT), pp. 351–360.
HCIHCI-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.
HCIHCI-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.
HCIHCI-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.
HCIHCI-AS-2013-TakanoS #learning
Nature Sound Ensemble Learning in Narrative-Episode Creation with Pictures (KT, SS), pp. 493–502.
HCIHCI-AS-2013-TogawaK #framework
Private Cloud Cooperation Framework for Reducing the Earthquake Damage on e-Learning Environment (ST, KK), pp. 503–510.
HCIHCI-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.
HCIHCI-IMT-2013-DruryPKL #design #lessons learnt #visualisation
Decision Space Visualization: Lessons Learned and Design Principles (JLD, MSP, GLK, YL), pp. 658–667.
HCIHCI-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.
HCIHCI-UC-2013-StarySF #interactive #learning
Agility Based on Stakeholder Interaction — Blending Organizational Learning with Interactive BPM (CS, WS, AF), pp. 456–465.
HCIHIMI-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.
HCIHIMI-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.
HCIHIMI-HSM-2013-SaitohI #detection #learning #using #visualisation
Visualization of Anomaly Data Using Peculiarity Detection on Learning Vector Quantization (FS, SI), pp. 181–188.
HCIHIMI-LCCB-2013-Canter #hybrid #student
A Hybrid Model for an E-learning System Which Develops Metacognitive Skills at Students (MC), pp. 9–15.
HCIHIMI-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.
HCIHIMI-LCCB-2013-HallLS #assessment #evaluation #learning #tool support
Psychophysiological Assessment Tools for Evaluation of Learning Technologies (RHH, NSL, HS), pp. 33–42.
HCIHIMI-LCCB-2013-HayashiON #collaboration #interactive #learning
An Experimental Environment for Analyzing Collaborative Learning Interaction (YH, YO, YIN), pp. 43–52.
HCIHIMI-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.
HCIHIMI-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.
HCIHIMI-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.
HCIHIMI-LCCB-2013-YamamotoKYMH #learning #online #problem
Learning by Problem-Posing with Online Connected Media Tablets (SY, TK, YY, KM, TH), pp. 165–174.
HCIHIMI-LCCB-2013-YuL #approach #feedback #mining
Exploring User Feedback of a E-Learning System: A Text Mining Approach (WBY, RL), pp. 182–191.
HCIOCSC-2013-Eustace #learning #network
Building and Sustaining a Lifelong Adult Learning Network (KE), pp. 260–268.
HCIOCSC-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.
VISSOFTVISSOFT-2013-Wijk #case study #experience #lessons learnt #visualisation
Keynote talk: Information visualization: Experiences and lessons learned (JJvW), p. 1.
EDOCEDOC-2013-Swenson #design #learning
Designing for an Innovative Learning Organization (KDS), pp. 209–213.
ICEISICEIS-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.
ICEISICEIS-v2-2013-EomA #testing
Developing and Testing a Model to Understand Relationships between e-Learning Outcomes and Human Factors (SBE, NJA), pp. 361–370.
ICEISICEIS-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.
ICEISICEIS-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.
ICEISICEIS-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.
ICEISICEIS-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.
ICEISICEIS-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.
CIKMCIKM-2013-BaragliaMNS #learning #named #predict
LearNext: learning to predict tourists movements (RB, CIM, FMN, FS), pp. 751–756.
CIKMCIKM-2013-CeccarelliLOPT #learning #metric
Learning relatedness measures for entity linking (DC, CL, SO, RP, ST), pp. 139–148.
CIKMCIKM-2013-ChengCLWAC #data type #learning #multi
Feedback-driven multiclass active learning for data streams (YC, ZC, LL, JW, AA, ANC), pp. 1311–1320.
CIKMCIKM-2013-ChenW #classification #learning #scalability
Cost-sensitive learning for large-scale hierarchical classification (JC, DW), pp. 1351–1360.
CIKMCIKM-2013-FangZ #feature model #learning #multi
Discriminative feature selection for multi-view cross-domain learning (ZF, Z(Z), pp. 1321–1330.
CIKMCIKM-2013-Guestrin #machine learning #scalability #usability
Usability in machine learning at scale with graphlab (CG), pp. 5–6.
CIKMCIKM-2013-HashemiNB #approach #learning #network #retrieval #topic
Expertise retrieval in bibliographic network: a topic dominance learning approach (SHH, MN, HB), pp. 1117–1126.
CIKMCIKM-2013-KamathC #learning #predict #what
Spatio-temporal meme prediction: learning what hashtags will be popular where (KYK, JC), pp. 1341–1350.
ECIRECIR-2013-DangBC #information retrieval #learning #rank
Two-Stage Learning to Rank for Information Retrieval (VD, MB, WBC), pp. 423–434.
ECIRECIR-2013-JuMJ #classification #learning #rank
Learning to Rank from Structures in Hierarchical Text Classification (QJ, AM, RJ), pp. 183–194.
ECIRECIR-2013-NguyenTT #classification #learning #rank #using
Folktale Classification Using Learning to Rank (DN, DT, MT), pp. 195–206.
ICMLICML-c1-2013-0005LSL #feature model #learning #modelling #online
Online Feature Selection for Model-based Reinforcement Learning (TTN, ZL, TS, TYL), pp. 498–506.
ICMLICML-c1-2013-AbernethyAKD #learning #problem #scalability
Large-Scale Bandit Problems and KWIK Learning (JA, KA, MK, MD), pp. 588–596.
ICMLICML-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.
ICMLICML-c1-2013-AnandkumarHJK #learning #linear #network
Learning Linear Bayesian Networks with Latent Variables (AA, DH, AJ, SK), pp. 249–257.
ICMLICML-c1-2013-BalcanBEL #learning #performance
Efficient Semi-supervised and Active Learning of Disjunctions (NB, CB, SE, YL), pp. 633–641.
ICMLICML-c1-2013-BootsG #approach #learning
A Spectral Learning Approach to Range-Only SLAM (BB, GJG), pp. 19–26.
ICMLICML-c1-2013-ChenK #adaptation #learning #optimisation
Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization (YC, AK), pp. 160–168.
ICMLICML-c1-2013-CotterSS #learning
Learning Optimally Sparse Support Vector Machines (AC, SSS, NS), pp. 266–274.
ICMLICML-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.
ICMLICML-c1-2013-GolubCY #learning
Learning an Internal Dynamics Model from Control Demonstration (MG, SC, BY), pp. 606–614.
ICMLICML-c1-2013-GonenSS #approach #learning #performance
Efficient Active Learning of Halfspaces: an Aggressive Approach (AG, SS, SSS), pp. 480–488.
ICMLICML-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.
ICMLICML-c1-2013-KadriGP #approach #kernel #learning
A Generalized Kernel Approach to Structured Output Learning (HK, MG, PP), pp. 471–479.
ICMLICML-c1-2013-KarbasiSS #learning
Iterative Learning and Denoising in Convolutional Neural Associative Memories (AK, AHS, AS), pp. 445–453.
ICMLICML-c1-2013-KumarB #bound #graph #learning
Convex Relaxations for Learning Bounded-Treewidth Decomposable Graphs (KSSK, FRB), pp. 525–533.
ICMLICML-c1-2013-LiLSHD #generative #learning #using
Learning Hash Functions Using Column Generation (XL, GL, CS, AvdH, ARD), pp. 142–150.
ICMLICML-c1-2013-LimLM #learning #metric #robust
Robust Structural Metric Learning (DL, GRGL, BM), pp. 615–623.
ICMLICML-c1-2013-MaatenCTW #learning
Learning with Marginalized Corrupted Features (LvdM, MC, ST, KQW), pp. 410–418.
ICMLICML-c1-2013-MaillardNOR #bound #learning #representation
Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning (OAM, PN, RO, DR), pp. 543–551.
ICMLICML-c1-2013-MenonTGLK #framework #machine learning #programming
A Machine Learning Framework for Programming by Example (AKM, OT, SG, BWL, AK), pp. 187–195.
ICMLICML-c1-2013-RuvoloE #algorithm #learning #named #performance
ELLA: An Efficient Lifelong Learning Algorithm (PR, EE), pp. 507–515.
ICMLICML-c1-2013-ZuluagaSKP #learning #multi #optimisation
Active Learning for Multi-Objective Optimization (MZ, GS, AK, MP), pp. 462–470.
ICMLICML-c2-2013-GaneshapillaiGL #learning
Learning Connections in Financial Time Series (GG, JVG, AL), pp. 109–117.
ICMLICML-c2-2013-GolovinSMY #learning #ram #scalability
Large-Scale Learning with Less RAM via Randomization (DG, DS, HBM, MY), pp. 325–333.
ICMLICML-c2-2013-KrummenacherOB #learning #multi
Ellipsoidal Multiple Instance Learning (GK, CSO, JMB), pp. 73–81.
ICMLICML-c2-2013-MaurerPR #learning #multi
Sparse coding for multitask and transfer learning (AM, MP, BRP), pp. 343–351.
ICMLICML-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.
ICMLICML-c2-2013-MinhBM #framework #learning #multi
A unifying framework for vector-valued manifold regularization and multi-view learning (HQM, LB, VM), pp. 100–108.
ICMLICML-c2-2013-RanganathWBX #adaptation #learning #probability
An Adaptive Learning Rate for Stochastic Variational Inference (RR, CW, DMB, EPX), pp. 298–306.
ICMLICML-c2-2013-SohnZLL #learning
Learning and Selecting Features Jointly with Point-wise Gated Boltzmann Machines (KS, GZ, CL, HL), pp. 217–225.
ICMLICML-c2-2013-Tran-DinhKC #framework #graph #learning #matrix
A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions (QTD, ATK, VC), pp. 271–279.
ICMLICML-c2-2013-TranPV #learning #multi
Thurstonian Boltzmann Machines: Learning from Multiple Inequalities (TT, DQP, SV), pp. 46–54.
ICMLICML-c2-2013-YangH #classification #learning
Activized Learning with Uniform Classification Noise (LY, SH), pp. 370–378.
ICMLICML-c3-2013-0002T #kernel #learning
Differentially Private Learning with Kernels (PJ, AT), pp. 118–126.
ICMLICML-c3-2013-AlmingolML #behaviour #learning #multi
Learning Multiple Behaviors from Unlabeled Demonstrations in a Latent Controller Space (JA, LM, ML), pp. 136–144.
ICMLICML-c3-2013-BalasubramanianYL #learning
Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations (KB, KY, GL), pp. 289–297.
ICMLICML-c3-2013-BalcanBM #learning #ontology
Exploiting Ontology Structures and Unlabeled Data for Learning (NB, AB, YM), pp. 1112–1120.
ICMLICML-c3-2013-BellemareVB #learning #recursion
Bayesian Learning of Recursively Factored Environments (MGB, JV, MB), pp. 1211–1219.
ICMLICML-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.
ICMLICML-c3-2013-ChattopadhyayFDPY #learning
Joint Transfer and Batch-mode Active Learning (RC, WF, ID, SP, JY), pp. 253–261.
ICMLICML-c3-2013-Cheng #learning #similarity
Riemannian Similarity Learning (LC), pp. 540–548.
ICMLICML-c3-2013-CoatesHWWCN #learning #off the shelf
Deep learning with COTS HPC systems (AC, BH, TW, DJW, BCC, AYN), pp. 1337–1345.
ICMLICML-c3-2013-DalalyanHMS #learning #modelling #programming
Learning Heteroscedastic Models by Convex Programming under Group Sparsity (ASD, MH, KM, JS), pp. 379–387.
ICMLICML-c3-2013-DimitrakakisT #learning
ABC Reinforcement Learning (CD, NT), pp. 684–692.
ICMLICML-c3-2013-GensD #learning #network
Learning the Structure of Sum-Product Networks (RG, PMD), pp. 873–880.
ICMLICML-c3-2013-GittensM #machine learning #scalability
Revisiting the Nystrom method for improved large-scale machine learning (AG, MWM), pp. 567–575.
ICMLICML-c3-2013-GuptaPV #approach #learning #multi #parametricity
Factorial Multi-Task Learning : A Bayesian Nonparametric Approach (SKG, DQP, SV), pp. 657–665.
ICMLICML-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.
ICMLICML-c3-2013-HoXV #learning #on the #taxonomy
On A Nonlinear Generalization of Sparse Coding and Dictionary Learning (JH, YX, BCV), pp. 1480–1488.
ICMLICML-c3-2013-HuangS #learning #markov #modelling
Spectral Learning of Hidden Markov Models from Dynamic and Static Data (TKH, JGS), pp. 630–638.
ICMLICML-c3-2013-JancsaryNR #learning #predict
Learning Convex QP Relaxations for Structured Prediction (JJ, SN, CR), pp. 915–923.
ICMLICML-c3-2013-JoseGAV #kernel #learning #performance #predict
Local Deep Kernel Learning for Efficient Non-linear SVM Prediction (CJ, PG, PA, MV), pp. 486–494.
ICMLICML-c3-2013-JoulaniGS #feedback #learning #online
Online Learning under Delayed Feedback (PJ, AG, CS), pp. 1453–1461.
ICMLICML-c3-2013-JunZSR #learning
Learning from Human-Generated Lists (KSJ, X(Z, BS, TTR), pp. 181–189.
ICMLICML-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.
ICMLICML-c3-2013-KontorovichNW #learning #on the
On learning parametric-output HMMs (AK, BN, RW), pp. 702–710.
ICMLICML-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.
ICMLICML-c3-2013-KraehenbuehlK #convergence #learning #parametricity #random
Parameter Learning and Convergent Inference for Dense Random Fields (PK, VK), pp. 513–521.
ICMLICML-c3-2013-KuzborskijO #learning
Stability and Hypothesis Transfer Learning (IK, FO), pp. 942–950.
ICMLICML-c3-2013-LattimoreHS #learning
The Sample-Complexity of General Reinforcement Learning (TL, MH, PS), pp. 28–36.
ICMLICML-c3-2013-MalioutovV #learning
Exact Rule Learning via Boolean Compressed Sensing (DMM, KRV), pp. 765–773.
ICMLICML-c3-2013-MemisevicE #invariant #learning #problem
Learning invariant features by harnessing the aperture problem (RM, GE), pp. 100–108.
ICMLICML-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.
ICMLICML-c3-2013-RamanJSS #learning
Stable Coactive Learning via Perturbation (KR, TJ, PS, TS), pp. 837–845.
ICMLICML-c3-2013-Romera-ParedesABP #learning #multi
Multilinear Multitask Learning (BRP, HA, NBB, MP), pp. 1444–1452.
ICMLICML-c3-2013-RossZYDB #learning #policy #predict
Learning Policies for Contextual Submodular Prediction (SR, JZ, YY, DD, DB), pp. 1364–1372.
ICMLICML-c3-2013-SchaulZL #learning
No more pesky learning rates (TS, SZ, YL), pp. 343–351.
ICMLICML-c3-2013-SilverNBWM #concurrent #interactive #learning
Concurrent Reinforcement Learning from Customer Interactions (DS, LN, DB, SW, JM), pp. 924–932.
ICMLICML-c3-2013-SimsekliCY #learning #matrix #modelling
Learning the β-Divergence in Tweedie Compound Poisson Matrix Factorization Models (US, ATC, YKY), pp. 1409–1417.
ICMLICML-c3-2013-SodomkaHLG #game studies #learning #named #probability
Coco-Q: Learning in Stochastic Games with Side Payments (ES, EH, MLL, AG), pp. 1471–1479.
ICMLICML-c3-2013-SutskeverMDH #learning #on the
On the importance of initialization and momentum in deep learning (IS, JM, GED, GEH), pp. 1139–1147.
ICMLICML-c3-2013-TarlowSCSZ #learning #probability
Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning (DT, KS, LC, IS, RSZ), pp. 199–207.
ICMLICML-c3-2013-WangNH #learning #robust #self
Robust and Discriminative Self-Taught Learning (HW, FN, HH), pp. 298–306.
ICMLICML-c3-2013-WangNH13a #clustering #learning #multi
Multi-View Clustering and Feature Learning via Structured Sparsity (HW, FN, HH), pp. 352–360.
ICMLICML-c3-2013-WangWBLT #learning #multi #taxonomy
Max-Margin Multiple-Instance Dictionary Learning (XW, BW, XB, WL, ZT), pp. 846–854.
ICMLICML-c3-2013-XuKHW #learning #representation
Anytime Representation Learning (ZEX, MJK, GH, KQW), pp. 1076–1084.
ICMLICML-c3-2013-YangLZ #learning #matrix #multi
Multi-Task Learning with Gaussian Matrix Generalized Inverse Gaussian Model (MY, YL, ZZ), pp. 423–431.
ICMLICML-c3-2013-YuLKJC #learning
∝SVM for Learning with Label Proportions (FXY, DL, SK, TJ, SFC), pp. 504–512.
ICMLICML-c3-2013-ZemelWSPD #learning
Learning Fair Representations (RSZ, YW, KS, TP, CD), pp. 325–333.
ICMLICML-c3-2013-ZhangYJLH #bound #kernel #learning #online
Online Kernel Learning with a Near Optimal Sparsity Bound (LZ, JY, RJ, ML, XH), pp. 621–629.
ICMLICML-c3-2013-ZhouZS #kernel #learning #multi #process
Learning Triggering Kernels for Multi-dimensional Hawkes Processes (KZ, HZ, LS), pp. 1301–1309.
ICMLICML-c3-2013-ZweigW #learning
Hierarchical Regularization Cascade for Joint Learning (AZ, DW), pp. 37–45.
KDDKDD-2013-BahadoriLX #learning #performance #probability #process
Fast structure learning in generalized stochastic processes with latent factors (MTB, YL, EPX), pp. 284–292.
KDDKDD-2013-ChakrabartiH #learning #scalability #social
Speeding up large-scale learning with a social prior (DC, RH), pp. 650–658.
KDDKDD-2013-ChenHKB #learning #named
DTW-D: time series semi-supervised learning from a single example (YC, BH, EJK, GEAPAB), pp. 383–391.
KDDKDD-2013-DasMGW #learning
Learning to question: leveraging user preferences for shopping advice (MD, GDFM, AG, IW), pp. 203–211.
KDDKDD-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.
KDDKDD-2013-GeGLZ #estimation #learning #multi
Multi-source deep learning for information trustworthiness estimation (LG, JG, XL, AZ), pp. 766–774.
KDDKDD-2013-GilpinED #algorithm #framework #learning
Guided learning for role discovery (GLRD): framework, algorithms, and applications (SG, TER, IND), pp. 113–121.
KDDKDD-2013-HaoCZ0RK #learning #towards
Towards never-ending learning from time series streams (YH, YC, JZ, BH, TR, EJK), pp. 874–882.
KDDKDD-2013-Howard #learning
The business impact of deep learning (JH), p. 1135.
KDDKDD-2013-KongY #automation #classification #distance #learning
Discriminant malware distance learning on structural information for automated malware classification (DK, GY), pp. 1357–1365.
KDDKDD-2013-KutzkovBBG #learning #named
STRIP: stream learning of influence probabilities (KK, AB, FB, AG), pp. 275–283.
KDDKDD-2013-LinWHY #information management #learning #modelling #social
Extracting social events for learning better information diffusion models (SL, FW, QH, PSY), pp. 365–373.
KDDKDD-2013-LiuFYX #learning #recommendation
Learning geographical preferences for point-of-interest recommendation (BL, YF, ZY, HX), pp. 1043–1051.
KDDKDD-2013-MorenoNK #graph #learning #modelling
Learning mixed kronecker product graph models with simulated method of moments (SM, JN, SK), pp. 1052–1060.
KDDKDD-2013-SutherlandPS #learning #matrix #rank
Active learning and search on low-rank matrices (DJS, BP, JGS), pp. 212–220.
KDDKDD-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.
KDDKDD-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.
KDDKDD-2013-WangY #learning #query
Querying discriminative and representative samples for batch mode active learning (ZW, JY), pp. 158–166.
KDDKDD-2013-Wright #data analysis #learning #optimisation
Optimization in learning and data analysis (SJW), p. 3.
KDDKDD-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.
KDDKDD-2013-ZhangHL #learning #multi #named
MI2LS: multi-instance learning from multiple informationsources (DZ, JH, RDL), pp. 149–157.
KDDKDD-2013-ZhaoH #detection #learning #online
Cost-sensitive online active learning with application to malicious URL detection (PZ, SCHH), pp. 919–927.
KDDKDD-2013-ZhaoYNG #framework #learning #twitter
A transfer learning based framework of crowd-selection on twitter (ZZ, DY, WN, SG), pp. 1514–1517.
KDIRKDIR-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.
KDIRKDIR-KMIS-2013-BerkaniN #collaboration #learning #recommendation #semantics
Semantic Collaborative Filtering for Learning Objects Recommendation (LB, ON), pp. 52–63.
KDIRKDIR-KMIS-2013-CastellanoS
Developing Innovative e-Learning Solutions (MC, FAS), pp. 484–489.
KDIRKDIR-KMIS-2013-Dessne #learning
Learning in an Organisation — Exploring the Nature of Relationships (KD), pp. 496–501.
KDIRKDIR-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.
KDIRKDIR-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.
KDIRKDIR-KMIS-2013-SaxenaBW #composition #learning
A Cognitive Reference based Model for Learning Compositional Hierarchies with Whole-composite Tags (ABS, AB, AW), pp. 119–127.
KEODKEOD-2013-WohlgenanntBS #automation #evolution #learning #ontology #prototype
A Prototype for Automating Ontology Learning and Ontology Evolution (GW, SB, MS), pp. 407–412.
MLDMMLDM-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.
MLDMMLDM-2013-ElGibreenA #learning #multi #product line
Multi Model Transfer Learning with RULES Family (HE, MSA), pp. 42–56.
MLDMMLDM-2013-GopalakrishnaOLL #algorithm #machine learning #metric
Relevance as a Metric for Evaluating Machine Learning Algorithms (AKG, TO, AL, JJL), pp. 195–208.
MLDMMLDM-2013-KoharaS #learning #self
Typhoon Damage Scale Forecasting with Self-Organizing Maps Trained by Selective Presentation Learning (KK, IS), pp. 16–26.
MLDMMLDM-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.
MLDMMLDM-2013-Suthaharan #big data #classification #network
A Single-Domain, Representation-Learning Model for Big Data Classification of Network Intrusion (SS), pp. 296–310.
RecSysRecSys-2013-HuY #learning #process #recommendation
Interview process learning for top-n recommendation (FH, YY), pp. 331–334.
RecSysRecSys-2013-KaratzoglouBS #learning #rank #recommendation
Learning to rank for recommender systems (AK, LB, YS), pp. 493–494.
RecSysRecSys-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.
RecSysRecSys-2013-SharmaY #community #learning #recommendation
Pairwise learning in recommendation: experiments with community recommendation on linkedin (AS, BY), pp. 193–200.
RecSysRecSys-2013-WestonYW #learning #rank #recommendation #statistics
Learning to rank recommendations with the k-order statistic loss (JW, HY, RJW), pp. 245–248.
SEKESEKE-2013-BarbosaFNM #architecture #learning #towards
Towards the Establishment of a Reference Architecture for Developing Learning Environments (EFB, MLF, EYN, JCM), pp. 350–355.
SEKESEKE-2013-CarrerasZO #machine learning
A Machine Learning Based File Archival Tool (S) (RC, DZ, JO), pp. 73–76.
SEKESEKE-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.
SIGIRSIGIR-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.
SIGIRSIGIR-2013-LimsopathamMO #learning
Learning to combine representations for medical records search (NL, CM, IO), pp. 833–836.
SIGIRSIGIR-2013-Moschitti #kernel #learning #rank #semantics
Kernel-based learning to rank with syntactic and semantic structures (AM), p. 1128.
SIGIRSIGIR-2013-Shokouhi #learning #personalisation #query
Learning to personalize query auto-completion (MS), pp. 103–112.
SIGIRSIGIR-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.
SIGIRSIGIR-2013-ZhangWYW #learning #network #predict
Learning latent friendship propagation networks with interest awareness for link prediction (JZ, CW, PSY, JW), pp. 63–72.
ICMTICMT-2013-FaunesSB #approach #model transformation
Genetic-Programming Approach to Learn Model Transformation Rules from Examples (MF, HAS, MB), pp. 17–32.
OOPSLAOOPSLA-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.
POPLPOPL-2013-BotincanB #learning #specification
Sigma*: symbolic learning of input-output specifications (MB, DB), pp. 443–456.
POPLPOPL-2013-DSilvaHK #learning
Abstract conflict driven learning (VD, LH, DK), pp. 143–154.
RERE-2013-ShiWL #evolution #learning #predict
Learning from evolution history to predict future requirement changes (LS, QW, ML), pp. 135–144.
RERE-2013-SultanovH #learning #requirements
Application of reinforcement learning to requirements engineering: requirements tracing (HS, JHH), pp. 52–61.
REFSQREFSQ-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.
SACSAC-2013-AkritidisB #algorithm #classification #machine learning #research
A supervised machine learning classification algorithm for research articles (LA, PB), pp. 115–120.
SACSAC-2013-BerralGT #automation #machine learning
Empowering automatic data-center management with machine learning (JLB, RG, JT), pp. 170–172.
SACSAC-2013-BlondelSU #classification #constraints #learning #using
Learning non-linear classifiers with a sparsity constraint using L1 regularization (MB, KS, KU), pp. 167–169.
SACSAC-2013-FilhoB #learning #mobile #requirements
A requirements catalog for mobile learning environments (NFDF, EFB), pp. 1266–1271.
SACSAC-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.
SACSAC-2013-LommatzschKA #hybrid #learning #modelling #recommendation #semantics
Learning hybrid recommender models for heterogeneous semantic data (AL, BK, SA), pp. 275–276.
SACSAC-2013-SeelandKP #graph #kernel #learning
Model selection based product kernel learning for regression on graphs (MS, SK, BP), pp. 136–143.
SACSAC-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.
ICSEICSE-2013-CotroneoPR #testing
A learning-based method for combining testing techniques (DC, RP, SR), pp. 142–151.
ICSEICSE-2013-Jonsson #machine learning #performance #scalability #using
Increasing anomaly handling efficiency in large organizations using applied machine learning (LJ), pp. 1361–1364.
ICSEICSE-2013-KimNSK #automation #generative
Automatic patch generation learned from human-written patches (DK, JN, JS, SK), pp. 802–811.
ICSEICSE-2013-MengKM #learning #named
LASE: locating and applying systematic edits by learning from examples (NM, MK, KSM), pp. 502–511.
ICSEICSE-2013-NamPK #fault #learning
Transfer defect learning (JN, SJP, SK), pp. 382–391.
ICSEICSE-2013-SykesCMKRI #adaptation #learning #modelling
Learning revised models for planning in adaptive systems (DS, DC, JM, JK, AR, KI), pp. 63–71.
ICSEICSE-2013-TillmannHXGB #education #game studies #interactive #learning #programming #re-engineering
Teaching and learning programming and software engineering via interactive gaming (NT, JdH, TX, SG, JB), pp. 1117–1126.
CCCC-2013-MooreC #automation #generative #machine learning #policy #using
Automatic Generation of Program Affinity Policies Using Machine Learning (RWM, BRC), pp. 184–203.
CGOCGO-2013-KulkarniCWS #automation #heuristic #machine learning #using
Automatic construction of inlining heuristics using machine learning (SK, JC, CW, DS), p. 12.
CAVCAV-2013-0001LMN #data type #invariant #learning #linear #quantifier
Learning Universally Quantified Invariants of Linear Data Structures (PG, CL, PM, DN), pp. 813–829.
CAVCAV-2013-ChagantyLNR #learning #relational #smt #using
Combining Relational Learning with SMT Solvers Using CEGAR (ATC, AL, AVN, SKR), pp. 447–462.
ICSTICST-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.
ICSTICST-2013-MeinkeS #named #testing
LBTest: A Learning-Based Testing Tool for Reactive Systems (KM, MAS), pp. 447–454.
ICTSSICTSS-2013-FengLMNSW #case study #testing
Case Studies in Learning-Based Testing (LF, SL, KM, FN, MAS, PYHW), pp. 164–179.
ISSTAISSTA-2013-HowarGR #analysis #generative #hybrid #interface #learning
Hybrid learning: interface generation through static, dynamic, and symbolic analysis (FH, DG, ZR), pp. 268–279.
ISSTAISSTA-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.
ICSTSAT-2013-Ben-Ari #education #named #satisfiability
LearnSAT: A SAT Solver for Education (MBA), pp. 403–407.
ICSTSAT-2013-Johannsen #exponential #learning #proving
Exponential Separations in a Hierarchy of Clause Learning Proof Systems (JJ), pp. 40–51.
ICSTSAT-2013-LonsingEG #learning #performance #pseudo #quantifier
Efficient Clause Learning for Quantified Boolean Formulas via QBF Pseudo Unit Propagation (FL, UE, AVG), pp. 100–115.
CBSECBSE-2012-AbateCTZ #component #future of #learning #repository
Learning from the future of component repositories (PA, RDC, RT, SZ), pp. 51–60.
ASEASE-2012-LuCC #fault #learning #predict #reduction #using
Software defect prediction using semi-supervised learning with dimension reduction (HL, BC, MC), pp. 314–317.
CASECASE-2012-AnKP #learning #modelling #process
Grasp motion learning with Gaussian Process Dynamic Models (BA, HK, FCP), pp. 1114–1119.
CASECASE-2012-YamamotoD #interface #learning
Robot interface learning user-defined voice instructions (DY, MD), pp. 926–929.
DACDAC-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.
DATEDATE-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.
DocEngDocEng-2012-MoulderM #how #layout #learning
Learning how to trade off aesthetic criteria in layout (PM, KM), pp. 33–36.
HTHT-2012-SchofeggerKSG #behaviour #learning #social
Learning user characteristics from social tagging behavior (KS, CK, PS, MG), pp. 207–212.
SIGMODSIGMOD-2012-AbiteboulAMS #learning #xml
Auto-completion learning for XML (SA, YA, TM, PS), pp. 669–672.
SIGMODSIGMOD-2012-LinK #machine learning #scalability #twitter
Large-scale machine learning at twitter (JL, AK), pp. 793–804.
VLDBVLDB-2012-IseleB #learning #programming #search-based #using
Learning Expressive Linkage Rules using Genetic Programming (RI, CB), pp. 1638–1649.
VLDBVLDB-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.
VLDBVLDB-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.
VLDBVLDB-2012-SinghG #learning #semantics #string
Learning Semantic String Transformations from Examples (RS, SG), pp. 740–751.
CSEETCSEET-2012-AroraG #collaboration #learning #programming #source code
Learning to Write Programs with Others: Collaborative Quadruple Programming (RA, SG), pp. 32–41.
CSEETCSEET-2012-BareissS
A Gentle Introduction to Learn by Doing (RB, TS), pp. 81–84.
CSEETCSEET-2012-TillmannHXB #education #game studies #learning #named #social
Pex4Fun: Teaching and Learning Computer Science via Social Gaming (NT, JdH, TX, JB), pp. 90–91.
ITiCSEITiCSE-2012-AsadB #aspect-oriented #concept #image #learning
Are children capable of learning image processing concepts?: cognitive and affective aspects (KA, MB), pp. 227–231.
ITiCSEITiCSE-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.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2012-CamaraPV #collaboration #evaluation #framework #learning #programming
Evaluation of a collaborative instructional framework for programming learning (LMSC, MPV, JÁVI), pp. 162–167.
ITiCSEITiCSE-2012-ChristensenC #learning
Lectures abandoned: active learning by active seminars (HBC, AVC), pp. 16–21.
ITiCSEITiCSE-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.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2012-HamadaN #learning
A learning tool for MP3 audio compression (MH, HN), p. 397.
ITiCSEITiCSE-2012-HiltonJ #array #education #learning #on the #testing
On teaching arrays with test-driven learning in WebIDE (MH, DSJ), pp. 93–98.
ITiCSEITiCSE-2012-KrausePR #learning
Formal learning groups in an introductory CS course: a qualitative exploration (JK, IP, CR), pp. 315–320.
ITiCSEITiCSE-2012-Larraza-MendiluzeGMMRALS
Nintendo® DS projects to learn computer input-output (ELM, NGV, JIM, JM, TRV, ISA, JFL, KS), p. 373.
ITiCSEITiCSE-2012-Luxton-ReillyDPS #how #learning #process #student
Activities, affordances and attitude: how student-generated questions assist learning (ALR, PD, BP, RS), pp. 4–9.
ITiCSEITiCSE-2012-MalekoHD #case study #experience #learning #mobile #programming #social
Novices’ perceptions and experiences of a mobile social learning environment for learning of programming (MM, MH, DJD), pp. 285–290.
ITiCSEITiCSE-2012-MehtaKP #algorithm #learning #network
Forming project groups while learning about matching and network flows in algorithms (DPM, TMK, IP), pp. 40–45.
ITiCSEITiCSE-2012-MeyerW #lessons learnt #programming
Programming studio: advances and lessons learned (CM, MW), p. 369.
ITiCSEITiCSE-2012-MussaiL #animation #concept #learning #object-oriented
An animation as an illustrate tool for learning concepts in oop (YM, NL), p. 386.
ITiCSEITiCSE-2012-MyllymakiH #case study #learning
Choosing a study mode in blended learning (MM, IH), pp. 291–296.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2012-Sudol-DeLyserSC #comprehension #learning #problem
Code comprehension problems as learning events (LASD, MS, SC), pp. 81–86.
ITiCSEITiCSE-2012-Velazquez-Iturbide #algorithm #approach #learning #refinement
Refinement of an experimental approach tocomputer-based, active learning of greedy algorithms (JÁVI), pp. 46–51.
FASEFASE-2012-AlrajehKRU #learning #satisfiability #specification
Learning from Vacuously Satisfiable Scenario-Based Specifications (DA, JK, AR, SU), pp. 377–393.
TACASTACAS-2012-DSilvaHKT #analysis #bound #learning
Numeric Bounds Analysis with Conflict-Driven Learning (VD, LH, DK, MT), pp. 48–63.
TACASTACAS-2012-MertenHSCJ #automaton #learning
Demonstrating Learning of Register Automata (MM, FH, BS, SC, BJ), pp. 466–471.
ICPCICPC-2012-Sajnani #approach #architecture #automation #machine learning
Automatic software architecture recovery: A machine learning approach (HS), pp. 265–268.
ICSMEICSM-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.
SASSAS-2012-GiannakopoulouRR #component #interface #learning
Symbolic Learning of Component Interfaces (DG, ZR, VR), pp. 248–264.
STOCSTOC-2012-DaskalakisDS #learning
Learning poisson binomial distributions (CD, ID, RAS), pp. 709–728.
DLTDLT-2012-BoiretLN #learning
Learning Rational Functions (AB, AL, JN), pp. 273–283.
LATALATA-2012-GeilkeZ #algorithm #learning #pattern matching #polynomial
Polynomial-Time Algorithms for Learning Typed Pattern Languages (MG, SZ), pp. 277–288.
LATALATA-2012-Yoshinaka #context-free grammar #integration #learning
Integration of the Dual Approaches in the Distributional Learning of Context-Free Grammars (RY), pp. 538–550.
FMFM-2012-AartsHKOV #abstraction #automaton #learning #refinement
Automata Learning through Counterexample Guided Abstraction Refinement (FA, FH, HK, PO, FWV), pp. 10–27.
CHICHI-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.
CHICHI-2012-ChinF #difference #health #learning
Age differences in exploratory learning from a health information website (JC, WTF), pp. 3031–3040.
CHICHI-2012-DongDJKNA #game studies #learning
Discovery-based games for learning software (TD, MD, DJ, KK, MWN, MSA), pp. 2083–2086.
CHICHI-2012-JainB #learning #performance
User learning and performance with bezel menus (MJ, RB), pp. 2221–2230.
CHICHI-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.
CHICHI-2012-ParkC12a #adaptation #deployment #design #learning
Adaptation as design: learning from an EMR deployment study (SYP, YC), pp. 2097–2106.
CHICHI-2012-VitakIDEG #learning
Gaze-augmented think-aloud as an aid to learning (SAV, JEI, ATD, SE, AKG), pp. 2991–3000.
CHICHI-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.
CSCWCSCW-2012-HemphillO #adaptation #bibliography #community #gender #learning
Learning the lingo?: gender, prestige and linguistic adaptation in review communities (LH, JO), pp. 305–314.
CSCWCSCW-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.
CSCWCSCW-2012-RzeszotarskiK #learning #predict #wiki #word
Learning from history: predicting reverted work at the word level in wikipedia (JMR, AK), pp. 437–440.
CSCWCSCW-2012-SarcevicPWSBA #coordination #distributed #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.
ICEISICEIS-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.
ICEISICEIS-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.
CIKMCIKM-2012-AgarwalRSMLGF #learning #rank #robust
Learning to rank for robust question answering (AA, HR, KS, PM, RDL, DG, JF), pp. 833–842.
CIKMCIKM-2012-AnHS #learning #ontology #web
Learning to discover complex mappings from web forms to ontologies (YA, XH, IYS), pp. 1253–1262.
CIKMCIKM-2012-CaiZ #injection #learning #rank
Variance maximization via noise injection for active sampling in learning to rank (WC, YZ), pp. 1809–1813.
CIKMCIKM-2012-ChaliHI #learning #performance
Improving the performance of the reinforcement learning model for answering complex questions (YC, SAH, KI), pp. 2499–2502.
CIKMCIKM-2012-ChengZXAC #classification #learning #on the
On active learning in hierarchical classification (YC, KZ, YX, AA, ANC), pp. 2467–2470.
CIKMCIKM-2012-Cohen #learning #metric #random #similarity
Learning similarity measures based on random walks (WWC), p. 3.
CIKMCIKM-2012-CuiMWGL #image #keyword #semantics
Semantically coherent image annotation with a learning-based keyword propagation strategy (CC, JM, SW, SG, TL), pp. 2423–2426.
CIKMCIKM-2012-FangS #approach #feedback #learning #recommendation
A latent pairwise preference learning approach for recommendation from implicit feedback (YF, LS), pp. 2567–2570.
CIKMCIKM-2012-GuoMCJ #learning #recommendation #social
Learning to recommend with social relation ensemble (LG, JM, ZC, HJ), pp. 2599–2602.
CIKMCIKM-2012-KanhabuaN #learning #query #rank
Learning to rank search results for time-sensitive queries (NK, KN), pp. 2463–2466.
CIKMCIKM-2012-LiBCH #clustering #learning #relational
Relational co-clustering via manifold ensemble learning (PL, JB, CC, ZH), pp. 1687–1691.
CIKMCIKM-2012-LuZZX #image #learning #scalability #semantics #set
Semantic context learning with large-scale weakly-labeled image set (YL, WZ, KZ, XX), pp. 1859–1863.
CIKMCIKM-2012-MacdonaldSO #learning #on the #query #rank
On the usefulness of query features for learning to rank (CM, RLTS, IO), pp. 2559–2562.
CIKMCIKM-2012-MetzgerSHS #interactive #learning
LUKe and MIKe: learning from user knowledge and managing interactive knowledge extraction (SM, MS, KH, RS), pp. 2671–2673.
CIKMCIKM-2012-MorenoSRS #learning #multi #named
TALMUD: transfer learning for multiple domains (OM, BS, LR, GS), pp. 425–434.
CIKMCIKM-2012-NegahbanRG #learning #multi #performance #scalability #statistics #using
Scaling multiple-source entity resolution using statistically efficient transfer learning (SN, BIPR, JG), pp. 2224–2228.
CIKMCIKM-2012-QuanzH #generative #learning #multi #named
CoNet: feature generation for multi-view semi-supervised learning with partially observed views (BQ, JH), pp. 1273–1282.
CIKMCIKM-2012-RamanSGB #algorithm #learning #towards
Learning from mistakes: towards a correctable learning algorithm (KR, KMS, RGB, CJCB), pp. 1930–1934.
CIKMCIKM-2012-RenCJ #learning #topic
Topic based pose relevance learning in dance archives (RR, JPC, JMJ), pp. 2571–2574.
CIKMCIKM-2012-ShangJLW #learning
Learning spectral embedding via iterative eigenvalue thresholding (FS, LCJ, YL, FW), pp. 1507–1511.
CIKMCIKM-2012-SunG #learning
Active learning for relation type extension with local and global data views (AS, RG), pp. 1105–1112.
CIKMCIKM-2012-SunSL #learning #multi #performance #query
Fast multi-task learning for query spelling correction (XS, AS, PL), pp. 285–294.
CIKMCIKM-2012-SunWGM #hybrid #learning #rank #recommendation
Learning to rank for hybrid recommendation (JS, SW, BJG, JM), pp. 2239–2242.
CIKMCIKM-2012-VolkovsLZ #learning #rank
Learning to rank by aggregating expert preferences (MV, HL, RSZ), pp. 843–851.
CIKMCIKM-2012-WangC #learning #predict #word
Learning to predict the cost-per-click for your ad words (CJW, HHC), pp. 2291–2294.
CIKMCIKM-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.
CIKMCIKM-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.
CIKMCIKM-2012-WangXY #learning
Importance weighted passive learning (SW, XX, YY), pp. 2243–2246.
CIKMCIKM-2012-YangTKZLDLW #learning #mining #network
Mining competitive relationships by learning across heterogeneous networks (YY, JT, JK, YZ, JL, YD, TL, LW), pp. 1432–1441.
CIKMCIKM-2012-YaoS #learning #relational #ubiquitous
Exploiting latent relevance for relational learning of ubiquitous things (LY, QZS), pp. 1547–1551.
CIKMCIKM-2012-ZhangHLL #learning #rank #realtime #twitter
Query-biased learning to rank for real-time twitter search (XZ, BH, TL, BL), pp. 1915–1919.
CIKMCIKM-2012-ZhangWW #framework #interactive #learning #ontology
An interaction framework of service-oriented ontology learning (JZ, YW, HW), pp. 2303–2306.
CIKMCIKM-2012-ZhouLZ #community #learning #quality
Joint relevance and answer quality learning for question routing in community QA (GZ, KL, JZ), pp. 1492–1496.
CIKMCIKM-2012-ZhouZ #debugging #learning #rank
Learning to rank duplicate bug reports (JZ, HZ), pp. 852–861.
ECIRECIR-2012-Lubell-DoughtieH #feedback #learning #rank
Learning to Rank from Relevance Feedback for e-Discovery (PLD, KH), pp. 535–539.
ECIRECIR-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.
ICMLICML-2012-AzarMK #complexity #generative #learning #on the
On the Sample Complexity of Reinforcement Learning with a Generative Model (MGA, RM, BK), p. 222.
ICMLICML-2012-AzimiFFBH #coordination #learning
Batch Active Learning via Coordinated Matching (JA, AF, XZF, GB, BH), p. 44.
ICMLICML-2012-BalleQC #learning #modelling #optimisation
Local Loss Optimization in Operator Models: A New Insight into Spectral Learning (BB, AQ, XC), p. 236.
ICMLICML-2012-BelletHS #classification #learning #linear #similarity
Similarity Learning for Provably Accurate Sparse Linear Classification (AB, AH, MS), p. 193.
ICMLICML-2012-BonillaR #learning #probability #prototype
Discriminative Probabilistic Prototype Learning (EVB, ARK), p. 155.
ICMLICML-2012-BronsteinSS #learning #modelling #performance
Learning Efficient Structured Sparse Models (AMB, PS, GS), p. 33.
ICMLICML-2012-ChambersJ #learning
Learning the Central Events and Participants in Unlabeled Text (NC, DJ), p. 3.
ICMLICML-2012-CharlinZB #learning #problem
Active Learning for Matching Problems (LC, RSZ, CB), p. 23.
ICMLICML-2012-DekelTA #adaptation #learning #online #policy
Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret (OD, AT, RA), p. 227.
ICMLICML-2012-DuanXT #adaptation #learning
Learning with Augmented Features for Heterogeneous Domain Adaptation (LD, DX, IWT), p. 89.
ICMLICML-2012-DundarAQR #learning #modelling #online
Bayesian Nonexhaustive Learning for Online Discovery and Modeling of Emerging Classes (MD, FA, AQ, BR), p. 18.
ICMLICML-2012-EbanBSG #learning #online #predict #sequence
Learning the Experts for Online Sequence Prediction (EE, AB, SSS, AG), p. 38.
ICMLICML-2012-FarabetCNL #learning #multi #parsing
Scene parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers (CF, CC, LN, YL), p. 241.
ICMLICML-2012-GeistSLG #approach #difference #learning
A Dantzig Selector Approach to Temporal Difference Learning (MG, BS, AL, MG), p. 49.
ICMLICML-2012-Gonen #kernel #learning #multi #performance
Bayesian Efficient Multiple Kernel Learning (MG), p. 17.
ICMLICML-2012-GongZM #learning #multi #robust
Robust Multiple Manifold Structure Learning (DG, XZ, GGM), p. 7.
ICMLICML-2012-GoodfellowCB #learning #scalability
Large-Scale Feature Learning With Spike-and-Slab Sparse Coding (IJG, ACC, YB), p. 180.
ICMLICML-2012-GuoX #classification #learning #multi
Cross Language Text Classification via Subspace Co-regularized Multi-view Learning (YG, MX), p. 120.
ICMLICML-2012-HanLC #learning #modelling #multi
Cross-Domain Multitask Learning with Latent Probit Models (SH, XL, LC), p. 51.
ICMLICML-2012-HazanK #learning #online
Projection-free Online Learning (EH, SK), p. 239.
ICMLICML-2012-HoiWZ #learning
Exact Soft Confidence-Weighted Learning (SCHH, JW, PZ), p. 19.
ICMLICML-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.
ICMLICML-2012-Honorio #convergence #learning #modelling #optimisation #probability
Convergence Rates of Biased Stochastic Optimization for Learning Sparse Ising Models (JH), p. 144.
ICMLICML-2012-JalaliS #dependence #graph #learning
Learning the Dependence Graph of Time Series with Latent Factors (AJ, SS), p. 83.
ICMLICML-2012-JawanpuriaN #learning
A Convex Feature Learning Formulation for Latent Task Structure Discovery (PJ, JSN), p. 199.
ICMLICML-2012-JiangLS #3d #learning #using
Learning Object Arrangements in 3D Scenes using Human Context (YJ, ML, AS), p. 119.
ICMLICML-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.
ICMLICML-2012-KalakrishnanRPS #learning #policy
Learning Force Control Policies for Compliant Robotic Manipulation (MK, LR, PP, SS), p. 10.
ICMLICML-2012-KarbasiIM #learning #rank
Comparison-Based Learning with Rank Nets (AK, SI, LM), p. 161.
ICMLICML-2012-KumarD #learning #multi
Learning Task Grouping and Overlap in Multi-task Learning (AK, HDI), p. 224.
ICMLICML-2012-KumarNKD #classification #framework #kernel #learning #multi
A Binary Classification Framework for Two-Stage Multiple Kernel Learning (AK, ANM, KK, HDI), p. 173.
ICMLICML-2012-KumarPK #learning #modelling #nondeterminism
Modeling Latent Variable Uncertainty for Loss-based Learning (MPK, BP, DK), p. 29.
ICMLICML-2012-LanctotGBB #game studies #learning
No-Regret Learning in Extensive-Form Games with Imperfect Recall (ML, RGG, NB, MB), p. 135.
ICMLICML-2012-LeRMDCCDN #learning #scalability #using
Building high-level features using large scale unsupervised learning (QVL, MR, RM, MD, GC, KC, JD, AYN), p. 69.
ICMLICML-2012-LinXWZ #learning
Total Variation and Euler’s Elastica for Supervised Learning (TL, HX, LW, HZ), p. 82.
ICMLICML-2012-LouH #learning
Structured Learning from Partial Annotations (XL, FAH), p. 52.
ICMLICML-2012-MakinoT #learning #parametricity
Apprenticeship Learning for Model Parameters of Partially Observable Environments (TM, JT), p. 117.
ICMLICML-2012-MatuszekFZBF #learning
A Joint Model of Language and Perception for Grounded Attribute Learning (CM, NF, LSZ, LB, DF), p. 186.
ICMLICML-2012-Memisevic #learning #multi #on the
On multi-view feature learning (RM), p. 140.
ICMLICML-2012-MnihH #image #learning #semistructured data
Learning to Label Aerial Images from Noisy Data (VM, GEH), p. 31.
ICMLICML-2012-MohamedHG #learning
Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning (SM, KAH, ZG), p. 91.
ICMLICML-2012-NiuDYS #learning #metric
Information-theoretic Semi-supervised Metric Learning via Entropy Regularization (GN, BD, MY, MS), p. 136.
ICMLICML-2012-Painter-WakefieldP #algorithm #learning
Greedy Algorithms for Sparse Reinforcement Learning (CPW, RP), p. 114.
ICMLICML-2012-PassosRWD #flexibility #learning #modelling #multi
Flexible Modeling of Latent Task Structures in Multitask Learning (AP, PR, JW, HDI), p. 167.
ICMLICML-2012-PeharzP #learning #network
Exact Maximum Margin Structure Learning of Bayesian Networks (RP, FP), p. 102.
ICMLICML-2012-PiresS #estimation #learning #linear #statistics
Statistical linear estimation with penalized estimators: an application to reinforcement learning (BAP, CS), p. 228.
ICMLICML-2012-PlessisS #learning
Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching (MCdP, MS), p. 159.
ICMLICML-2012-PrasseSLS #email #identification #learning #regular expression
Learning to Identify Regular Expressions that Describe Email Campaigns (PP, CS, NL, TS), p. 146.
ICMLICML-2012-RossB #identification #learning #modelling
Agnostic System Identification for Model-Based Reinforcement Learning (SR, DB), p. 247.
ICMLICML-2012-SamdaniR #learning #performance #predict
Efficient Decomposed Learning for Structured Prediction (RS, DR), p. 200.
ICMLICML-2012-ScholkopfJPSZM #learning #on the
On causal and anticausal learning (BS, DJ, JP, ES, KZ, JMM), p. 63.
ICMLICML-2012-ShiS #adaptation #clustering #learning
Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation (YS, FS), p. 166.
ICMLICML-2012-ShivaswamyJ #learning #online #predict
Online Structured Prediction via Coactive Learning (PS, TJ), p. 12.
ICMLICML-2012-SilvaKB #learning
Learning Parameterized Skills (BCdS, GK, AGB), p. 187.
ICMLICML-2012-SohnL #invariant #learning
Learning Invariant Representations with Local Transformations (KS, HL), p. 174.
ICMLICML-2012-StorkeyMG #machine learning
Isoelastic Agents and Wealth Updates in Machine Learning Markets (AJS, JM, KG), p. 133.
ICMLICML-2012-Wagstaff #machine learning #matter
Machine Learning that Matters (KW), p. 240.
ICMLICML-2012-WangWHL #learning #monte carlo
Monte Carlo Bayesian Reinforcement Learning (YW, KSW, DH, WSL), p. 105.
ICMLICML-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.
ICMLICML-2012-XuWC #learning
The Greedy Miser: Learning under Test-time Budgets (ZEX, KQW, OC), p. 169.
ICMLICML-2012-YackleyL #learning
Smoothness and Structure Learning by Proxy (BY, TL), p. 57.
ICMLICML-2012-YangMJZZ #kernel #learning #multi #probability #programming
Multiple Kernel Learning from Noisy Labels by Stochastic Programming (TY, MM, RJ, LZ, YZ), p. 21.
ICMLICML-2012-ZhongK #clustering #flexibility #learning #multi
Convex Multitask Learning with Flexible Task Clusters (WZ, JTYK), p. 66.
ICPRICPR-2012-AbeOD #image #learning #rank
Recognizing surface qualities from natural images based on learning to rank (TA, TO, KD), pp. 3712–3715.
ICPRICPR-2012-AntoniukFH #learning #markov #network
Learning Markov Networks by Analytic Center Cutting Plane Method (KA, VF, VH), pp. 2250–2253.
ICPRICPR-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.
ICPRICPR-2012-BaillyMPB #cost analysis #learning
Learning global cost function for face alignment (KB, MM, PP, EB), pp. 1112–1115.
ICPRICPR-2012-BanerjeeN #kernel #learning #multi #process #recognition #using
Pose based activity recognition using Multiple Kernel learning (PB, RN), pp. 445–448.
ICPRICPR-2012-CermanH #learning #problem
Tracking with context as a semi-supervised learning and labeling problem (LC, VH), pp. 2124–2127.
ICPRICPR-2012-ChernoffLN #fault #learning #metric
Metric learning by directly minimizing the k-NN training error (KC, ML, MN), pp. 1265–1268.
ICPRICPR-2012-DahmaneLDB #estimation #learning #symmetry
Learning symmetrical model for head pose estimation (AD, SL, CD, IMB), pp. 3614–3617.
ICPRICPR-2012-DAmbrosioIS #learning #re-engineering
A One-per-Class reconstruction rule for class imbalance learning (RD, GI, PS), pp. 1310–1313.
ICPRICPR-2012-DoTT #multi #representation #using
Text/graphic separation using a sparse representation with multi-learned dictionaries (THD, ST, ORT), pp. 689–692.
ICPRICPR-2012-DuanWLDC #learning #named
K-CPD: Learning of overcomplete dictionaries for tensor sparse coding (GD, HW, ZL, JD, YWC), pp. 493–496.
ICPRICPR-2012-FangZ #learning
I don’t know the label: Active learning with blind knowledge (MF, XZ), pp. 2238–2241.
ICPRICPR-2012-FiaschiKNH #learning
Learning to count with regression forest and structured labels (LF, UK, RN, FAH), pp. 2685–2688.
ICPRICPR-2012-GhanemKFZ #automation #learning #recognition
Context-aware learning for automatic sports highlight recognition (BG, MK, MF, TZ), pp. 1977–1980.
ICPRICPR-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.
ICPRICPR-2012-GranaCBC #image #learning #segmentation
Learning non-target items for interesting clothes segmentation in fashion images (CG, SC, DB, RC), pp. 3317–3320.
ICPRICPR-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.
ICPRICPR-2012-GutmannH #architecture #feature model #image #learning
Learning a selectivity-invariance-selectivity feature extraction architecture for images (MG, AH), pp. 918–921.
ICPRICPR-2012-HidoK #graph #learning #similarity
Hash-based structural similarity for semi-supervised Learning on attribute graphs (SH, HK), pp. 3009–3012.
ICPRICPR-2012-HinoO #kernel #learning #multi
An improved entropy-based multiple kernel learning (HH, TO), pp. 1189–1192.
ICPRICPR-2012-HiradeY #learning #predict
Ensemble learning for change-point prediction (RH, TY), pp. 1860–1863.
ICPRICPR-2012-HuangLT #invariant #learning #recognition
Learning modality-invariant features for heterogeneous face recognition (LH, JL, YPT), pp. 1683–1686.
ICPRICPR-2012-JinGYZ #algorithm #learning #multi
Multi-label learning vector quantization algorithm (XBJ, GG, JY, DZ), pp. 2140–2143.
ICPRICPR-2012-JiS12a #3d #estimation #learning #robust
Robust 3D human pose estimation via dual dictionaries learning (HJ, FS), pp. 3370–3373.
ICPRICPR-2012-KhanT #learning #taxonomy
Stable discriminative dictionary learning via discriminative deviation (NK, MFT), pp. 3224–3227.
ICPRICPR-2012-KongW #clustering #learning #multi
A multi-task learning strategy for unsupervised clustering via explicitly separating the commonality (SK, DW), pp. 771–774.
ICPRICPR-2012-KrijtheHL #classification #using
Improving cross-validation based classifier selection using meta-learning (JHK, TKH, ML), pp. 2873–2876.
ICPRICPR-2012-KumarRS #learning #predict
Learning to predict super resolution wavelet coefficients (NK, NKR, AS), pp. 3468–3471.
ICPRICPR-2012-KumarYD #classification #documentation #learning #retrieval
Learning document structure for retrieval and classification (JK, PY, DSD), pp. 1558–1561.
ICPRICPR-2012-LeeKD #induction #learning
Learning action symbols for hierarchical grammar induction (KL, TKK, YD), pp. 3778–3782.
ICPRICPR-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.
ICPRICPR-2012-LiHL #adaptation #learning #multi #online #people
Online adaptive learning for multi-camera people counting (JL, LH, CL), pp. 3415–3418.
ICPRICPR-2012-LiLLL #distance #estimation #learning #metric
Learning distance metric regression for facial age estimation (CL, QL, JL, HL), pp. 2327–2330.
ICPRICPR-2012-LinLZ #learning #representation #taxonomy
Incoherent dictionary learning for sparse representation (TL, SL, HZ), pp. 1237–1240.
ICPRICPR-2012-LiPMH #classification #email #incremental #learning #using
Business email classification using incremental subspace learning (ML, YP, RM, HYH), pp. 625–628.
ICPRICPR-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.
ICPRICPR-2012-LiuL #analysis #detection #learning #multi
Unsupervised multi-target trajectory detection, learning and analysis in complicated environments (HL, JL), pp. 3716–3720.
ICPRICPR-2012-LiuLWZ #learning #linear
Locally linear embedding based example learning for pan-sharpening (QL, LL, YW, ZZ), pp. 1928–1931.
ICPRICPR-2012-LiuLYZ #composition #learning #visual notation
Learning to describe color composition of visual objects (YL, YL, ZY, NZ), pp. 3337–3340.
ICPRICPR-2012-LiuML #learning #multi
Training data recycling for multi-level learning (JL, SM, YL), pp. 2314–2318.
ICPRICPR-2012-LiuSW #learning #recognition #taxonomy
Facial expression recognition based on discriminative dictionary learning (WL, CS, YW), pp. 1839–1842.
ICPRICPR-2012-LiVBB #clustering #learning #using
Feature learning using Generalized Extreme Value distribution based K-means clustering (ZL, OV, HB, RB), pp. 1538–1541.
ICPRICPR-2012-LuLY #adaptation #classification #kernel #learning
Adaptive kernel learning based on centered alignment for hierarchical classification (YL, JL, JY), pp. 569–572.
ICPRICPR-2012-LuLZSCO #using
Learning-based deformable registration using weighted mutual information (YL, RL, LZ, YS, CC, SHO), pp. 2626–2629.
ICPRICPR-2012-MarcaciniCR #approach #clustering #learning
An active learning approach to frequent itemset-based text clustering (RMM, GNC, SOR), pp. 3529–3532.
ICPRICPR-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.
ICPRICPR-2012-MoZW #classification #learning
Enhancing cross-view object classification by feature-based transfer learning (YM, ZZ, YW), pp. 2218–2221.
ICPRICPR-2012-Nagy #learning #web
Learning the characteristics of critical cells from web tables (GN), pp. 1554–1557.
ICPRICPR-2012-NamA #image #learning
Learning human preferences to sharpen images (MN, NA), pp. 2173–2176.
ICPRICPR-2012-NayefAB #learning
Learning feature weights of symbols, with application to symbol spotting (NN, MZA, TMB), pp. 2371–2374.
ICPRICPR-2012-Noh #analysis #classification #learning #metric #nearest neighbour
χ2 Metric learning for nearest neighbor classification and its analysis (SN), pp. 991–995.
ICPRICPR-2012-PangHYQW #analysis #classification #learning
Theoretical analysis of learning local anchors for classification (JP, QH, BY, LQ, DW), pp. 1803–1806.
ICPRICPR-2012-PanLS #kernel #learning
Learning kernels from labels with ideal regularization (BP, JHL, LS), pp. 505–508.
ICPRICPR-2012-PourdamghaniRZ #estimation #graph #learning #metric
Metric learning for graph based semi-supervised human pose estimation (NP, HRR, MZ), pp. 3386–3389.
ICPRICPR-2012-QinZCW #learning #online
Matting-driven online learning of Hough forests for object tracking (TQ, BZ, TJC, HW), pp. 2488–2491.
ICPRICPR-2012-San-BiagioUCCCM #approach #classification #kernel #learning #multi
A multiple kernel learning approach to multi-modal pedestrian classification (MSB, AU, MC, MC, UC, VM), pp. 2412–2415.
ICPRICPR-2012-SchauerteS #image #learning #modelling #robust #web
Learning robust color name models from web images (BS, RS), pp. 3598–3601.
ICPRICPR-2012-SharmaHN #classification #detection #incremental #learning #performance
Efficient incremental learning of boosted classifiers for object detection (PS, CH, RN), pp. 3248–3251.
ICPRICPR-2012-ShenMZ #analysis #graph #learning #online
Unsupervised online learning trajectory analysis based on weighted directed graph (YS, ZM, JZ), pp. 1306–1309.
ICPRICPR-2012-SommerFHG #detection #image
Learning-based mitotic cell detection in histopathological images (CS, LF, FAH, DG), pp. 2306–2309.
ICPRICPR-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.
ICPRICPR-2012-SunBM #learning
Unsupervised skeleton learning for manifold denoising (KS, EB, SMM), pp. 2719–2722.
ICPRICPR-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.
ICPRICPR-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.
ICPRICPR-2012-TiribuziPVR #detection #framework #kernel #learning #multi
A Multiple Kernel Learning framework for detecting altered fingerprints (MT, MP, PV, ER), pp. 3402–3405.
ICPRICPR-2012-TuS #adaptation #classification #learning
Dynamical ensemble learning with model-friendly classifiers for domain adaptation (WT, SS), pp. 1181–1184.
ICPRICPR-2012-VillamizarGSM #learning #online #random #using
Online human-assisted learning using Random Ferns (MV, AG, AS, FMN), pp. 2821–2824.
ICPRICPR-2012-VuralA #machine learning #video
A machine learning system for human-in-the-loop video surveillance (UV, YSA), pp. 1092–1095.
ICPRICPR-2012-WangJ12b #learning #network #process #recognition
Learning dynamic Bayesian network discriminatively for human activity recognition (XW, QJ), pp. 3553–3556.
ICPRICPR-2012-WangL12b #learning #recognition #string
String-level learning of confidence transformation for Chinese handwritten text recognition (DHW, CLL), pp. 3208–3211.
ICPRICPR-2012-WeberBLS #learning #segmentation
Unsupervised motion pattern learning for motion segmentation (MW, GB, ML, DS), pp. 202–205.
ICPRICPR-2012-XiaTWLL #categorisation #learning
Object categorization based on hierarchical learning (TX, YYT, YW, HL, LL), pp. 1419–1422.
ICPRICPR-2012-YangLZC #image #learning #multi #retrieval
Multi-view learning with batch mode active selection for image retrieval (WY, GL, LZ, EC), pp. 979–982.
ICPRICPR-2012-YanKMW #automation #game studies #learning
Automatic annotation of court games with structured output learning (FY, JK, KM, DW), pp. 3577–3580.
ICPRICPR-2012-YanRLS #classification #learning #multi
Active transfer learning for multi-view head-pose classification (YY, SR, OL, NS), pp. 1168–1171.
ICPRICPR-2012-YeD #learning #predict
Learning features for predicting OCR accuracy (PY, DSD), pp. 3204–3207.
ICPRICPR-2012-ZhangHR #classification #gender #learning
Hypergraph based semi-supervised learning for gender classification (ZZ, ERH, PR), pp. 1747–1750.
ICPRICPR-2012-ZhangZNH #learning #multi #recognition
Joint dynamic sparse learning and its application to multi-view face recognition (HZ, YZ, NMN, TSH), pp. 1671–1674.
ICPRICPR-2012-ZhaoSS #learning #predict
Importance-weighted label prediction for active learning with noisy annotations (LZ, GS, RS), pp. 3476–3479.
ICPRICPR-2012-ZhaoXY #learning #network #speech
Unsupervised Tibetan speech features Learning based on Dynamic Bayesian Networks (YZ, XX, GY), pp. 2319–2322.
ICPRICPR-2012-ZhaoYXJ #learning
A near-optimal non-myopic active learning method (YZ, GY, XX, QJ), pp. 1715–1718.
ICPRICPR-2012-ZhouWXZM #learning #recognition
Learning weighted features for human action recognition (WZ, CW, BX, ZZ, LM), pp. 1160–1163.
ICPRICPR-2012-ZhuoCQYX #algorithm #classification #image #learning #using
Image classification using HTM cortical learning algorithms (WZ, ZC, YQ, ZY, YX), pp. 2452–2455.
KDDKDD-2012-GongYZ #learning #multi #robust
Robust multi-task feature learning (PG, JY, CZ), pp. 895–903.
KDDKDD-2012-HalawiDGK #constraints #learning #scalability #word
Large-scale learning of word relatedness with constraints (GH, GD, EG, YK), pp. 1406–1414.
KDDKDD-2012-HoensC #learning
Learning in non-stationary environments with class imbalance (TRH, NVC), pp. 168–176.
KDDKDD-2012-JainVV #kernel #learning #multi #named
SPF-GMKL: generalized multiple kernel learning with a million kernels (AJ, SVNV, MV), pp. 750–758.
KDDKDD-2012-LiJPS #classification #learning #multi
Multi-domain active learning for text classification (LL, XJ, SJP, JTS), pp. 1086–1094.
KDDKDD-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.
KDDKDD-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.
KDDKDD-2012-Posse #lessons learnt #network #recommendation #scalability #social
Key lessons learned building recommender systems for large-scale social networks (CP), p. 587.
KDDKDD-2012-RamanSJ #feedback #learning #online
Online learning to diversify from implicit feedback (KR, PS, TJ), pp. 705–713.
KDDKDD-2012-SeelandKK #clustering #graph #kernel #learning
A structural cluster kernel for learning on graphs (MS, AK, SK), pp. 516–524.
KDDKDD-2012-ShangJW #learning
Semi-supervised learning with mixed knowledge information (FS, LCJ, FW), pp. 732–740.
KDDKDD-2012-ShenJ #learning #recommendation #social
Learning personal + social latent factor model for social recommendation (YS, RJ), pp. 1303–1311.
KDDKDD-2012-SilvaC #learning #matrix #online
Active learning for online bayesian matrix factorization (JGS, LC), pp. 325–333.
KDDKDD-2012-SindhwaniG #distributed #learning #scalability #taxonomy
Large-scale distributed non-negative sparse coding and sparse dictionary learning (VS, AG), pp. 489–497.
KDDKDD-2012-TianZ #learning
Learning from crowds in the presence of schools of thought (YT, JZ), pp. 226–234.
KDDKDD-2012-XiongJXC #dependence #learning #metric #random
Random forests for metric learning with implicit pairwise position dependence (CX, DMJ, RX, JJC), pp. 958–966.
KDDKDD-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.
KDDKDD-2012-ZhangH #induction #learning #multi
Inductive multi-task learning with multiple view data (JZ, JH), pp. 543–551.
KDDKDD-2012-ZhenY #learning #multimodal #probability
A probabilistic model for multimodal hash function learning (YZ, DYY), pp. 940–948.
KDDKDD-2012-ZhouKTX #machine learning
Adversarial support vector machine learning (YZ, MK, BMT, BX), pp. 1059–1067.
KDDKDD-2012-ZhouZ #collaboration #learning
Learning binary codes for collaborative filtering (KZ, HZ), pp. 498–506.
KDIRKDIR-2012-AbdullinN #clustering #data type #framework #learning
A Semi-supervised Learning Framework to Cluster Mixed Data Types (AA, ON), pp. 45–54.
KDIRKDIR-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.
KDIRKDIR-2012-Dagnino #approach #grid #information management #machine learning #smarttech
Knowledge Discovery in the Smart Grid — A Machine Learning Approach (AD), pp. 366–369.
KDIRKDIR-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.
KDIRKDIR-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.
KEODKEOD-2012-GarciaAGG #case study #metadata #ontology
Case Study: Ontology for Metadata in e-Learning (AMFG, SSA, MEBG, RBG), pp. 317–320.
KEODKEOD-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.
KEODKEOD-2012-WohlgenanntWSS #learning #ontology #web
Confidence Management for Learning Ontologies from Dynamic Web Sources (GW, AW, AS, MS), pp. 172–177.
KMISKMIS-2012-AkiyoshiSK #learning #problem #towards
A Project Manager Skill-up Simulator Towards Problem Solving-based Learning (MA, MS, NK), pp. 190–195.
KMISKMIS-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.
KMISKMIS-2012-HackerMHHM #collaboration #learning
Management of Collaboration — Impacts of Virtualization to Learning & Knowledge (GH, MM, PH, GH, MM), pp. 235–239.
KMISKMIS-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.
KMISKMIS-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.
KRKR-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).
MLDMMLDM-2012-BouhamedMLR #heuristic #learning #network
A New Learning Structure Heuristic of Bayesian Networks from Data (HB, AM, TL, AR), pp. 183–197.
MLDMMLDM-2012-ChanguelL #independence #machine learning #metadata #problem
Content Independent Metadata Production as a Machine Learning Problem (SC, NL), pp. 306–320.
MLDMMLDM-2012-HoaD #learning
A New Learning Strategy of General BAMs (NTH, TDB), pp. 213–221.
MLDMMLDM-2012-PitelisT #learning
Discriminant Subspace Learning Based on Support Vectors Machines (NP, AT), pp. 198–212.
MLDMMLDM-2012-TabatabaeiAKK #classification #internet #machine learning
Machine Learning-Based Classification of Encrypted Internet Traffic (TST, MA, FK, MK), pp. 578–592.
MLDMMLDM-2012-ToussaintB #comparison #empirical #learning
Proximity-Graph Instance-Based Learning, Support Vector Machines, and High Dimensionality: An Empirical Comparison (GTT, CB), pp. 222–236.
MLDMMLDM-2012-XuCG #concept #learning #multi #using
Constructing Target Concept in Multiple Instance Learning Using Maximum Partial Entropy (TX, DKYC, IG), pp. 169–182.
RecSysRecSys-2012-DeDGM #difference #learning #using
Local learning of item dissimilarity using content and link structure (AD, MSD, NG, PM), pp. 221–224.
RecSysRecSys-2012-Herbrich #distributed #learning #online #realtime
Distributed, real-time bayesian learning in online services (RH), pp. 203–204.
RecSysRecSys-2012-KarimiFNS #learning #matrix #recommendation
Exploiting the characteristics of matrix factorization for active learning in recommender systems (RK, CF, AN, LST), pp. 317–320.
RecSysRecSys-2012-Kohavi #online #statistics
Online controlled experiments: introduction, learnings, and humbling statistics (RK), pp. 1–2.
RecSysRecSys-2012-SalimansPG #collaboration #learning #ranking
Collaborative learning of preference rankings (TS, UP, TG), pp. 261–264.
RecSysRecSys-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.
SEKESEKE-2012-AlawawdehAL #adaptation #collaboration #learning #named
CLAT: Collaborative Learning Adaptive Tutor (AMHA, CA, LL), pp. 747–752.
SEKESEKE-2012-DagninoSR #fault #machine learning #using
Forecasting Fault Events in Power Distribution Grids Using Machine Learning (AD, KS, LR), pp. 458–463.
SEKESEKE-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.
SEKESEKE-2012-HaoWZ #classification #empirical #machine learning
An Empirical Study of Execution-Data Classification Based on Machine Learning (DH, XW, LZ), pp. 283–288.
SEKESEKE-2012-XavierOC #fuzzy #learning #logic
Evolutionary Learning and Fuzzy Logic Applied to a Load Balancer (FCX, MGdO, CLdC), pp. 256–260.
SEKESEKE-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.
SEKESEKE-2012-Zhang #bias #learning #named
i2Learning: Perpetual Learning through Bias Shifting (DZ), pp. 249–255.
SIGIRSIGIR-2012-BilgicB #learning #query
Active query selection for learning rankers (MB, PNB), pp. 1033–1034.
SIGIRSIGIR-2012-ChangHYLC #ranking #web
Learning-based time-sensitive re-ranking for web search (PTC, YCH, CLY, SDL, PJC), pp. 1101–1102.
SIGIRSIGIR-2012-GaoWL #graph #information retrieval #learning #mining #scalability
Large-scale graph mining and learning for information retrieval (BG, TW, TYL), pp. 1194–1195.
SIGIRSIGIR-2012-HongBAD #learning #rank #social
Learning to rank social update streams (LH, RB, JA, BDD), pp. 651–660.
SIGIRSIGIR-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.
SIGIRSIGIR-2012-KanhabuaBN #learning #retrieval
Learning to select a time-aware retrieval model (NK, KB, KN), pp. 1099–1100.
SIGIRSIGIR-2012-KovesiGA #categorisation #learning #multi #online #performance
Fast on-line learning for multilingual categorization (MK, CG, MRA), pp. 1071–1072.
SIGIRSIGIR-2012-LiX #machine learning #web
Beyond bag-of-words: machine learning for query-document matching in web search (HL, JX), p. 1177.
SIGIRSIGIR-2012-MacdonaldTO #learning #online #predict #query #scheduling
Learning to predict response times for online query scheduling (CM, NT, IO), pp. 621–630.
SIGIRSIGIR-2012-MacdonaldTO12a #effectiveness #learning #rank #safety
Effect of dynamic pruning safety on learning to rank effectiveness (CM, NT, IO), pp. 1051–1052.
SIGIRSIGIR-2012-NiuGLC #evaluation #learning #rank #ranking
Top-k learning to rank: labeling, ranking and evaluation (SN, JG, YL, XC), pp. 751–760.
SIGIRSIGIR-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.
SIGIRSIGIR-2012-SeverynM #learning #ranking #scalability
Structural relationships for large-scale learning of answer re-ranking (AS, AM), pp. 741–750.
SIGIRSIGIR-2012-ZhangWDH #detection #learning #performance #reuse
Learning hash codes for efficient content reuse detection (QZ, YW, ZD, XH), pp. 405–414.
OOPSLAOOPSLA-2012-KulkarniC #compilation #machine learning #optimisation #problem #using
Mitigating the compiler optimization phase-ordering problem using machine learning (SK, JC), pp. 147–162.
OOPSLAOOPSLA-2012-St-AmourTF #communication #optimisation
Optimization coaching: optimizers learn to communicate with programmers (VSA, STH, MF), pp. 163–178.
TOOLSTOOLS-EUROPE-2012-Sureka #component #debugging #learning
Learning to Classify Bug Reports into Components (AS), pp. 288–303.
PADLPADL-2012-ZhuFW #ad hoc #incremental
LearnPADS + + : Incremental Inference of Ad Hoc Data Formats (KQZ, KF, DW), pp. 168–182.
REFSQREFSQ-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.
REFSQREFSQ-2012-KnaussS #documentation #heuristic #learning #requirements
Supporting Learning Organisations in Writing Better Requirements Documents Based on Heuristic Critiques (EK, KS), pp. 165–171.
SACSAC-2012-MinervinidF #concept #learning #logic #probability
Learning probabilistic Description logic concepts: under different Assumptions on missing knowledge (PM, Cd, NF), pp. 378–383.
SACSAC-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.
SACSAC-2012-OongI #classification #fuzzy #learning #multi #performance #testing
Multilayer Fuzzy ARTMAP: fast learning and fast testing for pattern classification (THO, NAMI), pp. 27–32.
ICSEICSE-2012-Chioasca #automation #machine learning #model transformation #requirements #using
Using machine learning to enhance automated requirements model transformation (EVC), pp. 1487–1490.
ICSEICSE-2012-DagenaisR #api #learning #traceability
Recovering traceability links between an API and its learning resources (BD, MPR), pp. 47–57.
ICSEICSE-2012-FengC #behaviour #learning #multi
Multi-label software behavior learning (YF, ZC), pp. 1305–1308.
ICSEICSE-2012-GrechanikFX #automation #learning #performance #problem #testing
Automatically finding performance problems with feedback-directed learning software testing (MG, CF, QX), pp. 156–166.
ICSEICSE-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.
CAVCAV-2012-ChenW #incremental #learning
Learning Boolean Functions Incrementally (YFC, BYW), pp. 55–70.
CAVCAV-2012-LeeWY #algorithm #analysis #learning #termination
Termination Analysis with Algorithmic Learning (WL, BYW, KY), pp. 88–104.
CAVCAV-2012-SinhaSCS
Alternate and Learn: Finding Witnesses without Looking All over (NS, NS, SC, MS), pp. 599–615.
CSLCSL-2012-Berardid #learning
Knowledge Spaces and the Completeness of Learning Strategies (SB, Ud), pp. 77–91.
ICLPICLP-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.
ICLPICLP-2012-MarateaPR #machine learning
Applying Machine Learning Techniques to ASP Solving (MM, LP, FR), pp. 37–48.
ICSTICST-2012-RamlerKP #combinator #design #lessons learnt
Combinatorial Test Design in the TOSCA Testsuite: Lessons Learned and Practical Implications (RR, TK, WP), pp. 569–572.
ICSTICST-2012-SunSPR #cost analysis #learning #named #reliability
CARIAL: Cost-Aware Software Reliability Improvement with Active Learning (BS, GS, AP, SR), pp. 360–369.
ICTSSICTSS-2012-StrugS #approach #machine learning #mutation testing #testing
Machine Learning Approach in Mutation Testing (JS, BS), pp. 200–214.
ICTSSICTSS-2012-Vaandrager #finite #learning #state machine
Active Learning of Extended Finite State Machines (FWV), pp. 5–7.
LICSLICS-2012-KomuravelliPC #learning #probability
Learning Probabilistic Systems from Tree Samples (AK, CSP, EMC), pp. 441–450.
ICSTSAT-2012-BonetB #learning
An Improved Separation of Regular Resolution from Pool Resolution and Clause Learning (MLB, SRB), pp. 44–57.
ICSTSAT-2012-KatsirelosS #learning #satisfiability
Learning Polynomials over GF(2) in a SAT Solver — (Poster Presentation) (GK, LS), pp. 496–497.
ICSTSAT-2012-LaitinenJN #learning
Conflict-Driven XOR-Clause Learning (TL, TAJ, IN), pp. 383–396.
ICSTSAT-2012-MatsliahSS #learning
Augmenting Clause Learning with Implied Literals — (Poster Presentation) (AM, AS, HS), pp. 500–501.
ICSTSAT-2012-SabharwalSS #learning #satisfiability
Learning Back-Clauses in SAT — (Poster Presentation) (AS, HS, MS), pp. 498–499.
SMTSMT-2012-AzizWD #estimation #machine learning #problem #smt
A Machine Learning Technique for Hardness Estimation of QFBV SMT Problems (MAA, AGW, NMD), pp. 57–66.
CBSECBSE-2011-AletiM #component #deployment #learning #optimisation
Component deployment optimisation with bayesian learning (AA, IM), pp. 11–20.
ECSAECSA-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.
ASEASE-2011-ChenHX #approach #evaluation #machine learning #process
Software process evaluation: A machine learning approach (NC, SCHH, XX), pp. 333–342.
DACDAC-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.
DACDAC-2011-GeQ #machine learning #multi #using
Dynamic thermal management for multimedia applications using machine learning (YG, QQ), pp. 95–100.
DACDAC-2011-KatzRZS #architecture #behaviour #generative #learning #quality
Learning microarchitectural behaviors to improve stimuli generation quality (YK, MR, AZ, GS), pp. 848–853.
DACDAC-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.
DATEDATE-2011-ArslanO #adaptation #effectiveness #learning #optimisation #realtime
Adaptive test optimization through real time learning of test effectiveness (BA, AO), pp. 1430–1435.
DocEngDocEng-2011-ChidlovskiiB #learning #metric #network #recommendation #social
Local metric learning for tag recommendation in social networks (BC, AB), pp. 205–208.
ICDARICDAR-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.
ICDARICDAR-2011-KumarPD #classification #documentation #image #learning #multi #using
Document Image Classification and Labeling Using Multiple Instance Learning (JK, JP, DSD), pp. 1059–1063.
ICDARICDAR-2011-ShaoWXZZ11a #learning #multi
Multiple Instance Learning Based Method for Similar Handwritten Chinese Characters Discrimination (YS, CW, BX, RZ, YZ), pp. 1002–1006.
ICDARICDAR-2011-SuLZ #learning #polynomial
Perceptron Learning of Modified Quadratic Discriminant Function (THS, CLL, XYZ), pp. 1007–1011.
ICDARICDAR-2011-TaoLJG #learning #locality #recognition #using
Similar Handwritten Chinese Character Recognition Using Discriminative Locality Alignment Manifold Learning (DT, LL, LJ, YG), pp. 1012–1016.
ICDARICDAR-2011-VajdaJF #approach #learning
A Semi-supervised Ensemble Learning Approach for Character Labeling with Minimal Human Effort (SV, AJ, GAF), pp. 259–263.
ICDARICDAR-2011-WangDL #learning #recognition
MQDF Discriminative Learning Based Offline Handwritten Chinese Character Recognition (YW, XD, CL), pp. 1100–1104.
SIGMODSIGMOD-2011-GetoorM #learning #modelling #relational #statistics
Learning statistical models from relational data (LG, LM), pp. 1195–1198.
CSEETCSEET-2011-AndrianoMBR #assessment
A quantitative assessment method for simulation-based e-learnings (NA, MGM, CB, DR), pp. 159–168.
CSEETCSEET-2011-ChimalakondaN #education #learning #question #re-engineering
Can we make software engineering education better by applying learning theories? (SC, KVN), p. 561.
CSEETCSEET-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.
CSEETCSEET-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.
CSEETCSEET-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.
CSEETCSEET-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.
CSEETCSEET-2011-HattoriBLL #game studies #learning
Erase and rewind — Learning by replaying examples (LH, AB, ML, ML), p. 558.
CSEETCSEET-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.
CSEETCSEET-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.
CSEETCSEET-2011-TillmannHX #education #game studies #learning #named #social
Pex4Fun: Teaching and learning computer science via social gaming (NT, JdH, TX), pp. 546–548.
CSEETCSEET-2011-TuTOBHKY #learning
Turning real-world systems into verification-driven learning cases (ST, ST, SO, BB, BH, AK, ZY), pp. 129–138.
CSEETCSEET-2011-Virseda #education #learning #re-engineering #semantics
A learning methodology based on semantic tableaux for software engineering education (RdVV), pp. 401–405.
CSEETCSEET-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.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2011-BowerM #comparison #learning
Continual and explicit comparison to promote proactive facilitation during second computer language learning (MB, AM), pp. 218–222.
ITiCSEITiCSE-2011-BoyceCPCB #education #evaluation #game studies #how #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.
ITiCSEITiCSE-2011-CamachoM #learning #programming
Facilitating learning dynamic programming through a previous introduction of exhaustive search (AC, AM), p. 355.
ITiCSEITiCSE-2011-ChanK
Do educational software systems provide satisfactory learning opportunities for “multi-sensory learning” methodology? (PC, GK), p. 358.
ITiCSEITiCSE-2011-ChuaB #framework
Integrating scholarly articles within e-learning courses: a framework (BBC, DVB), p. 392.
ITiCSEITiCSE-2011-EllisH #learning #named #student
Courseware: student learning via FOSS field trips (HJCE, GWH), p. 329.
ITiCSEITiCSE-2011-GarciaMGH #interface #learning #unification
A system for usable unification of interfaces of learning objects in m-learning (EG, LdM, AGC, JRH), p. 347.
ITiCSEITiCSE-2011-Goldweber #learning #process #turing machine
Two kinesthetic learning activities: turing machines and basic computer organization (MG), p. 335.
ITiCSEITiCSE-2011-Goldweber11a #learning #social
Computing for the social good: a service learning project (MG), p. 379.
ITiCSEITiCSE-2011-HarrachA #collaboration #learning #optimisation #process #recommendation #using
Optimizing collaborative learning processes by using recommendation systems (SH, MA), p. 389.
ITiCSEITiCSE-2011-Hijon-NeiraV11a #design #learning
A first step mapping IMS learning design and Merlin-Mo (RHN, JÁVI), p. 365.
ITiCSEITiCSE-2011-HoverHR #collaboration #learning
A collaborative linked learning space (KMH, MH, GR), p. 380.
ITiCSEITiCSE-2011-HoverHRM #collaboration #how #learning #student
Evaluating how students would use a collaborative linked learning space (KMH, MH, GR, MM), pp. 88–92.
ITiCSEITiCSE-2011-JourjonKY #framework
Impact of an e-learning platform on CSE lectures (GJ, SSK, JY), pp. 83–87.
ITiCSEITiCSE-2011-KonertRGSB #ad hoc #community #learning
Supporting peer learning with ad-hoc communities (JK, KR, SG, RS, RB), p. 393.
ITiCSEITiCSE-2011-LasserreS #learning
Effects of team-based learning on a CS1 course (PL, CS), pp. 133–137.
ITiCSEITiCSE-2011-LuLJZJ #student
A bioinformatics e-learning lab for undergraduate students (FL, HL, YJ, YZ, ZJ), p. 356.
ITiCSEITiCSE-2011-MartinezC #algebra #education #relational
A cooperative learning-based strategy for teaching relational algebra (AM, AC), pp. 263–267.
ITiCSEITiCSE-2011-MesserK #problem #process
The use of mediating artifacts in embedding problem solving processes in an e-learning environment (OMM, AK), p. 390.
ITiCSEITiCSE-2011-MothVB #learning #named #syntax
SyntaxTrain: relieving the pain of learning syntax (ALAM, JV, MBA), p. 387.
ITiCSEITiCSE-2011-OliveiraMR #learning #problem #programming
From concrete to abstract?: problem domain in the learning of introductory programming (OLO, AMM, NTR), pp. 173–177.
ITiCSEITiCSE-2011-PollockH #learning #multi
Combining multiple pedagogies to boost learning and enthusiasm (LLP, TH), pp. 258–262.
ITiCSEITiCSE-2011-RussellMD #approach #learning #student
A contextualized project-based approach for improving student engagement and learning in AI courses (IR, ZM, JD), p. 368.
ITiCSEITiCSE-2011-Sanchez-TorrubiaTT #algorithm #assessment #automation #learning
GLMP for automatic assessment of DFS algorithm learning (MGST, CTB, GT), p. 351.
ITiCSEITiCSE-2011-ShuhidanHD #comprehension #learning
Understanding novice programmer difficulties via guided learning (SMS, MH, DJD), pp. 213–217.
ITiCSEITiCSE-2011-VanoM #learning #quote
“Computer science and nursery rhymes”: a learning path for the middle school (DDV, CM), pp. 238–242.
ITiCSEITiCSE-2011-WolzMS #learning #process
Kinesthetic learning of computing via “off-beat” activities (UW, MM, MS), pp. 68–72.
ESOPESOP-2011-BorgstromGGMG #machine learning #semantics
Measure Transformer Semantics for Bayesian Machine Learning (JB, ADG, MG, JM, JVG), pp. 77–96.
FASEFASE-2011-FengKP #automation #composition #learning #probability #reasoning
Automated Learning of Probabilistic Assumptions for Compositional Reasoning (LF, MZK, DP), pp. 2–17.
TACASTACAS-2011-JungLWY #generative #invariant #quantifier
Predicate Generation for Learning-Based Quantifier-Free Loop Invariant Inference (YJ, WL, BYW, KY), pp. 205–219.
TACASTACAS-2011-MertenSHM #generative
Next Generation LearnLib (MM, BS, FH, TMS), pp. 220–223.
ICPCICPC-J-2009-Sanz-RodriguezDA11 #evaluation #learning #reuse
Metrics-based evaluation of learning object reusability (JSR, JMD, SSA), pp. 121–140.
CSMRCSMR-2011-Borchers #assessment #re-engineering
Invited Talk: Reengineering from a Practitioner’s View — A Personal Lesson’s Learned Assessment (JB), pp. 1–2.
SASSAS-2011-NoriR #machine learning #program analysis
Program Analysis and Machine Learning: A Win-Win Deal (AVN, SKR), pp. 2–3.
STOCSTOC-2011-BalcanH #learning
Learning submodular functions (MFB, NJAH), pp. 793–802.
DLTDLT-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.
ICALPICALP-v1-2011-AroraG #algorithm #fault #learning
New Algorithms for Learning in Presence of Errors (SA, RG), pp. 403–415.
ICALPICALP-v1-2011-HarkinsH #algorithm #bound #game studies #learning
Exact Learning Algorithms, Betting Games, and Circuit Lower Bounds (RCH, JMH), pp. 416–423.
LATALATA-2011-CaseJLOSS #automation #learning #pattern matching #subclass
Automatic Learning of Subclasses of Pattern Languages (JC, SJ, TDL, YSO, PS, FS), pp. 192–203.
SFMSFM-2011-Jonsson #automaton #learning #modelling
Learning of Automata Models Extended with Data (BJ), pp. 327–349.
SFMSFM-2011-Moschitti #automation #kernel #learning #modelling
Kernel-Based Machines for Abstract and Easy Modeling of Automatic Learning (AM), pp. 458–503.
SFMSFM-2011-SteffenHM #automaton #learning #perspective
Introduction to Active Automata Learning from a Practical Perspective (BS, FH, MM), pp. 256–296.
CHICHI-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.
CHICHI-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.
CHICHI-2011-EdgeSCZL #learning #mobile #named
MicroMandarin: mobile language learning in context (DE, ES, KC, JZ, JAL), pp. 3169–3178.
CHICHI-2011-FiebrinkCT #evaluation #interactive #learning
Human model evaluation in interactive supervised learning (RF, PRC, DT), pp. 147–156.
CHICHI-2011-HowisonTRA #concept #interactive #learning
The mathematical imagery trainer: from embodied interaction to conceptual learning (MH, DT, DR, DA), pp. 1989–1998.
CHICHI-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.
CHICHI-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.
CHICHI-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.
CHICHI-2011-ShaerSVFLW #interactive #learning
Enhancing genomic learning through tabletop interaction (OS, MS, CV, TF, ML, HW), pp. 2817–2826.
CHICHI-2011-ToupsKHS #coordination #learning #simulation
Zero-fidelity simulation of fire emergency response: improving team coordination learning (ZOT, AK, WAH, NS), pp. 1959–1968.
CHICHI-2011-TrustyT #learning #web
Augmenting the web for second language vocabulary learning (AT, KNT), pp. 3179–3188.
CSCWCSCW-2011-NawahdahI #automation #education #learning
Automatic adjustment of a virtual teacher’s model in a learning support system (MN, TI), pp. 693–696.
HCIDHM-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.
HCIDHM-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.
HCIDUXU-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.
HCIDUXU-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.
HCIDUXU-v1-2011-GeorgeADMW #collaboration #learning #multi
Multitouch Tables for Collaborative Object-Based Learning (JG, EdA, DD, DSM, GW), pp. 237–246.
HCIDUXU-v1-2011-Innes #design #enterprise #why
Why Enterprises Can’t Innovate: Helping Companies Learn Design Thinking (JI), pp. 442–448.
HCIDUXU-v1-2011-LeeR #architecture #collaboration #concept #learning #mobile
Suggested Collaborative Learning Conceptual Architecture and Applications for Mobile Devices (KL, AR), pp. 611–620.
HCIDUXU-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.
HCIDUXU-v2-2011-ArditoLRSYAC #design #game studies #learning #pervasive
Designing Pervasive Games for Learning (CA, RL, DR, CS, NY, NMA, MFC), pp. 99–108.
HCIHCD-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.
HCIHCD-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.
HCIHCI-MIIE-2011-KarthikP #adaptation #approach #classification #email #machine learning
Adaptive Machine Learning Approach for Emotional Email Classification (KK, RP), pp. 552–558.
HCIHCI-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.
HCIHCI-UA-2011-AdamsS #learning
A Web-Based Learning Environment to Support Chemistry (CA, CS), pp. 3–11.
HCIHCI-UA-2011-EverardJM #learning #question #student #what
Are MIS Students Learning What They Need to Land a Job? (AE, BMJ, SM), pp. 235–236.
HCIHCI-UA-2011-GeorgeS #collaboration #game studies #learning
Introducing Mobility in Serious Games: Enhancing Situated and Collaborative Learning (SG, AS), pp. 12–20.
HCIHCI-UA-2011-HayakawaNOFN #framework #learning #visualisation
Visualization Framework for Computer System Learning (EH, YN, HO, MF, YN), pp. 21–26.
HCIHCI-UA-2011-Huseyinov #adaptation #fuzzy #learning #modelling #multi
Fuzzy Linguistic Modelling Cognitive / Learning Styles for Adaptation through Multi-level Granulation (IH), pp. 39–47.
HCIHCI-UA-2011-Klenner-Moore #learning #process
Creating a New Context for Activity in Blended Learning Environments: Engaging the Twitchy Fingers (JKM), pp. 61–67.
HCIHCI-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.
HCIHCI-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.
HCIHCI-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.
HCIHCI-UA-2011-YajimaT #collaboration #learning
Proposal of Collaborative Learning Support Method in Risk Communications (HY, NT), pp. 113–120.
HCIHCI-UA-2011-YamaguchiMT #evaluation #learning #online
Evaluation of Online Handwritten Characters for Penmanship Learning Support System (TY, NM, MT), pp. 121–130.
HCIHCI-UA-2011-YangCS #analysis #learning #recognition
Facial Expression Recognition for Learning Status Analysis (MTY, YJC, YCS), pp. 131–138.
HCIHIMI-v2-2011-PohlML #hybrid #learning #standard
Transforming a Standard Lecture into a Hybrid Learning Scenario (HMP, JTM, JL), pp. 55–61.
HCIOCSC-2011-AhmadL #learning
Promoting Reflective Learning: The Role of Blogs in the Classroom (RA, WGL), pp. 3–11.
HCIOCSC-2011-PujariK #approach #machine learning #predict #recommendation
A Supervised Machine Learning Link Prediction Approach for Tag Recommendation (MP, RK), pp. 336–344.
HCIOCSC-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.
AdaSIGAda-2011-Booch #ada
Everything i know i learned from ada (GB), pp. 17–18.
CAiSECAiSE-2011-DornD #process #self
Supporting Dynamic, People-Driven Processes through Self-learning of Message Flows (CD, SD), pp. 657–671.
ICEISICEIS-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.
ICEISICEIS-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.
ICEISICEIS-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.
ICEISICEIS-v4-2011-Marks #collaboration #learning #student
Students’ Acceptance of E-Group Collaboration Learning (AM), pp. 269–274.
CIKMCIKM-2011-ArguelloDC #learning #web
Learning to aggregate vertical results into web search results (JA, FD, JC), pp. 201–210.
CIKMCIKM-2011-CoffmanW #keyword #learning #rank #relational
Learning to rank results in relational keyword search (JC, ACW), pp. 1689–1698.
CIKMCIKM-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.
CIKMCIKM-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.
CIKMCIKM-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.
CIKMCIKM-2011-GiannopoulosBDS #learning #rank
Learning to rank user intent (GG, UB, TD, TKS), pp. 195–200.
CIKMCIKM-2011-KasiviswanathanMBS #detection #learning #taxonomy #topic #using
Emerging topic detection using dictionary learning (SPK, PM, AB, VS), pp. 745–754.
CIKMCIKM-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.
CIKMCIKM-2011-LiCHLJ #collaboration #learning #online
Collaborative online learning of user generated content (GL, KC, SCHH, WL, RJ), pp. 285–290.
CIKMCIKM-2011-LinC #data fusion #learning #query
Query sampling for learning data fusion (TCL, PJC), pp. 141–146.
CIKMCIKM-2011-LinLWX #learning #rank
Learning to rank with cross entropy (YL, HL, JW, KX), pp. 2057–2060.
CIKMCIKM-2011-LiSZ #feedback
Learning-based relevance feedback for web-based relation completion (ZL, LS, XZ), pp. 1535–1540.
CIKMCIKM-2011-LiuCZH #learning #random
Learning conditional random fields with latent sparse features for acronym expansion finding (JL, JC, YZ, YH), pp. 867–872.
CIKMCIKM-2011-LiuLH #bound #fault #kernel #learning
Learning kernels with upper bounds of leave-one-out error (YL, SL, YH), pp. 2205–2208.
CIKMCIKM-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.
CIKMCIKM-2011-OroR #approach #learning #named
SILA: a spatial instance learning approach for deep webpages (EO, MR), pp. 2329–2332.
CIKMCIKM-2011-PandeyABHCRZ #behaviour #learning #what
Learning to target: what works for behavioral targeting (SP, MA, AB, AOH, PC, AR, MZ), pp. 1805–1814.
CIKMCIKM-2011-QianHCZN #ambiguity #machine learning
Combining machine learning and human judgment in author disambiguation (YnQ, YH, JC, QZ, ZN), pp. 1241–1246.
CIKMCIKM-2011-RamanJS #learning #ranking
Structured learning of two-level dynamic rankings (KR, TJ, PS), pp. 291–296.
CIKMCIKM-2011-SellamanickamGS #approach #learning #ranking
A pairwise ranking based approach to learning with positive and unlabeled examples (SS, PG, SKS), pp. 663–672.
CIKMCIKM-2011-SzummerY #learning #rank
Semi-supervised learning to rank with preference regularization (MS, EY), pp. 269–278.
CIKMCIKM-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.
CIKMCIKM-2011-UllegaddiV #category theory #learning #query #rank #web
Learning to rank categories for web queries (PU, VV), pp. 2065–2068.
CIKMCIKM-2011-WangCWLWO #learning #similarity
Coupled nominal similarity in unsupervised learning (CW, LC, MW, JL, WW, YO), pp. 973–978.
CIKMCIKM-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.
CIKMCIKM-2011-WangHLCH #learning #recommendation
Learning to recommend questions based on public interest (JW, XH, ZL, WHC, BH), pp. 2029–2032.
CIKMCIKM-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.
CIKMCIKM-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.
CIKMCIKM-2011-YangZKL #how #learning #question #why
Can irrelevant data help semi-supervised learning, why and how? (HY, SZ, IK, MRL), pp. 937–946.
CIKMCIKM-2011-YanTLSL #learning #predict
Citation count prediction: learning to estimate future citations for literature (RY, JT, XL, DS, XL), pp. 1247–1252.
CIKMCIKM-2011-ZhangYCT #detection
A machine-learned proactive moderation system for auction fraud detection (LZ, JY, WC, BLT), pp. 2501–2504.
CIKMCIKM-2011-ZhaoYX #independence #information management #learning #web
Max margin learning on domain-independent web information extraction (BZ, XY, EPX), pp. 1305–1310.
CIKMCIKM-2011-ZhuZYGX #learning
Transfer active learning (ZZ, XZ, YY, YFG, XX), pp. 2169–2172.
ECIRECIR-2011-BuffoniTG #ranking
The Importance of the Depth for Text-Image Selection Strategy in Learning-To-Rank (DB, ST, PG), pp. 743–746.
ECIRECIR-2011-HofmannWR #learning #online #rank
Balancing Exploration and Exploitation in Learning to Rank Online (KH, SW, MdR), pp. 251–263.
ECIRECIR-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.
ECIRECIR-2011-MacdonaldO #learning #modelling #ranking
Learning Models for Ranking Aggregates (CM, IO), pp. 517–529.
ECIRECIR-2011-ZhouH #comprehension #learning #natural language #random
Learning Conditional Random Fields from Unaligned Data for Natural Language Understanding (DZ, YH), pp. 283–288.
ICMLICML-2011-BabenkoVDB #learning #multi
Multiple Instance Learning with Manifold Bags (BB, NV, PD, SB), pp. 81–88.
ICMLICML-2011-BabesMLS #learning #multi
Apprenticeship Learning About Multiple Intentions (MB, VNM, KS, MLL), pp. 897–904.
ICMLICML-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.
ICMLICML-2011-BuffoniCGU #learning #standard
Learning Scoring Functions with Order-Preserving Losses and Standardized Supervision (DB, CC, PG, NU), pp. 825–832.
ICMLICML-2011-Bylander #learning #linear #multi #polynomial
Learning Linear Functions with Quadratic and Linear Multiplicative Updates (TB), pp. 505–512.
ICMLICML-2011-ChakrabortyS #learning
Structure Learning in Ergodic Factored MDPs without Knowledge of the Transition Function’s In-Degree (DC, PS), pp. 737–744.
ICMLICML-2011-ChenPSDC #analysis #learning #process
The Hierarchical Beta Process for Convolutional Factor Analysis and Deep Learning (BC, GP, GS, DBD, LC), pp. 361–368.
ICMLICML-2011-ChoRI #adaptation #learning #strict
Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines (KC, TR, AI), pp. 105–112.
ICMLICML-2011-DauphinGB #learning #re-engineering #scalability
Large-Scale Learning of Embeddings with Reconstruction Sampling (YD, XG, YB), pp. 945–952.
ICMLICML-2011-DinuzzoOGP #coordination #kernel #learning
Learning Output Kernels with Block Coordinate Descent (FD, CSO, PVG, GP), pp. 49–56.
ICMLICML-2011-DudikLL #evaluation #learning #policy #robust
Doubly Robust Policy Evaluation and Learning (MD, JL, LL), pp. 1097–1104.
ICMLICML-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.
ICMLICML-2011-Gould #learning #linear #markov #random
Max-margin Learning for Lower Linear Envelope Potentials in Binary Markov Random Fields (SG), pp. 193–200.
ICMLICML-2011-GuilloryB #learning
Simultaneous Learning and Covering with Adversarial Noise (AG, JAB), pp. 369–376.
ICMLICML-2011-HarelM #learning #multi
Learning from Multiple Outlooks (MH, SM), pp. 401–408.
ICMLICML-2011-HeL #framework #learning #multi
A Graphbased Framework for Multi-Task Multi-View Learning (JH, RL), pp. 25–32.
ICMLICML-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.
ICMLICML-2011-JawanpuriaNR #kernel #learning #performance #using
Efficient Rule Ensemble Learning using Hierarchical Kernels (PJ, JSN, GR), pp. 161–168.
ICMLICML-2011-KangGS #learning #multi
Learning with Whom to Share in Multi-task Feature Learning (ZK, KG, FS), pp. 521–528.
ICMLICML-2011-KuwadekarN #classification #learning #modelling #relational
Relational Active Learning for Joint Collective Classification Models (AK, JN), pp. 385–392.
ICMLICML-2011-LeeW #identification #learning #online #probability
Manifold Identification of Dual Averaging Methods for Regularized Stochastic Online Learning (SL, SJW), pp. 1121–1128.
ICMLICML-2011-LeNCLPN #learning #on the #optimisation
On optimization methods for deep learning (QVL, JN, AC, AL, BP, AYN), pp. 265–272.
ICMLICML-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.
ICMLICML-2011-LuB #learning #modelling
Learning Mallows Models with Pairwise Preferences (TL, CB), pp. 145–152.
ICMLICML-2011-Maaten #kernel #learning
Learning Discriminative Fisher Kernels (LvdM), pp. 217–224.
ICMLICML-2011-MachartPARG #kernel #learning #probability #rank
Stochastic Low-Rank Kernel Learning for Regression (PM, TP, SA, LR, HG), pp. 969–976.
ICMLICML-2011-MartensS #learning #network #optimisation
Learning Recurrent Neural Networks with Hessian-Free Optimization (JM, IS), pp. 1033–1040.
ICMLICML-2011-NgiamCKN #energy #learning #modelling
Learning Deep Energy Models (JN, ZC, PWK, AYN), pp. 1105–1112.
ICMLICML-2011-NgiamKKNLN #learning #multimodal
Multimodal Deep Learning (JN, AK, MK, JN, HL, AYN), pp. 689–696.
ICMLICML-2011-NickelTK #learning #multi
A Three-Way Model for Collective Learning on Multi-Relational Data (MN, VT, HPK), pp. 809–816.
ICMLICML-2011-OrabonaL #algorithm #kernel #learning #multi #optimisation
Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning (FO, JL), pp. 249–256.
ICMLICML-2011-QuadriantoL #learning #multi
Learning Multi-View Neighborhood Preserving Projections (NQ, CHL), pp. 425–432.
ICMLICML-2011-RobbianoC #learning #plugin #ranking
Minimax Learning Rates for Bipartite Ranking and Plug-in Rules (SR, SC), pp. 441–448.
ICMLICML-2011-SaxeKCBSN #learning #on the #random
On Random Weights and Unsupervised Feature Learning (AMS, PWK, ZC, MB, BS, AYN), pp. 1089–1096.
ICMLICML-2011-SmallWBT #learning
The Constrained Weight Space SVM: Learning with Ranked Features (KS, BCW, CEB, TAT), pp. 865–872.
ICMLICML-2011-Sohl-DicksteinBD #learning #probability
Minimum Probability Flow Learning (JSD, PB, MRD), pp. 905–912.
ICMLICML-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.
ICMLICML-2011-TamuzLBSK #adaptation #kernel #learning
Adaptively Learning the Crowd Kernel (OT, CL, SB, OS, AK), pp. 673–680.
ICMLICML-2011-WellingT #learning #probability
Bayesian Learning via Stochastic Gradient Langevin Dynamics (MW, YWT), pp. 681–688.
ICMLICML-2011-YangR #learning #on the #visual notation
On the Use of Variational Inference for Learning Discrete Graphical Model (EY, PDR), pp. 1009–1016.
ICMLICML-2011-YanRFD #learning
Active Learning from Crowds (YY, RR, GF, JGD), pp. 1161–1168.
KDDKDD-2011-AttenbergP #learning #online
Online active inference and learning (JA, FJP), pp. 186–194.
KDDKDD-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.
KDDKDD-2011-ChakiCG #learning
Supervised learning for provenance-similarity of binaries (SC, CC, AG), pp. 15–23.
KDDKDD-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.
KDDKDD-2011-ChenRT #adaptation #detection #incremental #learning
Detecting bots via incremental LS-SVM learning with dynamic feature adaptation (FC, SR, PNT), pp. 386–394.
KDDKDD-2011-ChenZY #learning #multi #rank #robust
Integrating low-rank and group-sparse structures for robust multi-task learning (JC, JZ, JY), pp. 42–50.
KDDKDD-2011-ChuZLTT #data type #learning #online
Unbiased online active learning in data streams (WC, MZ, LL, AT, BLT), pp. 195–203.
KDDKDD-2011-Cormode #learning #privacy
Personal privacy vs population privacy: learning to attack anonymization (GC), pp. 1253–1261.
KDDKDD-2011-GhaniK #detection #fault #interactive #learning
Interactive learning for efficiently detecting errors in insurance claims (RG, MK), pp. 325–333.
KDDKDD-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.
KDDKDD-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.
KDDKDD-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.
KDDKDD-2011-MesterharmP #algorithm #learning #online #using
Active learning using on-line algorithms (CM, MJP), pp. 850–858.
KDDKDD-2011-MooreYZRL #classification #learning #network
Active learning for node classification in assortative and disassortative networks (CM, XY, YZ, JBR, TL), pp. 841–849.
KDDKDD-2011-RashidiC #induction #learning #query
Ask me better questions: active learning queries based on rule induction (PR, DJC), pp. 904–912.
KDDKDD-2011-ValizadeganJW #learning #multi #predict
Learning to trade off between exploration and exploitation in multiclass bandit prediction (HV, RJ, SW), pp. 204–212.
KDDKDD-2011-VijayaraghavanK #data mining #machine learning #mining #online
Applications of data mining and machine learning in online customer care (RV, PVK), p. 779.
KDDKDD-2011-ZhangHLSL #approach #learning #multi #scalability
Multi-view transfer learning with a large margin approach (DZ, JH, YL, LS, RDL), pp. 1208–1216.
KDDKDD-2011-ZhangLS #learning
Serendipitous learning: learning beyond the predefined label space (DZ, YL, LS), pp. 1343–1351.
KDDKDD-2011-ZhouYLY #learning #multi #predict
A multi-task learning formulation for predicting disease progression (JZ, LY, JL, JY), pp. 814–822.
KDIRKDIR-2011-ArmengolP #case study #classification #information management #lazy evaluation #learning
Combining Two Lazy Learning Methods for Classification and Knowledge Discovery — A Case Study for Malignant Melanoma Diagnosis (EA, SP), pp. 200–207.
KDIRKDIR-2011-FilhoRM #learning #named #rank
XHITS: Learning to Rank in a Hyperlinked Structure (FBF, RPR, RLM), pp. 385–389.
KDIRKDIR-2011-GriffithOS #collaboration #learning #parametricity
Learning Neighbourhood-based Collaborative Filtering Parameters (JG, CO, HS), pp. 452–455.
KDIRKDIR-2011-Liebowitz #information management
Knowledge Management and e-Learning: Putting Theory into Practice (JL), p. 5.
KDIRKDIR-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.
KDIRKDIR-2011-ReuterC #identification #learning #similarity #using
Learning Similarity Functions for Event Identification using Support Vector Machines (TR, PC), pp. 208–215.
KEODKEOD-2011-AbbesZN #learning #ontology #semantics
Evaluating Semantic Classes Used for Ontology Building and Learning from Texts (SBA, HZ, AN), pp. 445–448.
KEODKEOD-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.
KEODKEOD-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.
KEODKEOD-2011-YamasakiS #graph
A Graph Manipulation System Abstracted from e-Learning (SY, MS), pp. 466–469.
KMISKMIS-2011-BerkaniCN #community #online #semantics
Semantics and Knowledge Capitalization in Online Communities of Practice of e-Learning (LB, AC, ON), pp. 96–104.
KMISKMIS-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.
KMISKMIS-2011-Silva #approach #concept #learning
Learning Organization — Concept and Proposal of a New Approach (AFdS), pp. 384–389.
MLDMMLDM-2011-CelibertoM #learning
Investigation in Transfer Learning: Better Way to Apply Transfer Learning between Agents (LACJ, JPM), pp. 210–223.
MLDMMLDM-2011-LahbibBL #learning #multi
Informative Variables Selection for Multi-relational Supervised Learning (DL, MB, DL), pp. 75–87.
MLDMMLDM-2011-Sullins #smarttech
Exploration Strategies for Learned Probabilities in Smart Terrain (JS), pp. 224–238.
MLDMMLDM-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.
MLDMMLDM-2011-XuGC #adaptation #kernel #learning #multi
Adaptive Kernel Diverse Density Estimate for Multiple Instance Learning (TX, IG, DKYC), pp. 185–198.
MLDMMLDM-2011-XuM #learning #taxonomy
Dictionary Learning Based on Laplacian Score in Sparse Coding (JX, HM), pp. 253–264.
RecSysRecSys-2011-Makrehchi #learning #recommendation #social #topic
Social link recommendation by learning hidden topics (MM), pp. 189–196.
RecSysRecSys-2011-PaparrizosCG #recommendation
Machine learned job recommendation (IKP, BBC, AG), pp. 325–328.
RecSysRecSys-2011-WuCMW #detection #learning #named
Semi-SAD: applying semi-supervised learning to shilling attack detection (ZW, JC, BM, YW), pp. 289–292.
SEKESEKE-2011-GaoZHL #learning #modelling
Learning action models with indeterminate effects (JG, HHZ, DjH, LL), pp. 159–162.
SEKESEKE-2011-NoorianBD #classification #framework #machine learning #testing #towards
Machine Learning-based Software Testing: Towards a Classification Framework (MN, EB, WD), pp. 225–229.
SEKESEKE-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.
SEKESEKE-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.
SEKESEKE-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.
SIGIRSIGIR-2011-AminiU #automation #detection #learning #multi #summary
Transductive learning over automatically detected themes for multi-document summarization (MRA, NU), pp. 1193–1194.
SIGIRSIGIR-2011-AsadiMEL #learning #pseudo #ranking #web
Pseudo test collections for learning web search ranking functions (NA, DM, TE, JJL), pp. 1073–1082.
SIGIRSIGIR-2011-DaiSD #learning #rank
Learning to rank for freshness and relevance (ND, MS, BDD), pp. 95–104.
SIGIRSIGIR-2011-DaiSD11a #learning #multi #optimisation #rank
Multi-objective optimization in learning to rank (ND, MS, BDD), pp. 1241–1242.
SIGIRSIGIR-2011-GaoZLLW #feedback #learning
Learning features through feedback for blog distillation (DG, RZ, WL, RYKL, KFW), pp. 1085–1086.
SIGIRSIGIR-2011-Hofmann #online
Search engines that learn online (KH), pp. 1313–1314.
SIGIRSIGIR-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.
SIGIRSIGIR-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.
SIGIRSIGIR-2011-KumarL #learning #rank
Learning to rank from a noisy crowd (AK, ML), pp. 1221–1222.
SIGIRSIGIR-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.
SIGIRSIGIR-2011-Li #graph #learning
Learning for graphs with annotated edges (FL), pp. 1259–1260.
SIGIRSIGIR-2011-LinLJY #approach #machine learning #query #social
Social annotation in query expansion: a machine learning approach (YL, HL, SJ, ZY), pp. 405–414.
SIGIRSIGIR-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.
SIGIRSIGIR-2011-PolitzS #constraints #learning #rank
Learning to rank under tight budget constraints (CP, RS), pp. 1173–1174.
SIGIRSIGIR-2011-SantosMO11a #metric #on the #ranking
On the suitability of diversity metrics for learning-to-rank for diversity (RLTS, CM, IO), pp. 1185–1186.
SIGIRSIGIR-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.
SIGIRSIGIR-2011-SiJ #information retrieval #machine learning
Machine learning for information retrieval (LS, RJ), pp. 1293–1294.
SIGIRSIGIR-2011-TianL #information retrieval #interactive #learning
Active learning to maximize accuracy vs. effort in interactive information retrieval (AT, ML), pp. 145–154.
SIGIRSIGIR-2011-WangGWL #information retrieval #learning #parallel #rank
Parallel learning to rank for information retrieval (SW, BJG, KW, HWL), pp. 1083–1084.
SIGIRSIGIR-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.
SIGIRSIGIR-2011-WangWZH #learning #online #random
Learning online discussion structures by conditional random fields (HW, CW, CZ, JH), pp. 435–444.
SIGIRSIGIR-2011-WuYLLYX #learning #rank #using
Learning to rank using query-level regression (JW, ZY, YL, HL, ZY, KX), pp. 1091–1092.
SIGIRSIGIR-2011-YangLSZZ #collaboration #learning #recommendation #using
Collaborative competitive filtering: learning recommender using context of user choice (SHY, BL, AJS, HZ, ZZ), pp. 295–304.
ECMFAECMFA-2011-DolquesDFHNP #automation #learning #model transformation
Easing Model Transformation Learning with Automatically Aligned Examples (XD, AD, JRF, MH, CN, FP), pp. 189–204.
PADLPADL-2011-Mooney #learning
Learning Language from Its Perceptual Context (RJM), pp. 2–4.
POPLPOPL-2011-LiangTN #abstraction #learning
Learning minimal abstractions (PL, OT, MN), pp. 31–42.
RERE-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.
SACSAC-2011-BhaskaranNFG #behaviour #detection #learning #online
Deceit detection via online behavioral learning (NB, IN, MGF, VG), pp. 29–30.
SACSAC-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.
SACSAC-2011-GomesRS #concept #data type #learning
Learning recurring concepts from data streams with a context-aware ensemble (JBG, EMR, PACS), pp. 994–999.
SACSAC-2011-LiuLTL #framework #game studies #interactive #learning
A cognition-based interactive game platform for learning Chinese characters (CLL, CYL, JLT, CLL), pp. 1181–1186.
SACSAC-2011-NawahdahI #education #learning #physics
Positioning a virtual teacher in an MR physical task learning support system (MN, TI), pp. 1169–1174.
SACSAC-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.
SACSAC-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.
SACSAC-2011-ZhangZZZX #detection #learning #web
Harmonic functions based semi-supervised learning for web spam detection (WZ, DZ, YZ, GZ, BX), pp. 74–75.
ICSEICSE-2011-BorgesGLN #adaptation #evolution #learning #requirements #specification
Learning to adapt requirements specifications of evolving systems (RVB, ASdG, LCL, BN), pp. 856–859.
CGOCGO-2011-SanchezASPS #compilation #using
Using machines to learn method-specific compilation strategies (RNS, JNA, DS, MP, MGS), pp. 257–266.
ICLPICLP-J-2011-CorapiRVPS #design #induction #learning #using
Normative design using inductive learning (DC, AR, MDV, JAP, KS), pp. 783–799.
ICTSSICTSS-2011-MeinkeN #term rewriting #testing #using
Learning-Based Testing for Reactive Systems Using Term Rewriting Technology (KM, FN), pp. 97–114.
ICSTSAT-2011-SilverthornM #learning #satisfiability
Learning Polarity from Structure in SAT (BS, RM), pp. 377–378.
TAPTAP-2011-MeinkeS #incremental #testing
Incremental Learning-Based Testing for Reactive Systems (KM, MAS), pp. 134–151.
VMCAIVMCAI-2011-HowarSM #abstraction #automation #automaton #learning #refinement
Automata Learning with Automated Alphabet Abstraction Refinement (FH, BS, MM), pp. 263–277.
ECSAECSA-2010-MarcoGII #adaptation #learning #lifecycle #paradigm #self
Learning from the Cell Life-Cycle: A Self-adaptive Paradigm (ADM, FG, PI, RI), pp. 485–488.
CASECASE-2010-DoroodgarN #architecture #learning
A hierarchical reinforcement learning based control architecture for semi-autonomous rescue robots in cluttered environments (BD, GN), pp. 948–953.
CASECASE-2010-LiYG #learning
Learning compliance control of robot manipulators in contact with the unknown environment (YL, CY, SSG), pp. 644–649.
DACDAC-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.
DACDAC-2010-LaiJW #abstraction #learning #named
BooM: a decision procedure for boolean matching with abstraction and dynamic learning (CFL, JHRJ, KHW), pp. 499–504.
DATEDATE-2010-HuangSM #fault #machine learning
Fault diagnosis of analog circuits based on machine learning (KH, HGDS, SM), pp. 1761–1766.
DATEDATE-2010-LiuTQ #algorithm #constraints #performance #power management
Enhanced Q-learning algorithm for dynamic power management with performance constraint (WL, YT, QQ), pp. 602–605.
DATEDATE-2010-ShenHH #adaptation #configuration management
Learning-based adaptation to applications and environments in a reconfigurable Network-on-Chip (JSS, CHH, PAH), pp. 381–386.
DRRDRR-2010-LiuZ #detection #documentation #image #learning
Semi-supervised learning for detecting text-lines in noisy document images (ZL, HZ), pp. 1–10.
DRRDRR-2010-Obafemi-AjayiAF #documentation #learning
Learning shape features for document enhancement (TOA, GA, OF), pp. 1–10.
DRRDRR-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.
HTHT-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.
HTHT-2010-PaekHS #hypermedia #learning
Spatial contiguity and implicit learning in hypertext (SP, DH, AS), pp. 291–292.
HTHT-2010-PrataGC #learning #personalisation
Crossmedia personalized learning contexts (AP, NG, TC), pp. 305–306.
HTHT-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.
HTHT-2010-TielletPRLC10a #learning #named
HVet: a hypervideo environment to support veterinary surgery learning (CABT, AGP, EBR, JVdL, TC), pp. 313–314.
PODSPODS-2010-LemayMN #algorithm #learning #top-down #xml
A learning algorithm for top-down XML transformations (AL, SM, JN), pp. 285–296.
SIGMODSIGMOD-2010-ArasuGK #learning #on the
On active learning of record matching packages (AA, MG, RK), pp. 783–794.
SIGMODSIGMOD-2010-CortezSGM #information management #learning #named #on-demand
ONDUX: on-demand unsupervised learning for information extraction (EC, ASdS, MAG, ESdM), pp. 807–818.
ITiCSEITiCSE-2010-AydinolG10a #learning #spreadsheet #video
The effect of video tutorials on learning spreadsheets (ABA, ÖG), p. 323.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2010-Cross #learning
Promoting active learning through assignments (GWC), p. 306.
ITiCSEITiCSE-2010-Denny #collaboration #learning #online
Motivating online collaborative learning (PD), p. 300.
ITiCSEITiCSE-2010-EganJ #learning
Service learning in introductory computer science (MALE, MJ), pp. 8–12.
ITiCSEITiCSE-2010-HamadaS #learning
Lego NXT as a learning tool (MH, SS), p. 321.
ITiCSEITiCSE-2010-HowardJN #behaviour #design #learning #online #using
Reflecting on online learning designs using observed behavior (LH, JJ, CN), pp. 179–183.
ITiCSEITiCSE-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.
ITiCSEITiCSE-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.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2010-Mirolo #analysis #learning #multi #recursion #student
Learning (through) recursion: a multidimensional analysis of the competences achieved by CS1 students (CM), pp. 160–164.
ITiCSEITiCSE-2010-QianLYL #learning #programming
Inquiry-based active learning in introductory programming courses (KQ, CTDL, LY, JL), p. 312.
ITiCSEITiCSE-2010-TuOKKT #learning
Developing verification-driven learning cases (ST, SJO, RK, AK, ST), pp. 58–62.
ICSMEICSM-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.
PASTEPASTE-2010-FengG #fault #learning #locality #modelling #probability
Learning universal probabilistic models for fault localization (MF, RG), pp. 81–88.
SCAMSCAM-2010-Zeller #in the large #learning #mining #modelling
Learning from 6,000 Projects: Mining Models in the Large (AZ), pp. 3–6.
STOCSTOC-2010-KalaiMV #learning
Efficiently learning mixtures of two Gaussians (ATK, AM, GV), pp. 553–562.
LATALATA-2010-KasprzikK #learning #string #using
String Extension Learning Using Lattices (AK, TK), pp. 380–391.
CHICHI-2010-AmershiFKT #concept #interactive #learning #modelling #multi
Examining multiple potential models in end-user interactive concept learning (SA, JF, AK, DST), pp. 1357–1360.
CHICHI-2010-CapraMVM #collaboration #learning #multi
Tools-at-hand and learning in multi-session, collaborative search (RGC, GM, JVM, KM), pp. 951–960.
CHICHI-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.
CHICHI-2010-DuganGM #lessons learnt
Lessons learned from blog muse: audience-based inspiration for bloggers (CD, WG, DRM), pp. 1965–1974.
CHICHI-2010-HuangSDWKAL #learning #mobile #music
Mobile music touch: mobile tactile stimulation for passive learning (KH, TS, EYLD, GW, DK, CA, RL), pp. 791–800.
CHICHI-2010-IsbisterFH #design #game studies #learning
Designing games for learning: insights from conversations with designers (KI, MF, CH), pp. 2041–2044.
CHICHI-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.
CHICHI-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.
CHICHI-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.
ICEISICEIS-AIDSS-2010-AhdabG #learning #network #performance
Efficient Learning of Dynamic Bayesian Networks from Timed Data (AA, MLG), pp. 226–231.
ICEISICEIS-AIDSS-2010-CacoveanuBP #framework #predict
Evaluating Prediction Strategies in an Enhanced Meta-learning Framework (SC, CVB, RP), pp. 148–156.
ICEISICEIS-AIDSS-2010-MasvoulaKM #bibliography #learning
A Review of Learning Methods Enhanced in Strategies of Negotiating Agents (MM, PK, DM), pp. 212–219.
ICEISICEIS-AIDSS-2010-MoriyasuYN #learning #self #using
Supervised Learning for Agent Positioning by using Self-organizing Map (KM, TY, HN), pp. 368–372.
ICEISICEIS-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.
ICEISICEIS-HCI-2010-DelceaDC #collaboration #enterprise #performance
A Model for Improving Enterprise’s Performance based on Collaborative e-Learning (CD, MD, CC), pp. 5–12.
ICEISICEIS-HCI-2010-DiosERR #collaboration #learning
Virtual and Collaborative Environment for Learning Maths (AQD, AHE, IVR, ÁMdR), pp. 86–90.
ICEISICEIS-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.
ICEISICEIS-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.
ICEISICEIS-J-2010-DiosERR10a #collaboration #student
A Virtual Collaborative Environment Helps University Students to Learn Maths (AQD, AHE, IVR, ÁMdR), pp. 600–606.
ICEISICEIS-J-2010-PotoleaCL #evaluation #framework #predict
Meta-learning Framework for Prediction Strategy Evaluation (RP, SC, CL), pp. 280–295.
ICEISICEIS-SAIC-2010-MaximianoF #case study #mobile
Mobile e-Learning — Support Services Case Study (CM, VBF), pp. 106–113.
CIKMCIKM-2010-BethardJ #behaviour #learning #modelling
Who should I cite: learning literature search models from citation behavior (SB, DJ), pp. 609–618.
CIKMCIKM-2010-BilottiECN #constraints #learning #rank #semantics
Rank learning for factoid question answering with linguistic and semantic constraints (MWB, JLE, JGC, EN), pp. 459–468.
CIKMCIKM-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.
CIKMCIKM-2010-CebronB #learning #parallel
Active learning in parallel universes (NC, MRB), pp. 1621–1624.
CIKMCIKM-2010-ComarTJ #learning #multi #network
Multi task learning on multiple related networks (PMC, PNT, AKJ), pp. 1737–1740.
CIKMCIKM-2010-DuNL #adaptation #learning
Adapting cost-sensitive learning for reject option (JD, EAN, CXL), pp. 1865–1868.
CIKMCIKM-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.
CIKMCIKM-2010-FangSS #clustering #learning #multi
Multilevel manifold learning with application to spectral clustering (HrF, SS, YS), pp. 419–428.
CIKMCIKM-2010-FujinoUN #classification #learning #robust
A robust semi-supervised classification method for transfer learning (AF, NU, MN), pp. 379–388.
CIKMCIKM-2010-He #classification #learning #sentiment
Learning sentiment classification model from labeled features (YH), pp. 1685–1688.
CIKMCIKM-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.
CIKMCIKM-2010-KouCZZ #learning #ranking
Learning to blend rankings: a monotonic transformation to blend rankings from heterogeneous domains (ZK, YC, ZZ, HZ), pp. 1921–1924.
CIKMCIKM-2010-LadY #documentation #feedback #learning #novel #rank
Learning to rank relevant and novel documents through user feedback (AL, YY), pp. 469–478.
CIKMCIKM-2010-LinLYJS #learning #rank
Learning to rank with groups (YL, HL, ZY, SJ, XS), pp. 1589–1592.
CIKMCIKM-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.
CIKMCIKM-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.
CIKMCIKM-2010-ShiZT #learning
Combining link and content for collective active learning (LS, YZ, JT), pp. 1829–1832.
CIKMCIKM-2010-SonPS #classification #estimation #learning #naive bayes
Learning naïve bayes transfer classifier throughclass-wise test distribution estimation (JWS, SBP, HJS), pp. 1729–1732.
CIKMCIKM-2010-TakamuraO #learning #summary
Learning to generate summary as structured output (HT, MO), pp. 1437–1440.
CIKMCIKM-2010-YangKL #feature model #learning #multi #online
Online learning for multi-task feature selection (HY, IK, MRL), pp. 1693–1696.
CIKMCIKM-2010-ZhangWWCZHZ #learning #modelling
Learning click models via probit bayesian inference (YZ, DW, GW, WC, ZZ, BH, LZ), pp. 439–448.
CIKMCIKM-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.
CIKMCIKM-2010-ZhuZGX #classification #incremental #learning
Transfer incremental learning for pattern classification (ZZ, XZ, YFG, XX), pp. 1709–1712.
ECIRECIR-2010-MendozaMFP #learning #query #web
Learning to Distribute Queries into Web Search Nodes (MM, MM, FF, BP), pp. 281–292.
ECIRECIR-2010-PengMO #learning #ranking
Learning to Select a Ranking Function (JP, CM, IO), pp. 114–126.
ICMLICML-2010-Apte #machine learning #optimisation
The Role of Machine Learning in Business Optimization (CA), pp. 1–2.
ICMLICML-2010-BilgicMG #learning
Active Learning for Networked Data (MB, LM, LG), pp. 79–86.
ICMLICML-2010-BordesUW #ambiguity #learning #ranking #semantics
Label Ranking under Ambiguous Supervision for Learning Semantic Correspondences (AB, NU, JW), pp. 103–110.
ICMLICML-2010-BouzyM #game studies #learning #matrix #multi
Multi-agent Learning Experiments on Repeated Matrix Games (BB, MM), pp. 119–126.
ICMLICML-2010-BradleyG #learning #random
Learning Tree Conditional Random Fields (JKB, CG), pp. 127–134.
ICMLICML-2010-CaniniSG #categorisation #learning #modelling #process
Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process (KRC, MMS, TLG), pp. 151–158.
ICMLICML-2010-CaoLY #learning #multi #predict
Transfer Learning for Collective Link Prediction in Multiple Heterogenous Domains (BC, NNL, QY), pp. 159–166.
ICMLICML-2010-Cesa-BianchiSS #learning #performance
Efficient Learning with Partially Observed Attributes (NCB, SSS, OS), pp. 183–190.
ICMLICML-2010-ChakrabortyS #convergence #learning #multi #safety
Convergence, Targeted Optimality, and Safety in Multiagent Learning (DC, PS), pp. 191–198.
ICMLICML-2010-ChangSGR #learning
Structured Output Learning with Indirect Supervision (MWC, VS, DG, DR), pp. 199–206.
ICMLICML-2010-CortesMR #algorithm #kernel #learning
Two-Stage Learning Kernel Algorithms (CC, MM, AR), pp. 239–246.
ICMLICML-2010-CortesMR10a #bound #kernel #learning
Generalization Bounds for Learning Kernels (CC, MM, AR), pp. 247–254.
ICMLICML-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.
ICMLICML-2010-DavisD #bottom-up #learning #markov #network
Bottom-Up Learning of Markov Network Structure (JD, PMD), pp. 271–278.
ICMLICML-2010-DeselaersF #learning #multi #random
A Conditional Random Field for Multiple-Instance Learning (TD, VF), pp. 287–294.
ICMLICML-2010-DillonBL #analysis #generative #learning
Asymptotic Analysis of Generative Semi-Supervised Learning (JVD, KB, GL), pp. 295–302.
ICMLICML-2010-DruckM #generative #learning #modelling #using
High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models (GD, AM), pp. 319–326.
ICMLICML-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.
ICMLICML-2010-GomesK #data type #learning #parametricity
Budgeted Nonparametric Learning from Data Streams (RG, AK), pp. 391–398.
ICMLICML-2010-GregorL #approximate #learning #performance
Learning Fast Approximations of Sparse Coding (KG, YL), pp. 399–406.
ICMLICML-2010-GrubbB #composition #learning #network
Boosted Backpropagation Learning for Training Deep Modular Networks (AG, JAB), pp. 407–414.
ICMLICML-2010-HarpaleY #adaptation #learning #multi
Active Learning for Multi-Task Adaptive Filtering (AH, YY), pp. 431–438.
ICMLICML-2010-HonorioS #learning #modelling #multi #visual notation
Multi-Task Learning of Gaussian Graphical Models (JH, DS), pp. 447–454.
ICMLICML-2010-HuangG #independence #learning #ranking
Learning Hierarchical Riffle Independent Groupings from Rankings (JH, CG), pp. 455–462.
ICMLICML-2010-HueV #kernel #learning #on the
On learning with kernels for unordered pairs (MH, JPV), pp. 463–470.
ICMLICML-2010-JenattonMOB #learning #taxonomy
Proximal Methods for Sparse Hierarchical Dictionary Learning (RJ, JM, GO, FRB), pp. 487–494.
ICMLICML-2010-KimT10a #learning #multi #process
Gaussian Processes Multiple Instance Learning (MK, FDlT), pp. 535–542.
ICMLICML-2010-KokD #learning #logic #markov #network #using
Learning Markov Logic Networks Using Structural Motifs (SK, PMD), pp. 551–558.
ICMLICML-2010-KulisB #learning #online
Implicit Online Learning (BK, PLB), pp. 575–582.
ICMLICML-2010-LazaricG #learning #multi
Bayesian Multi-Task Reinforcement Learning (AL, MG), pp. 599–606.
ICMLICML-2010-LiangJK #approach #learning #source code
Learning Programs: A Hierarchical Bayesian Approach (PL, MIJ, DK), pp. 639–646.
ICMLICML-2010-LiangS #interactive #learning #multi #on the
On the Interaction between Norm and Dimensionality: Multiple Regimes in Learning (PL, NS), pp. 647–654.
ICMLICML-2010-LiPSG #learning #parametricity
Budgeted Distribution Learning of Belief Net Parameters (LL, BP, CS, RG), pp. 879–886.
ICMLICML-2010-LiuHC #graph #learning #scalability
Large Graph Construction for Scalable Semi-Supervised Learning (WL, JH, SFC), pp. 679–686.
ICMLICML-2010-LiuNLL #analysis #graph #learning #relational
Learning Temporal Causal Graphs for Relational Time-Series Analysis (YL, ANM, ACL, YL), pp. 687–694.
ICMLICML-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.
ICMLICML-2010-MaeiSBS #approximate #learning #towards
Toward Off-Policy Learning Control with Function Approximation (HRM, CS, SB, RSS), pp. 719–726.
ICMLICML-2010-Mahmud #learning
Constructing States for Reinforcement Learning (MMHM), pp. 727–734.
ICMLICML-2010-Martens #learning #optimisation
Deep learning via Hessian-free optimization (JM), pp. 735–742.
ICMLICML-2010-Martens10a #learning #linear
Learning the Linear Dynamical System with ASOS (JM), pp. 743–750.
ICMLICML-2010-McFeeL #learning #metric #rank
Metric Learning to Rank (BM, GRGL), pp. 775–782.
ICMLICML-2010-MeshiSJG #approximate #learning
Learning Efficiently with Approximate Inference via Dual Losses (OM, DS, TSJ, AG), pp. 783–790.
ICMLICML-2010-MorimuraSKHT #approximate #learning #parametricity
Nonparametric Return Distribution Approximation for Reinforcement Learning (TM, MS, HK, HH, TT), pp. 799–806.
ICMLICML-2010-OntanonP #approach #induction #learning #multi
Multiagent Inductive Learning: an Argumentation-based Approach (SO, EP), pp. 839–846.
ICMLICML-2010-Raphael #machine learning #music
Music Plus One and Machine Learning (CR), pp. 21–28.
ICMLICML-2010-Salakhutdinov #adaptation #learning #using
Learning Deep Boltzmann Machines using Adaptive MCMC (RS), pp. 943–950.
ICMLICML-2010-ShoebG #detection #machine learning
Application of Machine Learning To Epileptic Seizure Detection (AHS, JVG), pp. 975–982.
ICMLICML-2010-SlivkinsRG #documentation #learning #ranking #scalability
Learning optimally diverse rankings over large document collections (AS, FR, SG), pp. 983–990.
ICMLICML-2010-SnyderB #learning #multi
Climbing the Tower of Babel: Unsupervised Multilingual Learning (BS, RB), pp. 29–36.
ICMLICML-2010-SzitaS #bound #complexity #learning #modelling
Model-based reinforcement learning with nearly tight exploration complexity bounds (IS, CS), pp. 1031–1038.
ICMLICML-2010-TanWT #dataset #feature model #learning
Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets (MT, LW, IWT), pp. 1047–1054.
ICMLICML-2010-TomiokaSSK #algorithm #learning #matrix #performance #rank
A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices (RT, TS, MS, HK), pp. 1087–1094.
ICMLICML-2010-WalshSLD #learning
Generalizing Apprenticeship Learning across Hypothesis Classes (TJW, KS, MLL, CD), pp. 1119–1126.
ICMLICML-2010-WangKC #learning
Sequential Projection Learning for Hashing with Compact Codes (JW, SK, SFC), pp. 1127–1134.
ICMLICML-2010-WunderLB #multi
Classes of Multiagent Q-learning Dynamics with epsilon-greedy Exploration (MW, MLL, MB), pp. 1167–1174.
ICMLICML-2010-XuJYKL #kernel #learning #multi #performance
Simple and Efficient Multiple Kernel Learning by Group Lasso (ZX, RJ, HY, IK, MRL), pp. 1175–1182.
ICMLICML-2010-YangJJ #learning
Learning from Noisy Side Information by Generalized Maximum Entropy Model (TY, RJ, AKJ), pp. 1199–1206.
ICMLICML-2010-YangXKL #learning #online
Online Learning for Group Lasso (HY, ZX, IK, MRL), pp. 1191–1198.
ICMLICML-2010-ZhaoH #framework #learning #named #online
OTL: A Framework of Online Transfer Learning (PZ, SCHH), pp. 1231–1238.
ICMLICML-2010-ZhuGJRHK #learning #modelling
Cognitive Models of Test-Item Effects in Human Category Learning (XZ, BRG, KSJ, TTR, JH, CK), pp. 1247–1254.
ICPRICPR-2010-Al-HuseinyMN #approach #set
Gait Learning-Based Regenerative Model: A Level Set Approach (MSAH, SM, MSN), pp. 2644–2647.
ICPRICPR-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.
ICPRICPR-2010-AmateR #learning #modelling #probability
Learning Probabilistic Models of Contours (LA, MJR), pp. 645–648.
ICPRICPR-2010-AroraS #algorithm #learning #performance
An Efficient and Stable Algorithm for Learning Rotations (RA, WAS), pp. 2993–2996.
ICPRICPR-2010-AtmosukartoSH #3d #learning #programming #search-based
The Use of Genetic Programming for Learning 3D Craniofacial Shape Quantifications (IA, LGS, CH), pp. 2444–2447.
ICPRICPR-2010-BaghshahS #constraints #kernel #learning #performance
Efficient Kernel Learning from Constraints and Unlabeled Data (MSB, SBS), pp. 3364–3367.
ICPRICPR-2010-BalujaC #learning #performance #retrieval
Beyond “Near Duplicates”: Learning Hash Codes for Efficient Similar-Image Retrieval (SB, MC), pp. 543–547.
ICPRICPR-2010-BanderaMM #incremental #learning #mobile #visual notation
Incremental Learning of Visual Landmarks for Mobile Robotics (AB, RM, RVM), pp. 4255–4258.
ICPRICPR-2010-BlondelSU #learning #online #recognition
Unsupervised Learning of Stroke Tagger for Online Kanji Handwriting Recognition (MB, KS, KU), pp. 1973–1976.
ICPRICPR-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.
ICPRICPR-2010-BuyssensR #learning #verification
Learning Sparse Face Features: Application to Face Verification (PB, MR), pp. 670–673.
ICPRICPR-2010-CamposZJ
An Improved Structural EM to Learn Dynamic Bayesian Nets (CPdC, ZZ, QJ), pp. 601–604.
ICPRICPR-2010-CarneiroN #architecture #learning
The Fusion of Deep Learning Architectures and Particle Filtering Applied to Lip Tracking (GC, JCN), pp. 2065–2068.
ICPRICPR-2010-Casarrubias-VargasPB #machine learning #navigation #visual notation
EKF-SLAM and Machine Learning Techniques for Visual Robot Navigation (HCV, APB, EBC), pp. 396–399.
ICPRICPR-2010-Cevikalp #distance #learning #metric #polynomial #programming
Semi-supervised Distance Metric Learning by Quadratic Programming (HC), pp. 3352–3355.
ICPRICPR-2010-ChenF #graph #learning
Semi-supervised Graph Learning: Near Strangers or Distant Relatives (WC, GF), pp. 3368–3371.
ICPRICPR-2010-CiompiPR #approach #random #using
A Meta-Learning Approach to Conditional Random Fields Using Error-Correcting Output Codes (FC, OP, PR), pp. 710–713.
ICPRICPR-2010-CohenP #learning #performance #robust
Reinforcement Learning for Robust and Efficient Real-World Tracking (AC, VP), pp. 2989–2992.
ICPRICPR-2010-DagAKS #categorisation #learning
Learning Affordances for Categorizing Objects and Their Properties (ND, IA, SK, ES), pp. 3089–3092.
ICPRICPR-2010-DitzlerPC #algorithm #incremental #learning
An Incremental Learning Algorithm for Non-stationary Environments and Class Imbalance (GD, RP, NVC), pp. 2997–3000.
ICPRICPR-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.
ICPRICPR-2010-ErdoganS #classification #framework #learning #linear
A Unifying Framework for Learning the Linear Combiners for Classifier Ensembles (HE, MUS), pp. 2985–2988.
ICPRICPR-2010-FanHM #classification #learning #metric
Learning Metrics for Shape Classification and Discrimination (YF, DH, WM), pp. 2652–2655.
ICPRICPR-2010-FausserS #approximate #learning
Learning a Strategy with Neural Approximated Temporal-Difference Methods in English Draughts (SF, FS), pp. 2925–2928.
ICPRICPR-2010-FengZH #detection #learning #online #self
Online Learning with Self-Organizing Maps for Anomaly Detection in Crowd Scenes (JF, CZ, PH), pp. 3599–3602.
ICPRICPR-2010-FuLTZ #classification #learning #music #naive bayes #retrieval
Learning Naive Bayes Classifiers for Music Classification and Retrieval (ZF, GL, KMT, DZ), pp. 4589–4592.
ICPRICPR-2010-GuoBC #approach #learning #using
Support Vectors Selection for Supervised Learning Using an Ensemble Approach (LG, SB, NC), pp. 37–40.
ICPRICPR-2010-GuoZCZG #documentation #learning
Unsupervised Learning from Linked Documents (ZG, SZ, YC, ZZ, YG), pp. 730–733.
ICPRICPR-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.
ICPRICPR-2010-HanCR10a #concept #interactive #learning #recognition #semantics
Semi-supervised and Interactive Semantic Concept Learning for Scene Recognition (XHH, YWC, XR), pp. 3045–3048.
ICPRICPR-2010-HanFD #learning #prototype #recognition #set
Discriminative Prototype Learning in Open Set Face Recognition (ZH, CF, XD), pp. 2696–2699.
ICPRICPR-2010-HuangY #learning #recognition
Learning Virtual HD Model for Bi-model Emotional Speaker Recognition (TH, YY), pp. 1614–1617.
ICPRICPR-2010-HurWL #estimation #invariant #learning
View Invariant Body Pose Estimation Based on Biased Manifold Learning (DH, CW, SWL), pp. 3866–3869.
ICPRICPR-2010-JhuoL #kernel #learning #multi #recognition
Boosted Multiple Kernel Learning for Scene Category Recognition (IHJ, DTL), pp. 3504–3507.
ICPRICPR-2010-JiaCLW #image #learning #performance
Efficient Learning to Label Images (KJ, LC, NL, LW), pp. 942–945.
ICPRICPR-2010-JokoKY #learning #linear #modelling
Learning Non-linear Dynamical Systems by Alignment of Local Linear Models (MJ, YK, TY), pp. 1084–1087.
ICPRICPR-2010-JoshiP #adaptation #detection #incremental #learning
Scene-Adaptive Human Detection with Incremental Active Learning (AJJ, FP), pp. 2760–2763.
ICPRICPR-2010-KamarainenI #canonical #detection #learning
Learning and Detection of Object Landmarks in Canonical Object Space (JKK, JI), pp. 1409–1412.
ICPRICPR-2010-KappSM #adaptation #incremental #learning
Adaptive Incremental Learning with an Ensemble of Support Vector Machines (MNK, RS, PM), pp. 4048–4051.
ICPRICPR-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.
ICPRICPR-2010-LiLD #learning #using
Learning GMM Using Elliptically Contoured Distributions (BL, WL, LD), pp. 511–514.
ICPRICPR-2010-LiuA #learning #semantics #using
Learning Scene Semantics Using Fiedler Embedding (JL, SA), pp. 3627–3630.
ICPRICPR-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.
ICPRICPR-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.
ICPRICPR-2010-NiSRM #learning #multi #online
Particle Filter Tracking with Online Multiple Instance Learning (ZN, SS, AR, BSM), pp. 2616–2619.
ICPRICPR-2010-OhH #learning #process #using #video
Unsupervised Learning of Activities in Video Using Scene Context (SO, AH), pp. 3579–3582.
ICPRICPR-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.
ICPRICPR-2010-PhilippotBB #algorithm #classification #learning #network #online
Bayesian Networks Learning Algorithms for Online Form Classification (EP, YB, AB), pp. 1981–1984.
ICPRICPR-2010-PuS #learning #probability #verification
Probabilistic Measure for Signature Verification Based on Bayesian Learning (DP, SNS), pp. 1188–1191.
ICPRICPR-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.
ICPRICPR-2010-RicciTZ #kernel #learning
Learning Pedestrian Trajectories with Kernels (ER, FT, GZ), pp. 149–152.
ICPRICPR-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.
ICPRICPR-2010-Sarkar #classification #documentation #image #learning
Learning Image Anchor Templates for Document Classification and Data Extraction (PS), pp. 3428–3431.
ICPRICPR-2010-Sato #classification #design #kernel #learning
A New Learning Formulation for Kernel Classifier Design (AS), pp. 2897–2900.
ICPRICPR-2010-ShamiliBA #detection #distributed #machine learning #mobile #using
Malware Detection on Mobile Devices Using Distributed Machine Learning (ASS, CB, TA), pp. 4348–4351.
ICPRICPR-2010-ShenYS #learning
Learning Discriminative Features Based on Distribution (JS, WY, CS), pp. 1401–1404.
ICPRICPR-2010-SodaI #composition #dataset #integration #learning
Decomposition Methods and Learning Approaches for Imbalanced Dataset: An Experimental Integration (PS, GI), pp. 3117–3120.
ICPRICPR-2010-SternigRB #classification #learning #multi
Inverse Multiple Instance Learning for Classifier Grids (SS, PMR, HB), pp. 770–773.
ICPRICPR-2010-SuLT10a #documentation #framework #learning #self
A Self-Training Learning Document Binarization Framework (BS, SL, CLT), pp. 3187–3190.
ICPRICPR-2010-SunSHE #learning #locality #metric
Localized Supervised Metric Learning on Temporal Physiological Data (JS, DMS, JH, SE), pp. 4149–4152.
ICPRICPR-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.
ICPRICPR-2010-TorkiEL #learning #multi #representation #set
Learning a Joint Manifold Representation from Multiple Data Sets (MT, AME, CSL), pp. 1068–1071.
ICPRICPR-2010-TsagkatakisS #distance #modelling #random #recognition
Manifold Modeling with Learned Distance in Random Projection Space for Face Recognition (GT, AES), pp. 653–656.
ICPRICPR-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.
ICPRICPR-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.
ICPRICPR-2010-WangJHT #higher-order #kernel #learning #multi
Multiple Kernel Learning with High Order Kernels (SW, SJ, QH, QT), pp. 2138–2141.
ICPRICPR-2010-WangM #learning #order #process #using
Gaussian Process Learning from Order Relationships Using Expectation Propagation (RW, SJM), pp. 605–608.
ICPRICPR-2010-WidhalmB #learning
Learning Major Pedestrian Flows in Crowded Scenes (PW, NB), pp. 4064–4067.
ICPRICPR-2010-WuLW #image #learning #retrieval #using
Enhancing SVM Active Learning for Image Retrieval Using Semi-supervised Bias-Ensemble (JW, ML, CLW), pp. 3175–3178.
ICPRICPR-2010-XingAL #detection #learning #multi
Multiple Human Tracking Based on Multi-view Upper-Body Detection and Discriminative Learning (JX, HA, SL), pp. 1698–1701.
ICPRICPR-2010-YaegashiY #kernel #learning #multi #recognition #using
Geotagged Photo Recognition Using Corresponding Aerial Photos with Multiple Kernel Learning (KY, KY), pp. 3272–3275.
ICPRICPR-2010-ZhangLD #approach #kernel #learning #multi #named #novel
AdaMKL: A Novel Biconvex Multiple Kernel Learning Approach (ZZ, ZNL, MSD), pp. 2126–2129.
ICPRICPR-2010-ZhangWL #categorisation #kernel #learning
Learning the Kernel Combination for Object Categorization (DZ, XW, BL), pp. 2929–2932.
ICPRICPR-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.
ICPRICPR-2010-ZhouLLT #canonical #image #learning #visual notation
Canonical Image Selection by Visual Context Learning (WZ, YL, HL, QT), pp. 834–837.
ICPRICPR-2010-ZhuHYL #behaviour #learning #metric #prototype #recognition #using
Prototype Learning Using Metric Learning Based Behavior Recognition (PZ, WH, CY, LL), pp. 2604–2607.
ICPRICPR-2010-ZouY #image #kernel #learning
Learning the Relationship Between High and Low Resolution Images in Kernel Space for Face Super Resolution (WWWZ, PCY), pp. 1152–1155.
KDDKDD-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.
KDDKDD-2010-AgarwalCE #learning #online #performance #recommendation
Fast online learning through offline initialization for time-sensitive recommendation (DA, BCC, PE), pp. 703–712.
KDDKDD-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.
KDDKDD-2010-BozorgiSSV #heuristic #learning #predict
Beyond heuristics: learning to classify vulnerabilities and predict exploits (MB, LKS, SS, GMV), pp. 105–114.
KDDKDD-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.
KDDKDD-2010-ChenLY #learning #multi #rank
Learning incoherent sparse and low-rank patterns from multiple tasks (JC, JL, JY), pp. 1179–1188.
KDDKDD-2010-DasMSO #algorithm #case study #detection #kernel #learning #multi #safety
Multiple kernel learning for heterogeneous anomaly detection: algorithm and aviation safety case study (SD, BLM, ANS, NCO), pp. 47–56.
KDDKDD-2010-GuptaPATV #learning #retrieval #social #social media
Nonnegative shared subspace learning and its application to social media retrieval (SKG, DQP, BA, TT, SV), pp. 1169–1178.
KDDKDD-2010-HoTL #learning #metric #reduction #sequence #similarity
Tropical cyclone event sequence similarity search via dimensionality reduction and metric learning (SSH, WT, WTL), pp. 135–144.
KDDKDD-2010-HuhF #learning #modelling #topic
Discriminative topic modeling based on manifold learning (SH, SEF), pp. 653–662.
KDDKDD-2010-KhoslaCLCHL #approach #machine learning #predict
An integrated machine learning approach to stroke prediction (AK, YC, CCYL, HKC, JH, HL), pp. 183–192.
KDDKDD-2010-Lee #classification #learning
Learning to combine discriminative classifiers: confidence based (CHL), pp. 743–752.
KDDKDD-2010-LiuMTLL #learning #metric #optimisation #using
Semi-supervised sparse metric learning using alternating linearization optimization (WL, SM, DT, JL, PL), pp. 1139–1148.
KDDKDD-2010-LiuZ #learning
Learning with cost intervals (XYL, ZHZ), pp. 403–412.
KDDKDD-2010-SomaiyaJR #learning #modelling
Mixture models for learning low-dimensional roles in high-dimensional data (MS, CMJ, SR), pp. 909–918.
KDDKDD-2010-WallaceSBT #learning
Active learning for biomedical citation screening (BCW, KS, CEB, TAT), pp. 173–182.
KDDKDD-2010-ZhangY #learning #metric
Transfer metric learning by learning task relationships (YZ, DYY), pp. 1199–1208.
KDDKDD-2010-ZhangZ #dependence #learning #multi
Multi-label learning by exploiting label dependency (MLZ, KZ), pp. 999–1008.
KDDKDD-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.
KDIRKDIR-2010-CarulloB #analysis #machine learning #mining #web
Machine Learning and Link Analysis for Web Content Mining (MC, EB), pp. 156–161.
KDIRKDIR-2010-Cebron #learning #representation #towards
Towards Learning with Objects in a Hierarchical Representation (NC), pp. 326–329.
KDIRKDIR-2010-LourencoF #clustering #learning #multi
Selectively Learning Clusters in Multi-EAC (AL, ALNF), pp. 491–499.
KDIRKDIR-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.
KDIRKDIR-2010-WangSFR #concept
A Meta-learning Method for Concept Drift (RW, LS, MÓF, ER), pp. 257–262.
KEODKEOD-2010-ArdilaAL #kernel #learning #multi #ontology
Multiple Kernel Learning for Ontology Instance Matching (DA, JA, FL), pp. 311–318.
KEODKEOD-2010-Braham #assessment #learning #metric
A Knowledge Metric with Applications to Learning Assessment (RB), pp. 5–9.
KEODKEOD-2010-EynardMM #analysis #on the #ontology
On the Use of Correspondence Analysis to Learn Seed Ontologies from Text (DE, FM, MM), pp. 430–437.
KEODKEOD-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.
KEODKEOD-2010-Girardi #learning #ontology
Guiding Ontology Learning and Population by Knowledge System Goals (RG), pp. 480–484.
KMISKMIS-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.
KMISKMIS-2010-JuvonenO #learning
Studying IT Team Entrepreneurship as a Learning Organization (PJ, PO), pp. 332–337.
KMISKMIS-2010-LiDFF #behaviour #comprehension
Understanding Behavioral Intention of e-Learning System Re-use (YL, YD, ZF, WF), pp. 218–223.
RecSysRecSys-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.
RecSysRecSys-2010-LipczakM #learning #performance #recommendation
Learning in efficient tag recommendation (ML, EEM), pp. 167–174.
RecSysRecSys-2010-MelloAZ #impact analysis #learning #rating
Active learning driven by rating impact analysis (CERdM, MAA, GZ), pp. 341–344.
RecSysRecSys-2010-ShiLH #collaboration #learning #matrix #rank
List-wise learning to rank with matrix factorization for collaborative filtering (YS, ML, AH), pp. 269–272.
SEKESEKE-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.
SEKESEKE-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.
SEKESEKE-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.
SIGIRSIGIR-2010-BalasubramanianA #learning
Learning to select rankers (NB, JA), pp. 855–856.
SIGIRSIGIR-2010-DangBC #learning #query #rank
Learning to rank query reformulations (VD, MB, WBC), pp. 807–808.
SIGIRSIGIR-2010-DaveV #learning
Learning the click-through rate for rare/new ads from similar ads (KSD, VV), pp. 897–898.
SIGIRSIGIR-2010-GaoCWZ #learning #rank #using
Learning to rank only using training data from related domain (WG, PC, KFW, AZ), pp. 162–169.
SIGIRSIGIR-2010-HajishirziYK #adaptation #detection #learning #similarity
Adaptive near-duplicate detection via similarity learning (HH, WtY, AK), pp. 419–426.
SIGIRSIGIR-2010-LeeCW #machine learning #social
Uncovering social spammers: social honeypots + machine learning (KL, JC, SW), pp. 435–442.
SIGIRSIGIR-2010-Liu #information retrieval #learning #rank
Learning to rank for information retrieval (TYL), p. 904.
SIGIRSIGIR-2010-LiuW #email #learning #multi
Multi-field learning for email spam filtering (WL, TW), pp. 745–746.
SIGIRSIGIR-2010-LiuYSCCL #behaviour #learning #rank
Learning to rank audience for behavioral targeting (NL, JY, DS, DC, ZC, YL), pp. 719–720.
SIGIRSIGIR-2010-LongCZCZT #learning #optimisation #ranking
Active learning for ranking through expected loss optimization (BL, OC, YZ, YC, ZZ, BLT), pp. 267–274.
SIGIRSIGIR-2010-MojdehC #consistency #learning #using
Semi-supervised spam filtering using aggressive consistency learning (MM, GVC), pp. 751–752.
SIGIRSIGIR-2010-Wang #learning #modelling #retrieval
Learning hidden variable models for blog retrieval (MW), p. 922.
SIGIRSIGIR-2010-WangLM #learning #rank
Learning to efficiently rank (LW, JJL, DM), pp. 138–145.
SIGIRSIGIR-2010-WangWVL #clustering #documentation #learning #metric
Text document clustering with metric learning (JW, SW, HQV, GL), pp. 783–784.
SIGIRSIGIR-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.
SIGIRSIGIR-2010-YueGCZJ #evaluation #learning #retrieval #statistics
Learning more powerful test statistics for click-based retrieval evaluation (YY, YG, OC, YZ, TJ), pp. 507–514.
SIGIRSIGIR-2010-ZwolPMS #ranking
Machine learned ranking of entity facets (RvZ, LGP, MM, BS), pp. 879–880.
MODELSMoDELS-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.
RERE-2010-MashkoorJ #domain model #lessons learnt
Domain Engineering with Event-B: Some Lessons We Learned (AM, JPJ), pp. 252–261.
REFSQREFSQ-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.
SACSAC-2010-AppiceCM #learning
Transductive learning for spatial regression with co-training (AA, MC, DM), pp. 1065–1070.
SACSAC-2010-AyyappanWN #algorithm #constraints #learning #named #network #scalability
MICHO: a scalable constraint-based algorithm for learning Bayesian networks (MA, YKW, WKN), pp. 985–989.
SACSAC-2010-CostaFGMO #learning #mining #modelling
Mining models of exceptional objects through rule learning (GC, FF, MG, GM, RO), pp. 1078–1082.
FSEFSE-2010-Elkhodary #adaptation #approach #feature model #self
A learning-based approach for engineering feature-oriented self-adaptive software systems (AME), pp. 345–348.
ICSEICSE-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.
HPDCHPDC-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.
CAVCAV-2010-BolligKKLNP #automaton #framework #learning #named
libalf: The Automata Learning Framework (BB, JPK, CK, ML, DN, DRP), pp. 360–364.
CAVCAV-2010-ChenCFTTW #automation #learning #reasoning
Automated Assume-Guarantee Reasoning through Implicit Learning (YFC, EMC, AF, MHT, YKT, BYW), pp. 511–526.
CAVCAV-2010-SinghGP #abstraction #component #interface #learning
Learning Component Interfaces with May and Must Abstractions (RS, DG, CSP), pp. 527–542.
ICLPICLP-2010-Balduccini10 #heuristic #learning #set
Learning Domain-Specific Heuristics for Answer Set Solvers (MB), pp. 14–23.
ICLPICLP-2010-Pahlavi10 #higher-order #learning #logic
Higher-order Logic Learning and λ-Progol (NP), pp. 281–285.
ICLPICLP-J-2010-SneyersMVKS #learning #logic #probability
CHR(PRISM)-based probabilistic logic learning (JS, WM, JV, YK, TS), pp. 433–447.
ICSTICST-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.
ICTSSICTSS-2010-MeinkeN #approach #testing
A Learning-Based Approach to Unit Testing of Numerical Software (KM, FN), pp. 221–235.
ISSTAISSTA-2010-GruskaWZ #detection #learning #lightweight
Learning from 6, 000 projects: lightweight cross-project anomaly detection (NG, AW, AZ), pp. 119–130.
ICSTSAT-2010-Ben-SassonJ #bound #learning #strict
Lower Bounds for Width-Restricted Clause Learning on Small Width Formulas (EBS, JJ), pp. 16–29.
ICSTSAT-2010-KlieberSGC #learning
A Non-prenex, Non-clausal QBF Solver with Game-State Learning (WK, SS, SG, EMC), pp. 128–142.
VMCAIVMCAI-2010-JungKWY #abstraction #algorithm #invariant #learning
Deriving Invariants by Algorithmic Learning, Decision Procedures, and Predicate Abstraction (YJ, SK, BYW, KY), pp. 180–196.
CASECASE-2009-BountourelisR #algorithm #learning
Customized learning algorithms for episodic tasks with acyclic state spaces (TB, SR), pp. 627–634.
CASECASE-2009-Ray #lessons learnt #standard
Healthcare interoperability — lessons learned from the manufacturing standards sector (SRR), pp. 88–89.
CASECASE-2009-SolisT #comprehension #learning #towards
Towards enhancing the understanding of human motor learning (JS, AT), pp. 591–596.
DACDAC-2009-MarrBBH #learning
A learning digital computer (BM, AB, SB, PEH), pp. 617–618.
DATEDATE-2009-RichterJE #framework #learning #verification
Learning early-stage platform dimensioning from late-stage timing verification (KR, MJ, RE), pp. 851–857.
DATEDATE-2009-StratigopoulosMM #set
Enrichment of limited training sets in machine-learning-based analog/RF test (HGDS, SM, YM), pp. 1668–1673.
DATEDATE-2009-WangW #machine learning
Machine learning-based volume diagnosis (SW, WW), pp. 902–905.
DRRDRR-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.
HTHT-2009-AlAghaB #approach #hypermedia #learning #towards
Towards a constructivist approach to learning from hypertext (IA, LB), pp. 51–56.
HTHT-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.
ICDARICDAR-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.
ICDARICDAR-2009-AlmaksourA #incremental #learning #online #performance #recognition
Fast Incremental Learning Strategy Driven by Confusion Reject for Online Handwriting Recognition (AA, ÉA), pp. 81–85.
ICDARICDAR-2009-BallS #learning #recognition
Semi-supervised Learning for Handwriting Recognition (GRB, SNS), pp. 26–30.
ICDARICDAR-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.
ICDARICDAR-2009-KaeL #learning #on the fly #problem
Learning on the Fly: Font-Free Approaches to Difficult OCR Problems (AK, EGLM), pp. 571–575.
ICDARICDAR-2009-MansjurWJ #automation #categorisation #classification #kernel #learning #topic #using
Using Kernel Density Classifier with Topic Model and Cost Sensitive Learning for Automatic Text Categorization (DSM, TSW, BHJ), pp. 1086–1090.
ICDARICDAR-2009-Silva #analysis #documentation #learning #markov #modelling
Learning Rich Hidden Markov Models in Document Analysis: Table Location (ACeS), pp. 843–847.
ICDARICDAR-2009-StefanoFFM #classification #evolution #learning #network
Learning Bayesian Networks by Evolution for Classifier Combination (CDS, FF, ASdF, AM), pp. 966–970.
ICDARICDAR-2009-TewariN #adaptation #learning
Learning and Adaptation for Improving Handwritten Character Recognizers (NCT, AMN), pp. 86–90.
ICDARICDAR-2009-WangLJ #learning #modelling #segmentation #statistics #string
Statistical Modeling and Learning for Recognition-Based Handwritten Numeral String Segmentation (YW, XL, YJ), pp. 421–425.
ICDARICDAR-2009-ZhuGGZ #framework #learning #online #probability #recognition
A Probabilistic Framework for Soft Target Learning in Online Cursive Handwriting Recognition (XZ, YG, FJG, LXZ), pp. 1246–1250.
SIGMODSIGMOD-2009-BabuGM #learning #nondeterminism #scalability
Large-scale uncertainty management systems: learning and exploiting your data (SB, SG, KM), pp. 995–998.
VLDBVLDB-2009-ArasuCK #learning #string
Learning String Transformations From Examples (AA, SC, RK), pp. 514–525.
VLDBVLDB-2009-Ley #lessons learnt #named
DBLP — Some Lessons Learned (ML), pp. 1493–1500.
VLDBVLDB-2009-PandaHBB #learning #named #parallel #pipes and filters
PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce (BP, JH, SB, RJB), pp. 1426–1437.
CSEETCSEET-2009-Armarego #learning #student
Displacing the Sage on the Stage: Student Control of Learning (JA), pp. 198–201.
CSEETCSEET-2009-ChaoR #agile #learning #student
Agile Software Factory for Student Service Learning (JC, MR), pp. 34–40.
CSEETCSEET-2009-Goel #education #learning #re-engineering
Enriching the Culture of Software Engineering Education through Theories of Knowledge and Learning (SG), p. 279.
CSEETCSEET-2009-PadminiR #challenge #development
Issues in SE E-learning Development — Changing Phases and Challenges Going Forward (HAP, SSR), pp. 130–137.
CSEETCSEET-2009-RichardsonD #learning #problem #re-engineering
Problem Based Learning in the Software Engineering Classroom (IR, YD), pp. 174–181.
CSEETCSEET-2009-Rosso-Llopart #education #learning #re-engineering
An Examination of Learning Technologies That Support Software Engineering and Education (MRL), pp. 294–295.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2009-AndersonL #collaboration #community #learning #student
Exploring technologies for building collaborative learning communities among diverse student populations (NA, CCL), pp. 243–247.
ITiCSEITiCSE-2009-BlasGMB #analysis #automation #composition #using
Automatic E-learning contents composition by using gap analysis techniques (JMdB, JMG, LdM, RB), p. 369.
ITiCSEITiCSE-2009-BuendiaCB #approach #learning
An instructional approach to drive computer science courses through virtual learning environments (FB, JCC, JVB), pp. 6–10.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2009-DoerschukLM #experience #lessons learnt
INSPIRED broadening participation: first year experience and lessons learned (PID, JL, JM), pp. 238–242.
ITiCSEITiCSE-2009-Draganova #learning #mobile
Use of mobile phone technologies in learning (CD), p. 399.
ITiCSEITiCSE-2009-Ginat #composition #learning
Interleaved pattern composition and scaffolded learning (DG), pp. 109–113.
ITiCSEITiCSE-2009-Hwang09a #education #learning #operating system
Blended learning for teaching operating systems with Windows (SwH), p. 380.
ITiCSEITiCSE-2009-Lasserre #adaptation #learning #programming
Adaptation of team-based learning on a first term programming class (PL), pp. 186–190.
ITiCSEITiCSE-2009-Martin #learning
Cooperative learning to support the lacks of PBL (JGM), p. 343.
ITiCSEITiCSE-2009-MhiriR #development #learning #named
AARTIC: development of an intelligent environment for human learning (FM, SR), p. 359.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2009-Palmer-BrownDL #feedback #learning
Guided learning via diagnostic feedback to question responses (DPB, CD, SWL), p. 362.
ITiCSEITiCSE-2009-Pantaleev #learning #named #visual notation
Dzver: a visual computer science learning environment (AP), p. 387.
ITiCSEITiCSE-2009-Radenski #learning
Freedom of choice as motivational factor for active learning (AR), pp. 21–25.
ITiCSEITiCSE-2009-Sondergaard #learning #student
Learning from and with peers: the different roles of student peer reviewing (HS), pp. 31–35.
ITiCSEITiCSE-2009-TsengHH #collaboration #education #framework #learning #ubiquitous
A collaborative ubiquitous learning platform for computer science education (JCRT, SYYH, GJH), p. 368.
ITiCSEITiCSE-2009-Velazquez-IturbideP #algorithm #interactive #learning
Active learning of greedy algorithms by means of interactive experimentation (JÁVI, APC), pp. 119–123.
ITiCSEITiCSE-2009-VillalobosCJ #interactive #learning #programming #using
Developing programming skills by using interactive learning objects (JV, NAC, CJ), pp. 151–155.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2009-WhiteI #case study #education #experience #learning #research
Relating research and teaching: learning from experiences and beliefs (SW, AI), pp. 75–79.
ITiCSEITiCSE-2009-WiesnerB #concept #how #learning #question
How do robots foster the learning of basic concepts in informatics? (BW, TB), p. 403.
ITiCSEITiCSE-2009-ZanderTSMMHF #learning
Learning styles: novices decide (CZ, LT, BS, LM, RM, BH, SF), pp. 223–227.
ESOPESOP-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.
TACASTACAS-2009-ChenFCTW #automaton #composition #learning #verification
Learning Minimal Separating DFA’s for Compositional Verification (YFC, AF, EMC, YKT, BYW), pp. 31–45.
ICPCICPC-2009-JeffreyFGG #debugging #developer #named
BugFix: A learning-based tool to assist developers in fixing bugs (DJ, MF, NG, RG), pp. 70–79.
MSRMSR-2009-AyewahP #fault #learning
Learning from defect removals (NA, WP), pp. 179–182.
PLDIPLDI-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.
STOCSTOC-2009-KleinbergPT #game studies #learning #multi
Multiplicative updates outperform generic no-regret learning in congestion games: extended abstract (RK, GP, ÉT), pp. 533–542.
STOCSTOC-2009-Sellie #learning #random
Exact learning of random DNF over the uniform distribution (LS), pp. 45–54.
ICALPICALP-v1-2009-KlivansLS #learning
Learning Halfspaces with Malicious Noise (ARK, PML, RAS), pp. 609–621.
LATALATA-2009-Akama #commutative #learning
Commutative Regular Shuffle Closed Languages, Noetherian Property, and Learning Theory (YA), pp. 93–104.
LATALATA-2009-Gierasimczuk #learning #logic
Learning by Erasing in Dynamic Epistemic Logic (NG), pp. 362–373.
LATALATA-2009-Jain #learning
Hypothesis Spaces for Learning (SJ), pp. 43–58.
CHICHI-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.
CHICHI-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.
CHICHI-2009-HaradaWMBL #learning #people
Longitudinal study of people learning to use continuous voice-based cursor control (SH, JOW, JM, JAB, JAL), pp. 347–356.
CHICHI-2009-KammererNPC #learning #social
Signpost from the masses: learning effects in an exploratory social tag search browser (YK, RN, PP, EHhC), pp. 625–634.
CHICHI-2009-LoveJTH #assessment #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.
CHICHI-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.
CHICHI-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.
CHICHI-2009-Thom-SantelliM #learning
Learning by seeing: photo viewing in the workplace (JTS, DRM), pp. 2081–2090.
CHICHI-2009-TorreyCM #how #internet #learning
Learning how: the search for craft knowledge on the internet (CT, EFC, DWM), pp. 1371–1380.
HCIDHM-2009-FallonCP #assessment #learning #risk management
Learning from Risk Assessment in Radiotherapy (EFF, LC, WJvdP), pp. 502–511.
HCIDHM-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.
HCIDHM-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.
HCIHCD-2009-BlumK #challenge #e-commerce #lessons learnt #user interface
Evaluating E-Commerce User Interfaces: Challenges and Lessons Learned (RB, KK), pp. 653–660.
HCIHCD-2009-FerranGMM #design #learning #repository
User Centered Design of a Learning Object Repository (NF, AEGR, EM, JM), pp. 679–688.
HCIHCD-2009-NasozB #user interface
Affectively Intelligent User Interfaces for Enhanced E-Learning Applications (FN, MB), pp. 765–774.
HCIHCD-2009-ShibukawaFIN #effectiveness #using
Fundamental Studies on Effective e-Learning Using Physiology Indices (MS, MFF, YI, SPN), pp. 795–804.
HCIHCI-AUII-2009-McMullenW #assessment #design #learning
Relationship Learning Software: Design and Assessment (KAM, GHW), pp. 631–640.
HCIHCI-AUII-2009-ZarraonandiaVDA #learning #protocol
A Virtual Environment for Learning Aiport Emergency Management Protocols (TZ, MRRV, PD, IA), pp. 228–235.
HCIHCI-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.
HCIHCI-NIMT-2009-NagaiKI
A Basic Study on a Drawing-Learning Support System in the Networked Environment (TN, MK, KI), pp. 860–868.
HCIHCI-NT-2009-Wang09a #concept #design
Learn as Babies Learn: A Conceptual Model of Designing Optimum Learnability (DXW), pp. 745–751.
HCIHCI-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.
HCIHCI-VAD-2009-BuzziBL
Accessing e-Learning Systems via Screen Reader: An Example (MCB, MB, BL), pp. 21–30.
HCIHCI-VAD-2009-ChalfounF #3d #learning
Optimal Affective Conditions for Subconscious Learning in a 3D Intelligent Tutoring System (PC, CF), pp. 39–48.
HCIHCI-VAD-2009-ChenGSEJ #detection #learning
Computer-Based Learning to Improve Breast Cancer Detection Skills (YC, AGG, HJS, AE, JJ), pp. 49–57.
HCIHCI-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.
HCIHCI-VAD-2009-DogusoyC #comprehension #eye tracking #learning #process
An Innovative Way of Understanding Learning Processes: Eye Tracking (BD, ), pp. 94–100.
HCIHCI-VAD-2009-FicarraCV #evaluation #learning
Communicability for Virtual Learning: Evaluation (FVCF, MCF, PMV), pp. 68–77.
HCIHCI-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.
HCIHCI-VAD-2009-Lane #learning
Promoting Metacognition in Immersive Cultural Learning Environments (HCL), pp. 129–139.
HCIHCI-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.
HCIHCI-VAD-2009-MazzolaM #adaptation #learning #student
Supporting Learners in Adaptive Learning Environments through the Enhancement of the Student Model (LM, RM), pp. 166–175.
HCIHCI-VAD-2009-SaC #development #learning #mobile #personalisation #tool support
Supporting End-User Development of Personalized Mobile Learning Tools (MdS, LC), pp. 217–225.
HCIHCI-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.
HCIHCI-VAD-2009-TesorieroFGLP #interactive #learning
Interactive Learning Panels (RT, HF, JAG, MDL, VMRP), pp. 236–245.
HCIHCI-VAD-2009-UenoHY #education #framework #named
WebELS: A Content-Centered E-Learning Platform for Postgraduate Education in Engineering (HU, ZH, JY), pp. 246–255.
HCIHCI-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.
HCIHIMI-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.
HCIHIMI-II-2009-AyodeleZK #approach #email #machine learning #predict
Email Reply Prediction: A Machine Learning Approach (TA, SZ, RK), pp. 114–123.
HCIHIMI-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.
HCIHIMI-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.
HCIHIMI-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.
HCIHIMI-II-2009-NakamuraS #learning
Construction of Systematic Learning Support System of Business Theory and Method (YN, KS), pp. 669–678.
HCIHIMI-II-2009-NishinoH #embedded #learning #named #visualisation
Minato: Integrated Visualization Environment for Embedded Systems Learning (YN, EH), pp. 325–333.
HCIHIMI-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.
HCIHIMI-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.
HCIHIMI-II-2009-Terawaki #framework #learning
Framework for Supporting Decision Making in Learning Management System Selection (YT), pp. 699–707.
HCIHIMI-II-2009-Wang09c #adaptation #design #development #learning
The Design and Development of an Adaptive Web-Based Learning System (CW), pp. 716–725.
HCIIDGD-2009-AlsharaA #case study
The Effect of E-Learning on Business Organizations: A UAE Case Study (OKA, MKA), pp. 437–446.
HCIIDGD-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.
HCIIDGD-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.
HCIOCSC-2009-BramanVDJ #learning
Learning Computer Science Fundamentals through Virtual Environments (JB, GV, AMAD, AJ), pp. 423–431.
HCIOCSC-2009-ConlonP #distance #learning #video
A Discussion of Video Capturing to Assist in Distance Learning (MC, VP), pp. 432–441.
HCIOCSC-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.
HCIOCSC-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.
HCIOCSC-2009-Pozzi #community #learning #online #social
Evaluating the Social Dimension in Online Learning Communities (FP), pp. 498–506.
HCIOCSC-2009-PuseyM #education #heuristic #implementation #learning #wiki
Heuristics for Implementation of Wiki Technology in Higher Education Learning (PP, GM), pp. 507–514.
VISSOFTVISSOFT-2009-SensalireOT #evaluation #lessons learnt #tool support #visualisation
Evaluation of software visualization tools: Lessons learned (MS, PO, ACT), pp. 19–26.
CAiSECAiSE-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.
ICEISICEIS-DISI-2009-Mao #machine learning #online
Machine Learning in Online Advertising (JM), p. 27.
ICEISICEIS-AIDSS-2009-BombiniMBFE #framework #learning #logic programming
A Logic Programming Framework for Learning by Imitation (GB, NDM, TMAB, SF, FE), pp. 218–223.
ICEISICEIS-AIDSS-2009-YangLSKCGP #graph #learning
Graph Structure Learning for Task Ordering (YY, AL, HS, BK, CMC, RG, KP), pp. 164–169.
ICEISICEIS-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.
ICEISICEIS-J-2009-LealQ #learning #named #repository
CrimsonHex: A Service Oriented Repository of Specialised Learning Objects (JPL, RQ), pp. 102–113.
ICEISICEIS-J-2009-PenteadoM #authentication #web
A Video-Based Biometric Authentication for e-Learning Web Applications (BEP, ANM), pp. 770–779.
ICEISICEIS-J-2009-Prokhorov #categorisation #self
A Self-learning System for Object Categorization (DVP), pp. 265–274.
ICEISICEIS-J-2009-SiepermannS #automation #generative
e-Learning in Logistics Cost Accounting Automatic Generation and Marking of Exercises (MS, CS), pp. 665–676.
ICEISICEIS-SAIC-2009-CastroFSC #learning #programming
Fleshing Out Clues on Group Programming Learning (TC, HF, LS, ANdCJ), pp. 68–73.
CIKMCIKM-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.
CIKMCIKM-2009-CetintasSY #learning #query
Learning from past queries for resource selection (SC, LS, HY), pp. 1867–1870.
CIKMCIKM-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.
CIKMCIKM-2009-ChenWL #kernel #learning #novel #rank
Learning to rank with a novel kernel perceptron method (XwC, HW, XL), pp. 505–512.
CIKMCIKM-2009-GargS #classification #learning
Active learning in partially supervised classification (PG, SS), pp. 1783–1786.
CIKMCIKM-2009-HeLL #graph #learning
Graph-based transfer learning (JH, YL, RDL), pp. 937–946.
CIKMCIKM-2009-KuoCW #learning #rank
Learning to rank from Bayesian decision inference (JWK, PJC, HMW), pp. 827–836.
CIKMCIKM-2009-MeloW #learning #towards
Towards a universal wordnet by learning from combined evidence (GdM, GW), pp. 513–522.
CIKMCIKM-2009-Paranjpe #documentation #feedback #learning
Learning document aboutness from implicit user feedback and document structure (DP), pp. 365–374.
CIKMCIKM-2009-PasternackR #learning
Learning better transliterations (JP, DR), pp. 177–186.
CIKMCIKM-2009-QiCKKW #learning
Combining labeled and unlabeled data with word-class distribution learning (YQ, RC, PPK, KK, JW), pp. 1737–1740.
CIKMCIKM-2009-QuanzH #learning #scalability
Large margin transductive transfer learning (BQ, JH), pp. 1327–1336.
CIKMCIKM-2009-SunCSSWL #learning #recommendation
Learning to recommend questions based on user ratings (KS, YC, XS, YIS, XW, CYL), pp. 751–758.
CIKMCIKM-2009-SunMG09a #graph #learning #online #rank
Learning to rank graphs for online similar graph search (BS, PM, CLG), pp. 1871–1874.
CIKMCIKM-2009-SvoreB #approach #machine learning #retrieval
A machine learning approach for improved BM25 retrieval (KMS, CJCB), pp. 1811–1814.
CIKMCIKM-2009-TangL #behaviour #learning #scalability #social
Scalable learning of collective behavior based on sparse social dimensions (LT, HL), pp. 1107–1116.
CIKMCIKM-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.
CIKMCIKM-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.
CIKMCIKM-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.
CIKMCIKM-2009-YapB #learning
Experiments on pattern-based relation learning (WY, TB), pp. 1657–1660.
CIKMCIKM-2009-ZhangMCM #fuzzy #learning #ontology #semantics #uml #web
Fuzzy semantic web ontology learning from fuzzy UML model (FZ, ZMM, JC, XM), pp. 1007–1016.
CIKMCIKM-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.
CIKMCIKM-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.
CIKMCIKM-2009-ZhuWZ #learning
Label correspondence learning for part-of-speech annotation transformation (MZ, HW, JZ), pp. 1461–1464.
ECIRECIR-2009-DonmezC #learning #optimisation #rank
Active Sampling for Rank Learning via Optimizing the Area under the ROC Curve (PD, JGC), pp. 78–89.
ECIRECIR-2009-EsuliS #classification #learning #multi
Active Learning Strategies for Multi-Label Text Classification (AE, FS), pp. 102–113.
ECIRECIR-2009-GeraniCC #learning #retrieval
Investigating Learning Approaches for Blog Post Opinion Retrieval (SG, MJC, FC), pp. 313–324.
ECIRECIR-2009-LeaseAC #learning #query #rank
Regression Rank: Learning to Meet the Opportunity of Descriptive Queries (ML, JA, WBC), pp. 90–101.
ICMLICML-2009-AdamsG #learning #named #parametricity
Archipelago: nonparametric Bayesian semi-supervised learning (RPA, ZG), pp. 1–8.
ICMLICML-2009-BengioLCW #education #learning
Curriculum learning (YB, JL, RC, JW), pp. 41–48.
ICMLICML-2009-BennettBC #information retrieval #machine learning #summary #tutorial
Tutorial summary: Machine learning in IR: recent successes and new opportunities (PNB, MB, KCT), p. 17.
ICMLICML-2009-BeygelzimerDL #learning
Importance weighted active learning (AB, SD, JL), pp. 49–56.
ICMLICML-2009-BeygelzimerLZ #machine learning #reduction #summary #tutorial
Tutorial summary: Reductions in machine learning (AB, JL, BZ), p. 12.
ICMLICML-2009-BurlW #learning
Active learning for directed exploration of complex systems (MCB, EW), pp. 89–96.
ICMLICML-2009-CamposZJ #constraints #learning #network #using
Structure learning of Bayesian networks using constraints (CPdC, ZZ, QJ), pp. 113–120.
ICMLICML-2009-ChengHH #learning #ranking
Decision tree and instance-based learning for label ranking (WC, JCH, EH), pp. 161–168.
ICMLICML-2009-ChenGR #kernel #learning
Learning kernels from indefinite similarities (YC, MRG, BR), pp. 145–152.
ICMLICML-2009-ChenTLY #learning #multi
A convex formulation for learning shared structures from multiple tasks (JC, LT, JL, JY), pp. 137–144.
ICMLICML-2009-ChoS #analysis #learning #modelling
Learning dictionaries of stable autoregressive models for audio scene analysis (YC, LKS), pp. 169–176.
ICMLICML-2009-Cortes #kernel #learning #performance #question
Invited talk: Can learning kernels help performance? (CC), p. 1.
ICMLICML-2009-DaiJXYY #framework #learning #named
EigenTransfer: a unified framework for transfer learning (WD, OJ, GRX, QY, YY), pp. 193–200.
ICMLICML-2009-DasguptaL #learning #summary #tutorial
Tutorial summary: Active learning (SD, JL), p. 18.
ICMLICML-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.
ICMLICML-2009-DoLF #learning #online
Proximal regularization for online and batch learning (CBD, QVL, CSF), pp. 257–264.
ICMLICML-2009-FarhangfarGS #image #learning
Learning to segment from a few well-selected training images (AF, RG, CS), pp. 305–312.
ICMLICML-2009-FooDN #algorithm #learning #multi
A majorization-minimization algorithm for (multiple) hyperparameter learning (CSF, CBD, AYN), pp. 321–328.
ICMLICML-2009-Freund #game studies #learning #online
Invited talk: Drifting games, boosting and online learning (YF), p. 2.
ICMLICML-2009-GermainLLM #classification #learning #linear
PAC-Bayesian learning of linear classifiers (PG, AL, FL, MM), pp. 353–360.
ICMLICML-2009-GomesK #dynamic analysis #multi
Dynamic analysis of multiagent Q-learning with ε-greedy exploration (ERG, RK), pp. 369–376.
ICMLICML-2009-HazanS #algorithm #learning #performance
Efficient learning algorithms for changing environments (EH, CS), pp. 393–400.
ICMLICML-2009-HuangS #learning #linear #sequence
Learning linear dynamical systems without sequence information (TKH, JGS), pp. 425–432.
ICMLICML-2009-HuangZM #learning
Learning with structured sparsity (JH, TZ, DNM), pp. 417–424.
ICMLICML-2009-JebaraWC #graph #learning
Graph construction and b-matching for semi-supervised learning (TJ, JW, SFC), pp. 441–448.
ICMLICML-2009-JetchevT #learning #predict
Trajectory prediction: learning to map situations to robot trajectories (NJ, MT), pp. 449–456.
ICMLICML-2009-KarampatziakisK #learning #predict
Learning prediction suffix trees with Winnow (NK, DK), pp. 489–496.
ICMLICML-2009-KokD #learning #logic #markov #network
Learning Markov logic network structure via hypergraph lifting (SK, PMD), pp. 505–512.
ICMLICML-2009-KolterN09a #difference #feature model #learning
Regularization and feature selection in least-squares temporal difference learning (JZK, AYN), pp. 521–528.
ICMLICML-2009-KotlowskiS #constraints #learning
Rule learning with monotonicity constraints (WK, RS), pp. 537–544.
ICMLICML-2009-KowalskiSR #kernel #learning #multi
Multiple indefinite kernel learning with mixed norm regularization (MK, MS, LR), pp. 545–552.
ICMLICML-2009-KunegisL #graph transformation #learning #predict
Learning spectral graph transformations for link prediction (JK, AL), pp. 561–568.
ICMLICML-2009-LangfordSZ #learning #modelling
Learning nonlinear dynamic models (JL, RS, TZ), pp. 593–600.
ICMLICML-2009-LanLML #algorithm #analysis #ranking
Generalization analysis of listwise learning-to-rank algorithms (YL, TYL, ZM, HL), pp. 577–584.
ICMLICML-2009-LeeGRN #learning #network #scalability
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations (HL, RBG, RR, AYN), pp. 609–616.
ICMLICML-2009-LiangJK #exponential #learning #metric #product line
Learning from measurements in exponential families (PL, MIJ, DK), pp. 641–648.
ICMLICML-2009-LiKZ #learning #using
Semi-supervised learning using label mean (YFL, JTK, ZHZ), pp. 633–640.
ICMLICML-2009-LiYX #collaboration #generative #learning
Transfer learning for collaborative filtering via a rating-matrix generative model (BL, QY, XX), pp. 617–624.
ICMLICML-2009-LuJD #geometry #learning #metric
Geometry-aware metric learning (ZL, PJ, ISD), pp. 673–680.
ICMLICML-2009-MairalBPS #learning #online #taxonomy
Online dictionary learning for sparse coding (JM, FRB, JP, GS), pp. 689–696.
ICMLICML-2009-MaSSV #identification #learning #online #scalability
Identifying suspicious URLs: an application of large-scale online learning (JM, LKS, SS, GMV), pp. 681–688.
ICMLICML-2009-MobahiCW #learning #video
Deep learning from temporal coherence in video (HM, RC, JW), pp. 737–744.
ICMLICML-2009-NeumannMP #learning
Learning complex motions by sequencing simpler motion templates (GN, WM, JP), pp. 753–760.
ICMLICML-2009-Niv #learning #summary #tutorial
Tutorial summary: The neuroscience of reinforcement learning (YN), p. 16.
ICMLICML-2009-NowozinJ #clustering #graph #learning #linear #programming
Solution stability in linear programming relaxations: graph partitioning and unsupervised learning (SN, SJ), pp. 769–776.
ICMLICML-2009-PazisL #learning #policy
Binary action search for learning continuous-action control policies (JP, MGL), pp. 793–800.
ICMLICML-2009-PoczosASGS #exclamation #learning
Learning when to stop thinking and do something! (BP, YAY, CS, RG, NRS), pp. 825–832.
ICMLICML-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.
ICMLICML-2009-RainaMN #learning #scalability #using
Large-scale deep unsupervised learning using graphics processors (RR, AM, AYN), pp. 873–880.
ICMLICML-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.
ICMLICML-2009-RoyLW #consistency #learning #modelling #probability #visual notation
Learning structurally consistent undirected probabilistic graphical models (SR, TL, MWW), pp. 905–912.
ICMLICML-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.
ICMLICML-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.
ICMLICML-2009-SzitaL #learning #polynomial
Optimistic initialization and greediness lead to polynomial time learning in factored MDPs (IS, AL), pp. 1001–1008.
ICMLICML-2009-TaylorP #approximate #kernel #learning
Kernelized value function approximation for reinforcement learning (GT, RP), pp. 1017–1024.
ICMLICML-2009-Tillman #distributed #independence #learning
Structure learning with independent non-identically distributed data (RET), pp. 1041–1048.
ICMLICML-2009-TrespY #dependence #learning #summary #tutorial
Tutorial summary: Learning with dependencies between several response variables (VT, KY), p. 14.
ICMLICML-2009-VarmaB #kernel #learning #multi #performance
More generality in efficient multiple kernel learning (MV, BRB), pp. 1065–1072.
ICMLICML-2009-VlassisT #learning
Model-free reinforcement learning as mixture learning (NV, MT), pp. 1081–1088.
ICMLICML-2009-VolkovsZ #learning #named #ranking
BoltzRank: learning to maximize expected ranking gain (MV, RSZ), pp. 1089–1096.
ICMLICML-2009-WeinbergerDLSA #learning #multi #scalability
Feature hashing for large scale multitask learning (KQW, AD, JL, AJS, JA), pp. 1113–1120.
ICMLICML-2009-Welling
Herding dynamical weights to learn (MW), pp. 1121–1128.
ICMLICML-2009-XuWS #learning #predict
Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning (LX, MW, DS), pp. 1137–1144.
ICMLICML-2009-YangJY #learning #online
Online learning by ellipsoid method (LY, RJ, JY), pp. 1153–1160.
ICMLICML-2009-YuanH #feature model #learning #robust
Robust feature extraction via information theoretic learning (XY, BGH), pp. 1193–1200.
ICMLICML-2009-YuilleZ #composition #learning
Compositional noisy-logical learning (ALY, SZ), pp. 1209–1216.
ICMLICML-2009-YuJ #learning
Learning structural SVMs with latent variables (CNJY, TJ), pp. 1169–1176.
ICMLICML-2009-ZhangKP #learning #prototype #scalability
Prototype vector machine for large scale semi-supervised learning (KZ, JTK, BP), pp. 1233–1240.
ICMLICML-2009-ZhangSFD #learning
Learning non-redundant codebooks for classifying complex objects (WZ, AS, XF, TGD), pp. 1241–1248.
ICMLICML-2009-ZhanLLZ #learning #metric #using
Learning instance specific distances using metric propagation (DCZ, ML, YFL, ZHZ), pp. 1225–1232.
ICMLICML-2009-ZhouSL #learning #multi
Multi-instance learning by treating instances as non-I.I.D. samples (ZHZ, YYS, YFL), pp. 1249–1256.
ICMLICML-2009-ZhuangTH #kernel #learning #named #parametricity
SimpleNPKL: simple non-parametric kernel learning (JZ, IWT, SCHH), pp. 1273–1280.
KDDKDD-2009-BeygelzimerL #learning
The offset tree for learning with partial labels (AB, JL), pp. 129–138.
KDDKDD-2009-ChenCBT #learning #optimisation #random
Constrained optimization for validation-guided conditional random field learning (MC, YC, MRB, AET), pp. 189–198.
KDDKDD-2009-DonmezCS #learning
Efficiently learning the accuracy of labeling sources for selective sampling (PD, JGC, JGS), pp. 259–268.
KDDKDD-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.
KDDKDD-2009-GamaSR #algorithm #evaluation #learning
Issues in evaluation of stream learning algorithms (JG, RS, PPR), pp. 329–338.
KDDKDD-2009-GaoFSH #learning
Heterogeneous source consensus learning via decision propagation and negotiation (JG, WF, YS, JH), pp. 339–348.
KDDKDD-2009-GeXZSGW #learning #multi
Multi-focal learning and its application to customer service support (YG, HX, WZ, RKS, XG, WW), pp. 349–358.
KDDKDD-2009-GuptaBR #learning
Catching the drift: learning broad matches from clickthrough data (SG, MB, MR), pp. 1165–1174.
KDDKDD-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.
KDDKDD-2009-LiuKJ #graph #learning #monitoring
Learning dynamic temporal graphs for oil-production equipment monitoring system (YL, JRK, OJ), pp. 1225–1234.
KDDKDD-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.
KDDKDD-2009-MaSSV #detection #learning #web
Beyond blacklists: learning to detect malicious web sites from suspicious URLs (JM, LKS, SS, GMV), pp. 1245–1254.
KDDKDD-2009-RendleMNS #learning #ranking #recommendation
Learning optimal ranking with tensor factorization for tag recommendation (SR, LBM, AN, LST), pp. 727–736.
KDDKDD-2009-TangL #learning #relational #social
Relational learning via latent social dimensions (LT, HL), pp. 817–826.
KDDKDD-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.
KDDKDD-2009-WangSAL #fault #learning #network
Learning, indexing, and diagnosing network faults (TW, MS, DA, LL), pp. 857–866.
KDDKDD-2009-YangSWC #classification #effectiveness #learning #multi
Effective multi-label active learning for text classification (BY, JTS, TW, ZC), pp. 917–926.
KDDKDD-2009-YouHC #biology #learning #network
Learning patterns in the dynamics of biological networks (CHY, LBH, DJC), pp. 977–986.
KDIRKDIR-2009-CallejaFGA #learning #set
A Learning Method for Imbalanced Data Sets (JdlC, OF, JG, RMAP), pp. 307–310.
KDIRKDIR-2009-ZhouZK #collaboration #learning
The Collaborative Learning Agent (CLA) in Trident Warrior 08 Exercise (CZ, YZ, CK), pp. 323–328.
KEODKEOD-2009-Aussenac-GillesK #documentation #learning #ontology #xml
Ontology Learning by Analyzing XML Document Structure and Content (NAG, MK), pp. 159–165.
KEODKEOD-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.
KMISKMIS-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.
KMISKMIS-2009-DochevA #learning #semantics #towards #web
Towards Semantic Web Enhanced Learning (DD, GA), pp. 212–217.
KMISKMIS-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.
MLDMMLDM-2009-BouthinonSV #ambiguity #concept #learning
Concept Learning from (Very) Ambiguous Examples (DB, HS, VV), pp. 465–478.
MLDMMLDM-2009-ChanguelLB #automation #html #learning
A General Learning Method for Automatic Title Extraction from HTML Pages (SC, NL, BBM), pp. 704–718.
MLDMMLDM-2009-LeeCWL #learning
Learning with a Quadruped Chopstick Robot (WCL, JCC, SzW, KML), pp. 603–616.
MLDMMLDM-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.
MLDMMLDM-2009-SeredinKM #machine learning #order #set
Selection of Subsets of Ordered Features in Machine Learning (OS, AK, VM), pp. 16–28.
MLDMMLDM-2009-StrumbeljRK #learning
Learning Betting Tips from Users’ Bet Selections (ES, MRS, IK), pp. 678–688.
RecSysRecSys-2009-MaLK #learning #recommendation #trust
Learning to recommend with trust and distrust relationships (HM, MRL, IK), pp. 189–196.
RecSysRecSys-2009-OMahonyS #learning #recommendation
Learning to recommend helpful hotel reviews (MPO, BS), pp. 305–308.
SEKESEKE-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.
SEKESEKE-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.
SEKESEKE-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.
SEKESEKE-2009-LounisAS #approach #impact analysis #maintenance #predict
Predicting Maintainability expressed as Change Impact: A Machine-learning-based Approach (HL, MKA, HAS), pp. 122–128.
SEKESEKE-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.
SEKESEKE-2009-Ye #collaboration #education #learning #re-engineering
An Academia-Industry Collaborative Teaching and Learning Model for Software Engineering Education (HY), pp. 301–305.
SIGIRSIGIR-2009-AslamKPSY #documentation #effectiveness #performance #ranking
Document selection methodologies for efficient and effective learning-to-rank (JAA, EK, VP, SS, EY), pp. 468–475.
SIGIRSIGIR-2009-BanerjeeCR #learning #query #rank
Learning to rank for quantity consensus queries (SB, SC, GR), pp. 243–250.
SIGIRSIGIR-2009-CormackCB #learning #rank
Reciprocal rank fusion outperforms condorcet and individual rank learning methods (GVC, CLAC, SB), pp. 758–759.
SIGIRSIGIR-2009-CumminsO #framework #information retrieval #learning #proximity
Learning in a pairwise term-term proximity framework for information retrieval (RC, CO), pp. 251–258.
SIGIRSIGIR-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.
SIGIRSIGIR-2009-MaKL #learning #recommendation #social #trust
Learning to recommend with social trust ensemble (HM, IK, MRL), pp. 203–210.
SIGIRSIGIR-2009-SunQTW #learning #metric #rank #ranking #robust
Robust sparse rank learning for non-smooth ranking measures (ZS, TQ, QT, JW), pp. 259–266.
SIGIRSIGIR-2009-YangWGH #learning #query #ranking #web
Query sampling for ranking learning in web search (LY, LW, BG, XSH), pp. 754–755.
SIGIRSIGIR-2009-YilmazR #learning #rank
Deep versus shallow judgments in learning to rank (EY, SR), pp. 662–663.
RERE-2009-KnaussSS #heuristic #learning #requirements
Learning to Write Better Requirements through Heuristic Critiques (EK, KS, KS), pp. 387–388.
RERE-2009-KonradD #industrial #lessons learnt #modelling
Lessons Learned from the Use of Artifact Models in Industrial Projects (SK, HD), pp. 349–354.
REFSQREFSQ-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.
SACSAC-2009-GamaRS #algorithm #data type
Evaluating algorithms that learn from data streams (JG, PPR, RS), pp. 1496–1500.
SACSAC-2009-LeezerZ #simulation
Simulating human intuitive decisions by Q-learning (JL, YZ), pp. 2077–2081.
SACSAC-2009-LiuTS #classification #complexity #learning #using
Assessing complexity of service-oriented computing using learning classifier systems (LL, ST, HS), pp. 2170–2171.
SACSAC-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.
SACSAC-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.
SACSAC-2009-MartinsBPS #feedback #information retrieval
Implicit relevance feedback for context-aware information retrieval in UbiLearning environments (DSM, MB, AFdP, WLdS), pp. 659–663.
SACSAC-2009-RoeslerHC #case study #distance #learning #multi
A new multimedia synchronous distance learning system: the IVA study case (VR, RH, CHC), pp. 1765–1770.
SACSAC-2009-SchmitzbergerRNRP #architecture #learning
Thin client architecture in support of remote radiology learning (FFS, JER, SN, GDR, DSP), pp. 842–846.
SACSAC-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-FSEESEC-FSE-2009-BruchMM #code completion #learning
Learning from examples to improve code completion systems (MB, MM, MM), pp. 213–222.
ICSEICSE-2009-AlrajehKRU #learning #modelling #requirements
Learning operational requirements from goal models (DA, JK, AR, SU), pp. 265–275.
SPLCSPLC-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.
CGOCGO-2009-LeatherBO #automation #compilation #generative #machine learning #optimisation
Automatic Feature Generation for Machine Learning Based Optimizing Compilation (HL, EVB, MFPO), pp. 81–91.
CGOCGO-2009-MaoS #evolution #learning #predict #virtual machine
Cross-Input Learning and Discriminative Prediction in Evolvable Virtual Machines (FM, XS), pp. 92–101.
HPDCHPDC-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.
PPoPPPPoPP-2009-WangO #approach #machine learning #parallel
Mapping parallelism to multi-cores: a machine learning based approach (ZW, MFPO), pp. 75–84.
ICLPICLP-2009-Raedt #learning #logic #probability #tutorial
Probabilistic Logic Learning — A Tutorial Abstract (LDR), p. 39.
ICSTICST-2009-KoochakzadehGM #lessons learnt #metric
Test Redundancy Measurement Based on Coverage Information: Evaluations and Lessons Learned (NK, VG, FM), pp. 220–229.
ICSTSAT-2009-AtseriasFT #algorithm #bound
Clause-Learning Algorithms with Many Restarts and Bounded-Width Resolution (AA, JKF, MT), pp. 114–127.
ICSTSAT-2009-DilkinaGS #learning
Backdoors in the Context of Learning (BND, CPG, AS), pp. 73–79.
ICSTSAT-2009-HaimW #machine learning #using
Restart Strategy Selection Using Machine Learning Techniques (SH, TW), pp. 312–325.
ICSTSAT-2009-Johannsen #bound #exponential #learning #strict
An Exponential Lower Bound for Width-Restricted Clause Learning (JJ), pp. 128–140.
ICSTSAT-2009-PipatsrisawatD #policy #satisfiability
Width-Based Restart Policies for Clause-Learning Satisfiability Solvers (KP, AD), pp. 341–355.
ICSTSAT-2009-SorenssonB
Minimizing Learned Clauses (NS, AB), pp. 237–243.
TLCATLCA-2009-AschieriB #interactive
Interactive Learning-Based Realizability Interpretation for Heyting Arithmetic with EM1 (FA, SB), pp. 20–34.
ASEASE-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.
CASECASE-2008-StabelliniZ #approach #learning #network #self
Interference aware self-organization for wireless sensor networks: A reinforcement learning approach (LS, JZ), pp. 560–565.
CASECASE-2008-WeiP #implementation #industrial #learning
An implementation of iterative learning control in industrial production machines (DW, RP), pp. 472–477.
DACDAC-2008-BastaniKWC #learning #predict #set
Speedpath prediction based on learning from a small set of examples (PB, KK, LCW, EC), pp. 217–222.
DACDAC-2008-CoskunRG #learning #multi #online #using
Temperature management in multiprocessor SoCs using online learning (AKC, TSR, KCG), pp. 890–893.
DACDAC-2008-OzisikyilmazMC #design #machine learning #performance #using
Efficient system design space exploration using machine learning techniques (, GM, ANC), pp. 966–969.
DATEDATE-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.
HTHT-2008-HeoY #empirical #information management #learning
An empirical study of the learning effect of an ontology-driven information system (MH, MY), pp. 225–226.
HTHT-2008-KetterlEB #learning #social #web
Social selected learning content out of web lectures (MK, JE, JB), pp. 231–232.
HTHT-2008-LawlessHW #corpus #education #learning
Enhancing access to open corpus educational content: learning in the wild (SL, LH, VW), pp. 167–174.
SIGMODSIGMOD-2008-FisherWZ #ad hoc #automation #generative #named
LearnPADS: automatic tool generation from ad hoc data (KF, DW, KQZ), pp. 1299–1302.
VLDBVLDB-2008-NguyenNF #learning
Learning to extract form labels (HN, THN, JF), pp. 684–694.
VLDBVLDB-2008-TalukdarJMCIPG #learning #query
Learning to create data-integrating queries (PPT, MJ, MSM, KC, ZGI, FCNP, SG), pp. 785–796.
CSEETCSEET-2008-BarbosaSM #education #experience #learning #testing
An Experience on Applying Learning Mechanisms for Teaching Inspection and Software Testing (EFB, SdRSdS, JCM), pp. 189–196.
CSEETCSEET-2008-RasR #information management #learning #using
Improving Knowledge Acquisition in Capstone Projects Using Learning Spaces for Experiential Learning (ER, JR), pp. 77–84.
CSEETCSEET-2008-RyooFJ #design #education #game studies #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.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2008-Bower #learning #online
The “instructed-teacher”: a computer science online learning pedagogical pattern (MB), pp. 189–193.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2008-CerboDS #collaboration #learning
Extending moodle for collaborative learning (FDC, GD, GS), p. 324.
ITiCSEITiCSE-2008-CharltonMD #learning #performance #social
Evaluating the extent to which sociability and social presence affects learning performance (TC, LM, MD), p. 342.
ITiCSEITiCSE-2008-ChidanandanS #learning #question
Adopting pen-based technology to facilitate active learning in the classroom: is it right for you? (AC, SMS), p. 343.
ITiCSEITiCSE-2008-Goelman #collaboration #database #learning
Databases, non-majors and collaborative learning: a ternary relationships (DG), pp. 27–31.
ITiCSEITiCSE-2008-Jackova #learning #programming
Learning for mastery in an introductory programming course (JJ), p. 352.
ITiCSEITiCSE-2008-Kolikant #education #framework #learning
Computer-science education as a cultural encounter: a socio-cultural framework for articulating learning difficulties (YBDK), pp. 291–295.
ITiCSEITiCSE-2008-Kolling #ide #learning #named #object-oriented #programming #visual notation
Greenfoot: a highly graphical ide for learning object-oriented programming (MK), p. 327.
ITiCSEITiCSE-2008-MorenoICM #database #design #distance #education #learning #towards #using
Using accessible digital resources for teaching database design: towards an inclusive distance learning proposal (LM, AI, EC, PM), pp. 32–36.
ITiCSEITiCSE-2008-MurphyPK #approach #distance #education #learning #programming
A distance learning approach to teaching eXtreme programming (CM, DBP, GEK), pp. 199–203.
ITiCSEITiCSE-2008-PerezMF #learning #operating system
Cooperative learning in operating systems laboratory (JEP, JGM, IMF), p. 323.
ITiCSEITiCSE-2008-Shaban-NejadH #education #learning #towards
Web-based dynamic learning through lexical chaining: a step forward towards knowledge-driven education (ASN, VH), p. 375.
ITiCSEITiCSE-2008-SierraCF #learning
An environment for supporting active learning in courses on language processing (JLS, AMFPC, AFV), pp. 128–132.
ICSMEICSM-2008-Hou #design #framework #learning
Investigating the effects of framework design knowledge in example-based framework learning (DH), pp. 37–46.
ICSMEICSM-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.
MSRMSR-2008-Bernstein #data mining #how #mining
How to learn enough data mining to be dangerous in 60 minutes (AB), pp. 77–78.
STOCSTOC-2008-BlumLR #approach #database #learning #privacy
A learning theory approach to non-interactive database privacy (AB, KL, AR), pp. 609–618.
STOCSTOC-2008-Feldman #algorithm #learning
Evolvability from learning algorithms (VF), pp. 619–628.
STOCSTOC-2008-GopalanKK #learning
Agnostically learning decision trees (PG, ATK, ARK), pp. 527–536.
STOCSTOC-2008-KalaiMV #learning #on the
On agnostic boosting and parity learning (ATK, YM, EV), pp. 629–638.
STOCSTOC-2008-KhotS #learning #on the
On hardness of learning intersection of two halfspaces (SK, RS), pp. 345–354.
CIAACIAA-2008-GarciaPAR #automaton #finite #learning #nondeterminism #regular expression #using
Learning Regular Languages Using Nondeterministic Finite Automata (PG, MVdP, GIA, JR), pp. 92–101.
ICALPICALP-A-2008-Dachman-SoledLMSWW #encryption #learning
Optimal Cryptographic Hardness of Learning Monotone Functions (DDS, HKL, TM, RAS, AW, HW), pp. 36–47.
CHICHI-2008-CostabileALABP #challenge #exclamation #learning #mobile
Explore! possibilities and challenges of mobile learning (MFC, ADA, RL, CA, PB, TP), pp. 145–154.
CHICHI-2008-FogartyTKW #concept #image #interactive #learning #named
CueFlik: interactive concept learning in image search (JF, DST, AK, SAJW), pp. 29–38.
CHICHI-2008-Grammenos #game studies #learning
Game over: learning by dying (DG), pp. 1443–1452.
CHICHI-2008-McQuigganRL #learning
The effects of empathetic virtual characters on presence in narrative-centered learning environments (SWM, JPR, JCL), pp. 1511–1520.
CHICHI-2008-OganAJ #learning #predict
Pause, predict, and ponder: use of narrative videos to improve cultural discussion and learning (AO, VA, CJ), pp. 155–162.
CHICHI-2008-PatelFLH #development #machine learning #statistics
Investigating statistical machine learning as a tool for software development (KP, JF, JAL, BLH), pp. 667–676.
CHICHI-2008-WangM #interactive #learning
Human-Currency Interaction: learning from virtual currency use in China (YW, SDM), pp. 25–28.
ICEISICEIS-AIDSS-2008-MorgadoPR #evaluation #learning #quality
An Evaluation Instrument for Learning Object Quality and Management (EMM, FJGP, ÁBR), pp. 327–332.
ICEISICEIS-AIDSS-2008-StateCRP #algorithm #classification #learning
A New Learning Algorithm for Classification in the Reduced Space (LS, CC, IR, PV), pp. 155–160.
ICEISICEIS-HCI-2008-CarvalhoS #learning #lessons learnt #usability
The Importance of Usability Criteria on Learning Management Systems: Lessons Learned (AFPdC, JCAS), pp. 154–159.
ICEISICEIS-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.
ICEISICEIS-HCI-2008-GarciaMDS #interface #learning #visualisation
An Interface Environment for Learning Object Search and Pre-Visualisation (LSG, ROdOM, AID, MSS), pp. 240–247.
ICEISICEIS-HCI-2008-MileyRM #learning
Traditional Learning Vs. e-LEARNING — Some Results from Training Call Centre Personnel (MM, JAR, CM), pp. 299–307.
ICEISICEIS-ISAS1-2008-GullaBK #concept #ontology #using
Using Association Rules to Learn Concept Relationships in Ontologies (JAG, TB, GSK), pp. 58–65.
ICEISICEIS-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.
ICEISICEIS-J-2008-GullaBK08a #learning #ontology
Association Rules and Cosine Similarities in Ontology Relationship Learning (JAG, TB, GSK), pp. 201–212.
ICEISICEIS-SAIC-2008-CanalesP #architecture #learning #semantics #web
Learning Technology System Architecture Based on Agents and Semantic Web (ACC, RPV), pp. 127–132.
ICEISICEIS-SAIC-2008-RanW #adaptation #metric #performance #using
Develop Adaptive Workplace E-Learning Environments by Using Performance Measurement Systems (WR, MW), pp. 142–147.
CIKMCIKM-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.
CIKMCIKM-2008-DonmezC #learning #multi
Proactive learning: cost-sensitive active learning with multiple imperfect oracles (PD, JGC), pp. 619–628.
CIKMCIKM-2008-DouSYW #learning #question #ranking #web
Are click-through data adequate for learning web search rankings? (ZD, RS, XY, JRW), pp. 73–82.
CIKMCIKM-2008-HoefelE #classification #learning #sequence
Learning a two-stage SVM/CRF sequence classifier (GH, CE), pp. 271–278.
CIKMCIKM-2008-LuoZHXH #learning #multi
Transfer learning from multiple source domains via consensus regularization (PL, FZ, HX, YX, QH), pp. 103–112.
CIKMCIKM-2008-MaYKL #learning #query #semantics
Learning latent semantic relations from clickthrough data for query suggestion (HM, HY, IK, MRL), pp. 709–718.
CIKMCIKM-2008-MilneW #learning #wiki
Learning to link with wikipedia (DNM, IHW), pp. 509–518.
CIKMCIKM-2008-NiXLH #approach #learning
Group-based learning: a boosting approach (WN, JX, HL, YH), pp. 1443–1444.
CIKMCIKM-2008-WangCZL #constraints #learning #metric
Semi-supervised metric learning by maximizing constraint margin (FW, SC, CZ, TL), pp. 1457–1458.
ECIRECIR-2008-AyacheQ #corpus #learning #using #video
Video Corpus Annotation Using Active Learning (SA, GQ), pp. 187–198.
ICMLICML-2008-BarrettN #learning #multi #policy
Learning all optimal policies with multiple criteria (LB, SN), pp. 41–47.
ICMLICML-2008-BickelBLS #learning #multi
Multi-task learning for HIV therapy screening (SB, JB, TL, TS), pp. 56–63.
ICMLICML-2008-BryanS #learning
Actively learning level-sets of composite functions (BB, JGS), pp. 80–87.
ICMLICML-2008-CaruanaKY #empirical #evaluation #learning
An empirical evaluation of supervised learning in high dimensions (RC, NK, AY), pp. 96–103.
ICMLICML-2008-ChenM #learning
Learning to sportscast: a test of grounded language acquisition (DLC, RJM), pp. 128–135.
ICMLICML-2008-CoatesAN #learning #multi
Learning for control from multiple demonstrations (AC, PA, AYN), pp. 144–151.
ICMLICML-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.
ICMLICML-2008-DasguptaH #learning
Hierarchical sampling for active learning (SD, DH), pp. 208–215.
ICMLICML-2008-DekelS #learning
Learning to classify with missing and corrupted features (OD, OS), pp. 216–223.
ICMLICML-2008-DickHS #infinity #learning #semistructured data
Learning from incomplete data with infinite imputations (UD, PH, TS), pp. 232–239.
ICMLICML-2008-DiukCL #learning #object-oriented #performance #representation
An object-oriented representation for efficient reinforcement learning (CD, AC, MLL), pp. 240–247.
ICMLICML-2008-DonmezC #learning #optimisation #rank #reduction
Optimizing estimated loss reduction for active sampling in rank learning (PD, JGC), pp. 248–255.
ICMLICML-2008-DoshiPR #learning #using
Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs (FD, JP, NR), pp. 256–263.
ICMLICML-2008-DuchiSSC #learning #performance
Efficient projections onto the l1-ball for learning in high dimensions (JCD, SSS, YS, TC), pp. 272–279.
ICMLICML-2008-EpshteynVD #learning
Active reinforcement learning (AE, AV, GD), pp. 296–303.
ICMLICML-2008-FrankMP #learning
Reinforcement learning in the presence of rare events (JF, SM, DP), pp. 336–343.
ICMLICML-2008-GonenA #kernel #learning #locality #multi
Localized multiple kernel learning (MG, EA), pp. 352–359.
ICMLICML-2008-GordonGM #game studies #learning
No-regret learning in convex games (GJG, AG, CM), pp. 360–367.
ICMLICML-2008-HamL #analysis #learning
Grassmann discriminant analysis: a unifying view on subspace-based learning (JH, DDL), pp. 376–383.
ICMLICML-2008-HoiJ #kernel #learning
Active kernel learning (SCHH, RJ), pp. 400–407.
ICMLICML-2008-HuynhM #learning #logic #markov #network #parametricity
Discriminative structure and parameter learning for Markov logic networks (TNH, RJM), pp. 416–423.
ICMLICML-2008-KolterCNGD #learning #programming
Space-indexed dynamic programming: learning to follow trajectories (JZK, AC, AYN, YG, CD), pp. 488–495.
ICMLICML-2008-LanLQML #learning #rank
Query-level stability and generalization in learning to rank (YL, TYL, TQ, ZM, HL), pp. 512–519.
ICMLICML-2008-LazaricRB #learning
Transfer of samples in batch reinforcement learning (AL, MR, AB), pp. 544–551.
ICMLICML-2008-LiLW #framework #learning #self #what
Knows what it knows: a framework for self-aware learning (LL, MLL, TJW), pp. 568–575.
ICMLICML-2008-LoeffFR #approximate #learning #named
ManifoldBoost: stagewise function approximation for fully-, semi- and un-supervised learning (NL, DAF, DR), pp. 600–607.
ICMLICML-2008-MekaJCD #learning #online #rank
Rank minimization via online learning (RM, PJ, CC, ISD), pp. 656–663.
ICMLICML-2008-MeloMR #analysis #approximate #learning
An analysis of reinforcement learning with function approximation (FSM, SPM, MIR), pp. 664–671.
ICMLICML-2008-NowozinB #approach #learning
A decoupled approach to exemplar-based unsupervised learning (SN, GHB), pp. 704–711.
ICMLICML-2008-OuyangG #learning #ranking
Learning dissimilarities by ranking: from SDP to QP (HO, AGG), pp. 728–735.
ICMLICML-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.
ICMLICML-2008-PuolamakiAK #learning #query
Learning to learn implicit queries from gaze patterns (KP, AA, SK), pp. 760–767.
ICMLICML-2008-RadlinskiKJ #learning #multi #ranking
Learning diverse rankings with multi-armed bandits (FR, RK, TJ), pp. 784–791.
ICMLICML-2008-RanzatoS #documentation #learning #network
Semi-supervised learning of compact document representations with deep networks (MR, MS), pp. 792–799.
ICMLICML-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.
ICMLICML-2008-ReisingerSM #kernel #learning #online
Online kernel selection for Bayesian reinforcement learning (JR, PS, RM), pp. 816–823.
ICMLICML-2008-SakumaKW #learning #privacy
Privacy-preserving reinforcement learning (JS, SK, RNW), pp. 864–871.
ICMLICML-2008-ShiBY #learning #modelling #using
Data spectroscopy: learning mixture models using eigenspaces of convolution operators (TS, MB, BY), pp. 936–943.
ICMLICML-2008-SilverSM #learning
Sample-based learning and search with permanent and transient memories (DS, RSS, MM), pp. 968–975.
ICMLICML-2008-SindhwaniR #learning #multi
An RKHS for multi-view learning and manifold co-regularization (VS, DSR), pp. 976–983.
ICMLICML-2008-SokolovskaCY #learning #modelling #probability
The asymptotics of semi-supervised learning in discriminative probabilistic models (NS, OC, FY), pp. 984–991.
ICMLICML-2008-SuZLM #learning #network #parametricity
Discriminative parameter learning for Bayesian networks (JS, HZ, CXL, SM), pp. 1016–1023.
ICMLICML-2008-SyedBS #learning #linear #programming #using
Apprenticeship learning using linear programming (US, MHB, RES), pp. 1032–1039.
ICMLICML-2008-SzafranskiGR #kernel #learning
Composite kernel learning (MS, YG, AR), pp. 1040–1047.
ICMLICML-2008-WangYZ #adaptation #kernel #learning #multi
Adaptive p-posterior mixture-model kernels for multiple instance learning (HYW, QY, HZ), pp. 1136–1143.
ICMLICML-2008-WangZ #learning #multi #on the
On multi-view active learning and the combination with semi-supervised learning (WW, ZHZ), pp. 1152–1159.
ICMLICML-2008-WeinbergerS #distance #implementation #learning #metric #performance
Fast solvers and efficient implementations for distance metric learning (KQW, LKS), pp. 1160–1167.
ICMLICML-2008-WestonRC #learning
Deep learning via semi-supervised embedding (JW, FR, RC), pp. 1168–1175.
ICMLICML-2008-WingateS #exponential #learning #predict #product line
Efficiently learning linear-linear exponential family predictive representations of state (DW, SPS), pp. 1176–1183.
ICMLICML-2008-XiaLWZL #algorithm #approach #learning #rank
Listwise approach to learning to rank: theory and algorithm (FX, TYL, JW, WZ, HL), pp. 1192–1199.
ICMLICML-2008-YaoL #difference #learning
Preconditioned temporal difference learning (HY, ZQL), pp. 1208–1215.
ICPRICPR-2008-AlpcanB #algorithm #distributed #learning #parallel
A discrete-time parallel update algorithm for distributed learning (TA, CB), pp. 1–4.
ICPRICPR-2008-Arevalillo-HerraezFD #image #learning #metric #retrieval #similarity
Learning combined similarity measures from user data for image retrieval (MAH, FJF, JD), pp. 1–4.
ICPRICPR-2008-BasakLC #learning #summary #video
Video summarization with supervised learning (JB, VL, SC), pp. 1–4.
ICPRICPR-2008-CamposJ #constraints #learning #network #parametricity #using
Improving Bayesian Network parameter learning using constraints (CPdC, QJ), pp. 1–4.
ICPRICPR-2008-ChangLAH08a #collaboration #image #learning #using
Using collaborative learning for image contrast enhancement (YC, DJL, JKA, YH), pp. 1–4.
ICPRICPR-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.
ICPRICPR-2008-DuinP #difference #learning #matrix #on the
On refining dissimilarity matrices for an improved NN learning (RPWD, EP), pp. 1–4.
ICPRICPR-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.
ICPRICPR-2008-FerilliBBE #comprehension #documentation #incremental #layout #machine learning
Incremental machine learning techniques for document layout understanding (SF, MB, TMAB, FE), pp. 1–4.
ICPRICPR-2008-FuR #learning #multi #performance
Fast multiple instance learning via L1, 2 logistic regression (ZF, ARK), pp. 1–4.
ICPRICPR-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.
ICPRICPR-2008-GhanemVW #learning #relational
Learning in imbalanced relational data (ASG, SV, GAWW), pp. 1–4.
ICPRICPR-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.
ICPRICPR-2008-GuiHY #consistency #learning
An improvement on learning with local and global consistency (JG, DSH, ZY), pp. 1–4.
ICPRICPR-2008-HuAS08a #learning #using
Learning motion patterns in crowded scenes using motion flow field (MH, SA, MS), pp. 1–5.
ICPRICPR-2008-HuWJHG #detection #learning #online
Human reappearance detection based on on-line learning (LH, YW, SJ, QH, WG), pp. 1–4.
ICPRICPR-2008-JinLH #learning #prototype
Prototype learning with margin-based conditional log-likelihood loss (XJ, CLL, XH), pp. 1–4.
ICPRICPR-2008-JradGB #constraints #learning #multi #performance
Supervised learning rule selection for multiclass decision with performance constraints (NJ, EGM, PB), pp. 1–4.
ICPRICPR-2008-KarnickMP #approach #classification #concept #incremental #learning #multi #using
Incremental learning in non-stationary environments with concept drift using a multiple classifier based approach (MTK, MM, RP), pp. 1–4.
ICPRICPR-2008-LiaoJ #learning #network #parametricity #semistructured data
Exploiting qualitative domain knowledge for learning Bayesian network parameters with incomplete data (WL, QJ), pp. 1–4.
ICPRICPR-2008-LiaoL #kernel #learning #novel #robust
A novel robust kernel for appearance-based learning (CTL, SHL), pp. 1–4.
ICPRICPR-2008-LiDM #feature model #learning #locality #using
Localized feature selection for Gaussian mixtures using variational learning (YL, MD, YM), pp. 1–4.
ICPRICPR-2008-LiuWBM #kernel #learning #linear
Semi-supervised learning by locally linear embedding in kernel space (RL, YW, TB, DM), pp. 1–4.
ICPRICPR-2008-LiuZDY #detection #learning #sequence #video
Video attention: Learning to detect a salient object sequence (TL, NZ, WD, ZY), pp. 1–4.
ICPRICPR-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.
ICPRICPR-2008-NaYKC #learning
Relevant pattern selection for subspace learning (JHN, SMY, MK, JYC), pp. 1–4.
ICPRICPR-2008-NguyenBP #approach #learning #set
A supervised learning approach for imbalanced data sets (GHN, AB, SLP), pp. 1–4.
ICPRICPR-2008-NingXZGH #detection #difference #learning
Temporal difference learning to detect unsafe system states (HN, WX, YZ, YG, TSH), pp. 1–4.
ICPRICPR-2008-PerezO #invariant #learning #programming #search-based
Learning invariant region descriptor operators with genetic programming and the F-measure (CBP, GO), pp. 1–4.
ICPRICPR-2008-QuQY #learning
Learning a discriminative sparse tri-value transform (ZQ, GQ, PCY), pp. 1–4.
ICPRICPR-2008-SudoOTKA #detection #incremental #learning #online
Online anomal movement detection based on unsupervised incremental learning (KS, TO, HT, HK, KA), pp. 1–4.
ICPRICPR-2008-TorselloD #generative #graph #learning
Supervised learning of a generative model for edge-weighted graphs (AT, DLD), pp. 1–4.
ICPRICPR-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.
ICPRICPR-2008-WangZ #collaboration #distributed #learning
Collaborative learning by boosting in distributed environments (SW, CZ), pp. 1–4.
ICPRICPR-2008-WuF #3d #classification #learning #multi #using
Multiple view based 3D object classification using ensemble learning of local subspaces (JW, KF), pp. 1–4.
ICPRICPR-2008-ZhaoGLJ #learning #modelling
Spatio-temporal patches for night background modeling by subspace learning (YZ, HG, LL, YJ), pp. 1–4.
ICPRICPR-2008-Zhu #documentation #image #learning
Augment document image binarization by learning (YZ), pp. 1–4.
ICPRICPR-2008-ZhuBQ #lazy evaluation #learning
Bagging very weak learners with lazy local learning (XZ, CB, WQ), pp. 1–4.
KDDKDD-2008-ChakrabartiKSB #learning #ranking
Structured learning for non-smooth ranking losses (SC, RK, US, CB), pp. 88–96.
KDDKDD-2008-ChengT #learning
Semi-supervised learning with data calibration for long-term time series forecasting (HC, PNT), pp. 133–141.
KDDKDD-2008-ChenJCLWY #classification #kernel #learning
Learning subspace kernels for classification (JC, SJ, BC, QL, MW, JY), pp. 106–114.
KDDKDD-2008-CuiDSAJ #learning
Learning methods for lung tumor markerless gating in image-guided radiotherapy (YC, JGD, GCS, BMA, SBJ), pp. 902–910.
KDDKDD-2008-DavisD #learning #metric #problem
Structured metric learning for high dimensional problems (JVD, ISD), pp. 195–203.
KDDKDD-2008-ElkanN #classification #learning
Learning classifiers from only positive and unlabeled data (CE, KN), pp. 213–220.
KDDKDD-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.
KDDKDD-2008-LingD #learning #query
Active learning with direct query construction (CXL, JD), pp. 480–487.
KDDKDD-2008-LingDXYY #learning
Spectral domain-transfer learning (XL, WD, GRX, QY, YY), pp. 488–496.
KDDKDD-2008-MadaniH #learning #on the
On updates that constrain the features’ connections during learning (OM, JH), pp. 515–523.
KDDKDD-2008-SinghG #learning #matrix #relational
Relational learning via collective matrix factorization (APS, GJG), pp. 650–658.
KDDKDD-2008-SunJY #classification #learning #multi
Hypergraph spectral learning for multi-label classification (LS, SJ, JY), pp. 668–676.
KDDKDD-2008-WuLCC #learning #symmetry
Asymmetric support vector machines: low false-positive learning under the user tolerance (SHW, KPL, CMC, MSC), pp. 749–757.
KDDKDD-2008-WuXC #clustering #incremental #learning #named
SAIL: summation-based incremental learning for information-theoretic clustering (JW, HX, JC), pp. 740–748.
KDDKDD-2008-ZhangSPN #documentation #learning #multi #topic #web
Learning from multi-topic web documents for contextual advertisement (YZ, ACS, JCP, MN), pp. 1051–1059.
KRKR-2008-Rintanen #graph
Planning Graphs and Propositional Clause-Learning (JR), pp. 535–543.
RecSysRecSys-2008-DrachslerHK #learning #navigation
Navigation support for learners in informal learning environments (HD, HGKH, RK), pp. 303–306.
SEKESEKE-2008-MurphyKHW #machine learning #testing
Properties of Machine Learning Applications for Use in Metamorphic Testing (CM, GEK, LH, LW), pp. 867–872.
SEKESEKE-2008-Zhang #machine learning #re-engineering #research
Machine Learning and Value-based Software Engineering: a Research Agenda (DZ), pp. 285–290.
SEKESEKE-2008-ZhongYAF #using
Ontology-learning Supported Sematic Search Using Cooperative Agents (CZ, Z(Y, MA, BHF), pp. 123–128.
SIGIRSIGIR-2008-AminiTG #algorithm #learning #ranking
A boosting algorithm for learning bipartite ranking functions with partially labeled data (MRA, TVT, CG), pp. 99–106.
SIGIRSIGIR-2008-ChenJYW #clustering #debugging #information retrieval #learning
Information retrieval on bug locations by learning co-located bug report clusters (IXC, HJ, CZY, PJW), pp. 801–802.
SIGIRSIGIR-2008-DruckMM #learning #using
Learning from labeled features using generalized expectation criteria (GD, GSM, AM), pp. 595–602.
SIGIRSIGIR-2008-DuhK #learning #rank
Learning to rank with partially-labeled data (KD, KK), pp. 251–258.
SIGIRSIGIR-2008-GuiverS #learning #process #rank
Learning to rank with SoftRank and Gaussian processes (JG, ES), pp. 259–266.
SIGIRSIGIR-2008-HarpaleY #collaboration #learning #personalisation
Personalized active learning for collaborative filtering (AH, YY), pp. 91–98.
SIGIRSIGIR-2008-LeeKJ #algorithm #constraints #learning
Fixed-threshold SMO for Joint Constraint Learning Algorithm of Structural SVM (CL, HK, MGJ), pp. 829–830.
SIGIRSIGIR-2008-LiWA #graph #learning #query
Learning query intent from regularized click graphs (XL, YYW, AA), pp. 339–346.
SIGIRSIGIR-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.
SIGIRSIGIR-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.
SIGIRSIGIR-2008-VelosoAGM #learning #rank #using
Learning to rank at query-time using association rules (AV, HMdA, MAG, WMJ), pp. 267–274.
SIGIRSIGIR-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.
SIGIRSIGIR-2008-XuLLLM #evaluation #learning #metric #optimisation #rank
Directly optimizing evaluation measures in learning to rank (JX, TYL, ML, HL, WYM), pp. 107–114.
SIGIRSIGIR-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.
SIGIRSIGIR-2008-ZhangL #learning #multi
Learning with support vector machines for query-by-multiple-examples (DZ, WSL), pp. 835–836.
SIGIRSIGIR-2008-ZhouXZY #learning #rank
Learning to rank with ties (KZ, GRX, HZ, YY), pp. 275–282.
OOPSLAOOPSLA-2008-SimpkinsBIM #adaptation #learning #programming language #towards
Towards adaptive programming: integrating reinforcement learning into a programming language (CS, SB, CLIJ, MM), pp. 603–614.
RERE-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.
RERE-2008-RegevGW #approach #education #learning #requirements
Requirements Engineering Education in the 21st Century, An Experiential Learning Approach (GR, DCG, AW), pp. 85–94.
RERE-2008-SimAA #experience #requirements #what
Marginal Notes on Amethodical Requirements Engineering: What Experts Learned from Experience (SES, TAA, BAA), pp. 105–114.
SACSAC-2008-CarvalhoAZ #health #learning #process
Learning activities on health care supported by common sense knowledge (AFPdC, JCAS, SZM), pp. 1385–1389.
SACSAC-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.
SACSAC-2008-MartinsSBPS #information retrieval #learning #ubiquitous
Context-aware information retrieval on a ubiquitous medical learning environment (DSM, LHZS, MB, AFdP, WLdS), pp. 2348–2349.
SACSAC-2008-StrapparavaM #identification #learning
Learning to identify emotions in text (CS, RM), pp. 1556–1560.
SACSAC-2008-SuKZG #classification #collaboration #machine learning #using
Imputation-boosted collaborative filtering using machine learning classifiers (XS, TMK, XZ, RG), pp. 949–950.
SACSAC-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.
SACSAC-2008-TaghipourK #hybrid #recommendation #web
A hybrid web recommender system based on Q-learning (NT, AAK), pp. 1164–1168.
ATEMATEM-J-2006-DubeyJA #context-free grammar #learning #set
Learning context-free grammar rules from a set of program (AD, PJ, SKA), pp. 223–240.
ASPLOSASPLOS-2008-LuPSZ #concurrent #debugging #learning
Learning from mistakes: a comprehensive study on real world concurrency bug characteristics (SL, SP, ES, YZ), pp. 329–339.
ISSTAISSTA-2008-SankaranarayananCIG #learning
Dynamic inference of likely data preconditions over predicates by tree learning (SS, SC, FI, AG), pp. 295–306.
ICSTSAT-2008-StachniakB #learning #satisfiability
Speeding-Up Non-clausal Local Search for Propositional Satisfiability with Clause Learning (ZS, AB), pp. 257–270.
DATEDATE-2007-Huang #learning
Dynamic learning based scan chain diagnosis (YH0), pp. 510–515.
HTHT-2007-BrownFB #learning
Real users, real results: examining the limitations of learning styles within AEH (EJB, TF, TJB), pp. 57–66.
HTHT-2007-FigueiraL #interactive #learning #using #visualisation
Interaction visualization in web-based learning using igraphs (ÁRF, JBL), pp. 45–46.
HTHT-2007-GodboleJMR #concept #interactive #learning #towards
Toward interactive learning by concept ordering (SG, SJ, SM, GR), pp. 149–150.
HTHT-2007-LeblancA #learning #using
Using forum in an organizational learning context (AL, MHA), pp. 41–42.
ICDARICDAR-2007-ChenLJ #learning #pseudo #recognition
Learning Handwritten Digit Recognition by the Max-Min Posterior Pseudo-Probabilities Method (XC, XL, YJ), pp. 342–346.
ICDARICDAR-2007-Dengel #classification #documentation #learning
Learning of Pattern-Based Rules for Document Classification (AD), pp. 123–127.
ICDARICDAR-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.
ICDARICDAR-2007-YeVRSL #learning
Learning to Group Text Lines and Regions in Freeform Handwritten Notes (MY, PAV, SR, HS, CL), pp. 28–32.
CSEETCSEET-2007-Armarego #learning
Learning from Reflection: Practitioners as Adult Learners (JA), pp. 55–63.
CSEETCSEET-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.
CSEETCSEET-2007-KanerP #education #learning #testing
Practice and Transfer of Learning in the Teaching of Software Testing (CK, SP), pp. 157–166.
CSEETCSEET-2007-KrogstieB #collaboration #learning #re-engineering #student
Cross-Community Collaboration and Learning in Customer-Driven Software Engineering Student Projects (BRK, BB), pp. 336–343.
CSEETCSEET-2007-PortK #case study #experience #learning #re-engineering
Laptop Enabled Active Learning in the Software Engineering Classroom: An Experience Report (DP, RK), pp. 262–274.
CSEETCSEET-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.
CSEETCSEET-2007-Zeid #lessons learnt #re-engineering
Lessons Learned from Establishing a Software Engineering Academic Programme in Developing Countries (AZ), pp. 11–18.
ITiCSEITiCSE-2007-AlstesL #learning #named #network #online #programming
VERKKOKE: learning routing and network programming online (AA, JL), pp. 91–95.
ITiCSEITiCSE-2007-AmelungPR #component #named
eduComponents: a component-based e-learning environment (MA, MP, DFR), p. 352.
ITiCSEITiCSE-2007-Arnold #interactive #learning #logic
Introducing propositional logic and queueing theory with the infotraffic interactive learning environments (RA), p. 356.
ITiCSEITiCSE-2007-BagleyC #collaboration #java #learning #programming
Collaboration and the importance for novices in learning java computer programming (CAB, CCC), pp. 211–215.
ITiCSEITiCSE-2007-BarnesRPCG #game studies #learning #named
Game2Learn: building CS1 learning games for retention (TB, HR, EP, AC, AG), pp. 121–125.
ITiCSEITiCSE-2007-BuenoAC #adaptation #education #student
Assisting lecturers to adapt e-learning content for deaf students (FJB, MGA, JRFdC), p. 335.
ITiCSEITiCSE-2007-BuenoCGB #adaptation #student
E-learning content adaptation for deaf students (FJB, JRFdC, SG, RB), pp. 271–275.
ITiCSEITiCSE-2007-CassenSALN #generative #interactive #learning #visual notation
A visual learning engine for interactive generation ofinstructional materials (TC, KRS, JA, DL, AN), p. 319.
ITiCSEITiCSE-2007-CukiermanT #learning
Learning strategies sessions within the classroom in computing science university courses (DC, DMT), p. 341.
ITiCSEITiCSE-2007-GalpinSC #learning #student
Learning styles and personality types of computer science students at a South African university (VCG, IDS, PyC), pp. 201–205.
ITiCSEITiCSE-2007-HonigP #experience #learning #outsourcing #re-engineering
A classroom outsourcing experience for software engineering learning (WLH, TP), pp. 181–185.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2007-LeidlR #how #learning #question
How will future learning work in the third dimension? (ML, GR), p. 329.
ITiCSEITiCSE-2007-OliverGMA #learning #using
Using disruptive technology for explorative learning (IO, KG, AM, CA), pp. 96–100.
ITiCSEITiCSE-2007-Sanchez-TorrubiaTC #algorithm #graph #interactive #learning #tool support
New interactive tools for graph algorithms active learning (MGST, CTB, JC), p. 337.
TACASTACAS-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.
TACASTACAS-2007-Cleaveland #lessons learnt
THERE AND BACK AGAIN: Lessons Learned on the Way to the Market (RC), p. 1.
ICSMEICSM-2007-CorboGP #learning #source code
Smart Formatter: Learning Coding Style from Existing Source Code (FC, CDG, MDP), pp. 525–526.
WCREWCRE-2007-Kienle #component #lessons learnt #reverse engineering #tool support
Building Reverse Engineering Tools with Software Components: Ten Lessons Learned (HMK), pp. 289–292.
STOCSTOC-2007-GuhaM #algorithm #approximate #learning #problem
Approximation algorithms for budgeted learning problems (SG, KM), pp. 104–113.
IFMIFM-2007-OostdijkRTVW #encryption #learning #protocol #testing #verification
Integrating Verification, Testing, and Learning for Cryptographic Protocols (MO, VR, JT, RGdV, TACW), pp. 538–557.
CHICHI-2007-CockburnKAZ #interface #learning
Hard lessons: effort-inducing interfaces benefit spatial learning (AC, POK, JA, SZ), pp. 1571–1580.
CHICHI-2007-GrossmanDB #learning #online
Strategies for accelerating on-line learning of hotkeys (TG, PD, RB), pp. 1591–1600.
CHICHI-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.
CHICHI-2007-KelleherPK #motivation #programming #women
Storytelling alice motivates middle school girls to learn computer programming (CK, RFP, SBK), pp. 1455–1464.
CHICHI-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.
HCIHCI-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.
HCIHCI-AS-2007-ChenL #assessment #usability
Usability Assessment of an E-Learning Courseware for Basic Cataloging (XSC, TL), pp. 198–207.
HCIHCI-AS-2007-ChoK #collaboration #contest #learning
Suppressing Competition in a Computer-Supported Collaborative Learning System (KC, BK), pp. 208–214.
HCIHCI-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.
HCIHCI-AS-2007-KimJCHH #learning
The Effect of Tangible Pedagogical Agents on Children’s Interest and Learning (JhK, DhJ, HSC, JYH, KHH), pp. 270–277.
HCIHCI-AS-2007-LiuKL #approach #learning
Breaking the Traditional E-Learning Mould: Support for the Learning Preference Approach (FL, JK, LL), pp. 294–301.
HCIHCI-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.
HCIHCI-AS-2007-SaC07a #detection #learning #ubiquitous
Detecting Learning Difficulties on Ubiquitous Scenarios (MdS, LC), pp. 235–244.
HCIHCI-AS-2007-SanchezSS #game studies #learning #mobile
Mobile Game-Based Methodology for Science Learning (JS, AS, MS), pp. 322–331.
HCIHCI-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.
HCIHCI-AS-2007-ThengW #learning #usability
Perceived Usefulness and Usability of Weblogs for Collaborating Learning (YLT, ELYW), pp. 361–370.
HCIHCI-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.
HCIHCI-AS-2007-YuC #collaboration #learning #process
Creating Computer Supported Collaborative Learning Activities with IMS LD (DY, XC), pp. 391–400.
HCIHCI-MIE-2007-FabriEM #learning
Emotionally Expressive Avatars for Chatting, Learning and Therapeutic Intervention (MF, SYAE, DJM), pp. 275–285.
HCIHCI-MIE-2007-SerbanTM #behaviour #interface #learning #predict
A Learning Interface Agent for User Behavior Prediction (GS, AT, GSM), pp. 508–517.
HCIHCI-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.
HCIHCI-MIE-2007-ZhuL #case study #recognition #speech
Study on Speech Emotion Recognition System in E-Learning (AZ, QL), pp. 544–552.
HCIHIMI-IIE-2007-AlsharaI #integration #learning #using
Business Integration Using the Interdisciplinary Project Based Learning Model (IPBL) (OKA, MI), pp. 823–833.
HCIHIMI-IIE-2007-AnseT #evaluation
Evaluation Method of e-Learning Materials by α-Wave and β-Wave of EEG (MA, TT), pp. 252–259.
HCIHIMI-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.
HCIHIMI-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.
HCIHIMI-IIE-2007-DavcevAIK #human-computer #image
HCI for m-Learning in Image Processing by Handhelds (DD, MA, DI, AK), pp. 299–308.
HCIHIMI-IIE-2007-HorinouchiWAT #case study #effectiveness
A Study of an Effective Rehearsal Method in e-Learning (TH, SW, MA, TT), pp. 328–336.
HCIHIMI-IIE-2007-IbrahimA #interactive #learning
Impact of Interactive Learning on Knowledge Retention (MI, OAS), pp. 347–355.
HCIHIMI-IIE-2007-JeongL #interactive #learning #ubiquitous
Context Aware Human Computer Interaction for Ubiquitous Learning (CJ, EL), pp. 364–373.
HCIHIMI-IIE-2007-TsengLH #learning #mobile
A Mobile Environment for Chinese Language Learning (CCT, CHL, WLH), pp. 485–489.
HCIHIMI-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.
HCIHIMI-MTT-2007-MullerKDCB #human-computer #machine learning
Machine Learning and Applications for Brain-Computer Interfacing (KRM, MK, GD, GC, BB), pp. 705–714.
HCIOCSC-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.
HCIOCSC-2007-ChoC #collaboration #learning #self
Self-Awareness in a Computer Supported Collaborative Learning Environment (KC, MHC), pp. 284–291.
ICEISICEIS-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.
ICEISICEIS-AIDSS-2007-PessiotTUAG #collaboration #learning #rank
Learning to Rank for Collaborative Filtering (JFP, TVT, NU, MRA, PG), pp. 145–151.
ICEISICEIS-AIDSS-2007-RamabadranG #approach #flexibility #learning
Intelligent E-Learning Systems — An Intelligent Approach to Flexible Learning Methodologies (SR, VG), pp. 107–112.
ICEISICEIS-AIDSS-2007-YingboJJ #learning #predict #process #using #workflow
Using Decision Tree Learning to Predict Workflow Activity Time Consumption (YL, JW, JS), pp. 69–75.
ICEISICEIS-EIS-2007-Rodriguez #collaboration #coordination #education #learning #modelling #process
A Modeling Language for Collaborative Learning Educational Units — Supporting the Coordination of Collaborative Activities (MCR), pp. 334–339.
ICEISICEIS-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.
ICEISICEIS-J-2007-LuciaFPT07a #collaboration #distributed #learning
A Service Oriented Collaborative Distributed Learning Object Management System (ADL, RF, IP, GT), pp. 341–354.
ICEISICEIS-SAIC-2007-LuciaFPT #collaboration #distributed #learning #named
CD-LOMAS: A Collaborative Distributed Learning Object Management System (ADL, RF, IP, GT), pp. 34–44.
ICEISICEIS-SAIC-2007-MorgadoRP #evaluation #learning
Key Issues for Learning Objects Evaluation (EMM, ÁBR, FJGP), pp. 149–154.
ICEISICEIS-SAIC-2007-PetrieMKLZ #challenge #lessons learnt
SWS Challenge — Status, Perspectives, Lessons Learned So Far (CJP, TMS, UK, HL, MZ), pp. 447–452.
CIKMCIKM-2007-ErtekinHBG #classification #learning
Learning on the border: active learning in imbalanced data classification (SE, JH, LB, CLG), pp. 127–136.
CIKMCIKM-2007-LiuTZ #learning #network
Ensembling Bayesian network structure learning on limited data (FL, FT, QZ), pp. 927–930.
CIKMCIKM-2007-OuyangLL #learning #summary #topic
Developing learning strategies for topic-based summarization (OY, SL, WL), pp. 79–86.
CIKMCIKM-2007-Pereira #learning
Learning to join everything (FP0), pp. 9–10.
CIKMCIKM-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.
CIKMCIKM-2007-WangJZZ #learning #summary #web
Learning query-biased web page summarization (CW, FJ, LZ, HJZ), pp. 555–562.
ECIRECIR-2007-DavyL #categorisation #learning #query
Active Learning with History-Based Query Selection for Text Categorisation (MD, SL), pp. 695–698.
ECIRECIR-2007-Gori #learning
Learning in Hyperlinked Environments (MG), p. 3.
ECIRECIR-2007-Monz #learning #query
Model Tree Learning for Query Term Weighting in Question Answering (CM), pp. 589–596.
ECIRECIR-2007-MoreauCS #automation #machine learning #query #using
Automatic Morphological Query Expansion Using Analogy-Based Machine Learning (FM, VC, PS), pp. 222–233.
ECIRECIR-2007-XuAZ #feedback #learning
Incorporating Diversity and Density in Active Learning for Relevance Feedback (ZX, RA, YZ), pp. 246–257.
ECIRECIR-2007-YeungBCK #approach #documentation #learning
A Bayesian Approach for Learning Document Type Relevance (PCKY, SB, CLAC, MK), pp. 753–756.
ICMLICML-2007-AgarwalC #graph #learning #random #rank
Learning random walks to rank nodes in graphs (AA, SC), pp. 9–16.
ICMLICML-2007-AndoZ #generative #learning
Two-view feature generation model for semi-supervised learning (RKA, TZ), pp. 25–32.
ICMLICML-2007-Azran #algorithm #learning #markov #multi #random
The rendezvous algorithm: multiclass semi-supervised learning with Markov random walks (AA), pp. 49–56.
ICMLICML-2007-Bar-HillelW #distance #learning #similarity
Learning distance function by coding similarity (ABH, DW), pp. 65–72.
ICMLICML-2007-BickelBS #learning
Discriminative learning for differing training and test distributions (SB, MB, TS), pp. 81–88.
ICMLICML-2007-BunescuM #learning #multi
Multiple instance learning for sparse positive bags (RCB, RJM), pp. 105–112.
ICMLICML-2007-CaoQLTL #approach #learning #rank
Learning to rank: from pairwise approach to listwise approach (ZC, TQ, TYL, MFT, HL), pp. 129–136.
ICMLICML-2007-ChengV #image #learning
Learning to compress images and videos (LC, SVNV), pp. 161–168.
ICMLICML-2007-DaiYXY #learning
Boosting for transfer learning (WD, QY, GRX, YY), pp. 193–200.
ICMLICML-2007-DavisKJSD #learning #metric
Information-theoretic metric learning (JVD, BK, PJ, SS, ISD), pp. 209–216.
ICMLICML-2007-DollarRB #algorithm #analysis #learning
Non-isometric manifold learning: analysis and an algorithm (PD, VR, SJB), pp. 241–248.
ICMLICML-2007-Hanneke #bound #complexity #learning
A bound on the label complexity of agnostic active learning (SH), pp. 353–360.
ICMLICML-2007-HoiJL #constraints #kernel #learning #matrix #parametricity
Learning nonparametric kernel matrices from pairwise constraints (SCHH, RJ, MRL), pp. 361–368.
ICMLICML-2007-HulseKN #learning
Experimental perspectives on learning from imbalanced data (JVH, TMK, AN), pp. 935–942.
ICMLICML-2007-Jaeger #learning #network #parametricity #relational
Parameter learning for relational Bayesian networks (MJ), pp. 369–376.
ICMLICML-2007-KimP #learning #recursion
A recursive method for discriminative mixture learning (MK, VP), pp. 409–416.
ICMLICML-2007-KrauseG #approach #learning #process
Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach (AK, CG), pp. 449–456.
ICMLICML-2007-KropotovV #learning #on the
On one method of non-diagonal regularization in sparse Bayesian learning (DK, DV), pp. 457–464.
ICMLICML-2007-LeeCVK #learning #multi
Learning a meta-level prior for feature relevance from multiple related tasks (SIL, VC, DV, DK), pp. 489–496.
ICMLICML-2007-LiLL #learning #scalability
Large-scale RLSC learning without agony (WL, KHL, KSL), pp. 529–536.
ICMLICML-2007-LiYW #distance #framework #learning #metric #reduction
A transductive framework of distance metric learning by spectral dimensionality reduction (FL, JY, JW), pp. 513–520.
ICMLICML-2007-Mahadevan #3d #adaptation #learning #multi #using
Adaptive mesh compression in 3D computer graphics using multiscale manifold learning (SM), pp. 585–592.
ICMLICML-2007-MannM #learning #robust #scalability
Simple, robust, scalable semi-supervised learning via expectation regularization (GSM, AM), pp. 593–600.
ICMLICML-2007-MihalkovaM #bottom-up #learning #logic #markov #network
Bottom-up learning of Markov logic network structure (LM, RJM), pp. 625–632.
ICMLICML-2007-MoschittiZ #effectiveness #kernel #learning #performance #relational
Fast and effective kernels for relational learning from texts (AM, FMZ), pp. 649–656.
ICMLICML-2007-NiCD #learning #multi #process
Multi-task learning for sequential data via iHMMs and the nested Dirichlet process (KN, LC, DBD), pp. 689–696.
ICMLICML-2007-OsentoskiM #learning
Learning state-action basis functions for hierarchical MDPs (SO, SM), pp. 705–712.
ICMLICML-2007-ParkerFT #learning #performance #query #retrieval
Learning for efficient retrieval of structured data with noisy queries (CP, AF, PT), pp. 729–736.
ICMLICML-2007-PetersS #learning
Reinforcement learning by reward-weighted regression for operational space control (JP, SS), pp. 745–750.
ICMLICML-2007-PhuaF #approximate #learning #linear
Tracking value function dynamics to improve reinforcement learning with piecewise linear function approximation (CWP, RF), pp. 751–758.
ICMLICML-2007-RainaBLPN #learning #self
Self-taught learning: transfer learning from unlabeled data (RR, AB, HL, BP, AYN), pp. 759–766.
ICMLICML-2007-RakotomamonjyBCG #kernel #learning #multi #performance
More efficiency in multiple kernel learning (AR, FRB, SC, YG), pp. 775–782.
ICMLICML-2007-SternHG #game studies #learning
Learning to solve game trees (DHS, RH, TG), pp. 839–846.
ICMLICML-2007-SunJSF #algorithm #kernel #learning
A kernel-based causal learning algorithm (XS, DJ, BS, KF), pp. 855–862.
ICMLICML-2007-TaylorS #learning
Cross-domain transfer for reinforcement learning (MET, PS), pp. 879–886.
ICMLICML-2007-WachmanK #kernel #learning #order
Learning from interpretations: a rooted kernel for ordered hypergraphs (GW, RK), pp. 943–950.
ICMLICML-2007-WangYF #difference #learning #on the
On learning with dissimilarity functions (LW, CY, JF), pp. 991–998.
ICMLICML-2007-WangZQ #learning #metric #towards
Dirichlet aggregation: unsupervised learning towards an optimal metric for proportional data (HYW, HZ, HQ), pp. 959–966.
ICMLICML-2007-WilsonFRT #approach #learning #multi
Multi-task reinforcement learning: a hierarchical Bayesian approach (AW, AF, SR, PT), pp. 1015–1022.
ICMLICML-2007-WoznicaKH #learning
Learning to combine distances for complex representations (AW, AK, MH), pp. 1031–1038.
ICMLICML-2007-WuYYS #learning
Local learning projections (MW, KY, SY, BS), pp. 1039–1046.
ICMLICML-2007-XueDC #flexibility #learning #matrix #multi #process
The matrix stick-breaking process for flexible multi-task learning (YX, DBD, LC), pp. 1063–1070.
ICMLICML-2007-XuF #learning #linear #on the #ranking
On learning linear ranking functions for beam search (YX, AF), pp. 1047–1054.
ICMLICML-2007-YeCJ #kernel #learning #parametricity #programming
Discriminant kernel and regularization parameter learning via semidefinite programming (JY, JC, SJ), pp. 1095–1102.
ICMLICML-2007-YuTY #learning #multi #robust
Robust multi-task learning with t-processes (SY, VT, KY), pp. 1103–1110.
ICMLICML-2007-ZhangAV #learning #multi #random
Conditional random fields for multi-agent reinforcement learning (XZ, DA, SVNV), pp. 1143–1150.
ICMLICML-2007-ZhaoL #feature model #learning
Spectral feature selection for supervised and unsupervised learning (ZZ, HL), pp. 1151–1157.
ICMLICML-2007-ZhouB #clustering #learning #multi
Spectral clustering and transductive learning with multiple views (DZ, CJCB), pp. 1159–1166.
ICMLICML-2007-ZhouX #learning #multi #on the
On the relation between multi-instance learning and semi-supervised learning (ZHZ, JMX), pp. 1167–1174.
ICMLICML-2007-ZienO #kernel #learning #multi
Multiclass multiple kernel learning (AZ, CSO), pp. 1191–1198.
KDDKDD-2007-ChenZYL #adaptation #clustering #distance #learning #metric
Nonlinear adaptive distance metric learning for clustering (JC, ZZ, JY, HL), pp. 123–132.
KDDKDD-2007-DeodharG #clustering #framework #learning
A framework for simultaneous co-clustering and learning from complex data (MD, JG), pp. 250–259.
KDDKDD-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.
KDDKDD-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.
KDDKDD-2007-Parthasarathy #data mining #learning #mining
Data mining at the crossroads: successes, failures and learning from them (SP), pp. 1053–1055.
KDDKDD-2007-RadlinskiJ #learning #ranking
Active exploration for learning rankings from clickthrough data (FR, TJ), pp. 570–579.
KDDKDD-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.
KDDKDD-2007-Schickel-ZuberF #clustering #learning #recommendation #using
Using hierarchical clustering for learning theontologies used in recommendation systems (VSZ, BF), pp. 599–608.
KDDKDD-2007-Sculley #feedback #learning
Practical learning from one-sided feedback (DS), pp. 609–618.
KDDKDD-2007-ShengL #learning
Partial example acquisition in cost-sensitive learning (VSS, CXL), pp. 638–646.
KDDKDD-2007-YanL #machine learning
Machine learning for stock selection (RJY, CXL), pp. 1038–1042.
KDDKDD-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.
KDDKDD-2007-ZhaoB #web
Corroborate and learn facts from the web (SZ, JB), pp. 995–1003.
MLDMMLDM-2007-CeciABM #learning #relational
Transductive Learning from Relational Data (MC, AA, NB, DM), pp. 324–338.
MLDMMLDM-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.
MLDMMLDM-2007-EkdahlK #classification #learning #on the
On Concentration of Discrete Distributions with Applications to Supervised Learning of Classifiers (ME, TK), pp. 2–16.
MLDMMLDM-2007-GomezF #2d #algorithm #evolution #hybrid #image #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.
MLDMMLDM-2007-Holness #network
A Direct Measure for the Efficacy of Bayesian Network Structures Learned from Data (GH), pp. 601–615.
MLDMMLDM-2007-JiangI #learning
Dynamic Distance-Based Active Learning with SVM (JJ, HHSI), pp. 296–309.
MLDMMLDM-2007-Kertesz-FarkasKP #classification #equivalence #learning
Equivalence Learning in Protein Classification (AKF, AK, SP), pp. 824–837.
MLDMMLDM-2007-Lehmann #hybrid #learning #ontology
Hybrid Learning of Ontology Classes (JL), pp. 883–898.
MLDMMLDM-2007-NgaiY
Fast-Maneuvering Target Seeking Based on Double-Action Q-Learning (DCKN, NHCY), pp. 653–666.
MLDMMLDM-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.
MLDMMLDM-2007-VanderlooyMS #empirical #evaluation #learning
Off-Line Learning with Transductive Confidence Machines: An Empirical Evaluation (SV, LvdM, IGSK), pp. 310–323.
MLDMMLDM-2007-YuL #approach #classification #graph #named
PE-PUC: A Graph Based PU-Learning Approach for Text Classification (SY, CL), pp. 574–584.
RecSysRecSys-2007-RubensS #collaboration #learning
Influence-based collaborative active learning (NR, MS), pp. 145–148.
RecSysRecSys-2007-TaghipourKG #approach #learning #recommendation #web
Usage-based web recommendations: a reinforcement learning approach (NT, AAK, SSG), pp. 113–120.
RecSysRecSys-2007-TiemannP #hybrid #learning #music #recommendation #towards
Towards ensemble learning for hybrid music recommendation (MT, SP), pp. 177–178.
SEKESEKE-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–?.
SEKESEKE-2007-FollecoKHS #learning #quality
Learning from Software Quality Data with Class Imbalance and Noise (AF, TMK, JVH, CS), p. 487–?.
SEKESEKE-2007-MurphyKA #approach #machine learning #testing
An Approach to Software Testing of Machine Learning Applications (CM, GEK, MA), p. 167–?.
SIGIRSIGIR-2007-EfthimiadisF #education #information retrieval #learning #named
IR-Toolbox: an experiential learning tool for teaching IR (ENE, NGF), p. 914.
SIGIRSIGIR-2007-ErtekinHG #learning #problem
Active learning for class imbalance problem (SE, JH, CLG), pp. 823–824.
SIGIRSIGIR-2007-JansenSB #learning #online #paradigm
Viewing online searching within a learning paradigm (BJJ, BKS, DLB), pp. 859–860.
SIGIRSIGIR-2007-VelipasaogluSP #constraints #learning
Improving active learning recall via disjunctive boolean constraints (EV, HS, JOP), pp. 893–894.
SIGIRSIGIR-2007-WangZ #web
Learn from web search logs to organize search results (XW, CZ), pp. 87–94.
SIGIRSIGIR-2007-XuL07a #learning #rank
Learning to rank collections (JX, XL), pp. 765–766.
SIGIRSIGIR-2007-ZhangHRJ #learning #query #using
Query rewriting using active learning for sponsored search (WVZ, XH, BR, RJ), pp. 853–854.
SIGIRSIGIR-2007-ZhengCSZ #framework #learning #ranking #using
A regression framework for learning ranking functions using relative relevance judgments (ZZ, KC, GS, HZ), pp. 287–294.
RERE-2007-EgyedGHB #lessons learnt #requirements #traceability
Value-Based Requirements Traceability: Lessons Learned (AE, PG, MH, SB), pp. 115–118.
SACSAC-2007-BarratT #learning #recognition
A progressive learning method for symbols recognition (SB, ST), pp. 627–631.
SACSAC-2007-RulloCP #categorisation #learning
Learning rules with negation for text categorization (PR, CC, VLP), pp. 409–416.
SACSAC-2007-YingboJJ #approach #machine learning #workflow
A machine learning approach to semi-automating workflow staff assignment (YL, JW, JS), pp. 340–345.
ICSEICSE-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.
ICSEICSE-2007-Zualkernan #learning #programming #using
Using Soloman-Felder Learning Style Index to Evaluate Pedagogical Resources for Introductory Programming Classes (IAZ), pp. 723–726.
LCTESLCTES-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.
PPoPPPPoPP-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.
CAVCAV-2007-SinhaC #composition #lazy evaluation #learning #satisfiability #using #verification
SAT-Based Compositional Verification Using Lazy Learning (NS, EMC), pp. 39–54.
ICSTSAT-2007-ArgelichM #learning #satisfiability
Partial Max-SAT Solvers with Clause Learning (JA, FM), pp. 28–40.
FATESTestCom-FATES-2007-ShahbazLG #component #integration #learning #testing
Learning and Integration of Parameterized Components Through Testing (MS, KL, RG), pp. 319–334.
VMCAIVMCAI-2007-Madhusudan #algorithm #learning #tutorial #verification
Learning Algorithms and Formal Verification (Invited Tutorial) (PM), p. 214.
ASEASE-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.
CASECASE-2006-ReveliotisB #algorithm #learning #performance
Efficient learning algorithms for episodic tasks with acyclic state spaces (SR, TB), pp. 411–418.
CASECASE-2006-ZhouD #game studies #learning
An evolutionary game model on supply chains learning through imitation (MZ, FD), pp. 645–648.
DACDAC-2006-WangGG #deduction #difference #learning #logic
Predicate learning and selective theory deduction for a difference logic solver (CW, AG, MKG), pp. 235–240.
DocEngDocEng-2006-ChidlovskiiFL #documentation #interface #learning #named
ALDAI: active learning documents annotation interface (BC, JF, LL), pp. 184–185.
DocEngDocEng-2006-LecerfC #documentation #learning
Document annotation by active learning techniques (LL, BC), pp. 125–127.
HTHT-2006-Al-KhalifaD #evolution #metadata #semantics #standard
The evolution of metadata from standards to semantics in E-learning applications (HSAK, HCD), pp. 69–72.
VLDBVLDB-2006-ShivamBC #cost analysis #learning #modelling #optimisation
Active and Accelerated Learning of Cost Models for Optimizing Scientific Applications (PS, SB, JSC), pp. 535–546.
CSEETCSEET-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.
CSEETCSEET-2006-WangS #learning #re-engineering
Writing as a Tool for Learning Software Engineering (AIW, CFS), pp. 35–42.
ITiCSEITiCSE-2006-AmzadO #learning #modelling
Model based project centered team learning (IA, AJO), p. 328.
ITiCSEITiCSE-2006-BerglundW #empirical #student
Students learn CS in different ways: insights from an empirical study (AB, MW), pp. 265–269.
ITiCSEITiCSE-2006-BiancoL #game studies #named
PlayToLearn: a game adventure in the realm of Si Piuh (GMB, IL), p. 331.
ITiCSEITiCSE-2006-BirdC #learning #problem
Building a search engine to drive problem-based learning (SB, JRC), pp. 153–157.
ITiCSEITiCSE-2006-Ellis06a #approach #learning #named #self
Self-grading: an approach to supporting self-directed learning (HJCE), p. 349.
ITiCSEITiCSE-2006-GiangrandiM #quote
“Numeri e Macchine”: a virtual museum to learn the history of computing (PG, CM), pp. 78–82.
ITiCSEITiCSE-2006-GriswoldS #learning #performance #scalability #ubiquitous
Ubiquitous presenter: fast, scalable active learning for the whole classroom (WGG, BS), p. 358.
ITiCSEITiCSE-2006-HielscherW #automaton #education #formal method #learning #named
AtoCC: learning environment for teaching theory of automata and formal languages (MH, CW), p. 306.
ITiCSEITiCSE-2006-HughesP #learning #object-oriented #programming #student
ASSISTing CS1 students to learn: learning approaches and object-oriented programming (JH, DRP), pp. 275–279.
ITiCSEITiCSE-2006-KeenanPCM #agile #learning
Learning project planning the agile way (FK, SP, GC, KM), p. 324.
ITiCSEITiCSE-2006-OKellyG #approach #education #learning #problem #programming
RoboCode & problem-based learning: a non-prescriptive approach to teaching programming (JO, JPG), pp. 217–221.
ITiCSEITiCSE-2006-PlimmerA #education #human-computer #learning
Peer teaching extends HCI learning (BP, RA), pp. 53–57.
ITiCSEITiCSE-2006-Quade #hybrid #learning #re-engineering
Developing a hybrid software engineering curse that promotes project-based active learning (AMQ), p. 308.
ITiCSEITiCSE-2006-Rodger #automaton #formal method #learning
Learning automata and formal languages interactively with JFLAP (SHR), p. 360.
ITiCSEITiCSE-2006-RussellMN #education #machine learning
Teaching AI through machine learning projects (IR, ZM, TWN), p. 323.
FASEFASE-2006-RaffeltS #automaton #learning #library #named
LearnLib: A Library for Automata Learning and Experimentation (HR, BS), pp. 377–380.
ICPCICPC-2006-Tilley #challenge #documentation #lessons learnt
Program Redocumentation: Lessons Learned & Future Challenges (SRT), p. xiv.
STOCSTOC-2006-AngluinACW #injection #learning
Learning a circuit by injecting values (DA, JA, JC, YW), pp. 584–593.
STOCSTOC-2006-Feldman #approximate #learning #logic #query
Hardness of approximate two-level logic minimization and PAC learning with membership queries (VF), pp. 363–372.
CHICHI-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.
CHICHI-2006-Moher #distributed #embedded #learning #simulation
Embedded phenomena: supporting science learning with classroom-sized distributed simulations (TM), pp. 691–700.
CHICHI-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.
CHICHI-2006-SiekCR #how #learning #people
Pride and prejudice: learning how chronically ill people think about food (KAS, KHC, YR), pp. 947–950.
CSCWCSCW-2006-Danis #collaboration #learning #performance
Forms of collaboration in high performance computing: exploring implications for learning (CD), pp. 501–504.
CSCWCSCW-2006-RazaviI #behaviour #information management #learning
A grounded theory of information sharing behavior in a personal learning space (MNR, LI), pp. 459–468.
ICEISICEIS-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.
ICEISICEIS-HCI-2006-Patokorpi #learning
Constructivist Instructional Principles, Learner Psychology and Technological Enablers of Learning (EP), pp. 103–109.
ICEISICEIS-SAIC-2006-LuciaFGPT #learning #legacy #migration #multi #video
Migrating Legacy Video Lectures to Multimedia Learning Objects (ADL, RF, MG, IP, GT), pp. 51–58.
ICEISICEIS-SAIC-2006-MarjanovicSMRG #approach #collaboration #learning #process
Supporting Complex Collaborative Learning Activities — The Libresource Approach (OM, HSM, PM, FAR, CG), pp. 59–65.
ICEISICEIS-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.
CIKMCIKM-2006-Flake #how #internet
How I learned to stop worrying and love the imminent internet singularity (GWF), p. 2.
CIKMCIKM-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.
CIKMCIKM-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.
ECIRECIR-2006-VildjiounaiteK #learning
Learning Links Between a User’s Calendar and Information Needs (EV, VK), pp. 557–560.
ECIRECIR-2006-VittautG #information retrieval #machine learning #ranking
Machine Learning Ranking for Structured Information Retrieval (JNV, PG), pp. 338–349.
ICMLICML-2006-AbbeelQN #learning #modelling #using
Using inaccurate models in reinforcement learning (PA, MQ, AYN), pp. 1–8.
ICMLICML-2006-AgarwalBB #graph #higher-order #learning
Higher order learning with graphs (SA, KB, SB), pp. 17–24.
ICMLICML-2006-AsgharbeygiSL #difference #learning #relational
Relational temporal difference learning (NA, DJS, PL), pp. 49–56.
ICMLICML-2006-BalcanB #formal method #learning #on the #similarity
On a theory of learning with similarity functions (MFB, AB), pp. 73–80.
ICMLICML-2006-BalcanBL #learning
Agnostic active learning (MFB, AB, JL), pp. 65–72.
ICMLICML-2006-BowlingMJNW #learning #policy #predict #using
Learning predictive state representations using non-blind policies (MHB, PM, MJ, JN, DFW), pp. 129–136.
ICMLICML-2006-BrefeldS #learning
Semi-supervised learning for structured output variables (UB, TS), pp. 145–152.
ICMLICML-2006-CaruanaN #algorithm #comparison #empirical #learning
An empirical comparison of supervised learning algorithms (RC, ANM), pp. 161–168.
ICMLICML-2006-CheungK #framework #learning #multi
A regularization framework for multiple-instance learning (PMC, JTK), pp. 193–200.
ICMLICML-2006-ConitzerG #algorithm #learning #online #problem
Learning algorithms for online principal-agent problems (and selling goods online) (VC, NG), pp. 209–216.
ICMLICML-2006-DegrisSW #learning #markov #problem #process
Learning the structure of Factored Markov Decision Processes in reinforcement learning problems (TD, OS, PHW), pp. 257–264.
ICMLICML-2006-DenisMR #classification #learning #naive bayes #performance
Efficient learning of Naive Bayes classifiers under class-conditional classification noise (FD, CNM, LR), pp. 265–272.
ICMLICML-2006-desJardinsEW #learning #set
Learning user preferences for sets of objects (Md, EE, KW), pp. 273–280.
ICMLICML-2006-EpshteynD #learning
Qualitative reinforcement learning (AE, GD), pp. 305–312.
ICMLICML-2006-FinkSSU #learning #multi #online
Online multiclass learning by interclass hypothesis sharing (MF, SSS, YS, SU), pp. 313–320.
ICMLICML-2006-GlobersonR #learning #robust
Nightmare at test time: robust learning by feature deletion (AG, STR), pp. 353–360.
ICMLICML-2006-Haffner #kernel #learning #performance
Fast transpose methods for kernel learning on sparse data (PH), pp. 385–392.
ICMLICML-2006-Hanneke #analysis #graph #learning
An analysis of graph cut size for transductive learning (SH), pp. 393–399.
ICMLICML-2006-HertzBW #classification #kernel #learning
Learning a kernel function for classification with small training samples (TH, ABH, DW), pp. 401–408.
ICMLICML-2006-HoiJZL #classification #image #learning
Batch mode active learning and its application to medical image classification (SCHH, RJ, JZ, MRL), pp. 417–424.
ICMLICML-2006-KellerMP #approximate #automation #learning #programming
Automatic basis function construction for approximate dynamic programming and reinforcement learning (PWK, SM, DP), pp. 449–456.
ICMLICML-2006-KonidarisB #information management #learning
Autonomous shaping: knowledge transfer in reinforcement learning (GK, AGB), pp. 489–496.
ICMLICML-2006-KulisSD #kernel #learning #matrix #rank
Learning low-rank kernel matrices (BK, MAS, ISD), pp. 505–512.
ICMLICML-2006-McAuleyCSF #higher-order #image #learning
Learning high-order MRF priors of color images (JJM, TSC, AJS, MOF), pp. 617–624.
ICMLICML-2006-NaorR #learning
Learning to impersonate (MN, GNR), pp. 649–656.
ICMLICML-2006-NejatiLK #learning #network
Learning hierarchical task networks by observation (NN, PL, TK), pp. 665–672.
ICMLICML-2006-NevmyvakaFK #execution #learning
Reinforcement learning for optimized trade execution (YN, YF, MK), pp. 673–680.
ICMLICML-2006-PoupartVHR #learning
An analytic solution to discrete Bayesian reinforcement learning (PP, NAV, JH, KR), pp. 697–704.
ICMLICML-2006-RahmaniG #learning #multi #named
MISSL: multiple-instance semi-supervised learning (RR, SAG), pp. 705–712.
ICMLICML-2006-RainaNK #learning #using
Constructing informative priors using transfer learning (RR, AYN, DK), pp. 713–720.
ICMLICML-2006-RuckertK #approach #learning #statistics
A statistical approach to rule learning (UR, SK), pp. 785–792.
ICMLICML-2006-SenG #learning #markov #network
Cost-sensitive learning with conditional Markov networks (PS, LG), pp. 801–808.
ICMLICML-2006-SilvaS #learning #metric #modelling
Bayesian learning of measurement and structural models (RBdAeS, RS), pp. 825–832.
ICMLICML-2006-SinghiL #bias #classification #learning #set
Feature subset selection bias for classification learning (SKS, HL), pp. 849–856.
ICMLICML-2006-SongE #human-computer #interface #learning
Classifying EEG for brain-computer interfaces: learning optimal filters for dynamical system features (LS, JE), pp. 857–864.
ICMLICML-2006-StrehlLWLL #learning
PAC model-free reinforcement learning (ALS, LL, EW, JL, MLL), pp. 881–888.
ICMLICML-2006-StrehlMLH #learning #problem
Experience-efficient learning in associative bandit problems (ALS, CM, MLL, HH), pp. 889–896.
ICMLICML-2006-XuWSS #learning #predict
Discriminative unsupervised learning of structured predictors (LX, DFW, FS, DS), pp. 1057–1064.
ICMLICML-2006-YuBT #design #learning
Active learning via transductive experimental design (KY, JB, VT), pp. 1081–1088.
ICPRICPR-v1-2006-Al-ZubiS #adaptation #learning
Learning to Imitate Human Movement to Adapt to Environmental Changes (SAZ, GS), pp. 191–194.
ICPRICPR-v1-2006-FredJ #clustering #learning #similarity
Learning Pairwise Similarity for Data Clustering (ALNF, AKJ), pp. 925–928.
ICPRICPR-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.
ICPRICPR-v1-2006-JiangXT #learning
Shape Alignment by Learning a Landmark-PDM Coupled Model (YJ, JX, HTT), pp. 959–962.
ICPRICPR-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.
ICPRICPR-v1-2006-Lampert #machine learning #video
Machine Learning for Video Compression: Macroblock Mode Decision (CHL), pp. 936–940.
ICPRICPR-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.
ICPRICPR-v1-2006-OngB #clustering #learning
Learning Wormholes for Sparsely Labelled Clustering (EJO, RB), pp. 916–919.
ICPRICPR-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.
ICPRICPR-v1-2006-TrujilloO #detection #evolution #how #using
Using Evolution to Learn How to Perform Interest Point Detection (LT, GO), pp. 211–214.
ICPRICPR-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.
ICPRICPR-v2-2006-AutioL #learning #online #sequence
Online Learning of Discriminative Patterns from Unlimited Sequences of Candidates (IA, JTL), pp. 437–440.
ICPRICPR-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.
ICPRICPR-v2-2006-ChenJY #learning #reduction #robust
Robust Nonlinear Dimensionality Reduction for Manifold Learning (HC, GJ, KY), pp. 447–450.
ICPRICPR-v2-2006-DagliRH #information management
Utilizing Information Theoretic Diversity for SVM Active Learn (CKD, SR, TSH), pp. 506–511.
ICPRICPR-v2-2006-GaoLL #approach #classification #learning #optimisation
An ensemble classifier learning approach to ROC optimization (SG, CHL, JHL), pp. 679–682.
ICPRICPR-v2-2006-GuoQ #3d #learning
Learning and Inference of 3D Human Poses from Gaussian Mixture Modeled Silhouettes (FG, GQ), pp. 43–47.
ICPRICPR-v2-2006-HarpazH #geometry #learning
Exploiting the Geometry of Gene Expression Patterns for Unsupervised Learning (RH, RMH), pp. 670–674.
ICPRICPR-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.
ICPRICPR-v2-2006-JonssonF #learning
Correspondence-free Associative Learning (EJ, MF), pp. 441–446.
ICPRICPR-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.
ICPRICPR-v2-2006-LernerM #classification #image #learning #network
Learning Bayesian Networks for Cytogenetic Image Classification (BL, RM), pp. 772–775.
ICPRICPR-v2-2006-PungprasertyingCK #analysis #approach #learning #migration #performance
Migration Analysis: An Alternative Approach for Analyzing Learning Performance (PP, RC, BK), pp. 837–840.
ICPRICPR-v2-2006-ScalzoP #learning
Unsupervised Learning of Dense Hierarchical Appearance Represe (FS, JHP), pp. 395–398.
ICPRICPR-v2-2006-StefanoDMF #learning
Improving Dynamic Learning Vector Quantization (CDS, CD, AM, ASdF), pp. 804–807.
ICPRICPR-v2-2006-SungZL #learning #scalability #set
Accelerating the SVM Learning for Very Large Data Sets (ES, YZ, XL), pp. 484–489.
ICPRICPR-v2-2006-WuLZH #learning
A Semi-supervised SVM for Manifold Learning (ZW, ChL, JZ, JH), pp. 490–493.
ICPRICPR-v2-2006-XuWH #algorithm #learning
A maximum margin discriminative learning algorithm for temporal signals (WX, JW, ZH), pp. 460–463.
ICPRICPR-v2-2006-ZhangJHW #detection #using
Learning-Based License Plate Detection Using Global and Local Features (HZ, WJ, XH, QW), pp. 1102–1105.
ICPRICPR-v2-2006-ZhangPB #classification #learning #representation
Learning Optimal Filter Representation for Texture Classification (PZ, JP, BPB), pp. 1138–1141.
ICPRICPR-v2-2006-ZhangR #incremental #learning
A New Data Selection Principle for Semi-Supervised Incremental Learning (RZ, AIR), pp. 780–783.
ICPRICPR-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.
ICPRICPR-v2-2006-ZhengLY #kernel #learning #problem
Weakly Supervised Learning on Pre-image Problem in Kernel Methods (WSZ, JHL, PCY), pp. 711–715.
ICPRICPR-v2-2006-ZouL #learning #performance #sequence
The Generalization Performance of Learning Machine Based on Phi-mixing Sequence (BZ, LL), pp. 548–551.
ICPRICPR-v3-2006-AlahariPJ #learning #online #recognition
Learning Mixtures of Offline and Online features for Handwritten Stroke Recognition (KA, SLP, CVJ), pp. 379–382.
ICPRICPR-v3-2006-GunselK #learning
Perceptual Audio Watermarking by Learning in Wavelet Domain (BG, SK), pp. 383–386.
ICPRICPR-v3-2006-IsukapalliE #identification #learning #policy
Learning Policies for Efficiently Identifying Objects of Many Classes (RI, AME, RG), pp. 356–361.
ICPRICPR-v3-2006-Martinez-ArroyoS #classification #learning #naive bayes
Learning an Optimal Naive Bayes Classifier (MMA, LES), pp. 1236–1239.
ICPRICPR-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.
ICPRICPR-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.
ICPRICPR-v4-2006-Martinez-ArroyoS06a #classification #learning #naive bayes
Learning an Optimal Naive Bayes Classifier (MMA, LES), p. 958.
ICPRICPR-v4-2006-YangLPZZ #detection #learning
Active Learning Based Pedestrian Detection in Real Scenes (TY, JL, QP, CZ, YZ), pp. 904–907.
ICPRICPR-v4-2006-ZhengLL #learning #network
Control Double Inverted Pendulum by Reinforcement Learning with Double CMAC Network (YZ, SL, ZL), pp. 639–642.
KDDKDD-2006-AbeZL #detection #learning
Outlier detection by active learning (NA, BZ, JL), pp. 504–509.
KDDKDD-2006-AgarwalCA #learning #rank
Learning to rank networked entities (AA, SC, SA), pp. 14–23.
KDDKDD-2006-CarvalhoC #feature model #learning #online #performance
Single-pass online learning: performance, voting schemes and online feature selection (VRC, WWC), pp. 548–553.
KDDKDD-2006-HettichP #lessons learnt #mining
Mining for proposal reviewers: lessons learned at the national science foundation (SH, MJP), pp. 862–871.
KDDKDD-2006-HoiLC #classification #kernel #learning
Learning the unified kernel machines for classification (SCHH, MRL, EYC), pp. 187–196.
KDDKDD-2006-LongWZY #graph #learning
Unsupervised learning on k-partite graphs (BL, XW, Z(Z, PSY), pp. 317–326.
KDDKDD-2006-RosalesF #learning #linear #metric #programming
Learning sparse metrics via linear programming (RR, GF), pp. 367–373.
SIGIRSIGIR-2006-AgichteinBDR #interactive #learning #modelling #predict #web
Learning user interaction models for predicting web search result preferences (EA, EB, STD, RR), pp. 3–10.
SIGIRSIGIR-2006-AngelovaW #classification #graph
Graph-based text classification: learn from your neighbors (RA, GW), pp. 485–492.
SIGIRSIGIR-2006-CarteretteP #learning #ranking
Learning a ranking from pairwise preferences (BC, DP), pp. 629–630.
SIGIRSIGIR-2006-HuangZL #learning #taxonomy
Refining hierarchical taxonomy structure via semi-supervised learning (RH, ZZ, WL), pp. 653–654.
SIGIRSIGIR-2006-LacerdaCGFZR #learning
Learning to advertise (AL, MC, MAG, WF, NZ, BARN), pp. 549–556.
SIGIRSIGIR-2006-MaoPH #information management #named #ontology
DiLight: an ontology-based information access system for e-learning environments (MM, YP, DH), p. 733.
SIGIRSIGIR-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.
SIGIRSIGIR-2006-ZhaZFS #difference #information retrieval #learning #query
Incorporating query difference for learning retrieval functions in information retrieval (HZ, ZZ, HF, GS), pp. 721–722.
SACSAC-2006-CraigL #classification #learning #using
Protein classification using transductive learning on phylogenetic profiles (RAC, LL), pp. 161–166.
SACSAC-2006-Ferrer-TroyanoAS #classification #data type #incremental #learning
Data streams classification by incremental rule learning with parameterized generalization (FJFT, JSAR, JCRS), pp. 657–661.
SACSAC-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.
SACSAC-2006-SoaresB #kernel #parametricity #using
Selecting parameters of SVM using meta-learning and kernel matrix-based meta-features (CS, PB), pp. 564–568.
ICSEICSE-2006-Venkatagiri #approach #requirements
Engineering the software requirements of nonprofits: a service-learning approach (SV), pp. 643–648.
CGOCGO-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.
CAVCAV-2006-VardhanV #learning #named #verification
LEVER: A Tool for Learning Based Verification (AV, MV), pp. 471–474.
FATESFATES-RV-2006-VeanesRC #learning #online #testing
Online Testing with Reinforcement Learning (MV, PR, CC), pp. 240–253.
ICLPICLP-2006-Aguilar-Solis #approach #constraints #learning #parsing #semantics
Learning Semantic Parsers: A Constraint Handling Rule Approach (DAS), pp. 447–448.
ICSTSAT-2006-YuM #constraints #learning #linear #smt
Lemma Learning in SMT on Linear Constraints (YY, SM), pp. 142–155.
ASEASE-2005-Swartout #lessons learnt #scalability
Virtual humans: lessons learned in integrating a large-scale AI project (WRS), p. 2.
ASEASE-2005-VardhanV #branch #learning #verification
Learning to verify branching time properties (AV, MV), pp. 325–328.
DACDAC-2005-ParthasarathyICB #learning
Structural search for RTL with predicate learning (GP, MKI, KTC, FB), pp. 451–456.
DATEDATE-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.
DATEDATE-2005-IyerPC #constraints #learning #performance #theorem proving
Efficient Conflict-Based Learning in an RTL Circuit Constraint Solver (MKI, GP, KTC), pp. 666–671.
HTHT-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.
ICDARICDAR-2005-BargeronVS #detection #learning
Boosting-based Transductive Learning for Text Detection (DB, PAV, PYS), pp. 1166–1171.
ICDARICDAR-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.
ICDARICDAR-2005-FengHG #approach #learning #semantics #web
A Learning Approach to Discovering Web Page Semantic Structures (JF, PH, MG), pp. 1055–1059.
ICDARICDAR-2005-LavenLR #analysis #approach #documentation #image #learning #statistics
A Statistical Learning Approach To Document Image Analysis (KL, SL, STR), pp. 357–361.
ICDARICDAR-2005-LiuCL #identification #image #machine learning #using
Language Identification of Character Images Using Machine Learning Techniques (YHL, FC, CCL), pp. 630–634.
ICDARICDAR-2005-RaghavendraNSRS #learning #online #prototype #recognition
Prototype Learning Methods for Online Handwriting Recognition (BSR, CKN, GS, AGR, MS), pp. 287–291.
ICDARICDAR-2005-SteinkrauSB #algorithm #machine learning #using
Using GPUs for Machine Learning Algorithms (DS, PYS, IB), pp. 1115–1119.
ICDARICDAR-2005-Szummer #diagrams #learning #random
Learning Diagram Parts with Hidden Random Fields (MS), pp. 1188–1193.
SIGMODSIGMOD-2005-BragaCCR #learning #named #query #visual notation #xml
XQBE: a visual environment for learning XML query languages (DB, AC, SC, AR), pp. 903–905.
VLDBVLDB-2005-ZhangHJLZ #cost analysis #learning #query #statistics #xml
Statistical Learning Techniques for Costing XML Queries (NZ, PJH, VJ, GML, CZ), pp. 289–300.
CSEETCSEET-2005-BunseGOPS #education #learning #re-engineering
xd Software Engineering Education Applying a Blended Learning Strategy for (CB, IG, MO, CP, SSN), pp. 95–102.
CSEETCSEET-2005-Ellis #learning #online #re-engineering
Autonomous Learning in Online and Traditional Versions of a Software Engineering Course (HJCE), pp. 69–76.
CSEETCSEET-2005-Liu #communication #issue tracking #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.
CSEETCSEET-2005-Selic #developer #what
What I Wish I Had Learned in School: Reflections on 30+ Years as a Software Developer (BS), p. 5.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2005-ChamillardS #education #learning
Learning styles across the curriculum (ATC, RES), pp. 241–245.
ITiCSEITiCSE-2005-DavisW #convergence #education #learning #multi
A research-led curriculum in multimedia: learning about convergence (HCD, SW), pp. 29–33.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2005-Granger #collaboration #communication #concept #learning
Learning technical concepts with collaboration and communication skills (MJG), p. 391.
ITiCSEITiCSE-2005-HurtadoV #learning
Learning UNIX in first year of computer engineering (MASH, CVP), p. 392.
ITiCSEITiCSE-2005-LiccardiW #comprehension #difference #effectiveness
Understanding disciplinary differences: an insight into selecting effective e-learning approaches (IL, SW), p. 411.
ITiCSEITiCSE-2005-LoftusR #learning #programming #question
Extreme programming promotes extreme learning? (CWL, MR), pp. 311–315.
ITiCSEITiCSE-2005-Ludi #process #re-engineering #student
Active-learning activities that introduce students to software engineering fundamentals (SL), pp. 128–132.
ITiCSEITiCSE-2005-Marcelino #learning #programming
Learning repetition structures in programming (MJM), p. 351.
ITiCSEITiCSE-2005-NugentSSPL #design #development #learning #validation
Design, development, and validation of a learning object for CS1 (GN, LKS, AS, SP, JL), p. 370.
ITiCSEITiCSE-2005-Olsevicova #topic
Application of topic maps in e-learning environment (KO), p. 363.
ITiCSEITiCSE-2005-Truong #learning
The environment for learning to program (NT), p. 383.
ITiCSEITiCSE-2005-TruongBR #learning #web
Learning to program through the web (NT, PB, PR), pp. 9–13.
ITiCSEITiCSE-2005-Vinha #learning #reuse #theory and practice
Reusable learning objects: theory to practice (AV), p. 413.
ICSMEICSM-2005-FerencBFL #design pattern #machine learning #mining
Design Pattern Mining Enhanced by Machine Learning (RF, ÁB, LJF, JL), pp. 295–304.
ICSMEICSM-2005-ZvegintzovP #lessons learnt #maintenance
Sixty Years of Software Maintenance: Lessons Learned (NZ, GP), pp. 726–727.
MSRMSR-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.
STOCSTOC-2005-KaplanKM #learning
Learning with attribute costs (HK, EK, YM), pp. 356–365.
STOCSTOC-2005-MosselR #learning #markov #modelling
Learning nonsingular phylogenies and hidden Markov models (EM, SR), pp. 366–375.
STOCSTOC-2005-Regev #encryption #fault #learning #linear #on the #random
On lattices, learning with errors, random linear codes, and cryptography (OR), pp. 84–93.
CIAACIAA-2005-GarciaRCA #learning #question
Is Learning RFSAs Better Than Learning DFAs? (PG, JR, AC, GIA), pp. 343–344.
CIAACIAA-2005-HigueraPT #automaton #finite #learning #probability #recognition
Learning Stochastic Finite Automata for Musical Style Recognition (CdlH, FP, FT), pp. 345–346.
CHICHI-2005-BondarenkoJ #learning
Dcuments at Hand: Learning from Paper to Improve Digital Technologies (OB, RJ), pp. 121–130.
CHICHI-2005-XieLGM #image #learning
Learning user interest for image browsing on small-form-factor devices (XX, HL, SG, WYM), pp. 671–680.
CHICHI-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.
EDOCEDOC-2005-FerreiraF #learning #lifecycle #workflow
Learning, planning, and the life cycle of workflow management (DRF, HMF), pp. 39–46.
ICEISICEIS-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.
ICEISICEIS-v2-2005-LokugeA #hybrid #learning #multi
Handling Multiple Events in Hybrid BDI Agents with Reinforcement Learning: A Container Application (PL, DA), pp. 83–90.
ICEISICEIS-v2-2005-MashechkinPR #anti #approach #enterprise #machine learning
Enterprise Anti-Spam Solution Based on Machine Learning Approach (IM, MP, AR), pp. 188–193.
ICEISICEIS-v5-2005-DexterP #assurance #quality
Cross-Domain Mapping: Quality Assurance and E-Learning Provision (HD, JP), pp. 199–205.
ICEISICEIS-v5-2005-DixitM #classification #documentation #using
Electronic Document Classification Using Support Vector Machine — An Application for E-Learning (SD, LKM), pp. 191–198.
ICEISICEIS-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.
ICEISICEIS-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.
ICEISICEIS-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.
ICEISICEIS-v5-2005-IslamARR #distance #learning #mobile
Mobile Telephone Technology as a Distance Learning Tool (YMI, MA, ZR, MR), pp. 226–232.
ICEISICEIS-v5-2005-LeR #learning #named
LINC: A Web-Based Learning Tool for Mixed-Mode Learning (THL, JR), pp. 154–160.
ICEISICEIS-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.
CIKMCIKM-2005-AminiTULG #documentation #learning #using #xml
Learning to summarise XML documents using content and structure (MRA, AT, NU, ML, PG), pp. 297–298.
CIKMCIKM-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.
CIKMCIKM-2005-NottelmannS #information retrieval #machine learning #probability
Information retrieval and machine learning for probabilistic schema matching (HN, US), pp. 295–296.
CIKMCIKM-2005-RoussinovFN05a #approach #information retrieval #learning
Discretization based learning approach to information retrieval (DR, WF, FADN), pp. 321–322.
CIKMCIKM-2005-XiongSK #database #learning #multi #privacy
Privacy leakage in multi-relational databases via pattern based semi-supervised learning (HX, MS, VK), pp. 355–356.
ICMLICML-2005-AbbeelN #learning
Exploration and apprenticeship learning in reinforcement learning (PA, AYN), pp. 1–8.
ICMLICML-2005-AndersonM #algorithm #learning #markov #modelling
Active learning for Hidden Markov Models: objective functions and algorithms (BA, AM), pp. 9–16.
ICMLICML-2005-BlockeelPS #learning #multi
Multi-instance tree learning (HB, DP, AS), pp. 57–64.
ICMLICML-2005-BurgeL #learning #network
Learning class-discriminative dynamic Bayesian networks (JB, TL), pp. 97–104.
ICMLICML-2005-BurgesSRLDHH #learning #rank #using
Learning to rank using gradient descent (CJCB, TS, ER, AL, MD, NH, GNH), pp. 89–96.
ICMLICML-2005-ChangK #learning
Hedged learning: regret-minimization with learning experts (YHC, LPK), pp. 121–128.
ICMLICML-2005-ChuG #learning #process
Preference learning with Gaussian processes (WC, ZG), pp. 137–144.
ICMLICML-2005-CortesMW #learning
A general regression technique for learning transductions (CC, MM, JW), pp. 153–160.
ICMLICML-2005-CrandallG #game studies #learning
Learning to compete, compromise, and cooperate in repeated general-sum games (JWC, MAG), pp. 161–168.
ICMLICML-2005-DaumeM #approximate #learning #optimisation #predict #scalability
Learning as search optimization: approximate large margin methods for structured prediction (HDI, DM), pp. 169–176.
ICMLICML-2005-DrakeV #learning
A practical generalization of Fourier-based learning (AD, DV), pp. 185–192.
ICMLICML-2005-DriessensD #first-order #learning #modelling
Combining model-based and instance-based learning for first order regression (KD, SD), pp. 193–200.
ICMLICML-2005-EngelMM #learning #process
Reinforcement learning with Gaussian processes (YE, SM, RM), pp. 201–208.
ICMLICML-2005-GirolamiR #kernel #learning #modelling
Hierarchic Bayesian models for kernel learning (MG, SR), pp. 241–248.
ICMLICML-2005-GroisW #approach #comprehension #learning
Learning strategies for story comprehension: a reinforcement learning approach (EG, DCW), pp. 257–264.
ICMLICML-2005-HerbsterPW #graph #learning #online
Online learning over graphs (MH, MP, LW), pp. 305–312.
ICMLICML-2005-IlghamiMNA #approximate #learning
Learning approximate preconditions for methods in hierarchical plans (OI, HMA, DSN, DWA), pp. 337–344.
ICMLICML-2005-IresonCCFKL #information management #machine learning
Evaluating machine learning for information extraction (NI, FC, MEC, DF, NK, AL), pp. 345–352.
ICMLICML-2005-JinCS #information retrieval #using
Learn to weight terms in information retrieval using category information (RJ, JYC, LS), pp. 353–360.
ICMLICML-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.
ICMLICML-2005-JodogneP #interactive #learning #visual notation
Interactive learning of mappings from visual percepts to actions (SJ, JHP), pp. 393–400.
ICMLICML-2005-KokD #learning #logic #markov #network
Learning the structure of Markov logic networks (SK, PMD), pp. 441–448.
ICMLICML-2005-LangfordZ #classification #learning #performance
Relating reinforcement learning performance to classification performance (JL, BZ), pp. 473–480.
ICMLICML-2005-Mahadevan #learning
Proto-value functions: developmental reinforcement learning (SM), pp. 553–560.
ICMLICML-2005-MichelsSN #learning #using
High speed obstacle avoidance using monocular vision and reinforcement learning (JM, AS, AYN), pp. 593–600.
ICMLICML-2005-NatarajanT #learning #multi
Dynamic preferences in multi-criteria reinforcement learning (SN, PT), pp. 601–608.
ICMLICML-2005-NatarajanTADFR #first-order #learning #modelling #probability
Learning first-order probabilistic models with combining rules (SN, PT, EA, TGD, AF, ACR), pp. 609–616.
ICMLICML-2005-Niculescu-MizilC #learning #predict
Predicting good probabilities with supervised learning (ANM, RC), pp. 625–632.
ICMLICML-2005-OntanonP #learning #multi
Recycling data for multi-agent learning (SO, EP), pp. 633–640.
ICMLICML-2005-PalettaFS #recognition #visual notation
Q-learning of sequential attention for visual object recognition from informative local descriptors (LP, GF, CS), pp. 649–656.
ICMLICML-2005-PernkopfB #classification #generative #learning #network #parametricity
Discriminative versus generative parameter and structure learning of Bayesian network classifiers (FP, JAB), pp. 657–664.
ICMLICML-2005-RayC #comparison #empirical #learning #multi
Supervised versus multiple instance learning: an empirical comparison (SR, MC), pp. 697–704.
ICMLICML-2005-RosellHRP #learning #why
Why skewing works: learning difficult Boolean functions with greedy tree learners (BR, LH, SR, DP), pp. 728–735.
ICMLICML-2005-RousuSSS #classification #learning #modelling #multi
Learning hierarchical multi-category text classification models (JR, CS, SS, JST), pp. 744–751.
ICMLICML-2005-ScholkopfSB #machine learning #problem
Object correspondence as a machine learning problem (BS, FS, VB), pp. 776–783.
ICMLICML-2005-SiddiqiM #learning #performance
Fast inference and learning in large-state-space HMMs (SMS, AWM), pp. 800–807.
ICMLICML-2005-SilvaS #identification #learning #modelling
New d-separation identification results for learning continuous latent variable models (RBdAeS, RS), pp. 808–815.
ICMLICML-2005-SimsekWB #clustering #graph #identification #learning
Identifying useful subgoals in reinforcement learning by local graph partitioning (ÖS, APW, AGB), pp. 816–823.
ICMLICML-2005-SindhwaniNB #learning
Beyond the point cloud: from transductive to semi-supervised learning (VS, PN, MB), pp. 824–831.
ICMLICML-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.
ICMLICML-2005-SunD #approach #learning
Explanation-Augmented SVM: an approach to incorporating domain knowledge into SVM learning (QS, GD), pp. 864–871.
ICMLICML-2005-TaskarCKG #approach #learning #modelling #predict #scalability
Learning structured prediction models: a large margin approach (BT, VC, DK, CG), pp. 896–903.
ICMLICML-2005-ToussaintV #learning #modelling
Learning discontinuities with products-of-sigmoids for switching between local models (MT, SV), pp. 904–911.
ICMLICML-2005-Wiewiora #learning #predict
Learning predictive representations from a history (EW), pp. 964–971.
ICMLICML-2005-WolfeJS #learning #predict
Learning predictive state representations in dynamical systems without reset (BW, MRJ, SPS), pp. 980–987.
ICMLICML-2005-XuTYYK #learning #relational
Dirichlet enhanced relational learning (ZX, VT, KY, SY, HPK), pp. 1004–1011.
ICMLICML-2005-YuTS #learning #multi #process
Learning Gaussian processes from multiple tasks (KY, VT, AS), pp. 1012–1019.
ICMLICML-2005-ZhouHS #graph #learning
Learning from labeled and unlabeled data on a directed graph (DZ, JH, BS), pp. 1036–1043.
ICMLICML-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.
KDDKDD-2005-FanLH #image #learning #mining #semantics #statistics
Mining images on semantics via statistical learning (JF, HL, MSH), pp. 22–31.
KDDKDD-2005-LowdM #learning
Adversarial learning (DL, CM), pp. 641–647.
KDDKDD-2005-MeruguG #data flow #distributed #framework #learning #semistructured data
A distributed learning framework for heterogeneous data sources (SM, JG), pp. 208–217.
KDDKDD-2005-PhanNHH #learning
Improving discriminative sequential learning with rare--but--important associations (XHP, MLN, TBH, SH), pp. 304–313.
KDDKDD-2005-RadlinskiJ #feedback #learning #query #rank
Query chains: learning to rank from implicit feedback (FR, TJ), pp. 239–248.
KDDKDD-2005-YangL #learning #predict
Learning to predict train wheel failures (CY, SL), pp. 516–525.
LSOLSO-2005-DedeneSBL #generative #web #web service
New generation E-Learning technology by Web Services (GD, MS, MDB, WL), pp. 77–81.
LSOLSO-2005-Fajtak #learning
Kick-off Workshops and Project Retrospectives: A Good Learning Software Organization Practice (FFF), pp. 112–114.
LSOLSO-2005-Salo #agile #development #learning #validation
Systematical Validation of Learning in Agile Software Development Environment (OS), pp. 92–96.
MLDMMLDM-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.
MLDMMLDM-2005-EickRBV #assessment #clustering #distance #similarity #using
Using Clustering to Learn Distance Functions for Supervised Similarity Assessment (CFE, AR, AB, RV), pp. 120–131.
MLDMMLDM-2005-GhoshGYB05a #learning #parametricity
Determining Regularization Parameters for Derivative Free Neural Learning (RG, MG, JY, AMB), pp. 71–79.
MLDMMLDM-2005-KuhnertK #feedback #learning
Autonomous Vehicle Steering Based on Evaluative Feedback by Reinforcement Learning (KDK, MK), pp. 405–414.
MLDMMLDM-2005-ScalzoP #learning #visual notation
Unsupervised Learning of Visual Feature Hierarchies (FS, JHP), pp. 243–252.
MLDMMLDM-2005-SilvaJNP #geometry #learning #metric #using
Diagnosis of Lung Nodule Using Reinforcement Learning and Geometric Measures (ACS, VRdSJ, AdAN, ACdP), pp. 295–304.
SEKESEKE-2005-GaoCMYB #learning #modelling #object-oriented
An Object-Oriented Modeling Learning Support System With Inspection Comments (TG, KMLC, HM, ILY, FBB), pp. 211–216.
SEKESEKE-2005-HongCC #fuzzy #learning #performance
Learning Efficiency Improvement of Fuzzy CMAC by Aitken Acceleration Method (CMH, CMC, HYC), pp. 556–595.
SEKESEKE-2005-KinjoH #learning #modelling #object-oriented
An Object-Oriented Modeling Learning Support System With Inspection Comments (TK, AH), pp. 223–228.
SEKESEKE-2005-SiciliaCR #learning #ontology #process
Ontologies of Software Artifacts and Activities: Resource Annotation and Application to Learning Technologies (MÁS, JJC, DR), pp. 145–150.
SIGIRSIGIR-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.
SIGIRSIGIR-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.
SIGIRSIGIR-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.
MODELSMoDELS-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.
MODELSMoDELS-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.
MODELSMoDELS-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.
MODELSMoDELS-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.
RERE-2005-AvesaniBPS #machine learning #requirements #scalability
Facing Scalability Issues in Requirements Prioritization with Machine Learning Techniques (PA, CB, AP, AS), pp. 297–306.
RERE-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.
SACSAC-2005-BoninoCP #automation #concept #learning #network
Automatic learning of text-to-concept mappings exploiting WordNet-like lexical networks (DB, FC, FP), pp. 1639–1644.
SACSAC-2005-Ferrer-TroyanoAS #data type #incremental #learning
Incremental rule learning based on example nearness from numerical data streams (FJFT, JSAR, JCRS), pp. 568–572.
SACSAC-2005-FradkinK #classification #learning
Methods for learning classifier combinations: no clear winner (DF, PBK), pp. 1038–1043.
SACSAC-2005-GamaMR #data type #learning
Learning decision trees from dynamic data streams (JG, PM, PPR), pp. 573–577.
SACSAC-2005-KatayamaKN #learning #process
Reinforcement learning agents with primary knowledge designed by analytic hierarchy process (KK, TK, HN), pp. 14–21.
SACSAC-2005-LunaLSHHB #learning
Learning system to introduce GIS to civil engineers (RL, WTL, JMS, RHH, MGH, MB), pp. 1737–1738.
SACSAC-2005-PandeyGM #algorithm #learning #probability #scheduling
Stochastic scheduling of active support vector learning algorithms (GP, HG, PM), pp. 38–42.
SACSAC-2005-TebriBC #incremental #learning
Incremental profile learning based on a reinforcement method (HT, MB, CC), pp. 1096–1101.
SACSAC-2005-ZhangM #learning #privacy
Privacy preserving learning in negotiation (SZ, FM), pp. 821–825.
ESEC-FSEESEC-FSE-2005-ChatleyT #eclipse #learning #named
KenyaEclipse: learning to program in eclipse (RC, TT), pp. 245–248.
ICSEICSE-2005-BernerWK #automation #lessons learnt #testing
Observations and lessons learned from automated testing (SB, RW, RKK), pp. 571–579.
ICSEICSE-2005-Fox #dependence #machine learning #statistics
Addressing software dependability with statistical and machine learning techniques (AF), p. 8.
ICSEICSE-2005-Liu #approach #open source #re-engineering
Enriching software engineering courses with service-learning projects and the open-source approach (CL), pp. 613–614.
CGOCGO-2005-Hind #architecture #machine learning #virtual machine
Virtual Machine Learning: Thinking like a Computer Architect (MH), p. 11.
CAVCAV-2005-AlurMN #composition #learning #verification
Symbolic Compositional Verification by Learning Assumptions (RA, PM, WN), pp. 548–562.
CAVCAV-2005-LoginovRS #abstraction #induction #learning #refinement
Abstraction Refinement via Inductive Learning (AL, TWR, SS), pp. 519–533.
ICSTSAT-2005-GentR #learning
Local and Global Complete Solution Learning Methods for QBF (IPG, AGDR), pp. 91–106.
WICSAWICSA-2004-BardramCH #approach #architecture #design #learning #prototype
Architectural Prototyping: An Approach for Grounding Architectural Design and Learning (JB, HBC, KMH), pp. 15–24.
DACDAC-2004-WangMCA #learning #on the
On path-based learning and its applications in delay test and diagnosis (LCW, TMM, KTC, MSA), pp. 492–497.
DATEDATE-v1-2004-Wang #learning #simulation #validation
Regression Simulation: Applying Path-Based Learning In Delay Test and Post-Silicon Validation (LCW), pp. 692–695.
DocEngDocEng-2004-ChidlovskiiF #documentation #learning #legacy
Supervised learning for the legacy document conversion (BC, JF), pp. 220–228.
HTHT-2004-DavisB #case study #experience #learning #migration
Experiences migrating microcosm learning materials (HCD, RAB), pp. 141–142.
CSEETCSEET-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.
CSEETCSEET-2004-Milewski #human-computer #learning
Software Engineers and HCI Practitioners Learning to Work Together: A Preliminary Look at Expectations (AEM), pp. 45–49.
ITiCSEITiCSE-2004-ArgolloHMBFBLMR #collaboration #learning #research #student
Graduate students learning strategies through research collaboration (EA, MH, DM, GB, PCF, FB, EL, JCM, DR), p. 262.
ITiCSEITiCSE-2004-ChesnevarGM #automaton #formal method #learning
Didactic strategies for promoting significant learning in formal languages and automata theory (CIC, MPG, AGM), pp. 7–11.
ITiCSEITiCSE-2004-Dixon #automation #education #learning
A single CASE environment for teaching and learning (MD), p. 271.
ITiCSEITiCSE-2004-Ford04a #generative #learning #programming
A learning object generator for programming (LF), p. 268.
ITiCSEITiCSE-2004-Garner #learning #programming
The use of a code restructuring tool in the learning of programming (SG), p. 277.
ITiCSEITiCSE-2004-Kerren #education #generative #learning
Generation as method for explorative learning in computer science education (AK), pp. 77–81.
ITiCSEITiCSE-2004-Kumar #java #learning #programming
Web-based tutors for learning programming in C++/Java (AK), p. 266.
ITiCSEITiCSE-2004-LeskaR #concept #game studies #learning #using
Learning O-O concepts in CS I using game projects (CL, JRR), p. 237.
ITiCSEITiCSE-2004-McKennaL #concept #learning
Constructivist or instructivist: pedagogical concepts practically applied to a computer learning environment (PM, BL), pp. 166–170.
ITiCSEITiCSE-2004-MelinC #learning #student
Project oriented student work: learning & examination (UM, SC), pp. 87–91.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2004-PaciniFF #database #learning #problem #spreadsheet #tool support
Learning problem solving with spreadsheet and database tools (GP, GF, AF), p. 267.
ITiCSEITiCSE-2004-PahlBK #database #interactive #learning #multi
Supporting active database learning and training through interactive multimedia (CP, RB, CK), pp. 27–31.
ITiCSEITiCSE-2004-PowellMGFR #learning #programming
Dyslexia and learning computer programming (NP, DJM, JG, JF, JR), p. 242.
ITiCSEITiCSE-2004-RamalingamLW #learning #modelling #self
Self-efficacy and mental models in learning to program (VR, DL, SW), pp. 171–175.
ITiCSEITiCSE-2004-RatcliffeHE #collaboration #learning #student
Enhancing student learning through collaboration (MR, JH, WE), p. 272.
ITiCSEITiCSE-2004-SadiqOSL #learning #named #online #sql
SQLator: an online SQL learning workbench (SWS, MEO, WS, JYCL), pp. 223–227.
ITiCSEITiCSE-2004-Sheard #community #learning
Electronic learning communities: strategies for establishment and management (JS), pp. 37–41.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2004-SitthiworachartJ #assessment #effectiveness #learning #programming
Effective peer assessment for learning computer programming (JS, MJ), pp. 122–126.
ITiCSEITiCSE-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.
CSMRCSMR-2004-Kajko-MattssonJKW
Lesson Learned from Attempts to Implement Daily Build (MKM, MJ, SK, FW), pp. 137–146.
CSMRCSMR-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.
IWPCIWPC-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.
STOCSTOC-2004-AwerbuchK #adaptation #distributed #feedback #geometry #learning
Adaptive routing with end-to-end feedback: distributed learning and geometric approaches (BA, RDK), pp. 45–53.
ICALPICALP-2004-AlonA #learning
Learning a Hidden Subgraph (NA, VA), pp. 110–121.
CHICHI-2004-KierasS #lessons learnt #modelling
Computational GOMS modeling of a complex team task: lessons learned (DEK, TPS), pp. 97–104.
CSCWCSCW-2004-CubranicMSB #case study #development #learning
Learning from project history: a case study for software development (DC, GCM, JS, KSB), pp. 82–91.
ICEISICEIS-v2-2004-BendouM #graph #learning #network
Learning Bayesian Networks with Largest Chain Graphs (MB, PM), pp. 184–190.
ICEISICEIS-v2-2004-ColaceSVF #algorithm #automation #learning #ontology
A Semi-Automatic Bayesian Algorithm for Ontology Learning (FC, MDS, MV, PF), pp. 191–196.
ICEISICEIS-v2-2004-ColaceSVF04a #algorithm #comparison #learning #network
Bayesian Network Structural Learning from Data: An Algorithms Comparison (FC, MDS, MV, PF), pp. 527–530.
ICEISICEIS-v2-2004-Kabiri #approximate #comparison #learning #network
A Comparison Between the Proportional Keen Approximator and the Neural Networks Learning Methods (PK), pp. 159–164.
ICEISICEIS-v3-2004-Nobre #complexity #design #learning
Organisational Learning — Foundational Roots for Design for Complexity (ÂLN), pp. 85–93.
ICEISICEIS-v4-2004-Carneiro #challenge #learning #network #process
Learning Processes and the Role of Technological Networks as an Innovative Challenge (AC), pp. 497–501.
ICEISICEIS-v4-2004-DunkelBO #semantics #web
Semantic E-Learning Agents — Supporting Elearning by Semantic Web and Agents Technologies (JD, RB, SO), pp. 271–278.
ICEISICEIS-v4-2004-FloresGVS #learning
Amplia Learning Environment: A Proposal for Pedagogical Negotiation (CDF, JCG, RMV, LJS), pp. 279–286.
ICEISICEIS-v5-2004-ChenLK #assessment #perspective
Assessment of E-Learning Satisfaction from Critical Incidents Perspective (NSC, KML, K), pp. 27–34.
ICEISICEIS-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.
ICEISICEIS-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.
ICEISICEIS-v5-2004-SantanaS #hypermedia #learning
Accessing Hypermedia Systems Efectiveness in Learning Contexts (SS, AS), pp. 250–253.
ICEISICEIS-v5-2004-SoferM #requirements
An Investigation into the Requirements for an E-Learning System (YYS, SBM), pp. 233–237.
CIKMCIKM-2004-LiO #identification #learning #music
Semi-supervised learning for music artists style identification (TL, MO), pp. 152–153.
CIKMCIKM-2004-LiuZYYYCBM #learning #metric #similarity
Learning similarity measures in non-orthogonal space (NL, BZ, JY, QY, SY, ZC, FB, WYM), pp. 334–341.
CIKMCIKM-2004-MaZMS #framework #learning #query #similarity #using
A framework for refining similarity queries using learning techniques (YM, QZ, SM, DYS), pp. 158–159.
ICMLICML-2004-AgarwalT #3d #learning
Learning to track 3D human motion from silhouettes (AA, BT).
ICMLICML-2004-BachLJ #algorithm #kernel #learning #multi
Multiple kernel learning, conic duality, and the SMO algorithm (FRB, GRGL, MIJ).
ICMLICML-2004-BahamondeBDQLCAG #case study #learning #set
Feature subset selection for learning preferences: a case study (AB, GFB, JD, JRQ, OL, JJdC, JA, FG).
ICMLICML-2004-BilenkoBM #clustering #constraints #learning #metric
Integrating constraints and metric learning in semi-supervised clustering (MB, SB, RJM).
ICMLICML-2004-BlumLRR #learning #random #using
Semi-supervised learning using randomized mincuts (AB, JDL, MRR, RR).
ICMLICML-2004-Bouckaert #classification #learning
Estimating replicability of classifier learning experiments (RRB).
ICMLICML-2004-BrefeldS #learning
Co-EM support vector learning (UB, TS).
ICMLICML-2004-Brinker #learning #ranking
Active learning of label ranking functions (KB).
ICMLICML-2004-CastilloW #case study #comparative #learning #multi
A comparative study on methods for reducing myopia of hill-climbing search in multirelational learning (LPC, SW).
ICMLICML-2004-ConitzerS #bound #communication #complexity #game studies #learning
Communication complexity as a lower bound for learning in games (VC, TS).
ICMLICML-2004-EliazarP #learning #mobile #modelling #probability
Learning probabilistic motion models for mobile robots (AIE, RP).
ICMLICML-2004-GaoWLC #approach #categorisation #learning #multi #robust
A MFoM learning approach to robust multiclass multi-label text categorization (SG, WW, CHL, TSC).
ICMLICML-2004-GoldenbergM #learning #scalability
Tractable learning of large Bayes net structures from sparse data (AG, AWM).
ICMLICML-2004-GrossmanD #classification #learning #network
Learning Bayesian network classifiers by maximizing conditional likelihood (DG, PMD).
ICMLICML-2004-HuangYKL #classification #learning #scalability
Learning large margin classifiers locally and globally (KH, HY, IK, MRL).
ICMLICML-2004-JamesS #learning #predict
Learning and discovery of predictive state representations in dynamical systems with reset (MRJ, SPS).
ICMLICML-2004-KashimaT #algorithm #graph #kernel #learning #sequence
Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs (HK, YT).
ICMLICML-2004-KokV
Sparse cooperative Q-learning (JRK, NAV).
ICMLICML-2004-LawrenceP #learning
Learning to learn with the informative vector machine (NDL, JCP).
ICMLICML-2004-MannorMHK #abstraction #clustering #learning
Dynamic abstraction in reinforcement learning via clustering (SM, IM, AH, UK).
ICMLICML-2004-MelvilleM #learning
Diverse ensembles for active learning (PM, RJM).
ICMLICML-2004-MerkeS #approximate #convergence #learning #linear
Convergence of synchronous reinforcement learning with linear function approximation (AM, RS).
ICMLICML-2004-MoralesS #behaviour #learning
Learning to fly by combining reinforcement learning with behavioural cloning (EFM, CS).
ICMLICML-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).
ICMLICML-2004-NguyenS #clustering #learning #using
Active learning using pre-clustering (HTN, AWMS).
ICMLICML-2004-OngMCS #kernel #learning
Learning with non-positive kernels (CSO, XM, SC, AJS).
ICMLICML-2004-PieterN #learning
Apprenticeship learning via inverse reinforcement learning (PA, AYN).
ICMLICML-2004-Potts #incremental #learning #linear
Incremental learning of linear model trees (DP).
ICMLICML-2004-RosalesAF #clustering #learning #using
Learning to cluster using local neighborhood structure (RR, KA, BJF).
ICMLICML-2004-RosencrantzGT #learning #predict
Learning low dimensional predictive representations (MR, GJG, ST).
ICMLICML-2004-RuckertK #bound #learning #towards
Towards tight bounds for rule learning (UR, SK).
ICMLICML-2004-RudarySP #adaptation #constraints #learning #reasoning
Adaptive cognitive orthotics: combining reinforcement learning and constraint-based temporal reasoning (MRR, SPS, MEP).
ICMLICML-2004-Ryabko #learning #online
Online learning of conditionally I.I.D. data (DR).
ICMLICML-2004-Shalev-ShwartzSN #learning #online #pseudo
Online and batch learning of pseudo-metrics (SSS, YS, AYN).
ICMLICML-2004-SimsekB #abstraction #identification #learning #using
Using relative novelty to identify useful temporal abstractions in reinforcement learning (ÖS, AGB).
ICMLICML-2004-SzepesvariS
Interpolation-based Q-learning (CS, WDS).
ICMLICML-2004-TaoSVO #approximate #learning #multi
SVM-based generalized multiple-instance learning via approximate box counting (QT, SDS, NVV, TTO).
ICMLICML-2004-TaskarCK #learning #markov #network
Learning associative Markov networks (BT, VC, DK).
ICMLICML-2004-ToutanovaMN #dependence #learning #modelling #random #word
Learning random walk models for inducing word dependency distributions (KT, CDM, AYN).
ICMLICML-2004-TsochantaridisHJA #machine learning
Support vector machine learning for interdependent and structured output spaces (IT, TH, TJ, YA).
ICMLICML-2004-WeinbergerSS #kernel #learning #matrix #reduction
Learning a kernel matrix for nonlinear dimensionality reduction (KQW, FS, LKS).
ICMLICML-2004-Zadrozny #bias #classification #learning
Learning and evaluating classifiers under sample selection bias (BZ).
ICMLICML-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).
ICPRICPR-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.
ICPRICPR-v1-2004-GocciaSD #classification #fuzzy #learning #recognition
Learning Optimal Classifier Through Fuzzy Recognition Rate Maximization (MG, CS, SGD), pp. 204–207.
ICPRICPR-v1-2004-GokcenJD #bound #learning
Comparing Optimal Bounding Ellipsoid and Support Vector Machine Active Learning (IG, DJ, JRD), pp. 172–175.
ICPRICPR-v1-2004-LeangB #learning
Learning Integrated Perception-Based Speed Control (PL, BB), pp. 813–816.
ICPRICPR-v1-2004-YiKZ #classification #learning
Classifier Combination based on Active Learning (XY, ZK, CZ), pp. 184–187.
ICPRICPR-v2-2004-BeginF #approach #using
Blind Super-Resolution Using a Learning-Based Approach (IB, FPF), pp. 85–89.
ICPRICPR-v2-2004-FangQ #detection #learning
Learning Sample Subspace with Application to Face Detection (JF, GQ), pp. 423–426.
ICPRICPR-v2-2004-JingZLZZ #image #learning #retrieval
Learning in Hidden Annotation-Based Image Retrieval (FJ, BZ, ML, HZ, JZ), pp. 1001–1004.
ICPRICPR-v2-2004-KaneS #classification #image #learning #network
Bayesian Network Structure Learning and Inference in Indoor vs. Outdoor Image Classification (MJK, AES), pp. 479–482.
ICPRICPR-v2-2004-LindgrenH #component #image #independence #learning #representation
Learning High-level Independent Components of Images through a Spectral Representation (JTL, AH), pp. 72–75.
ICPRICPR-v2-2004-LiuS #learning
Reinforcement Learning-Based Feature Learning for Object Tracking (FL, JS), pp. 748–751.
ICPRICPR-v2-2004-SageB #learning
Joint Spatial and Temporal Structure Learning for Task based Control (KS, HB), pp. 48–51.
ICPRICPR-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.
ICPRICPR-v3-2004-FanG #learning
Hierarchical Object Indexing and Sequential Learning (XF, DG), pp. 65–68.
ICPRICPR-v3-2004-KoKB04a #learning #multi #problem
Improved N-Division Output Coding for Multiclass Learning Problems (JK, EK, HB), pp. 470–473.
ICPRICPR-v3-2004-LuoKGHSRH #learning #multi
Active Learning to Recognize Multiple Types of Plankton (TL, KK, DBG, LOH, SS, AR, TH), pp. 478–481.
ICPRICPR-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.
ICPRICPR-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.
ICPRICPR-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.
ICPRICPR-v3-2004-ShiNGY #classification #learning
Critical Vector Learning to Construct RBF Classifiers (DS, GSN, JG, DSY), pp. 359–362.
ICPRICPR-v4-2004-Cardenas #classification #learning #multi #prototype #string
A Learning Model for Multiple-Prototype Classification of Strings (RAM), pp. 420–423.
ICPRICPR-v4-2004-ChenC04a #bidirectional #dependence #learning #network
Improvement of Bidirectional Recurrent Neural Network for Learning Long-Term Dependencies (JC, NSC), pp. 593–596.
ICPRICPR-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.
ICPRICPR-v4-2004-McKennaN #learning #using
Learning Spatial Context from Tracking using Penalised Likelihoods (SJM, HNC), pp. 138–141.
ICPRICPR-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.
ICPRICPR-v4-2004-PiriouBY #detection #image #modelling #probability
Learned Probabilistic Image Motion Models for Event Detection in Videos (GP, PB, JFY), pp. 207–210.
ICPRICPR-v4-2004-QinandS04a #algorithm #kernel #learning #novel #prototype
A Novel Kernel Prototype-Based Learning Algorithm (AKQ, PNS), pp. 621–624.
ICPRICPR-v4-2004-RaytchevYS #estimation #learning
Head Pose Estimation by Nonlinear Manifold Learning (BR, IY, KS), pp. 462–466.
ICPRICPR-v4-2004-SamsonB #clustering #learning #parallel #robust #video
Learning Classes for Video Interpretation with a Robust Parallel Clustering Method (VS, PB), pp. 569–572.
ICPRICPR-v4-2004-StefanoDM #approach #learning
A Dynamic Approach to Learning Vector Quantization (CDS, CD, AM), pp. 601–604.
ICPRICPR-v4-2004-WuCW04a #learning #recognition
Face Recognition Based on Discriminative Manifold Learning (YW, KLC, LW), pp. 171–174.
KDDKDD-2004-AbeVAS #learning
Cross channel optimized marketing by reinforcement learning (NA, NKV, CA, RS), pp. 767–772.
KDDKDD-2004-AbeZL #learning #multi
An iterative method for multi-class cost-sensitive learning (NA, BZ, JL), pp. 3–11.
KDDKDD-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.
KDDKDD-2004-EvgeniouP #learning #multi
Regularized multi--task learning (TE, MP), pp. 109–117.
KDDKDD-2004-KolterM #bytecode #detection #learning
Learning to detect malicious executables in the wild (JZK, MAM), pp. 470–478.
KDDKDD-2004-KummamuruKA #difference #learning #metric
Learning spatially variant dissimilarity (SVaD) measures (KK, RK, RA), pp. 611–616.
KDDKDD-2004-Muslea #machine learning #online #query
Machine learning for online query relaxation (IM), pp. 246–255.
KDDKDD-2004-PopesculU #clustering #concept #learning #relational #statistics
Cluster-based concept invention for statistical relational learning (AP, LHU), pp. 665–670.
KDDKDD-2004-TruongLB #dataset #learning #random #using
Learning a complex metabolomic dataset using random forests and support vector machines (YT, XL, CB), pp. 835–840.
KRKR-2004-PasulaZK #learning #probability #relational
Learning Probabilistic Relational Planning Rules (HP, LSZ, LPK), pp. 683–691.
LSOLSO-2004-ChauM #agile #learning #tool support
Tool Support for Inter-team Learning in Agile Software Organizations (TC, FM), pp. 98–109.
LSOLSO-2004-FalboRBT #how #learning #risk management #using
Learning How to Manage Risks Using Organizational Knowledge (RdAF, FBR, GB, DFT), pp. 7–18.
LSOLSO-2004-HolzM #learning #past present future #research
Research on Learning Software Organizations — Past, Present, and Future (HH, GM), pp. 1–6.
LSOLSO-2004-MelnikR #learning
Impreciseness and Its Value from the Perspective of Software Organizations and Learning (GM, MMR), pp. 122–130.
LSOLSO-2004-Roth-Berghofer #learning
Learning from HOMER, a Case-Based Help Desk Support System (TRB), pp. 88–97.
LSOLSO-2004-SousaAO #learning #maintenance
Learning Software Maintenance Organizations (KDdS, NA, KMdO), pp. 67–77.
SEKESEKE-2004-AvesaniBPS #approach #machine learning #process #requirements
Supporting the Requirements Prioritization Process. A Machine Learning approach (PA, CB, AP, AS), pp. 306–311.
SEKESEKE-2004-DantasBW #game studies #learning #project management
A Simulation-Based Game for Project Management Experiential Learning (ARD, MdOB, CMLW), pp. 19–24.
SEKESEKE-2004-MaxvilleLA #component #learning
Learning to Select Software Components (VM, CPL, JA), pp. 421–426.
SIGIRSIGIR-2004-LamHC #learning #mining #similarity
Learning phonetic similarity for matching named entity translations and mining new translations (WL, RH, PSC), pp. 289–296.
SIGIRSIGIR-2004-RoussinovR #learning #web
Learning patterns to answer open domain questions on the web (DR, JARF), pp. 500–501.
SIGIRSIGIR-2004-XiLB #effectiveness #learning #ranking
Learning effective ranking functions for newsgroup search (WX, JL, EB), pp. 394–401.
SIGIRSIGIR-2004-ZengHCMM #clustering #learning #web
Learning to cluster web search results (HJZ, QCH, ZC, WYM, JM), pp. 210–217.
SIGIRSIGIR-2004-ZhangPZ #machine learning #recognition #using
Focused named entity recognition using machine learning (LZ, YP, TZ), pp. 281–288.
RERE-2004-HaleyNST #categorisation #learning #requirements
The Conundrum of Categorising Requirements: Managing Requirements for Learning on the Move (DTH, BN, HCS, JT), pp. 309–314.
SACSAC-2004-BergholzC #interface #learning #query #web
Learning query languages of Web interfaces (AB, BC), pp. 1114–1121.
SACSAC-2004-Binemann-Zdanowicz #named #towards
SiteLang: : Edu: towards a context-driven e-learning content utilization model (ABZ), pp. 924–928.
SACSAC-2004-CesariniMT #process #workflow
Carrying on the e-learning process with a workflow management engine (MC, MM, RT), pp. 940–945.
SACSAC-2004-ChakravarthySJP #approach
A learning-based approach for fetching pages in WebVigiL (SC, AS, JJ, NP), pp. 1725–1731.
SACSAC-2004-DerntlM #case study #concept #evaluation #experience #learning
Patterns for blended, Person-Centered learning: strategy, concepts, experiences, and evaluation (MD, RMP), pp. 916–923.
SACSAC-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.
SACSAC-2004-LischkaK #execution #modelling
Modeling and execution of E-Learning resources (JL, DK), pp. 971–972.
SACSAC-2004-NeelyLEBNG #architecture #distributed #learning
An architecture for supporting vicarious learning in a distributed environment (SN, HL, DME, JB, JN, XG), pp. 963–970.
SACSAC-2004-OBrienH #analysis #authoring
Training Needs Analysis: the first step in authoring e-learning content (EO, TH), pp. 935–939.
SACSAC-2004-Vrasidas #design
Issues of pedagogy and design in e-learning systems (CV), pp. 911–915.
SACSAC-2004-ZaneroS #detection #learning
Unsupervised learning techniques for an intrusion detection system (SZ, SMS), pp. 412–419.
ICSEICSE-2004-BrunE #fault #machine learning
Finding Latent Code Errors via Machine Learning over Program Executions (YB, MDE), pp. 480–490.
SATSAT-2004-SangBBKP #component #effectiveness #learning
Combining Component Caching and Clause Learning for Effective Model Counting (TS, FB, PB, HAK, TP), pp. 20–28.
DACDAC-2003-GuptaGWYA #bound #learning #model checking #satisfiability
Learning from BDDs in SAT-based bounded model checking (AG, MKG, CW, ZY, PA), pp. 824–829.
DATEDATE-2003-LuWCH #correlation #learning #satisfiability
A Circuit SAT Solver With Signal Correlation Guided Learning (FL, LCW, KTC, RCYH), pp. 10892–10897.
ICDARICDAR-2003-Legal-AyalaF #approach #image #learning #segmentation
Image Segmentation By Learning Approach (HALA, JF), pp. 819–823.
ICDARICDAR-2003-MalerbaEACB #approach #documentation #layout #machine learning
Correcting the Document Layout: A Machine Learning Approach (DM, FE, OA, MC, MB), p. 97–?.
ICDARICDAR-2003-RyuK #learning #recognition #word
Learning the lexicon from raw texts for open-vocabulary Korean word recognition (SR, JHK), pp. 202–206.
ICDARICDAR-2003-ShimizuOWK #image #learning #network
Mirror Image Learning for Autoassociative Neural Networks (SS, WO, TW, FK), pp. 804–808.
ICDARICDAR-2003-TakahashiN #learning #recognition
A class-modular GLVQ ensemble with outlier learning for handwritten digit recognition (KT, DN), pp. 268–272.
CSEETCSEET-2003-AlfonsoM #learning #re-engineering
Learning Software Engineering with Group Work (MIA, FM), p. 309–?.
ITiCSEITiCSE-2003-ChalkBP #design #education #learning #programming
Designing and evaluating learning objects for introductory programming education (PC, CB, PP), p. 240.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2003-EkateriniSP #education #learning #problem
Teaching IT in secondary education through problem-based learning could be really beneficial (GE, BS, GP), p. 243.
ITiCSEITiCSE-2003-Garvin-DoxasB #interactive #learning
Creating learning environments that support interaction (KGD, LJB), p. 276.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2003-GunawardenaA #approach #education #learning #programming
A customized learning objects approach to teaching programming (AG, VA), p. 264.
ITiCSEITiCSE-2003-KurhilaMNFT #learning #peer-to-peer #web
Peer-to-peer learning with open-ended writable Web (JK, MM, PN, PF, HT), pp. 173–177.
ITiCSEITiCSE-2003-Leska #java #learning #user interface #using
Learning to develop GUIs in Java using closed labs (CL), p. 228.
ITiCSEITiCSE-2003-LinosHL #re-engineering
A service-learning program for computer science and software engineering (PKL, SH, JL), pp. 30–34.
ITiCSEITiCSE-2003-LynchM #learning #student
The winds of change: students’ comfort level in different learning environments (KL, SM), pp. 70–73.
ITiCSEITiCSE-2003-MirmotahariHK #architecture #learning
Difficulties learning computer architecture (OM, CH, JK), p. 247.
ITiCSEITiCSE-2003-Nodelman #learning #programming #theory and practice
Learning computer graphics by programming: linking theory and practice (VN), p. 261.
ITiCSEITiCSE-2003-PearsPE #learning #online
Enriching online learning resources with “explanograms” (ANP, LP, CE), p. 237.
ITiCSEITiCSE-2003-RagonisH #distance #multi
A multi-level distance learning-based course for high-school computer science leading-teachers (NR, BH), p. 224.
ITiCSEITiCSE-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.
TACASTACAS-2003-CobleighGP #composition #learning #verification
Learning Assumptions for Compositional Verification (JMC, DG, CSP), pp. 331–346.
CSMRCSMR-2003-Lanza #lessons learnt #named #visualisation
CodeCrawler — Lessons Learned in Building a Software Visualization Tool (ML), pp. 409–418.
ICSMEICSM-2003-LinosB #learning #maintenance #re-engineering
Service Learning in Software Engineering and Maintenance (PKL, CBK), p. 336–?.
WCREWCRE-2003-Murphy #learning
Learning from the Past (GCM), pp. 2–3.
PLDIPLDI-2003-StephensonAMO #compilation #heuristic #machine learning #optimisation
Meta optimization: improving compiler heuristics with machine learning (MS, SPA, MCM, UMO), pp. 77–90.
STOCSTOC-2003-MosselOS #learning
Learning juntas (EM, RO, RAS), pp. 206–212.
DLTDLT-2003-DrewesH #education #learning
Learning a Regular Tree Language from a Teacher (FD, JH), pp. 279–291.
FMFME-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.
CHICHI-2003-KitamuraYHKK #tool support
Things happening in the brain while humans learn to use new tools (YK, YY, HI, FK, MK), pp. 417–424.
ICEISICEIS-v2-2003-BendouM #learning #network #semistructured data
Learning Bayesian Networks From Noisy Data (MB, PM), pp. 26–33.
ICEISICEIS-v2-2003-ColaceSFV #learning #network #ontology
Ontology Learning Through Bayesian Networks (FC, MDS, PF, MV), pp. 430–433.
ICEISICEIS-v2-2003-KeeniGS #learning #network #on the #performance #using
On Fast Learning of Neural Networks Using Back Propagation (KK, KG, HS), pp. 266–271.
ICEISICEIS-v2-2003-Koehler #automation #database #health #learning #network
Tool for Automatic Learning of Bayesian Networks From Database: An Application in the Health Area (CK), pp. 474–481.
ICEISICEIS-v4-2003-BrunsDH #smarttech
Secure Smart Card-Based Access to an E-Learning Portal (RB, JD, JvH), pp. 167–172.
ICEISICEIS-v4-2003-LiuWS #design
Knowledge Construction in E-Learning — Designing an E-Learning Environment (KL, SW, LS), pp. 111–118.
ICEISICEIS-v4-2003-SemeraroLDL #learning
Learning User Profiles for Intelligent Search (GS, PL, MD, OL), pp. 426–429.
ICEISICEIS-v4-2003-TyrvainenJS #case study #learning #on the
On Estimating the Amount of Learning Materials a Case Study (PT, MJ, AS), pp. 127–135.
CIKMCIKM-2003-ZhangOR #learning #using
Learning cross-document structural relationships using boosting (ZZ, JO, DRR), pp. 124–130.
ECIRECIR-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.
ECIRECIR-2003-TianC #collaboration #learning #rating #recommendation #similarity
Learning User Similarity and Rating Style for Collaborative Recommendation (LFT, KWC), pp. 135–145.
ICMLICML-2003-Bar-HillelHSW #distance #equivalence #learning #using
Learning Distance Functions using Equivalence Relations (ABH, TH, NS, DW), pp. 11–18.
ICMLICML-2003-BaramEL #algorithm #learning #online
Online Choice of Active Learning Algorithms (YB, REY, KL), pp. 19–26.
ICMLICML-2003-BerardiCEM #analysis #layout #learning #logic programming #source code
Learning Logic Programs for Layout Analysis Correction (MB, MC, FE, DM), pp. 27–34.
ICMLICML-2003-Bouckaert #algorithm #learning #testing
Choosing Between Two Learning Algorithms Based on Calibrated Tests (RRB), pp. 51–58.
ICMLICML-2003-Brinker #learning
Incorporating Diversity in Active Learning with Support Vector Machines (KB), pp. 59–66.
ICMLICML-2003-BrownW #ambiguity #composition #learning #network
The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods (GB, JLW), pp. 67–74.
ICMLICML-2003-CerquidesM #learning #modelling #naive bayes
Tractable Bayesian Learning of Tree Augmented Naive Bayes Models (JC, RLdM), pp. 75–82.
ICMLICML-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.
ICMLICML-2003-CozmanCC #learning #modelling
Semi-Supervised Learning of Mixture Models (FGC, IC, MCC), pp. 99–106.
ICMLICML-2003-CumbyR #kernel #learning #on the #relational
On Kernel Methods for Relational Learning (CMC, DR), pp. 107–114.
ICMLICML-2003-DriessensR #learning #relational
Relational Instance Based Regression for Relational Reinforcement Learning (KD, JR), pp. 123–130.
ICMLICML-2003-EngelMM #approach #difference #learning #process
Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning (YE, SM, RM), pp. 154–161.
ICMLICML-2003-Even-DarMM #learning
Action Elimination and Stopping Conditions for Reinforcement Learning (EED, SM, YM), pp. 162–169.
ICMLICML-2003-Flach #comprehension #geometry #machine learning #metric
The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics (PAF), pp. 194–201.
ICMLICML-2003-GargR #learning
Margin Distribution and Learning (AG, DR), pp. 210–217.
ICMLICML-2003-GeibelW #learning
Perceptron Based Learning with Example Dependent and Noisy Costs (PG, FW), pp. 218–225.
ICMLICML-2003-GreenwaldH #correlation
Correlated Q-Learning (AG, KH), pp. 242–249.
ICMLICML-2003-IsaacS #learning
Goal-directed Learning to Fly (AI, CS), pp. 258–265.
ICMLICML-2003-Joachims #clustering #graph #learning
Transductive Learning via Spectral Graph Partitioning (TJ), pp. 290–297.
ICMLICML-2003-KalousisH
Representational Issues in Meta-Learning (AK, MH), pp. 313–320.
ICMLICML-2003-KennedyJ #learning #problem
Characteristics of Long-term Learning in Soar and its Application to the Utility Problem (WGK, KADJ), pp. 337–344.
ICMLICML-2003-KirshnerPS #learning #permutation
Unsupervised Learning with Permuted Data (SK, SP, PS), pp. 345–352.
ICMLICML-2003-KotnikK #learning #self
The Significance of Temporal-Difference Learning in Self-Play Training TD-Rummy versus EVO-rummy (CK, JKK), pp. 369–375.
ICMLICML-2003-KrawiecB #learning #synthesis #visual notation
Visual Learning by Evolutionary Feature Synthesis (KK, BB), pp. 376–383.
ICMLICML-2003-KwokT #kernel #learning
Learning with Idealized Kernels (JTK, IWT), pp. 400–407.
ICMLICML-2003-LagoudakisP #classification #learning
Reinforcement Learning as Classification: Leveraging Modern Classifiers (MGL, RP), pp. 424–431.
ICMLICML-2003-LaudD #analysis #learning
The Influence of Reward on the Speed of Reinforcement Learning: An Analysis of Shaping (AL, GD), pp. 440–447.
ICMLICML-2003-LeeL #learning #using
Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression (WSL, BL), pp. 448–455.
ICMLICML-2003-McGovernJ #identification #learning #multi #predict #relational #using
Identifying Predictive Structures in Relational Data Using Multiple Instance Learning (AM, DJ), pp. 528–535.
ICMLICML-2003-MooreW #learning #network
Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure Learning (AWM, WKW), pp. 552–559.
ICMLICML-2003-OngS #kernel #machine learning
Machine Learning with Hyperkernels (CSO, AJS), pp. 568–575.
ICMLICML-2003-OntanonP #learning #multi
Justification-based Multiagent Learning (SO, EP), pp. 576–583.
ICMLICML-2003-RichardsonD #learning #multi
Learning with Knowledge from Multiple Experts (MR, PMD), pp. 624–631.
ICMLICML-2003-RivestP #network
Combining TD-learning with Cascade-correlation Networks (FR, DP), pp. 632–639.
ICMLICML-2003-RuckertK #learning #probability
Stochastic Local Search in k-Term DNF Learning (UR, SK), pp. 648–655.
ICMLICML-2003-RussellZ #learning
Q-Decomposition for Reinforcement Learning Agents (SJR, AZ), pp. 656–663.
ICMLICML-2003-SinghLJPS #learning #predict
Learning Predictive State Representations (SPS, MLL, NKJ, DP, PS), pp. 712–719.
ICMLICML-2003-StimpsonG #approach #learning #social
Learning To Cooperate in a Social Dilemma: A Satisficing Approach to Bargaining (JLS, MAG), pp. 728–735.
ICMLICML-2003-TaskarWK #learning #testing
Learning on the Test Data: Leveraging Unseen Features (BT, MFW, DK), pp. 744–751.
ICMLICML-2003-WangD #learning #modelling #policy
Model-based Policy Gradient Reinforcement Learning (XW, TGD), pp. 776–783.
ICMLICML-2003-WangSPZ #learning #modelling #principle
Learning Mixture Models with the Latent Maximum Entropy Principle (SW, DS, FP, YZ), pp. 784–791.
ICMLICML-2003-WiewioraCE #learning
Principled Methods for Advising Reinforcement Learning Agents (EW, GWC, CE), pp. 792–799.
ICMLICML-2003-WinnerV #learning #named
DISTILL: Learning Domain-Specific Planners by Example (EW, MMV), pp. 800–807.
ICMLICML-2003-WuC #adaptation #learning
Adaptive Feature-Space Conformal Transformation for Imbalanced-Data Learning (GW, EYC), pp. 816–823.
ICMLICML-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.
ICMLICML-2003-ZhangH #learning #taxonomy
Learning from Attribute Value Taxonomies and Partially Specified Instances (JZ, VH), pp. 880–887.
ICMLICML-2003-ZhangXC #adaptation #learning
Exploration and Exploitation in Adaptive Filtering Based on Bayesian Active Learning (YZ, WX, JPC), pp. 896–903.
ICMLICML-2003-ZhuGL #learning #using
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions (XZ, ZG, JDL), pp. 912–919.
KDDKDD-2003-FradkinM #machine learning #random
Experiments with random projections for machine learning (DF, DM), pp. 517–522.
KDDKDD-2003-Koller #learning #relational #statistics
Statistical learning from relational data (DK), p. 4.
KDDKDD-2003-NevilleJFH #learning #probability #relational
Learning relational probability trees (JN, DJ, LF, MH), pp. 625–630.
KDDKDD-2003-SarawagiCG #learning #named #probability #topic
Cross-training: learning probabilistic mappings between topics (SS, SC, SG), pp. 177–186.
MLDMMLDM-2003-Bunke #data mining #graph #machine learning #mining #tool support
Graph-Based Tools for Data Mining and Machine Learning (HB), pp. 7–19.
MLDMMLDM-2003-ComiteGT #learning #multi
Learning Multi-label Alternating Decision Trees from Texts and Data (FDC, RG, MT), pp. 35–49.
MLDMMLDM-2003-Craw #learning #reasoning
Introspective Learning to Build Case-Based Reasoning (CBR) Knowledge Containers (SC), pp. 1–6.
MLDMMLDM-2003-KrawiecB #learning #recognition
Coevolutionary Feature Learning for Object Recognition (KK, BB), pp. 224–238.
MLDMMLDM-2003-KuhnertK #classification #image #learning
A Learning Autonomous Driver System on the Basis of Image Classification and Evolutional Learning (KDK, MK), pp. 400–412.
MLDMMLDM-2003-PiwowarskiG #documentation #information retrieval #machine learning
A Machine Learning Model for Information Retrieval with Structured Documents (BP, PG), pp. 425–438.
SEKESEKE-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.
SEKESEKE-2003-SpanoudakisGZ #approach #machine learning #requirements #traceability
Revising Rules to Capture Requirements Traceability Relations: A Machine Learning Approach (GS, ASdG, AZ), pp. 570–577.
SIGIRSIGIR-2003-GaoWLC #approach #categorisation #learning
A maximal figure-of-merit learning approach to text categorization (SG, WW, CHL, TSC), pp. 174–181.
SACSAC-2003-LiZLO #classification #functional #learning #semistructured data
Gene Functional Classification by Semisupervised Learning from Heterogeneous Data (TL, SZ, QL, MO), pp. 78–82.
SACSAC-2003-RumetshoferW #adaptation #approach #aspect-oriented #learning
An Approach for Adaptable Learning Systems with Respect to Psychological Aspects (HR, WW), pp. 558–563.
PPoPPPPoPP-2003-Puppin #adaptation #convergence #machine learning #scheduling #using
Adapting convergent scheduling using machine learning (DP), p. 1.
CAVCAV-2003-HungarNS #automaton #learning #optimisation
Domain-Specific Optimization in Automata Learning (HH, ON, BS), pp. 315–327.
ICSTSAT-2003-SabharwalBK #learning #performance #problem #using
Using Problem Structure for Efficient Clause Learning (AS, PB, HAK), pp. 242–256.
SIGMODSIGMOD-2002-MarklL #learning
Learning table access cardinalities with LEO (VM, GML), p. 613.
VLDBVLDB-2002-SarawagiBKM #alias #interactive #learning #named
ALIAS: An Active Learning led Interactive Deduplication System (SS, AB, AK, CM), pp. 1103–1106.
CSEETCSEET-2002-Armarego #design #learning #problem
Advanced Software Design: A Case in Problem-Based Learning (JA), pp. 44–54.
CSEETCSEET-2002-JovanovicMMSM #lessons learnt #re-engineering #source code
Panel 3: Software Engineering Masters Programs — Lessons Learned (VMJ, KLM, DM, DS, PEM), pp. 253–255.
CSEETCSEET-2002-UmphressH #education #learning #process
Software Process as a Foundation for Teaching, Learning and Accrediting (DAU, JAHJ), pp. 160–169.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2002-Cassel #learning #network
Very active learning of network routing (LNC), p. 195.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2002-FabregaMJM #learning #network
A virtual network laboratory for learning IP networking (LF, JM, TJ, DM), pp. 161–164.
ITiCSEITiCSE-2002-GarciaM #how #learning #using
Learning how to develop software using the toy LEGO mindstorms (MAG, HPM), p. 239.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2002-Hazzan #abstraction #concept #learning
Reducing abstraction level when learning computability theory concepts (OH), pp. 156–160.
ITiCSEITiCSE-2002-KasyanovK #education #learning
Web-based systems for supporting computer-science teaching and learning (VNK, EVK), p. 238.
ITiCSEITiCSE-2002-Lapidot #experience #learning #self
Self-assessment as a powerful learning experience (TL), p. 198.
ITiCSEITiCSE-2002-LastDHW #collaboration #learning #student
Learning from students: continuous improvement in international collaboration (MZL, MD, MLH, MW), pp. 136–140.
ITiCSEITiCSE-2002-Nygaard #learning #object-oriented
COOL (comprehensive object-oriented learning) (KN), p. 218.
ITiCSEITiCSE-2002-ParkinsonR #learning #performance #question
Do cognitive styles affect learning performance in different computer media? (AP, JAR), pp. 39–43.
ITiCSEITiCSE-2002-PlekhanovaM #learning #process #re-engineering
Learning processes in software engineering projects (VP, WM), p. 230.
ITiCSEITiCSE-2002-StewartKM #authoring #named
MediaMime: after-the-fact authoring annotation system for an e-learning environment (AS, PK, MM), p. 243.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2002-WaltersASBK #learning
Increasing learning and decreasing costs in a computer fluency course (DW, CA, BS, DTB, HK), pp. 208–212.
STOCSTOC-2002-HellersteinR #learning #using
Exact learning of DNF formulas using DNF hypotheses (LH, VR), pp. 465–473.
CHICHI-2002-Ehret #learning #user interface #visual notation
Learning where to look: location learning in graphical user interfaces (BDE), pp. 211–218.
CHICHI-2002-SnowdonG #experience #learning
Diffusing information in organizational settings: learning from experience (DS, AG), pp. 331–338.
CHICHI-2002-ZhaiSA #learning
Movement model, hits distribution and learning in virtual keyboarding (SZ, AES, JA), pp. 17–24.
CAiSECAiSE-2002-BerlinM #database #feature model #machine learning #using
Database Schema Matching Using Machine Learning with Feature Selection (JB, AM), pp. 452–466.
ICEISICEIS-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.
ICEISICEIS-2002-IglesiasMCCF #database #design #education #fault #learning
Learning to Teach Database Design by Trial and Error (AI, PM, DC, EC, FF), pp. 500–505.
ICEISICEIS-2002-Oliver #automation
A Training Environment for Automated Sales Agents to Learn Negotiation Strategies (JRO), pp. 410–417.
ICEISICEIS-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.
CIKMCIKM-2002-HuangCA #comparison #learning #web
Comparison of interestingness functions for learning web usage patterns (XH, NC, AA), pp. 617–620.
ICMLICML-2002-BianchettiRS #concept #constraints #learning #relational
Constraint-based Learning of Long Relational Concepts (JAB, CR, MS), pp. 35–42.
ICMLICML-2002-ChisholmT #learning #random
Learning Decision Rules by Randomized Iterative Local Search (MC, PT), pp. 75–82.
ICMLICML-2002-DietterichBMS #learning #probability #refinement
Action Refinement in Reinforcement Learning by Probability Smoothing (TGD, DB, RLdM, CS), pp. 107–114.
ICMLICML-2002-DriessensD #learning #relational
Integrating Experimentation and Guidance in Relational Reinforcement Learning (KD, SD), pp. 115–122.
ICMLICML-2002-FerriFH #learning #using
Learning Decision Trees Using the Area Under the ROC Curve (CF, PAF, JHO), pp. 139–146.
ICMLICML-2002-GhavamzadehM #learning
Hierarchically Optimal Average Reward Reinforcement Learning (MG, SM), pp. 195–202.
ICMLICML-2002-GonzalezHC #concept #graph #learning #relational
Graph-Based Relational Concept Learning (JAG, LBH, DJC), pp. 219–226.
ICMLICML-2002-GuestrinLP #coordination #learning
Coordinated Reinforcement Learning (CG, MGL, RP), pp. 227–234.
ICMLICML-2002-GuestrinPS #learning #modelling
Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs (CG, RP, DS), pp. 235–242.
ICMLICML-2002-Hengst #learning
Discovering Hierarchy in Reinforcement Learning with HEXQ (BH), pp. 243–250.
ICMLICML-2002-JensenN #bias #feature model #learning #relational
Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning (DJ, JN), pp. 259–266.
ICMLICML-2002-KakadeL #approximate #learning
Approximately Optimal Approximate Reinforcement Learning (SK, JL), pp. 267–274.
ICMLICML-2002-LanckrietCBGJ #kernel #learning #matrix #programming
Learning the Kernel Matrix with Semi-Definite Programming (GRGL, NC, PLB, LEG, MIJ), pp. 323–330.
ICMLICML-2002-LaudD #behaviour #learning
Reinforcement Learning and Shaping: Encouraging Intended Behaviors (AL, GD), pp. 355–362.
ICMLICML-2002-LeckieR #distributed #learning #probability
Learning to Share Distributed Probabilistic Beliefs (CL, KR), pp. 371–378.
ICMLICML-2002-MerkeS #approximate #convergence #learning
A Necessary Condition of Convergence for Reinforcement Learning with Function Approximation (AM, RS), pp. 411–418.
ICMLICML-2002-Mladenic #learning #normalisation #using #word
Learning word normalization using word suffix and context from unlabeled data (DM), pp. 427–434.
ICMLICML-2002-MusleaMK #learning #multi #robust
Active + Semi-supervised Learning = Robust Multi-View Learning (IM, SM, CAK), pp. 435–442.
ICMLICML-2002-OatesDB #context-free grammar #learning
Learning k-Reversible Context-Free Grammars from Positive Structural Examples (TO, DD, VB), pp. 459–465.
ICMLICML-2002-OLZ #learning #using
Stock Trading System Using Reinforcement Learning with Cooperative Agents (JO, JWL, BTZ), pp. 451–458.
ICMLICML-2002-PanangadanD #2d #correlation #learning #navigation
Learning Spatial and Temporal Correlation for Navigation in a 2-Dimensional Continuous World (AP, MGD), pp. 474–481.
ICMLICML-2002-ParkZ #learning
A Boosted Maximum Entropy Model for Learning Text Chunking (SBP, BTZ), pp. 482–489.
ICMLICML-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.
ICMLICML-2002-PeshkinS #experience #learning
Learning from Scarce Experience (LP, CRS), pp. 498–505.
ICMLICML-2002-PickettB #algorithm #learning #named
PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning (MP, AGB), pp. 506–513.
ICMLICML-2002-Ryan #automation #behaviour #learning #modelling #using
Using Abstract Models of Behaviours to Automatically Generate Reinforcement Learning Hierarchies (MRKR), pp. 522–529.
ICMLICML-2002-SeriT #learning #modelling
Model-based Hierarchical Average-reward Reinforcement Learning (SS, PT), pp. 562–569.
ICMLICML-2002-ShapiroL #learning #using
Separating Skills from Preference: Using Learning to Program by Reward (DGS, PL), pp. 570–577.
ICMLICML-2002-Stirling #learning
Learning to Fly by Controlling Dynamic Instabilities (DS), pp. 586–593.
ICMLICML-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.
ICMLICML-2002-ZhangGYF #image #learning #multi #retrieval #using
Content-Based Image Retrieval Using Multiple-Instance Learning (QZ, SAG, WY, JEF), pp. 682–689.
ICMLICML-2002-ZubekD #heuristic #learning
Pruning Improves Heuristic Search for Cost-Sensitive Learning (VBZ, TGD), pp. 19–26.
ICPRICPR-v1-2002-HadidKP #analysis #learning #linear #using
Unsupervised Learning Using Locally Linear Embedding: Experiments with Face Pose Analysis (AH, OK, MP), pp. 111–114.
ICPRICPR-v1-2002-HaroE #learning #video
Learning Video Processing by Example (AH, IAE), pp. 487–491.
ICPRICPR-v1-2002-RobertsMR #3d #learning #online
Online Appearance Learning or 3D Articulated Human Tracking (TJR, SJM, IWR), pp. 425–428.
ICPRICPR-v2-2002-Al-ShaherH #learning #modelling #online #performance
Fast On-Line learning of Point Distribution Models (AAAS, ERH), pp. 208–211.
ICPRICPR-v2-2002-Amin #learning #prototype #using
Prototyping Structural Description Using Decision Tree Learning Techniques (AA), pp. 76–79.
ICPRICPR-v2-2002-ChiuLY #learning #personalisation
Learning User Preference in a Personalized CBIR Systeml (CYC, HCL, SNY), p. 532–?.
ICPRICPR-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.
ICPRICPR-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.
ICPRICPR-v2-2002-Lashkia #learning
Learning with Relevant Features and Examples (GVL), pp. 68–71.
ICPRICPR-v2-2002-LiuB #concept #learning #semantics #video #visual notation
Learning Semantic Visual Concepts from Video (JL, BB), pp. 1061–1064.
ICPRICPR-v2-2002-Maloof #analysis #machine learning #on the #statistics #testing
On Machine Learning, ROC Analysis, and Statistical Tests of Significance (MAM), pp. 204–207.
ICPRICPR-v2-2002-PhungDV #analysis #education
Narrative Structure Analysis with Education and Training Videos for E-Learning (DQP, CD, SV), p. 835–?.
ICPRICPR-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.
ICPRICPR-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.
ICPRICPR-v2-2002-ShiWOK #case study #comparative #image #learning
Comparative Study on Mirror Image Learning (MIL) and GLVQ (MS, TW, WO, FK), p. 248–?.
ICPRICPR-v2-2002-TohM #approach #learning #network
A Global Transformation Approach to RBF Neural Network Learning (KAT, KZM), pp. 96–99.
ICPRICPR-v2-2002-Torkkola02a #feature model #learning #problem
Learning Feature Transforms Is an Easier Problem Than Feature Selection (KT), pp. 104–107.
ICPRICPR-v2-2002-WechslerDL #learning #process #using
Hierarchical Interpretation of Human Activities Using Competitive Learning (HW, ZD, FL), pp. 338–341.
ICPRICPR-v3-2002-ArtacJL #incremental #learning #online #recognition #visual notation
Incremental PCA or On-Line Visual Learning and Recognition (MA, MJ, AL), pp. 781–784.
ICPRICPR-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.
ICPRICPR-v3-2002-ChartierL #image #learning #network
Learning and Extracting Edges from Images by a Modified Hopfield Neural Network (SC, RL), pp. 431–434.
ICPRICPR-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–?.
ICPRICPR-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.
ICPRICPR-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.
ICPRICPR-v3-2002-LuoWH02a #approach #graph #learning
Graph Spectral Approach for Learning View Structure (BL, RCW, ERH), pp. 785–788.
ICPRICPR-v3-2002-Sakano #how #learning #query #search-based
Genetic Translator: How to Apply Query Learning to Practical OCR (HS), pp. 184–187.
ICPRICPR-v3-2002-SinghR #learning #recognition #robust
Background Learning for Robust Face Recognition (RKS, ANR), pp. 525–528.
ICPRICPR-v3-2002-SuW #identification #learning #process
A Learning Process to the Identification of Feature Points on Chinese Characters (YMS, JFW), pp. 93–97.
ICPRICPR-v4-2002-KubotaMK #fault #learning #optimisation
A Discriminative Learning Criterion for the Overall Optimization of Error and Reject (SK, HM, YK), pp. 98–102.
ICPRICPR-v4-2002-LiuSF #classification #learning #polynomial
Learning Quadratic Discriminant Function for Handwritten Character Classification (CLL, HS, HF), pp. 44–47.
KDDKDD-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.
KDDKDD-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.
KDDKDD-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.
KDDKDD-2002-KruengkraiJ #algorithm #classification #learning #parallel
A parallel learning algorithm for text classification (CK, CJ), pp. 201–206.
KDDKDD-2002-MahoneyC #detection #learning #modelling #network #novel
Learning nonstationary models of normal network traffic for detecting novel attacks (MVM, PKC), pp. 376–385.
KDDKDD-2002-PednaultAZ #learning
Sequential cost-sensitive decision making with reinforcement learning (EPDP, NA, BZ), pp. 259–268.
KDDKDD-2002-SarawagiB #interactive #learning #using
Interactive deduplication using active learning (SS, AB), pp. 269–278.
KDDKDD-2002-TejadaKM #identification #independence #learning #string
Learning domain-independent string transformation weights for high accuracy object identification (ST, CAK, SM), pp. 350–359.
KDDKDD-2002-YuHC #classification #learning #named #using #web
PEBL: positive example based learning for Web page classification using SVM (HY, JH, KCCC), pp. 239–248.
KRKR-2002-BeygelzimerR #complexity #learning #network
Inference Complexity as a Model-Selection Criterion for Learning Bayesian Networks (AB, IR), pp. 558–567.
LSOLSO-2002-AngkasaputraPRT #collaboration #implementation #learning
The Collaborative Learning Methodology CORONET-Train: Implementation and Guidance (NA, DP, ER, ST), pp. 13–24.
LSOLSO-2002-HenningerM #agile #concept #development #learning #question
Learning Software Organizations and Agile Software Development: Complementary or Contradictory Concepts? (SH, FM), pp. 1–3.
LSOLSO-2002-HofmannW #approach #community #learning
Building Communities among Software Engineers: The ViSEK Approach to Intra- and Inter-Organizational Learning (BH, VW), pp. 25–33.
LSOLSO-2002-NeuB #comprehension #learning #process #simulation
Learning and Understanding a Software Process through Simulation of Its Underlying Model (HN, UBK), pp. 81–93.
LSOLSO-2002-Ruhe #learning #paradigm #re-engineering
Software Engineering Decision Support ? A New Paradigm for Learning Software Organizations (GR), pp. 104–113.
SEKESEKE-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.
SEKESEKE-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.
SEKESEKE-2002-LoiaSS #deduction #named #web
LearnMiner: deductive, tolerant agents for discovering didactic resources on the web (VL, SS, MIS), pp. 109–115.
SEKESEKE-2002-MaidantchikMS #learning #requirements
Learning organizational knowledge: an evolutionary proposal for requirements engineering (CM, MM, GS), pp. 151–157.
SEKESEKE-2002-TortoraSVD #learning #multi
A multilevel learning management system (GT, MS, GV, PD), pp. 541–547.
SIGIRSIGIR-2002-AminiG #learning #summary
The use of unlabeled data to improve supervised learning for text summarization (MRA, PG), pp. 105–112.
UMLUML-2002-AnidoCRS #concept #corba
Applying MDA Concepts to Develop a Domain CORBA Facility for E-learning (LEAR, MC, JSR, JMS), pp. 321–335.
SACSAC-2002-BoughanemT #adaptation #incremental #learning
Incremental adaptive filtering: profile learning and threshold calibration (MB, MT), pp. 640–644.
SACSAC-2002-ElishRF #collaboration #learning #network
Evaluating collaborative software in supporting organizational learning with Bayesian Networks (MOE, DCR, JEF), pp. 992–996.
SACSAC-2002-NevesBR #classification #game studies #learning
Learning the risk board game with classifier systems (AN, OB, ACR), pp. 585–589.
SACSAC-2002-SeleznyovM #detection #learning
Learning temporal patterns for anomaly intrusion detection (AS, OM), pp. 209–213.
ICSEICSE-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.
HPCAHPCA-2002-CintraT #learning #parallel #thread
Speculative Multithreading Eliminating Squashes through Learning Cross-Thread Violations in Speculative Parallelization for Multiprocessors (MHC, JT), pp. 43–54.
CADECADE-2002-JamnikKP
Learn Omega-matic: System Description (MJ, MK, MP), pp. 150–155.
CAVCAV-2002-ClarkeGKS #abstraction #machine learning #satisfiability #using
SAT Based Abstraction-Refinement Using ILP and Machine Learning Techniques (EMC, AG, JHK, OS), pp. 265–279.
ICLPICLP-2002-MartinNSS #learning #logic #prolog
Learning in Logic with RichProlog (EM, PMN, AS, FS), pp. 239–254.
DACDAC-2001-GizdarskiF #complexity #framework #learning
A Framework for Low Complexity Static Learning (EG, HF), pp. 546–549.
DATEDATE-2001-NovikovG #learning #multi #performance
An efficient learning procedure for multiple implication checks (YN, EIG), pp. 127–135.
HTHT-2001-ConlanHLWA #adaptation #learning #metadata
Extending eductional metadata schemas to describe adaptive learning resources (OC, CH, PL, VPW, DA), pp. 161–162.
ICDARICDAR-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.
ICDARICDAR-2001-HoqueF #classification #learning
An Improved Learning Scheme for the Moving Window Classifier (SH, MCF), pp. 607–611.
ICDARICDAR-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.
ICDARICDAR-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.
ICDARICDAR-2001-ValvenyM #learning #using
Learning of Structural Descriptions of Graphic Symbols Using Deformable Template Matching (EV, EM), pp. 455–459.
ICDARICDAR-2001-WakabayashiSOK #image #learning #recognition
Accuracy Improvement of Handwritten Numeral Recognition by Mirror Image Learning (TW, MS, WO, FK), pp. 338–343.
CSEETCSEET-2001-ArmaregoFR #development #learning #online #re-engineering
Constructing Software Engineering Knowledge: Development of an Online Learning Environment (JA, LF, GGR), pp. 258–267.
CSEETCSEET-2001-RatcliffeTW #learning
A Learning Environment for First Year Software Engineers (MR, LT, JW), pp. 268–275.
ITiCSEITiCSE-2001-BlankPKHJR #collaboration #multi #named
CIMEL: constructive, collaborative inquiry-based multimedia E-learning (GDB, WMP, GDK, MH, HJ, SR), p. 179.
ITiCSEITiCSE-2001-CarboneHMG #learning #programming
Characteristics of programming exercises that lead to poor learning tendencies: Part II (AC, JH, IM, DG), pp. 93–96.
ITiCSEITiCSE-2001-Chalk #learning
Scaffolding learning in virtual environments (PC), pp. 85–88.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2001-CiesielskiM #algorithm #animation #learning #student #using
Using animation of state space algorithms to overcome student learning difficulties (VC, PM), pp. 97–100.
ITiCSEITiCSE-2001-Ginat #algorithm #learning #problem
Metacognitive awareness utilized for learning control elements in algorithmic problem solving (DG), pp. 81–84.
ITiCSEITiCSE-2001-Kumar #c++ #interactive #learning #pointer
Learning the interaction between pointers and scope in C++ (ANK), pp. 45–48.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2001-Putnik #integration #learning #on the
On integration of learning and technology (ZP), p. 185.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2001-Thomas #student
The coach supporting students as they learn to program (PT), p. 177.
ITiCSEITiCSE-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.
CSMRCSMR-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.
WCREWCRE-2001-Davis #lessons learnt #reverse engineering
Lessons Learned in Data Reverse Engineering (KHD), pp. 323–327.
STOCSTOC-2001-KlivansS01a #learning
Learning DNF in time 2Õ(n1/3) (AK, RAS), pp. 258–265.
STOCSTOC-2001-SanjeevK #learning
Learning mixtures of arbitrary gaussians (SA, RK), pp. 247–257.
FLOPSFLOPS-2001-Ferri-RamirezHR #functional #incremental #learning #logic programming #source code
Incremental Learning of Functional Logic Programs (CF, JHO, MJRQ), pp. 233–247.
FLOPSFLOPS-2001-Sato #learning #logic programming #source code
Parameterized Logic Programs where Computing Meets Learning (TS), pp. 40–60.
ICALPICALP-2001-Servedio #learning #quantum
Separating Quantum and Classical Learning (RAS), pp. 1065–1080.
CHICHI-2001-CorbettA #feedback #learning
Locus of feedback control in computer-based tutoring: impact on learning rate, achievement and attitudes (ATC, JRA), pp. 245–252.
CHICHI-2001-RossonS #education #learning #reuse #simulation
Teachers as simulation programmers: minimalist learning and reuse (MBR, CDS), pp. 237–244.
VISSOFTSVIS-2001-Faltin #algorithm #constraints #interactive #learning
Structure and Constraints in Interactive Exploratory Algorithm Learning (NF), pp. 213–226.
VISSOFTSVIS-2001-RossG #education #learning #named #web
Hypertextbooks: Animated, Active Learning, Comprehensive Teaching and Learning Resources for the Web (RJR, MTG), pp. 269–284.
ICEISICEIS-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.
ICEISICEIS-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.
ICEISICEIS-v2-2001-AudyBF #information management #learning
Information Systems Planning: Contributions from Organizational Learning (JLNA, JLB, HF), pp. 873–879.
ICEISICEIS-v2-2001-BressanAAG #3d #learning #multi #web
Multiuser 3D Learning Environments in the Web (CMB, SdA, RBdA, CG), pp. 1170–1173.
CIKMCIKM-2001-NottelmannF #classification #datalog #learning #probability
Learning Probabilistic Datalog Rules for Information Classification and Transformation (HN, NF), pp. 387–394.
ICMLICML-2001-AmarDGZ #learning #multi
Multiple-Instance Learning of Real-Valued Data (RAA, DRD, SAG, QZ), pp. 3–10.
ICMLICML-2001-BlumC #graph #learning #using
Learning from Labeled and Unlabeled Data using Graph Mincuts (AB, SC), pp. 19–26.
ICMLICML-2001-BowlingV #convergence #learning
Convergence of Gradient Dynamics with a Variable Learning Rate (MHB, MMV), pp. 27–34.
ICMLICML-2001-ChajewskaKO #behaviour #learning
Learning an Agent’s Utility Function by Observing Behavior (UC, DK, DO), pp. 35–42.
ICMLICML-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.
ICMLICML-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.
ICMLICML-2001-EngelM #embedded #learning #markov #process
Learning Embedded Maps of Markov Processes (YE, SM), pp. 138–145.
ICMLICML-2001-Furnkranz #learning
Round Robin Rule Learning (JF), pp. 146–153.
ICMLICML-2001-Geibel #bound #learning
Reinforcement Learning with Bounded Risk (PG), pp. 162–169.
ICMLICML-2001-GetoorFKT #learning #modelling #probability #relational
Learning Probabilistic Models of Relational Structure (LG, NF, DK, BT), pp. 170–177.
ICMLICML-2001-GhavamzadehM #learning
Continuous-Time Hierarchical Reinforcement Learning (MG, SM), pp. 186–193.
ICMLICML-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.
ICMLICML-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.
ICMLICML-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.
ICMLICML-2001-Krawiec #comparison #learning
Pairwise Comparison of Hypotheses in Evolutionary Learning (KK), pp. 266–273.
ICMLICML-2001-Lee #collaboration #learning #recommendation
Collaborative Learning and Recommender Systems (WSL), pp. 314–321.
ICMLICML-2001-Littman #game studies
Friend-or-Foe Q-learning in General-Sum Games (MLL), pp. 322–328.
ICMLICML-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.
ICMLICML-2001-MarchandS #learning #set
Learning with the Set Covering Machine (MM, JST), pp. 345–352.
ICMLICML-2001-McGovernB #automation #learning #using
Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density (AM, AGB), pp. 361–368.
ICMLICML-2001-PerkinsB #learning #set
Lyapunov-Constrained Action Sets for Reinforcement Learning (TJP, AGB), pp. 409–416.
ICMLICML-2001-PrecupSD #approximate #difference #learning
Off-Policy Temporal Difference Learning with Function Approximation (DP, RSS, SD), pp. 417–424.
ICMLICML-2001-RoyM #estimation #fault #learning #reduction #towards
Toward Optimal Active Learning through Sampling Estimation of Error Reduction (NR, AM), pp. 441–448.
ICMLICML-2001-SatoK #learning #markov #problem
Average-Reward Reinforcement Learning for Variance Penalized Markov Decision Problems (MS, SK), pp. 473–480.
ICMLICML-2001-SingerV #implementation #learning #performance
Learning to Generate Fast Signal Processing Implementations (BS, MMV), pp. 529–536.
ICMLICML-2001-StoneS #learning #scalability #towards
Scaling Reinforcement Learning toward RoboCup Soccer (PS, RSS), pp. 537–544.
ICMLICML-2001-Venkataraman #learning
A procedure for unsupervised lexicon learning (AV), pp. 569–576.
ICMLICML-2001-Wiering #learning #using
Reinforcement Learning in Dynamic Environments using Instantiated Information (MW), pp. 585–592.
ICMLICML-2001-Wyatt #learning #using
Exploration Control in Reinforcement Learning using Optimistic Model Selection (JLW), pp. 593–600.
ICMLICML-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–?.
KDDKDD-2001-KaltonLWY #clustering #learning
Generalized clustering, supervised learning, and data assignment (AK, PL, KW, JPY), pp. 299–304.
KDDKDD-2001-ZadroznyE #learning
Learning and making decisions when costs and probabilities are both unknown (BZ, CE), pp. 204–213.
LSOLSO-2001-FeldmannA #learning #on the
On the Status of Learning Software Organizations in the Year 2001 (RLF, KDA), pp. 2–7.
LSOLSO-2001-Henninger #learning
Organizational Learning in Dynamic Domains (SH), pp. 8–16.
LSOLSO-2001-Lehner #how
Keynote Address: How do Companies Learn? Selected Applications from the IT Sector (FL), p. 17.
LSOLSO-2001-LindvallFCT #experience #lessons learnt
Lessons Learned about Structuring and Describing Experience for Three Experience Bases (ML, MF, PC, RT), pp. 106–119.
LSOLSO-2001-PfahlADR #collaboration #learning #named
CORONET-Train: A Methodology for Web-Based Collaborative Learning in Software Organisations (DP, NA, CD, GR), pp. 37–51.
LSOLSO-2001-Segal #case study #learning #process
Organisational Learning and Software Process Improvement: A Case Study (JS), pp. 68–82.
LSOLSO-2001-StarkloffP #approach #development #learning
Process-Integrated Learning: The ADVISOR Approach for Corporate Development (PS, KP), pp. 152–162.
MLDMMLDM-2001-BhanuD #clustering #concept #feedback #fuzzy #learning
Concepts Learning with Fuzzy Clustering and Relevance Feedback (BB, AD), pp. 102–116.
MLDMMLDM-2001-DongKS #framework #learning #recognition
Local Learning Framework for Recognition of Lowercase Handwritten Characters (JxD, AK, CYS), pp. 226–238.
MLDMMLDM-2001-Fernau #learning #xml
Learning XML Grammars (HF), pp. 73–87.
MLDMMLDM-2001-KollmarH #feature model #learning
Feature Selection for a Real-World Learning Task (DK, DHH), pp. 157–172.
MLDMMLDM-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.
MLDMMLDM-2001-Krzyzak #classification #learning #network #using
Nonlinear Function Learning and Classification Using Optimal Radial Basis Function Networks (AK), pp. 217–225.
MLDMMLDM-2001-LinderP #how #learning
How to Automate Neural Net Based Learning (RL, SJP), pp. 206–216.
MLDMMLDM-2001-ShiWOK #image #learning #recognition
Mirror Image Learning for Handwritten Numeral Recognition (MS, TW, WO, FK), pp. 239–248.
SEKESEKE-2001-NavarroH #adaptation #game studies #learning
Adapting Game Technology to Support Individual and Organizational Learning (EON, AvdH), pp. 347–354.
SEKESEKE-2001-PfahlR #learning
System Dynamics as an Enabling Technology for Learning in Software Organizations (DP, GR), pp. 355–362.
SEKESEKE-2001-VincenziNMDR #guidelines
Bayesian-Learning Based Guidelines to determine Equivalente Mutants (AMRV, EYN, JCM, MED, RAFR), pp. 180–187.
SIGIRSIGIR-2001-Joachims #classification #learning #statistics
A Statistical Learning Model of Text Classification for Support Vector Machines (TJ), pp. 128–136.
SIGIRSIGIR-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.
SIGIRSIGIR-2001-LamL #approach #categorisation
A Meta-Learning Approach for Text Categorization (WL, KYL), pp. 303–309.
SIGIRSIGIR-2001-LeeS #clustering #image #learning #retrieval #using
Intelligent Object-based Image Retrieval Using Cluster-driven Personal Preference Learning (KML, WNS), pp. 436–437.
RERE-2001-Kovitz #backtracking #development #learning
Is Backtracking so Bad? The Role of Learning in Software Development (BK), p. 272.
SACSAC-2001-DeermanLP #algorithm #predict #problem #search-based
Linkage-learning genetic algorithm application to the protein structure prediction problem (KRD, GBL, RP), pp. 333–339.
SACSAC-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.
SACSAC-2001-LeeGA #learning #multi
A multi-neural-network learning for lot sizing and sequencing on a flow-shop (IL, JNDG, ADA), pp. 36–40.
SACSAC-2001-OkabeY #documentation #interactive #learning #relational #retrieval
Interactive document retrieval with relational learning (MO, SY), pp. 27–31.
ICSTSAT-2001-LagoudakisL #branch #learning #satisfiability
Learning to Select Branching Rules in the DPLL Procedure for Satisfiability (MGL, MLL), pp. 344–359.
HTHT-2000-FischerS #adaptation #automation #hypermedia #learning
Automatic creation of exercises in adaptive hypermedia learning systems (SF, RS), pp. 49–55.
HTHT-2000-Larsen #flexibility #hypermedia #what
Providing flexibility within hypertext systems: what we’ve learned at HT workshops, CyberMountain, and elsewhere (DL), pp. 268–269.
HTHT-2000-SpalterS #distance #hypermedia #jit #learning #reuse
Reusable hypertext structures for distance and JIT learning (AMS, RMS), pp. 29–38.
SIGMODSIGMOD-2000-ChenDLT #learning #named #query #web
Fact: A Learning Based Web Query Processing System (SC, YD, HL, ZT), p. 587.
SIGMODSIGMOD-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.
VLDBVLDB-2000-DiaoLCT #learning #query #towards #web
Toward Learning Based Web Query Processing (YD, HL, SC, ZT), pp. 317–328.
CSEETCSEET-2000-Cusick #education #lessons learnt #re-engineering #student
Lessons Learned from Teaching Software Engineering to Adult Students (JJC), p. 39–?.
CSEETCSEET-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.
CSEETCSEET-2000-KorneckiZE #concept #learning #programming #realtime
Learning Real-Time Programming Concepts through VxWorks Lab Experiments (AJK, JZ, DE), p. 294–?.
CSEETCSEET-2000-WilliamsK #education #re-engineering
The Effects of “Pair-Pressure” and “Pair-Learning” on Software Engineering Education (LAW, RRK), pp. 59–65.
ITiCSEITiCSE-2000-BlandL #learning
Agents, profiles, learning styles and tutors (poster session) (CGB, PBL), p. 185.
ITiCSEITiCSE-2000-Chalk #learning #re-engineering #using
Apprenticeship learning of software engineering using Webworlds (PC), pp. 112–115.
ITiCSEITiCSE-2000-Chang #analysis #concept #learning #web
Discovering learning patterns from Web logs by concept transformation analysis (poster session) (CKC), pp. 186–187.
ITiCSEITiCSE-2000-Eremin
Software system to learn objects (poster session) (EE), p. 188.
ITiCSEITiCSE-2000-Hobbs #assessment #email #learning
Email groups for learning and assessment (MH), p. 183.
ITiCSEITiCSE-2000-KhuriH #algorithm #image #interactive #learning
Interactive packages for learning image compression algorithms (SK, HCH), pp. 73–76.
ITiCSEITiCSE-2000-OuCLL #learning #web
Instructional instruments for Web group learning systems: the grouping, intervention, and strategy (KLO, GDC, CCL, BJL), pp. 69–72.
ITiCSEITiCSE-2000-RosbottomCF #learning #online
A generic model for on-line learning (JR, JC, DF), pp. 108–111.
ITiCSEITiCSE-2000-ShinYLL #database #education #learning
Plan of teaching & learning for database software through situated learning (poster session) (SBS, IHY, CHL, TWL), pp. 193–194.
ITiCSEITiCSE-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.
ITiCSEITiCSE-2000-Thompson #learning #maturity #process
Learning process maturity (poster session) (ET), p. 195.
FASEFASE-2000-Hernandez-OralloR #learning #lifecycle #quality
Software as Learning: Quality Factors and Life-Cycle Revised (JHO, MJRQ), pp. 147–162.
STOCSTOC-2000-BlumKW #learning #problem #query #statistics
Noise-tolerant learning, the parity problem, and the statistical query model (AB, AK, HW), pp. 435–440.
CHICHI-2000-ConwayABCC #3d #lessons learnt #named
Alice: lessons learned from building a 3D system for novices (MC, SA, TB, DC, KC), pp. 486–493.
CHICHI-2000-CorbettT #difference #learning
Instructional interventions in computer-based tutoring: differential impact on learning time and accuracy (ATC, HJT), pp. 97–104.
CSCWCSCW-2000-CadizBSGGJ #collaboration #distance #distributed #learning #video
Distance learning through distributed collaborative video viewing (JJC, AB, ES, AG, JG, GJ), pp. 135–144.
CSCWCSCW-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.
ICEISICEIS-2000-KleinerSB #estimation #learning
Self Organizing Maps for Value Estimation to Solve Reinforcement Learning Tasks (AK, BS, OB), pp. 149–156.
ICEISICEIS-2000-NobreC #information management #learning
Information Systems and Learning Organisations (ALN, MPeC), pp. 327–332.
ICEISICEIS-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.
CIKMCIKM-2000-GhaniJ #database #learning #multi
Learning a Monolingual Language Model from a Multilingual Text Database (RG, RJ), pp. 187–193.
CIKMCIKM-2000-LamL #documentation #learning
Learning to Extract Hierarchical Information from Semi-structured Documents (WL, WYL), pp. 250–257.
ICMLICML-2000-AlerBI #information management #learning #representation
Knowledge Representation Issues in Control Knowledge Learning (RA, DB, PI), pp. 1–8.
ICMLICML-2000-AllenG #comparison #empirical #learning
Model Selection Criteria for Learning Belief Nets: An Empirical Comparison (TVA, RG), pp. 1047–1054.
ICMLICML-2000-BaxterB #learning
Reinforcement Learning in POMDP’s via Direct Gradient Ascent (JB, PLB), pp. 41–48.
ICMLICML-2000-BoschZ #in memory #learning #multi
Unpacking Multi-valued Symbolic Features and Classes in Memory-Based Language Learning (AvdB, JZ), pp. 1055–1062.
ICMLICML-2000-Bowling #convergence #learning #multi #problem
Convergence Problems of General-Sum Multiagent Reinforcement Learning (MHB), pp. 89–94.
ICMLICML-2000-CampbellCS #classification #learning #query #scalability
Query Learning with Large Margin Classifiers (CC, NC, AJS), pp. 111–118.
ICMLICML-2000-ChangCM #learning
Learning to Create Customized Authority Lists (HC, DC, AM), pp. 127–134.
ICMLICML-2000-ChoiY #database #learning
Learning to Select Text Databases with Neural Nets (YSC, SIY), pp. 135–142.
ICMLICML-2000-ChownD #approach #divide and conquer #information management #learning
A Divide and Conquer Approach to Learning from Prior Knowledge (EC, TGD), pp. 143–150.
ICMLICML-2000-CoelhoG #approach #learning
Learning in Non-stationary Conditions: A Control Theoretic Approach (JACJ, RAG), pp. 151–158.
ICMLICML-2000-Cohen #automation #concept #learning #web
Automatically Extracting Features for Concept Learning from the Web (WWC), pp. 159–166.
ICMLICML-2000-CohnC #documentation #identification #learning
Learning to Probabilistically Identify Authoritative Documents (DC, HC), pp. 167–174.
ICMLICML-2000-ConradtTVS #learning #online
On-line Learning for Humanoid Robot Systems (JC, GT, SV, SS), pp. 191–198.
ICMLICML-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.
ICMLICML-2000-DeJong #empirical #learning
Hidden Strengths and Limitations: An Empirical Investigation of Reinforcement Learning (GD), pp. 215–222.
ICMLICML-2000-DyB #identification #learning #order #set
Feature Subset Selection and Order Identification for Unsupervised Learning (JGD, CEB), pp. 247–254.
ICMLICML-2000-Eskin #detection #probability #semistructured data #using
Anomaly Detection over Noisy Data using Learned Probability Distributions (EE), pp. 255–262.
ICMLICML-2000-FariasR #approximate #fixpoint #learning
Fixed Points of Approximate Value Iteration and Temporal-Difference Learning (DPdF, BVR), pp. 207–214.
ICMLICML-2000-FernG #empirical #learning #online
Online Ensemble Learning: An Empirical Study (AF, RG), pp. 279–286.
ICMLICML-2000-FiechterR #learning #scalability
Learning Subjective Functions with Large Margins (CNF, SR), pp. 287–294.
ICMLICML-2000-ForsterW #bound #learning
Relative Loss Bounds for Temporal-Difference Learning (JF, MKW), pp. 295–302.
ICMLICML-2000-GiordanaSSB #framework #learning #relational
Analyzing Relational Learning in the Phase Transition Framework (AG, LS, MS, MB), pp. 311–318.
ICMLICML-2000-GoldbergM #learning #modelling #multi
Learning Multiple Models for Reward Maximization (DG, MJM), pp. 319–326.
ICMLICML-2000-GoldmanZ #learning
Enhancing Supervised Learning with Unlabeled Data (SAG, YZ), pp. 327–334.
ICMLICML-2000-GordonM #learning
Learning Filaments (GJG, AM), pp. 335–342.
ICMLICML-2000-Hall #feature model #machine learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning (MAH), pp. 359–366.
ICMLICML-2000-HallH #information retrieval #learning #multi #natural language
Learning Curved Multinomial Subfamilies for Natural Language Processing and Information Retrieval (KBH, TH), pp. 351–358.
ICMLICML-2000-Heskes #empirical #learning
Empirical Bayes for Learning to Learn (TH), pp. 367–374.
ICMLICML-2000-HosteDSG #corpus
Meta-Learning for Phonemic Annotation of Corpora (VH, WD, EFTKS, SG), pp. 375–382.
ICMLICML-2000-HougenGS #approach #learning
An Integrated Connectionist Approach to Reinforcement Learning for Robotic Control (DFH, MLG, JRS), pp. 383–390.
ICMLICML-2000-HuangSK #constraints #declarative #learning
Learning Declarative Control Rules for Constraint-BAsed Planning (YCH, BS, HAK), pp. 415–422.
ICMLICML-2000-HuW #game studies #probability
Experimental Results on Q-Learning for General-Sum Stochastic Games (JH, MPW), pp. 407–414.
ICMLICML-2000-KatayamaKK #learning #using
A Universal Generalization for Temporal-Difference Learning Using Haar Basis Functions (SK, HK, SK), pp. 447–454.
ICMLICML-2000-KephartT #pseudo
Pseudo-convergent Q-Learning by Competitive Pricebots (JOK, GT), pp. 463–470.
ICMLICML-2000-Khardon #learning
Learning Horn Expressions with LogAn-H (RK), pp. 471–478.
ICMLICML-2000-KimN #learning #network #set
Learning Bayesian Networks for Diverse and Varying numbers of Evidence Sets (ZWK, RN), pp. 479–486.
ICMLICML-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.
ICMLICML-2000-LagoudakisL #algorithm #learning #using
Algorithm Selection using Reinforcement Learning (MGL, MLL), pp. 511–518.
ICMLICML-2000-LaneB #interface #learning #reduction
Data Reduction Techniques for Instance-Based Learning from Human/Computer Interface Data (TL, CEB), pp. 519–526.
ICMLICML-2000-Langley #machine learning
Crafting Papers on Machine Learning (PL), pp. 1207–1216.
ICMLICML-2000-LauerR #algorithm #distributed #learning #multi
An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems (ML, MAR), pp. 535–542.
ICMLICML-2000-Li #learning #online
Selective Voting for Perception-like Online Learning (YL), pp. 559–566.
ICMLICML-2000-MamitsukaA #database #learning #mining #performance #query #scalability
Efficient Mining from Large Databases by Query Learning (HM, NA), pp. 575–582.
ICMLICML-2000-MollPB #machine learning #problem
Machine Learning for Subproblem Selection (RM, TJP, AGB), pp. 615–622.
ICMLICML-2000-MorimotoD #behaviour #learning #using
Acquisition of Stand-up Behavior by a Real Robot using Hierarchical Reinforcement Learning (JM, KD), pp. 623–630.
ICMLICML-2000-MuggletonBS #biology #learning #product line #sequence
Learning Chomsky-like Grammars for Biological Sequence Families (SM, CHB, AS), pp. 631–638.
ICMLICML-2000-NgR #algorithm #learning
Algorithms for Inverse Reinforcement Learning (AYN, SJR), pp. 663–670.
ICMLICML-2000-NikovskiN #learning #mobile #modelling #navigation #probability
Learning Probabilistic Models for Decision-Theoretic Navigation of Mobile Robots (DN, IRN), pp. 671–678.
ICMLICML-2000-OSullivanLCB #algorithm #named #robust
FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness (JO, JL, RC, AB), pp. 703–710.
ICMLICML-2000-PaccanaroH #concept #distributed #learning #linear
Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space (AP, GEH), pp. 711–718.
ICMLICML-2000-PennockMGH #algorithm #learning
A Normative Examination of Ensemble Learning Algorithms (DMP, PMRI, CLG, EH), pp. 735–742.
ICMLICML-2000-PfahringerBG #algorithm #learning
Meta-Learning by Landmarking Various Learning Algorithms (BP, HB, CGGC), pp. 743–750.
ICMLICML-2000-PiaterG #development #learning #visual notation
Constructive Feature Learning and the Development of Visual Expertise (JHP, RAG), pp. 751–758.
ICMLICML-2000-Randlov #learning #physics #problem
Shaping in Reinforcement Learning by Changing the Physics of the Problem (JR), pp. 767–774.
ICMLICML-2000-RandlovBR #algorithm #learning
Combining Reinforcement Learning with a Local Control Algorithm (JR, AGB, MTR), pp. 775–782.
ICMLICML-2000-Reynolds #adaptation #bound #clustering #learning
Adaptive Resolution Model-Free Reinforcement Learning: Decision Boundary Partitioning (SIR), pp. 783–790.
ICMLICML-2000-RichterS #learning #modelling
Knowledge Propagation in Model-based Reinforcement Learning Tasks (CR, JS), pp. 791–798.
ICMLICML-2000-RyanR #learning
Learning to Fly: An Application of Hierarchical Reinforcement Learning (MRKR, MDR), pp. 807–814.
ICMLICML-2000-SannerALL #learning #performance
Achieving Efficient and Cognitively Plausible Learning in Backgammon (SS, JRA, CL, MCL), pp. 823–830.
ICMLICML-2000-SchohnC #learning #less is more
Less is More: Active Learning with Support Vector Machines (GS, DC), pp. 839–846.
ICMLICML-2000-SchuurmansS #adaptation #learning
An Adaptive Regularization Criterion for Supervised Learning (DS, FS), pp. 847–854.
ICMLICML-2000-SegalK #incremental #learning
Incremental Learning in SwiftFile (RS, JOK), pp. 863–870.
ICMLICML-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.
ICMLICML-2000-SilvaL #hybrid #learning
Obtaining Simplified Rule Bases by Hybrid Learning (RBdAeS, TBL), pp. 879–886.
ICMLICML-2000-SingerV #learning #modelling #performance #predict
Learning to Predict Performance from Formula Modeling and Training Data (BS, MMV), pp. 887–894.
ICMLICML-2000-SmartK #learning
Practical Reinforcement Learning in Continuous Spaces (WDS, LPK), pp. 903–910.
ICMLICML-2000-SmolaS #approximate #machine learning #matrix
Sparse Greedy Matrix Approximation for Machine Learning (AJS, BS), pp. 911–918.
ICMLICML-2000-SohT #image #learning #using
Using Learning by Discovery to Segment Remotely Sensed Images (LKS, CT), pp. 919–926.
ICMLICML-2000-SridharanT #automation #multi
Multi-agent Q-learning and Regression Trees for Automated Pricing Decisions (MS, GT), pp. 927–934.
ICMLICML-2000-Strens #framework #learning
A Bayesian Framework for Reinforcement Learning (MJAS), pp. 943–950.
ICMLICML-2000-Talavera #concept #feature model #incremental #learning #probability
Feature Selection and Incremental Learning of Probabilistic Concept Hierarchies (LT), pp. 951–958.
ICMLICML-2000-TellerV #evolution #learning #performance #programming
Efficient Learning Through Evolution: Neural Programming and Internal Reinforcement (AT, MMV), pp. 959–966.
ICMLICML-2000-TongK #classification #learning
Support Vector Machine Active Learning with Application sto Text Classification (ST, DK), pp. 999–1006.
ICMLICML-2000-TorkkolaC #learning
Mutual Information in Learning Feature Transformations (KT, WMC), pp. 1015–1022.
ICMLICML-2000-TowellPM #learning
Learning Priorities From Noisy Examples (GGT, TP, MRM), pp. 1031–1038.
ICMLICML-2000-VaithyanathanD #learning
Hierarchical Unsupervised Learning (SV, BD), pp. 1039–1046.
ICMLICML-2000-Veeser #approach #automaton #finite #learning
An Evolutionary Approach to Evidence-Based Learning of Deterministic Finite Automata (SV), pp. 1071–1078.
ICMLICML-2000-VijayakumarS #incremental #learning #realtime
Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space (SV, SS), pp. 1079–1086.
ICMLICML-2000-WnagZ #approach #lazy evaluation #learning #multi #problem
Solving the Multiple-Instance Problem: A Lazy Learning Approach (JW, JDZ), pp. 1119–1126.
ICMLICML-2000-YangAP #effectiveness #learning #multi #validation
Combining Multiple Learning Strategies for Effective Cross Validation (YY, TA, TP), pp. 1167–1174.
ICMLICML-2000-Zaanen #learning #recursion #syntax #using
Bootstrapping Syntax and Recursion using Alginment-Based Learning (MvZ), pp. 1063–1070.
ICPRICPR-v1-2000-BhanuF #image #interactive #learning #segmentation
Learning Based Interactive Image Segmentation (BB, SF), pp. 1299–1302.
ICPRICPR-v1-2000-LiuW #learning #recognition #representation
Learning the Face Space — Representation and Recognition (CL, HW), pp. 1249–1256.
ICPRICPR-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.
ICPRICPR-v1-2000-PalettaPP #analysis #learning #recognition #using
Learning Temporal Context in Active Object Recognition Using Bayesian Analysis (LP, MP, AP), pp. 1695–1699.
ICPRICPR-v1-2000-PiaterG #learning #network #recognition
Feature Learning for Recognition with Bayesian Networks (JHP, RAG), pp. 1017–1020.
ICPRICPR-v2-2000-BuhmannZ #clustering #learning
Active Learning for Hierarchical Pairwise Data Clustering (JMB, TZ), pp. 2186–2189.
ICPRICPR-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.
ICPRICPR-v2-2000-Caelli #feature model #image #learning #modelling #performance #predict
Learning Image Feature Extraction: Modeling, Tracking and Predicting Human Performance (TC), pp. 2215–2218.
ICPRICPR-v2-2000-ChouS #algorithm #classification #learning #multi
A Hierarchical Multiple Classifier Learning Algorithm (YYC, LGS), pp. 2152–2155.
ICPRICPR-v2-2000-Figueiredo #approximate #learning #on the
On Gaussian Radial Basis Function Approximations: Interpretation, Extensions, and Learning Strategies (MATF), pp. 2618–2621.
ICPRICPR-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.
ICPRICPR-v2-2000-HongH #learning #sequence
Learning to Extract Temporal Signal Patterns from Temporal Signal Sequence (PH, TSH), pp. 2648–2651.
ICPRICPR-v2-2000-KavallieratouSFK #learning #segmentation #using
Handwritten Character Segmentation Using Transformation-Based Learning (EK, ES, NF, GKK), pp. 2634–2637.
ICPRICPR-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.
ICPRICPR-v2-2000-LawK #clustering #learning #modelling #sequence
Rival Penalized Competitive Learning for Model-Based Sequence Clustering (MHCL, JTK), pp. 2195–2198.
ICPRICPR-v2-2000-LohRW #incremental #learning #named #network
IFOSART: A Noise Resistant Neural Network Capable of Incremental Learning (AWKL, MCR, GAWW), pp. 2985–2988.
ICPRICPR-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.
ICPRICPR-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.
ICPRICPR-v2-2000-NaphadeCHF #learning #modelling #multi
Learning Sparse Multiple Cause Models (MRN, LSC, TSH, BJF), pp. 2642–2647.
ICPRICPR-v2-2000-Sato #classification #fault #learning
A Learning Method for Definite Canonicalization Based on Minimum Classification Error (AS), pp. 2199–2202.
ICPRICPR-v4-2000-HeisterkampPD #image #learning #query #retrieval
Feature Relevance Learning with Query Shifting for Content-Based Image Retrieval (DRH, JP, HKD), pp. 4250–4253.
ICPRICPR-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.
KDDKDD-2000-IyengarAZ #adaptation #learning #using
Active learning using adaptive resampling (VSI, CA, TZ), pp. 91–98.
KDDKDD-2000-KimSM #feature model #learning #search-based
Feature selection in unsupervised learning via evolutionary search (YK, WNS, FM), pp. 365–369.
KDDKDD-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.
KRKR-2000-BisoRS #constraints #learning
Experimental Results on Learning Soft Constraints (AB, FR, AS), pp. 435–444.
KRKR-2000-CumbyR #learning #relational
Relational Representations that Facilitate Learning (CMC, DR), pp. 425–434.
KRKR-2000-MartinG #concept #learning #policy #using
Learning Generalized Policies in Planning Using Concept Languages (MM, HG), pp. 667–677.
SIGIRSIGIR-2000-AsadovS #documentation #learning #navigation #semantics
Semantic Explorer — navigation in documents collections, Proxima Daily — learning personal newspaper (VA, SS), p. 388.
SIGIRSIGIR-2000-ChuangY #approach #machine learning #summary
Extracting sentence segments for text summarization: a machine learning approach (WTC, JY), pp. 152–159.
SIGIRSIGIR-2000-Hofmann #learning #modelling #probability #web
Learning probabilistic models of the Web (TH), pp. 369–371.
SIGIRSIGIR-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.
SIGIRSIGIR-2000-ZhaiJE #adaptation #approach #heuristic #learning
Exploration of a heuristic approach to threshold learning in adaptive filtering (CZ, PJ, DAE), pp. 360–362.
OOPSLAOOPSLA-2000-BastidePSN #corba #experience #lessons learnt #specification
Formal specification of CORBA services: experience and lessons learned (RB, PAP, OS, DN), pp. 105–117.
TOOLSTOOLS-EUROPE-2000-NobleW #game studies #learning
GOF Pursuit — Learning Patterns by Playing (JN, CW), p. 462.
SACSAC-2000-BarraCPGRS #distance #education #learning
Teach++: A Cooperative Distance Learning and Teaching Environment (MB, GC, UFP, VG, CR, VS), pp. 124–130.
SACSAC-2000-PereiraC #adaptation #behaviour #information retrieval #learning
The Influence of Learning in the Behaviour of Information Retrieval Adaptive Agents (FBP, EC), pp. 452–457.
SACSAC-2000-RoselliCLPS #learning
WWW-Based Cooperative Learning (TR, CC, SL, MVP, GS), pp. 1014–1020.
ICSEICSE-2000-Curtis00a #lessons learnt #process #tutorial
Software process improvement (tutorial session): best practices and lessons learned (BC), p. 828.
ICSEICSE-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.
ICSEICSE-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.
ICLPCL-2000-KameyaS #learning #logic programming #performance #source code
Efficient EM Learning with Tabulation for Parameterized Logic Programs (YK, TS), pp. 269–284.
DATEDATE-1999-Marques-SilvaG #equivalence #learning #recursion #satisfiability #using
Combinational Equivalence Checking Using Satisfiability and Recursive Learning (JPMS, TG), pp. 145–149.
HTHT-1999-SeebergSRFS #learning
Individual Tables of Contents in Web-Based Learning Systems (CS, AS, KR, SF, RS), pp. 167–168.
ICDARICDAR-1999-HebertPG #detection #incremental #learning #using
Cursive Character Detection using Incremental Learning (JFH, MP, NG), pp. 808–811.
ICDARICDAR-1999-Ho #identification #keyword #learning #performance #word
Fast Identification of Stop Words for Font Learning and Keyword Spotting (TKH), pp. 333–336.
ICDARICDAR-1999-LebourgeoisBE #learning #using
Structure Relation between Classes for Supervised Learning using Pretopology (FL, MB, HE), pp. 33–36.
ICDARICDAR-1999-LiN #classification #documentation #learning
A Document Classification and Extraction System with Learning Ability (XL, PAN), pp. 197–200.
ICDARICDAR-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.
ICDARICDAR-1999-MiletzkiBS #learning
Continuous Learning Systems: Postal Address Readers with Built-In Learning Capability (UM, TB, HS), pp. 329–332.
ICDARICDAR-1999-Walischewski #automation #learning
Learning Regions of Interest in Postal Automation (HW), pp. 317–320.
ITiCSEITiCSE-1999-Ben-AriK #concurrent #learning #parallel #process
Thinking parallel: the process of learning concurrency (MBA, YBDK), pp. 13–16.
ITiCSEITiCSE-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.
ITiCSEITiCSE-1999-DavyJ #education #learning #programming
Research-led innovation in teaching and learning programming (JD, TJ), pp. 5–8.
ITiCSEITiCSE-1999-DeeR #approach #education #learning
ACOM (“computing for all”): an integrated approach to the teaching and learning of information technology (HD, PR), p. 195.
ITiCSEITiCSE-1999-Faltin #algorithm #design #game studies #learning
Designing courseware on algorithms for active learning with virtual board games (NF), pp. 135–138.
ITiCSEITiCSE-1999-HabermanG #distance #education #learning
Distance learning model with local workshop sessions applied to in-service teacher training (BH, DG), pp. 64–67.
ITiCSEITiCSE-1999-LowderH #feedback #learning #student
Web-based student feedback to improve learning (JL, DH), pp. 151–154.
ITiCSEITiCSE-1999-MiaoPW #collaboration #learning
Combining the metaphors of an institute and of networked computers for building collaborative learning environments (YM, HRP, MW), p. 188.
ITiCSEITiCSE-1999-ScherzP #learning
An organizer for project-based learning and instruction in computer science (ZS, SP), pp. 88–90.
ITiCSEITiCSE-1999-SheardH #learning #student
A special learning environment for repeat students (JS, DH), pp. 56–59.
ITiCSEITiCSE-1999-Taylor99a #education #learning
Math link: linking curriculum, instructional strategies, and technology to enhance teaching and learning (HGT), p. 201.
ITiCSEITiCSE-1999-Utting #education #learning
Gathering and disseminating good practice at teaching and learning conferences (IU), p. 202.
ITiCSEITiCSE-1999-YoungDM #online #question
Who wants to learn online? (SY, RD, MM), p. 207.
STOCSTOC-1999-Servedio #complexity #learning
Computational Sample Complexity and Attribute-Efficient Learning (RAS), pp. 701–710.
ICALPICALP-1999-Watanabe #learning
From Computational Learning Theory to Discovery Science (OW0), pp. 134–148.
CIAAWIA-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.
AGTIVEAGTIVE-1999-FischerKB #fuzzy #graph #learning
Learning and Rewriting in Fuzzy Rule Graphs (IF, MK, MRB), pp. 263–270.
CHICHI-1999-MoherJOG #learning
Bridging Strategies for VR-Based Learning (TGM, AEJ, SO, MG), pp. 536–543.
CHICHI-1999-PlowmanKLST #design #learning #multi
Designing Multimedia for Learning: Narrative Guidance and Narrative Construction (LP, RL, DL, MS, JT), pp. 310–317.
CHICHI-1999-Soto #analysis #learning #quality #semantics
Learning and Performing by Exploration: Label Quality Measured by Latent Semantic Analysis (RS), pp. 418–425.
HCIHCI-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.
HCIHCI-CCAD-1999-CarroMR #adaptation #education #learning
Teaching tasks in an adaptive learning environment (RMC, RM, EP, PR), pp. 740–744.
HCIHCI-CCAD-1999-Chiu #algorithm #approach #learning #search-based #using
Learning path planning using genetic algorithm approach (CC), pp. 71–75.
HCIHCI-CCAD-1999-Danielsson #learning #network
Learning in networks (UD), pp. 407–411.
HCIHCI-CCAD-1999-EngelKM #lessons learnt
Conventions for cooperation — lessons learned from videoconferencing (AE, SK, AM), pp. 382–386.
HCIHCI-CCAD-1999-FachB #adaptation #design #learning
Training wheels: an “old” method for designing modern and adaptable learning environments (PWF, MB), pp. 725–729.
HCIHCI-CCAD-1999-HartmannSMGS #learning #tool support
Tools for computer-supported learning in organisations (EAH, DS, KM, MG, HS), pp. 377–381.
HCIHCI-CCAD-1999-JohnsonO #learning #multi #problem #using
Innovative mathematical learning environments — Using multimedia to solve real world problems (LFJ, POJ), pp. 677–681.
HCIHCI-CCAD-1999-KashiharaUT #learning #visualisation
Visualizing knowledge structure for exploratory learning in hyperspace (AK, HU, JT), pp. 667–671.
HCIHCI-CCAD-1999-KasviKVPR #learning
Supporting a learning operative organization (JJJK, IK, MV, AP, LR), pp. 197–201.
HCIHCI-CCAD-1999-KutayHW #human-computer #learning
Achieving learning outcomes in HCI for computing — an experiential testbed (CK, PH, GW), pp. 626–631.
HCIHCI-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.
HCIHCI-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.
HCIHCI-CCAD-1999-NealI #case study #distance #education #experience #learning
Asynchronous distance learning for corporate education: experiences with Lotus LearningSpace (LN, DI), pp. 750–754.
HCIHCI-CCAD-1999-OppermannS #adaptation #learning #mobile
Adaptive mobile museum guide for information and learning on demand (RO, MS), pp. 642–646.
HCIHCI-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.
HCIHCI-CCAD-1999-PatelKR #learning
Cognitive apprenticeship based learning environment in numeric domains (AP, K, DR), pp. 637–641.
HCIHCI-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.
HCIHCI-CCAD-1999-Seufert #learning #named #network
PLATO — “electronic cookbook” for Internet-based learning networks (SS), pp. 707–711.
HCIHCI-CCAD-1999-Siemer-Matravers #collaboration #learning
Collaborative learning — a cure for intelligent tutoring systems (JSM), pp. 652–656.
HCIHCI-CCAD-1999-SinitsaM #interactive #learning #taxonomy
Interactive dictionary in a context of learning (KMS, AM), pp. 662–666.
HCIHCI-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.
HCIHCI-EI-1999-AzarovM #aspect-oriented #distance #learning
Psychological Aspects of the Organization of the Distance Learning (SSA, OVM), pp. 124–128.
HCIHCI-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.
HCIHCI-EI-1999-HuangWC #learning #programming
A Flow-chart Based Learning System for Computer Programming (KHH, KW, SYC), pp. 1298–1302.
HCIHCI-EI-1999-Nyssen #learning #towards
Training Simulators in Anesthesia: Towards a Hierarchy of Learning Situations (ASN), pp. 890–894.
HCIHCI-EI-1999-PentlandRW #adaptation #gesture #interface #learning #word
Perceptual Intelligence: learning gestures and words for individualized, adaptive interfaces (AP, DR, CRW), pp. 286–290.
HCIHCI-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.
HCIHCI-EI-1999-TanoT #adaptation #learning #user interface
User Adaptation of the Pen-based User Interface by Reinforcement Learning (ST, MT), pp. 233–237.
HCIHCI-EI-1999-ThissenS #concept #design #internet #learning #student
A New Concept for Designing Internet Learning Applications for Students of Electrical Engineering (DT, BS), pp. 590–594.
ICEISICEIS-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.
CIKMCIKM-1999-AponWD #approach #learning #parallel
A Learning Approach to Processor Allocation in Parallel Systems (AWA, TDW, LWD), pp. 531–537.
CIKMCIKM-1999-WidyantoroIY #adaptation #algorithm #learning
An Adaptive Algorithm for Learning Changes in User Interests (DHW, TRI, JY), pp. 405–412.
ICMLICML-1999-AbeL #concept #learning #linear #probability #using
Associative Reinforcement Learning using Linear Probabilistic Concepts (NA, PML), pp. 3–11.
ICMLICML-1999-AbeN #internet #learning
Learning to Optimally Schedule Internet Banner Advertisements (NA, AN), pp. 12–21.
ICMLICML-1999-BontempiBB #learning #predict
Local Learning for Iterated Time-Series Prediction (GB, MB, HB), pp. 32–38.
ICMLICML-1999-Bosch #abstraction #in memory #learning
Instance-Family Abstraction in Memory-Based Language Learning (AvdB), pp. 39–48.
ICMLICML-1999-Boyan #difference #learning
Least-Squares Temporal Difference Learning (JAB), pp. 49–56.
ICMLICML-1999-BrodieD #induction #learning #using
Learning to Ride a Bicycle using Iterated Phantom Induction (MB, GD), pp. 57–66.
ICMLICML-1999-FreundM #algorithm #learning
The Alternating Decision Tree Learning Algorithm (YF, LM), pp. 124–133.
ICMLICML-1999-GervasioIL #adaptation #evaluation #learning #scheduling
Learning User Evaluation Functions for Adaptive Scheduling Assistance (MTG, WI, PL), pp. 152–161.
ICMLICML-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.
ICMLICML-1999-Kadous #learning #multi
Learning Comprehensible Descriptions of Multivariate Time Series (MWK), pp. 454–463.
ICMLICML-1999-KimuraK #linear #performance
Efficient Non-Linear Control by Combining Q-learning with Local Linear Controllers (HK, SK), pp. 210–219.
ICMLICML-1999-LentL #learning #performance
Learning Hierarchical Performance Knowledge by Observation (MvL, JEL), pp. 229–238.
ICMLICML-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.
ICMLICML-1999-PalhangS #induction #learning #logic programming
Learning Discriminatory and Descriptive Rules by an Inductive Logic Programming System (MP, AS), pp. 288–297.
ICMLICML-1999-PeshkinMK #learning #memory management #policy
Learning Policies with External Memory (LP, NM, LPK), pp. 307–314.
ICMLICML-1999-PriceB #learning #multi
Implicit Imitation in Multiagent Reinforcement Learning (BP, CB), pp. 325–334.
ICMLICML-1999-RennieM #learning #using #web
Using Reinforcement Learning to Spider the Web Efficiently (JR, AM), pp. 335–343.
ICMLICML-1999-SakakibaraK #context-free grammar #learning #using
GA-based Learning of Context-Free Grammars using Tabular Representations (YS, MK), pp. 354–360.
ICMLICML-1999-ThompsonCM #information management #learning #natural language #parsing
Active Learning for Natural Language Parsing and Information Extraction (CAT, MEC, RJM), pp. 406–414.
ICMLICML-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.
ICMLICML-1999-VaithyanathanD #clustering #documentation #learning
Model Selection in Unsupervised Learning with Applications To Document Clustering (SV, BD), pp. 433–443.
ICMLICML-1999-VovkGS #algorithm
Machine-Learning Applications of Algorithmic Randomness (VV, AG, CS), pp. 444–453.
ICMLICML-1999-Zhang #approach #learning
An Region-Based Learning Approach to Discovering Temporal Structures in Data (WZ), pp. 484–492.
ICMLICML-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.
ICMLICML-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–?.
KDDKDD-1999-FanSZ #distributed #learning #online #scalability
The Application of AdaBoost for Distributed, Scalable and On-Line Learning (WF, SJS, JZ), pp. 362–366.
KDDKDD-1999-SyedLS99a #concept #incremental #learning
Handling Concept Drifts in Incremental Learning with Support Vector Machines (NAS, HL, KKS), pp. 317–321.
MLDMMLDM-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.
MLDMMLDM-1999-AltamuraELM #documentation #learning
Symbolic Learning Techniques in Paper Document Processing (OA, FE, FAL, DM), pp. 159–173.
MLDMMLDM-1999-GiacintoR #automation #classification #design #learning #multi
Automatic Design of Multiple Classifier Systems by Unsupervised Learning (GG, FR), pp. 131–143.
MLDMMLDM-1999-Jahn #image #learning #preprocessor
Unsupervised Learning of Local Mean Gray Values for Image Pre-processing (HJ), pp. 64–74.
MLDMMLDM-1999-KingL #clustering #information retrieval #learning
Non-hierarchical Clustering with Rival Penalized Competitive Learning for Information Retrieval (IK, TKL), pp. 116–130.
MLDMMLDM-1999-Petrou #learning #pattern matching #pattern recognition #recognition
Learning in Pattern Recognition (MP), pp. 1–12.
SIGIRSIGIR-1999-LamY #adaptation #learning #online
An Intelligent Adaptive Filtering Agent Based on an On-Line Learning Model (poster abstract) (WL, KLY), pp. 287–288.
OOPSLAOOPSLA-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.
TOOLSTOOLS-EUROPE-1999-Ishaq #industrial #lessons learnt #object-oriented
Lessons Learned Introducing an Object-Oriented Databse in the Telecom Industry (AI), pp. 214–223.
TOOLSTOOLS-USA-1999-Ramakrishnan #community #distributed #education #learning #testing #visualisation
Visualizing O-O Testing in Virtual Communities — Distributed Teaching and Learning (SR), p. 300–?.
TOOLSTOOLS-USA-1999-YannakopoulosFS #framework #lessons learnt
Object Lessons Learned from an Intelligent Agents Framework for Telephony-Based Applications (DY, MF, MS), pp. 222–236.
SACSAC-1999-VenkataramanaR #automaton #framework #learning
A Learning Automata Based Framework for Task Assignment in Heterogeneous Computing Systems (RDV, NR), pp. 541–547.
ESECESEC-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.
ICSEICSE-1999-WoodmanGMH #programming #smalltalk
OU LearningWorks: A Customized Programming Environment for Smalltalk Modules (MW, RG, MM, SH), pp. 638–641.
CSLCSL-1999-Balcazar #consistency #learning #query
The Consistency Dimension, Compactness, and Query Learning (JLB), pp. 2–13.
ICLPICLP-1999-SatoF #learning #logic programming
Reactive Logic Programming by Reinforcement Learning (TS, SF), p. 617.
ASEASE-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.
DACDAC-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.
CSEETCSEET-1998-Hislop #education #learning #network
Teaching Via Asynchronous Learning Networks (GWH), pp. 16–35.
ITiCSEITiCSE-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.
ITiCSEITiCSE-1998-Casey #education #learning #modelling #web
Learning “from” or “through” the Web: models of Web based education (DC), pp. 51–54.
ITiCSEITiCSE-1998-Daly #approach #learning
A proposed structure for a computer based learning environment — a pragmatic approach (poster) (CD), p. 276.
ITiCSEITiCSE-1998-DavidovicT #learning
Open learning environment and instruction system (OLEIS) (AD, ET), pp. 69–73.
ITiCSEITiCSE-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.
ITiCSEITiCSE-1998-Goldberg #artificial reality
Building a system in virtual reality with LearningWorks (AG), pp. 5–9.
ITiCSEITiCSE-1998-GrayBS #java #learning
A constructivist learning environment implemented in Java (JG, TB, CS), pp. 94–97.
ITiCSEITiCSE-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.
ITiCSEITiCSE-1998-LewisM #comparison #compilation #learning
A comparison between novice and experienced compiler users in a learning environment (SL, GM), pp. 157–161.
ITiCSEITiCSE-1998-MooreS #c #learning #multi #programming
A multimedia C programming course that supports different learning situations (poster) (SM, MS), p. 295.
ITiCSEITiCSE-1998-Richardson #information management #learning #optimisation
First year information systems papers — optimising learning — minimising administration (poster) (ASR), p. 301.
ITiCSEITiCSE-1998-Thomas98a #student
Observing students electronically as they learn (poster) (PGT), p. 307.
ITiCSEITiCSE-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.
ITiCSEITiCSE-1998-Wans #interactive #learning #multi
An interactive multimedia learning system for the postlingually deaf (poster) (CW), p. 309.
ITiCSEITiCSE-1998-WhitehurstPI #distance #learning #student
Utilising the student model in distance learning (RAW, CLP, JSI), pp. 254–256.
ITiCSEITiCSE-1998-Zagursky #flexibility #learning
Information technology for flexible and learning and training (poster) (VZ), p. 312.
FASEFASE-1998-Jones #what
Some Mistakes I Have and What I Have Learned from Them (CBJ), pp. 7–20.
STOCSTOC-1998-Bshouty #algorithm #composition #learning #theorem
A New Composition Theorem for Learning Algorithms (NHB), pp. 583–589.
STOCSTOC-1998-Damaschke #adaptation #learning
Adaptive versus Nonadaptive Attribute-Efficient Learning (PD), pp. 590–596.
CHICHI-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.
CHICHI-1998-JacksonKS #adaptation #design #interactive #learning
The Design of Guided Learner-Adaptable Scaffolding in Interactive Learning Environments (SLJ, JK, ES), pp. 187–194.
CHICHI-1998-RoseDMBN #community #design #implementation #learning
Building an Electronic Learning Community: From Design to Implementation (AR, WD, GM, JBJ, VN), pp. 203–210.
CHICHI-1998-Strommen #interface #learning
When the Interface is a Talking Dinosaur: Learning Across Media with ActiMates Barney (ES), pp. 288–295.
CHICHI-1998-SumnerT #case study #design #experience #learning
New Media, New Practices: Experiences in Open Learning Course Design (TS, JT), pp. 432–439.
CIKMCIKM-1998-DumaisPHS #algorithm #categorisation #induction #learning
Inductive Learning Algorithms and Representations for Text Categorization (STD, JCP, DH, MS), pp. 148–155.
CIKMCIKM-1998-HongL #fuzzy #learning
Learning Fuzzy Knowledge from Training Examples (TPH, CYL), pp. 161–166.
CIKMCIKM-1998-YuL #adaptation #algorithm #learning #online
A New On-Line Learning Algorithm for Adaptive Text Filtering (KLY, WL), pp. 156–160.
ICMLICML-1998-AbeM #learning #query #using
Query Learning Strategies Using Boosting and Bagging (NA, HM), pp. 1–9.
ICMLICML-1998-AlerBI #approach #learning #multi #programming #search-based
Genetic Programming and Deductive-Inductive Learning: A Multi-Strategy Approach (RA, DB, PI), pp. 10–18.
ICMLICML-1998-AnglanoGBS #concept #evaluation #learning
An Experimental Evaluation of Coevolutive Concept Learning (CA, AG, GLB, LS), pp. 19–27.
ICMLICML-1998-BaxterTW #named
KnightCap: A Chess Programm That Learns by Combining TD(λ) with Game-Tree Search (JB, AT, LW), pp. 28–36.
ICMLICML-1998-BillsusP #collaboration #learning
Learning Collaborative Information Filters (DB, MJP), pp. 46–54.
ICMLICML-1998-BonetG #learning #sorting
Learning Sorting and Decision Trees with POMDPs (BB, HG), pp. 73–81.
ICMLICML-1998-Dietterich #learning
The MAXQ Method for Hierarchical Reinforcement Learning (TGD), pp. 118–126.
ICMLICML-1998-DzeroskiRB #learning #relational
Relational Reinforcement Learning (SD, LDR, HB), pp. 136–143.
ICMLICML-1998-Freitag #information management #learning #multi
Multistrategy Learning for Information Extraction (DF), pp. 161–169.
ICMLICML-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.
ICMLICML-1998-GaborKS #learning #multi
Multi-criteria Reinforcement Learning (ZG, ZK, CS), pp. 197–205.
ICMLICML-1998-GarciaN #algorithm #analysis #learning
A Learning Rate Analysis of Reinforcement Learning Algorithms in Finite-Horizon (FG, SMN), pp. 215–223.
ICMLICML-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.
ICMLICML-1998-HuW #algorithm #framework #learning #multi
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm (JH, MPW), pp. 242–250.
ICMLICML-1998-JuilleP #case study #learning
Coevolutionary Learning: A Case Study (HJ, JBP), pp. 251–259.
ICMLICML-1998-KearnsS #learning
Near-Optimal Reinforcement Learning in Polynominal Time (MJK, SPS), pp. 260–268.
ICMLICML-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.
ICMLICML-1998-KollerF #approximate #learning #probability #process #using
Using Learning for Approximation in Stochastic Processes (DK, RF), pp. 287–295.
ICMLICML-1998-LiquiereS #graph #machine learning
Structural Machine Learning with Galois Lattice and Graphs (ML, JS), pp. 305–313.
ICMLICML-1998-LittmanJK #corpus #independence #learning #representation
Learning a Language-Independent Representation for Terms from a Partially Aligned Corpus (MLL, FJ, GAK), pp. 314–322.
ICMLICML-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.
ICMLICML-1998-MaronR #classification #learning #multi
Multiple-Instance Learning for Natural Scene Classification (OM, ALR), pp. 341–349.
ICMLICML-1998-McCallumN #classification #learning
Employing EM and Pool-Based Active Learning for Text Classification (AM, KN), pp. 350–358.
ICMLICML-1998-MooreSBL #learning #named #optimisation
Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions (AWM, JGS, JAB, MSL), pp. 386–394.
ICMLICML-1998-Ng #feature model #learning #on the
On Feature Selection: Learning with Exponentially Many Irrelevant Features as Training Examples (AYN), pp. 404–412.
ICMLICML-1998-PendrithM #analysis #learning #markov
An Analysis of Direct Reinforcement Learning in Non-Markovian Domains (MDP, MM), pp. 421–429.
ICMLICML-1998-RandlovA #learning #using
Learning to Drive a Bicycle Using Reinforcement Learning and Shaping (JR, PA), pp. 463–471.
ICMLICML-1998-ReddyT #first-order #learning #source code
Learning First-Order Acyclic Horn Programs from Entailment (CR, PT), pp. 472–480.
ICMLICML-1998-RyanP #architecture #composition #learning #named
RL-TOPS: An Architecture for Modularity and Re-Use in Reinforcement Learning (MRKR, MDP), pp. 481–487.
ICMLICML-1998-SamuelCV #learning
An Investigation of Transformation-Based Learning in Discourse (KS, SC, KVS), pp. 497–505.
ICMLICML-1998-SaundersGV #algorithm #learning
Ridge Regression Learning Algorithm in Dual Variables (CS, AG, VV), pp. 515–521.
ICMLICML-1998-StuartB #learning
Learning the Grammar of Dance (JMS, EB), pp. 547–555.
ICMLICML-1998-SuttonPS #learning
Intra-Option Learning about Temporally Abstract Actions (RSS, DP, SPS), pp. 556–564.
ICPRICPR-1998-BukerK #hybrid #learning
Learning in an active hybrid vision system (UB, BK), pp. 178–181.
ICPRICPR-1998-ConnellJ #learning #online #prototype
Learning prototypes for online handwritten digits (SDC, AKJ), pp. 182–184.
ICPRICPR-1998-DayP #learning #modelling
A projection filter for use with parameterised learning models (MJSD, JSP), pp. 867–869.
ICPRICPR-1998-DutaJ #concept #image #learning
Learning the human face concept in black and white images (ND, AKJ), pp. 1365–1367.
ICPRICPR-1998-Gimelfarb #interactive #modelling #question #segmentation #what
Supervised segmentation by pairwise interactions: do Gibbs models learn what we expect? (GLG), pp. 817–819.
ICPRICPR-1998-HickinbothamHA #learning
Learning feature characteristics (SJH, ERH, JA), pp. 1160–1164.
ICPRICPR-1998-KeglKN #classification #learning #network #parametricity
Radial basis function networks in nonparametric classification and function learning (BK, AK, HN), pp. 565–570.
ICPRICPR-1998-KnutssonBL #learning #multi
Learning multidimensional signal processing (HK, MB, TL), pp. 1416–1420.
ICPRICPR-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.
ICPRICPR-1998-Mizutani #classification #fault #learning
Discriminative learning for minimum error and minimum reject classification (HM), pp. 136–140.
ICPRICPR-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.
ICPRICPR-1998-Nagy #estimation #learning #persistent
Persistent issues in learning and estimation (GN), pp. 561–564.
ICPRICPR-1998-OrnesDS #network #visual notation
A visual neural network that learns perceptual relationships (CO, AD, JS), pp. 873–875.
ICPRICPR-1998-PengB #learning #recognition
Local reinforcement learning for object recognition (JP, BB), pp. 272–274.
ICPRICPR-1998-SatoY #classification #learning #using
A formulation of learning vector quantization using a new misclassification measure (AS, KY), pp. 322–325.
ICPRICPR-1998-WengH #learning #recognition #sequence
Sensorimotor action sequence learning with application to face recognition under discourse (J(W, WSH), pp. 252–254.
KDDKDD-1998-AndersonM #learning #performance
ADtrees for Fast Counting and for Fast Learning of Association Rules (BSA, AWM), pp. 134–138.
KDDKDD-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.
KDDKDD-1998-GrecuB #data mining #distributed #learning #mining
Coactive Learning for Distributed Data Mining (DLG, LAB), pp. 209–213.
KDDKDD-1998-HandleyLR #learning #predict
Learning to Predict the Duration of an Automobile Trip (SH, PL, FAR), pp. 219–223.
KDDKDD-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.
KDDKDD-1998-MoodyS #learning
Reinforcement Learning for Trading Systems and Portfolios (JEM, MS), pp. 279–283.
KDDKDD-1998-WeissH #learning #predict #sequence
Learning to Predict Rare Events in Event Sequences (GMW, HH), pp. 359–363.
SIGIRSIGIR-1998-Callan #learning
Learning While Filtering Focuments (JPC), pp. 224–231.
OOPSLAOOPSLA-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.
REICRE-1998-SongHMS #lessons learnt #requirements
Lessons Learned from Building a Web-Based Requirements Tracing System (XS, WMH, GM, WS), pp. 41–50.
SACSAC-1998-BillardL #automaton #behaviour #distributed #learning #simulation
Simulation of period-doubling behavior in distributed learning automata (EB, SL), pp. 690–695.
SACSAC-1998-ChungC #interactive #learning #multi
A multimedia system for interactive learning of organ literature (SC, SC), pp. 117–121.
FSEFSE-1998-MasudaSU #design pattern #learning
Applying Design Patterns to Decision Tree Learning System (GM, NS, KU), pp. 111–120.
ICSEICSE-1998-AlmeidaLM #modelling
An Investigation on the Use of Machine Learned Models for Estimating Correction Costs (MAdA, HL, WLM), pp. 473–476.
ICSEICSE-1998-AprilAM #assurance #lessons learnt #process
Process Assurance Audits: Lessons Learned (AA, AA, EM), pp. 482–485.
ICSEICSE-1998-BoehmE #lessons learnt #requirements
Software Requirements Negotiation: Some Lessons Learned (BWB, AE), pp. 503–506.
ICSEICSE-1998-HanakawaMM #development #learning #simulation
A Learning Curve Based Simulation Model for Software Development (NH, SM, KiM), pp. 350–359.
ISSTAISSTA-1998-Hamlet #question #testing #what
What Can We Learn by Testing a Program? (RGH), pp. 50–52.
ICDARICDAR-1997-AminKS #machine learning #recognition
Hand Printed Chinese Character Recognition via Machine Learning (AA, SGK, CS), pp. 190–194.
ICDARICDAR-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.
ICDARICDAR-1997-JunkerH #classification #documentation #learning
Evaluating OCR and Non-OCR Text Representations for Learning Document Classifiers (MJ, RH), pp. 1060–1066.
ICDARICDAR-1997-WaizumiKSN #classification #learning #using
High speed rough classification for handwritten characters using hierarchical learning vector quantization (YW, NK, KS, YN), pp. 23–27.
ICDARICDAR-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.
PODSPODS-1997-GunopulosKMT #data mining #machine learning #mining
Data mining, Hypergraph Transversals, and Machine Learning (DG, RK, HM, HT), pp. 209–216.
ITiCSEITiCSE-1997-BerghelNSTT #design #education
You learned all you need to design educational software design in kindergarten (panel) (HB, CAN, ES, HGT, JT), p. 139.
ITiCSEITiCSE-1997-Boulet #distance #learning
Distance learning of the management of software projects (MMB), pp. 136–138.
ITiCSEITiCSE-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.
ITiCSEITiCSE-1997-DankelH #distance #learning
The use of the WWW to support distance learning through NTU (DDDI, JH), pp. 8–10.
ITiCSEITiCSE-1997-Janser #algorithm #interactive #learning #visualisation
An interactive learning system visualizing computer graphics algorithms (AWJ), pp. 21–23.
ITiCSEITiCSE-1997-Lawhead97a #distance #learning #web #what
The Web and distance learning (panel): what is appropriate and what is not (PBL), p. 144.
ITiCSEITiCSE-1997-Makkonen #collaboration #hypermedia #learning #question
Does collaborative hypertext support better engagement in learning of the basics in informatics? (PM), pp. 130–132.
ITiCSEITiCSE-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.
ITiCSEITiCSE-1997-RoblesFPA #communication #distance #learning #multi #using
Using multimedia communication technologies in distance learning (TR, DF, EP, SA), pp. 6–7.
ITiCSEITiCSE-WGR-1997-Barikzai #collaboration #learning
Integrating courseware into collaborative learning enviroments (demonstration) (SB), p. 145.
ITiCSEITiCSE-WGR-1997-CarlssonKO #education #flexibility #learning
Networked PBL teaching the teacher on flexible learning (poster) (RC, GK, BO), p. 147.
ITiCSEITiCSE-WGR-1997-GavrilovaSU #distance #internet #learning
Teletutor workbench for Internet distance learning environment (poster) (TG, TS, SU), p. 149.
ITiCSEITiCSE-WGR-1997-Goldberg97a #learning
WebCT, a tool for the creation of sophisticated web-based learning environments (demonstration) (MWG), p. 149.
ITiCSEITiCSE-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.
ITiCSEITiCSE-WGR-1997-Maurer #distributed #education #learning
The emergence of sophisticated distributed teaching and learning environments (HM), pp. 112–113.
STOCSTOC-1997-AuerLS #approximate #learning #pseudo #set
Approximating Hyper-Rectangles: Learning and Pseudo-Random Sets (PA, PML, AS), pp. 314–323.
STOCSTOC-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.
DLTDLT-1997-DavidES #learning #string
Learning String Adjunct and Tree Adjunct Languages (NGD, JDE, KGS), pp. 411–427.
CHICHI-1997-RappinGRL #interface #learning #usability
Balancing Usability and Learning in an Interface (NR, MG, MR, PL), pp. 479–486.
CHICHI-1997-ScaifeRAD #design #interactive #learning
Designing For or Designing With? Informant Design For Interactive Learning Environments (MS, YR, FA, MD), pp. 343–350.
HCIHCI-CC-1997-Brodner #process
The Process of Organisational Learning-Experiences from a Joint Project (PB), pp. 253–256.
HCIHCI-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.
HCIHCI-CC-1997-Nishimura #empirical
Building Cyber-Community-Learning from CyberCampus[TM] Experiment (TN), pp. 35–39.
HCIHCI-SEC-1997-DasaiKY #collaboration #distance #learning
A Collaborative Distance Learning System and its Experimental Results (TD, HK, KY), pp. 165–168.
HCIHCI-SEC-1997-EnyedyVG #design #interactive #learning
Designing Interactions for Guided Inquiry Learning Environments (NE, PV, BG), pp. 157–160.
HCIHCI-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.
HCIHCI-SEC-1997-Keating #learning
Computer Based Learning: GroupSystems[R] in the Wireless Classroom (CCK), pp. 119–122.
HCIHCI-SEC-1997-Moustakis #human-computer #machine learning #people #question
Do People in HCI Use Machine Learning? (VM), pp. 95–98.
HCIHCI-SEC-1997-MurphyKG #interface #learning
Enhancing the Interface to Provide Intelligent Computer Aided Language Learning (MM, AK, AG), pp. 149–152.
HCIHCI-SEC-1997-Neal #distance #learning #multi #using
Using Multiple Technologies for Distance Learning (LN), pp. 111–114.
HCIHCI-SEC-1997-Nguifo #interactive #machine learning
An Interactive Environment for Dynamic Control of Machine Learning Systems (EMN), pp. 31–34.
HCIHCI-SEC-1997-PatelK #design #interactive #interface #learning
Granular Interface Design: Decomposing Learning Tasks and Enhancing Tutoring Interaction (AP, K), pp. 161–164.
HCIHCI-SEC-1997-Pohl #machine learning #modelling #named
LaboUr — Machine Learning for User Modeling (WP), pp. 27–30.
HCIHCI-SEC-1997-WilliamsFSTE #education #learning #named #student
PEBBLES: Providing Education by Bringing Learning Environments to Students (LAW, DIF, GS, JT, RE), pp. 115–118.
AdaEuropeAdaEurope-1997-BakerO #ada #c #implementation #interface #lessons learnt
Ada Bindings for C Interfaces: Lessons Learned from the Florist Implementation (TPB, DIO), pp. 13–22.
CIKMCIKM-1997-ChengBL #approach #learning #network
Learning Belief Networks from Data: An Information Theory Based Approach (JC, DAB, WL), pp. 325–331.
ICMLICML-1997-AtkesonS #learning
Robot Learning From Demonstration (CGA, SS), pp. 12–20.
ICMLICML-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.
ICMLICML-1997-BottaGP #first-order #learning #logic #named
FONN: Combining First Order Logic with Connectionist Learning (MB, AG, RP), pp. 46–56.
ICMLICML-1997-DattaK #learning #prototype
Learning Symbolic Prototypes (PD, DFK), pp. 75–82.
ICMLICML-1997-Decatur #classification #induction #learning
PAC Learning with Constant-Partition Classification Noise and Applications to Decision Tree Induction (SED), pp. 83–91.
ICMLICML-1997-Fiechter #bound #learning #online
Expected Mistake Bound Model for On-Line Reinforcement Learning (CNF), pp. 116–124.
ICMLICML-1997-Friedman #learning #network
Learning Belief Networks in the Presence of Missing Values and Hidden Variables (NF), pp. 125–133.
ICMLICML-1997-KimuraMK #approximate #learning
Reinforcement Learning in POMDPs with Function Approximation (HK, KM, SK), pp. 152–160.
ICMLICML-1997-PrecupS #learning
Exponentiated Gradient Methods for Reinforcement Learning (DP, RSS), pp. 272–277.
ICMLICML-1997-ReddyT #learning #using
Learning Goal-Decomposition Rules using Exercises (CR, PT), pp. 278–286.
ICMLICML-1997-RistadY #distance #edit distance #learning #string
Learning String Edit Distance (ESR, PNY), pp. 287–295.
ICMLICML-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.
ICMLICML-1997-Schapire #learning #multi #problem #using
Using output codes to boost multiclass learning problems (RES), pp. 313–321.
ICMLICML-1997-SuematsuHL #approach #learning #markov
A Bayesian Approach to Model Learning in Non-Markovian Environments (NS, AH, SL), pp. 349–357.
ICMLICML-1997-TadepalliD #learning
Hierarchical Explanation-Based Reinforcement Learning (PT, TGD), pp. 358–366.
ICMLICML-1997-ZupanBBD #composition #machine learning
Machine Learning by Function Decomposition (BZ, MB, IB, JD), pp. 421–429.
KDDKDD-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.
KDDKDD-1997-Hekanaho #concept #learning
GA-Based Rule Enhancement in Concept Learning (JH), pp. 183–186.
KDDKDD-1997-KramerPH #machine learning #mining
Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail (SK, BP, CH), pp. 223–226.
KDDKDD-1997-PazzaniMS #learning
Beyond Concise and Colorful: Learning Intelligible Rules (MJP, SM, WRS), pp. 235–238.
KDDKDD-1997-RubinsteinH #learning
Discriminative vs Informative Learning (YDR, TH), pp. 49–53.
KDDKDD-1997-Soderland #learning #web
Learning to Extract Text-Based Information from the World Wide Web (SS), pp. 251–254.
KDDKDD-1997-StolfoPTLFC #database #distributed #java #named
JAM: Java Agents for Meta-Learning over Distributed Databases (SJS, ALP, ST, WL, DWF, PKC), pp. 74–81.
KDDKDD-1997-ZighedRF #learning #multi
Optimal Multiple Intervals Discretization of Continuous Attributes for Supervised Learning (DAZ, RR, FF), pp. 295–298.
SIGIRSIGIR-1997-NgGL #case study #categorisation #feature model #learning #usability
Feature Selection, Perceptron Learning, and a Usability Case Study for Text Categorization (HTN, WBG, KLL), pp. 67–73.
SIGIRSIGIR-1997-SinghalMB #learning #query
Learning Routing Queries in a Query Zone (AS, MM, CB), pp. 25–32.
PPDPPLILP-1997-WhittleBL #editing #ml #standard
An Editor for Helping Novices to Learn Standard ML (JW, AB, HL), pp. 389–405.
RERE-1997-Viravan #lessons learnt
Lessons Learned from Applying the Spiral Model in the Software (CV), p. 40.
SACSAC-1997-Goldberg #learning
Virtual teams virtual projects = real learning (abstract only) (AG), p. 1.
SACSAC-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.
ICSEICSE-1997-Curtis #lessons learnt #process #tutorial
Software Process Improvement: Methods and Lessons Learned (Tutorial) (BC), pp. 624–625.
ICSEICSE-1997-Hefner #lessons learnt #maturity #security
Lessons Learned with the Systems Security Engineering Capability Maturity Model (RH), pp. 566–567.
CADECADE-1997-KolbeB #learning #named #proving
Plagiator — A Learning Prover (TK, JB), pp. 256–259.
CSEETCSEE-1996-Boehm #requirements #student
Helping Students Learn Requirements Engineering (BWB), pp. 96–99.
ITiCSEITiCSE-1996-BrodlieWW #learning #novel #visualisation
Scientific visualization — some novel approaches to learning (KB, JDW, HW), pp. 28–32.
ITiCSEITiCSE-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.
ITiCSEITiCSE-1996-FinkelW #learning
Computer supported peer learning in an introductory computer science course (DF, CEW), pp. 55–56.
ITiCSEITiCSE-1996-JohansenKB #interactive #learning
Interactive learning with gateway labs (MJ, JK, DB), p. 232.
ITiCSEITiCSE-1996-LeesC #learning #natural language #operating system
Applying natural language technology to the learning of operating systems functions (BL, JC), pp. 11–13.
ITiCSEITiCSE-1996-McConnell #learning
Active learning and its use in computer science (JJM), pp. 52–54.
ITiCSEITiCSE-1996-Prey #education #learning
Cooperative learning and closed laboratories in an undergraduate computer science curriculum (JCP), pp. 23–24.
ITiCSEITiCSE-1996-Tjaden #how #learning #student #visual notation
How visual software influences learning in college students (BJT), p. 229.
STOCSTOC-1996-BergadanoCV #learning #query
Learning Sat-k-DNF Formulas from Membership Queries (FB, DC, SV), pp. 126–130.
STOCSTOC-1996-BshoutyGMST #concept #geometry #learning
Noise-Tolerant Distribution-Free Learning of General Geometric Concepts (NHB, SAG, HDM, SS, HT), pp. 151–160.
STOCSTOC-1996-Cesa-BianchiDFS #bound #learning
Noise-Tolerant Learning Near the Information-Theoretic Bound (NCB, ED, PF, HUS), pp. 141–150.
STOCSTOC-1996-KearnsM #algorithm #learning #on the #top-down
On the Boosting Ability of Top-Down Decision Tree Learning Algorithms (MJK, YM), pp. 459–468.
CHICHI-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.
CSCWCSCW-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.
CSCWCSCW-1996-OlsonT #lessons learnt
Groupware in the Wild: Lessons Learned from a Year of Virtual Collocation (JSO, SDT), pp. 419–427.
AdaTRI-Ada-1996-NebeshF #ada #component #html #learning #using
Learning to Use Ada 95 Components Using HTML Linking (BN, MBF), pp. 207–210.
AdaTRI-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.
KDDAKDDM-1996-HsuK #induction #learning #optimisation #query #semantics #using
Using Inductive Learning To Generate Rules for Semantic Query Optimization (CNH, CAK), pp. 425–445.
CIKMCIKM-1996-Huffman #learning
Learning to Extract Information From Text Based on User-Provided Examples (SBH), pp. 154–163.
ICMLICML-1996-AbeL #learning #modelling #using #word
Learning Word Association Norms Using Tree Cut Pair Models (NA, HL), pp. 3–11.
ICMLICML-1996-BanderaVBHB #visual notation
Residual Q-Learning Applied to Visual Attention (CB, FJV, JMB, MEH, LCBI), pp. 20–27.
ICMLICML-1996-BlanzieriK #learning #network #online
Learning Radial Basis Function Networks On-line (EB, PK), pp. 37–45.
ICMLICML-1996-BoyanM #evaluation #learning #scalability
Learning Evaluation Functions for Large Acyclic Domains (JAB, AWM), pp. 63–70.
ICMLICML-1996-Caruana #algorithm #learning #multi
Algorithms and Applications for Multitask Learning (RC), pp. 87–95.
ICMLICML-1996-DietterichKM #framework #learning
Applying the Waek Learning Framework to Understand and Improve C4.5 (TGD, MJK, YM), pp. 96–104.
ICMLICML-1996-EmdeW #learning #relational
Relational Instance-Based Learning (WE, DW), pp. 122–130.
ICMLICML-1996-EzawaSN #learning #network #risk management
Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management (KJE, MS, SWN), pp. 139–147.
ICMLICML-1996-FriedmanG #learning #network
Discretizing Continuous Attributes While Learning Bayesian Networks (NF, MG), pp. 157–165.
ICMLICML-1996-GeibelW #concept #learning #relational
Learning Relational Concepts with Decision Trees (PG, FW), pp. 166–174.
ICMLICML-1996-GoetzKM #adaptation #learning #online
On-Line Adaptation of a Signal Predistorter through Dual Reinforcement Learning (PG, SK, RM), pp. 175–181.
ICMLICML-1996-GoldmanS #algorithm #empirical
A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geormetric Patterns (SAG, SDS), pp. 191–199.
ICMLICML-1996-GordonS #learning #parametricity #statistics
Nonparametric Statistical Methods for Experimental Evaluations of Speedup Learning (GJG, AMS), pp. 200–206.
ICMLICML-1996-GreinerGR #classification #learning
Learning Active Classifiers (RG, AJG, DR), pp. 207–215.
ICMLICML-1996-Hekanaho #concept #learning
Background Knowledge in GA-based Concept Learning (JH), pp. 234–242.
ICMLICML-1996-JappyNG #horn clause #learning #robust #source code
Negative Robust Learning Results from Horn Clause Programs (PJ, RN, OG), pp. 258–265.
ICMLICML-1996-KoenigS #distance #learning #navigation
Passive Distance Learning for Robot Navigation (SK, RGS), pp. 266–274.
ICMLICML-1996-LittmanS #convergence
A Generalized Reinforcement-Learning Model: Convergence and Applications (MLL, CS), pp. 310–318.
ICMLICML-1996-Mahadevan #learning
Sensitive Discount Optimality: Unifying Discounted and Average Reward Reinforcement Learning (SM), pp. 328–336.
ICMLICML-1996-Mannila #data mining #machine learning #mining
Data Mining and Machine Learning (Abstract) (HM), p. 555.
ICMLICML-1996-Moore #learning
Reinforcement Learning in Factories: The Auton Project (Abstract) (AWM0), p. 556.
ICMLICML-1996-Munos #algorithm #convergence #learning
A Convergent Reinforcement Learning Algorithm in the Continuous Case: The Finite-Element Reinforcement Learning (RM), pp. 337–345.
ICMLICML-1996-OliverBW #learning #using
Unsupervised Learning Using MML (JJO, RAB, CSW), pp. 364–372.
ICMLICML-1996-PendrithR #difference #learning
Actual Return Reinforcement Learning versus Temporal Differences: Some Theoretical and Experimental Results (MDP, MRKR), pp. 373–381.
ICMLICML-1996-Perez #learning #representation
Representing and Learning Quality-Improving Search Control Knowledge (MAP), pp. 382–390.
ICMLICML-1996-PerezR #concept #learning
Learning Despite Concept Variation by Finding Structure in Attribute-based Data (EP, LAR), pp. 391–399.
ICMLICML-1996-ReddyTR #composition #empirical #learning
Theory-guided Empirical Speedup Learning of Goal Decomposition Rules (CR, PT, SR), pp. 409–417.
ICMLICML-1996-Saerens #fault #learning
Non Mean Square Error Criteria for the Training of Learning Machines (MS), pp. 427–434.
ICMLICML-1996-SinghP #classification #learning #network #performance
Efficient Learning of Selective Bayesian Network Classifiers (MS, GMP), pp. 453–461.
ICMLICML-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.
ICMLICML-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.
ICMLICML-1996-ThrunO #algorithm #learning #multi
Discovering Structure in Multiple Learning Tasks: The TC Algorithm (ST, JO), pp. 489–497.
ICMLICML-1996-TirriKM #learning
Prababilistic Instance-Based Learning (HT, PK, PM), pp. 507–515.
ICMLICML-1996-Widmer #incremental #recognition
Recognition and Exploitation of Contextual CLues via Incremental Meta-Learning (GW), pp. 525–533.
ICMLICML-1996-ZuckerG #learning #performance #representation
Representation Changes for Efficient Learning in Structural Domains (JDZ, JGG), pp. 543–551.
ICPRICPR-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.
ICPRICPR-1996-AlquezarS #context-sensitive grammar #learning #regular expression
Learning of context-sensitive languages described by augmented regular expressions (RA, AS), pp. 745–749.
ICPRICPR-1996-BebisGLS #learning #modelling #recognition
Learning affine transformations of the plane for model-based object recognition (GB, MG, NdVL, MS), pp. 60–64.
ICPRICPR-1996-Bobrowski #classification #learning #set
Piecewise-linear classifiers, formal neurons and separability of the learning sets (LB), pp. 224–228.
ICPRICPR-1996-BurgeBM #component #learning #polymorphism #recognition
Recognition and learning with polymorphic structural components (MB, WB, WM), pp. 19–23.
ICPRICPR-1996-DemsarS #image #machine learning #using
Using machine learning for content-based image retrieving (JD, FS), pp. 138–142.
ICPRICPR-1996-FischlS #adaptation #approximate #image
Learned adaptive nonlinear filtering for anisotropic diffusion approximation in image processing (BF, ELS), pp. 276–280.
ICPRICPR-1996-FrankH #approach #learning
Pretopological approach for supervised learning (FL, HE), pp. 256–260.
ICPRICPR-1996-HoogsB #learning #modelling
Model-based learning of segmentations (AH, RB), pp. 494–499.
ICPRICPR-1996-KositskyU #learning
Learning class regions by the union of ellipsoids (MK, SU), pp. 750–757.
ICPRICPR-1996-LuettinTB96a #learning
Learning to recognise talking faces (JL, NAT, SWB), pp. 55–59.
ICPRICPR-1996-Muraki #fault #learning #statistics
Error correction scheme augmented with statistical and lexical learning capability, for Japanese OCR (KM), pp. 560–564.
ICPRICPR-1996-MuraseN #approach #generative #learning #recognition
Learning by a generation approach to appearance-based object recognition (HM, SKN), pp. 24–29.
ICPRICPR-1996-PelilloF #learning #network
Autoassociative learning in relaxation labeling networks (MP, AMF), pp. 105–110.
ICPRICPR-1996-PengB #learning #recognition
Delayed reinforcement learning for closed-loop object recognition (JP, BB), pp. 310–314.
ICPRICPR-1996-SainzS #context-sensitive grammar #learning #modelling #using
Learning bidimensional context-dependent models using a context-sensitive language (MS, AS), pp. 565–569.
ICPRICPR-1996-Stoyanov #learning #network
An improved backpropagation neural network learning (IPS), pp. 586–588.
ICPRICPR-1996-WengC #incremental #learning #navigation
Incremental learning for vision-based navigation (JW, SC), pp. 45–49.
ICPRICPR-1996-Yamakawa #feature model #learning #recognition
Matchability-oriented feature selection for recognition structure learning (HY), pp. 123–127.
ICPRICPR-1996-ZanardiHC #interactive #learning #mobile
Mutual learning or unsupervised interactions between mobile robots (CZ, JYH, PC), pp. 40–44.
ICPRICPR-1996-ZhengB #adaptation #detection #learning
Adaptive object detection based on modified Hebbian learning (YJZ, BB), pp. 164–168.
KDDKDD-1996-ChanS #database #modelling
Sharing Learned Models among Remote Database Partitions by Local Meta-Learning (PKC, SJS), pp. 2–7.
KDDKDD-1996-FawcettP #data mining #effectiveness #machine learning #mining #profiling
Combining Data Mining and Machine Learning for Effective User Profiling (TF, FJP), pp. 8–13.
KDDKDD-1996-Feelders #learning #modelling #using
Learning from Biased Data Using Mixture Models (AJF), pp. 102–107.
KDDKDD-1996-LakshminarayanHGS #machine learning #using
Imputation of Missing Data Using Machine Learning Techniques (KL, SAH, RPG, TS), pp. 140–145.
KDDKDD-1996-Musick #learning #network
Rethinking the Learning of Belief Network Probabilities (RM), pp. 120–125.
KDDKDD-1996-Sahami #classification #dependence #learning
Learning Limited Dependence Bayesian Classifiers (MS), pp. 335–338.
KDDKDD-1996-StolorzC #learning #markov #monte carlo #visual notation
Harnessing Graphical Structure in Markov Chain Monte Carlo Learning (PES, PCC), pp. 134–139.
KDDKDD-1996-TeranoI #induction #information management #interactive #learning #using
Interactive Knowledge Discovery from Marketing Questionnaire Using Simulated Breeding and Inductive Learning Methods (TT, YI), pp. 279–282.
KRKR-1996-Ghallab #learning #on the #online #recognition #representation
On Chronicles: Representation, On-line Recognition and Learning (MG), pp. 597–606.
SIGIRSIGIR-1996-CohenS #categorisation #learning
Context-sensitive Learning Methods for Text Categorization (WWC, YS), pp. 307–315.
OOPSLAOOPSLA-1996-KleindienstPT #corba #implementation #lessons learnt #persistent
Lessons Learned from Implementing the CORBA Persistent Object Service (JK, FP, PT), pp. 150–167.
REICRE-1996-NobeW #lessons learnt #modelling #requirements #using
Lessons Learned from a Trial Application of Requirements Modeling Using Statecharts (CRN, WEW), pp. 86–93.
ICSEICSE-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.
HPDCHPDC-1996-WangK #multi
A Broadband Multimedia TeleLearning System (RW, AK), pp. 132–139.
CADECADE-1996-DenzingerS #learning #proving #theorem proving
Learning Domain Knowledge to Improve Theorem Proving (JD, SS), pp. 62–76.
DACDAC-1995-JainMF #learning #verification
Advanced Verification Techniques Based on Learning (JJ, RM, MF), pp. 420–426.
ICDARICDAR-v1-1995-TakasuSK #documentation #image #learning
A rule learning method for academic document image processing (AT, SS, EK), pp. 239–242.
ICDARICDAR-v2-1995-DengelD #approach #classification #clustering #documentation #machine learning
Clustering and classification of document structure-a machine learning approach (AD, FD), pp. 587–591.
ICDARICDAR-v2-1995-MatsunagaK #case study #classification #learning #statistics
An experimental study of learning curves for statistical pattern classifiers (TM, HK), pp. 1103–1106.
ICDARICDAR-v2-1995-ZiinoAS #machine learning #recognition #using
Recognition of hand printed Latin characters using machine learning (DZ, AA, CS), pp. 1098–1102.
CSEETCSEE-1995-DickJ #education #industrial #learning
Industry Involvement in Undergraduate Curricula: Reinforcing Learning by Applying the Principles (GND, SFJ), pp. 51–63.
CSEETCSEE-1995-Mahy #learning #re-engineering
From TRAINING to LEARNING: The Reengineering of Training at DMR Group Inc. (IM), p. 433.
STOCSTOC-1995-HellersteinPRW #how #query #question
How many queries are needed to learn? (LH, KP, VR, DW), pp. 190–199.
ICALPICALP-1995-FortnowFGKKSS #learning
Measure, Category and Learning Theory (LF, RF, WIG, MK, SAK, CHS, FS), pp. 558–569.
CHICHI-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.
CHICHI-1995-BauerJ #interactive #learning #modelling
Modeling Time-Constrained Learning in a Highly Interactive Task (MIB, BEJ), pp. 19–26.
CHICHI-1995-JohnP #approach #case study #learning #using
Learning and Using the Cognitive Walkthrough Method: A Case Study Approach (BEJ, HP), pp. 429–436.
CHICHI-1995-MitchellPB #learning #using
Learning to Write Together Using Groupware (AM, IP, RB), pp. 288–295.
CIKMCIKM-1995-ChenM #information management #learning
Learning Subjective Relevance to Facilitate Information Access (JRC, NM), pp. 218–225.
ICMLICML-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.
ICMLICML-1995-AlmuallimAK #learning #on the
On Handling Tree-Structured Attributed in Decision Tree Learning (HA, YA, SK), pp. 12–20.
ICMLICML-1995-AuerHM #theory and practice
Theory and Applications of Agnostic PAC-Learning with Small Decision Trees (PA, RCH, WM), pp. 21–29.
ICMLICML-1995-Baird #algorithm #approximate #learning
Residual Algorithms: Reinforcement Learning with Function Approximation (LCBI), pp. 30–37.
ICMLICML-1995-Benson #induction #learning #modelling
Inductive Learning of Reactive Action Models (SB), pp. 47–54.
ICMLICML-1995-ChanS #comparative #evaluation
A Comparative Evaluation of Voting and Meta-learning on Partitioned Data (PKC, SJS), pp. 90–98.
ICMLICML-1995-CichoszM #difference #learning #performance
Fast and Efficient Reinforcement Learning with Truncated Temporal Differences (PC, JJM), pp. 99–107.
ICMLICML-1995-Cohen95a #categorisation #learning #relational
Text Categorization and Relational Learning (WWC), pp. 124–132.
ICMLICML-1995-Croft #information retrieval #machine learning
Machine Learning and Information Retrieval (Abstract) (WBC), p. 587.
ICMLICML-1995-Cussens #algorithm #analysis #finite #learning
A Bayesian Analysis of Algorithms for Learning Finite Functions (JC), pp. 142–149.
ICMLICML-1995-DattaK #concept #learning #prototype
Learning Prototypical Concept Descriptions (PD, DFK), pp. 158–166.
ICMLICML-1995-DietterichF #learning #perspective
Explanation-Based Learning and Reinforcement Learning: A Unified View (TGD, NSF), pp. 176–184.
ICMLICML-1995-Duff #problem
Q-Learning for Bandit Problems (MOD), pp. 209–217.
ICMLICML-1995-Fuchs #adaptation #heuristic #learning #parametricity #proving
Learning Proof Heuristics by Adaptive Parameters (MF), pp. 235–243.
ICMLICML-1995-GambardellaD #approach #learning #named #problem
Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem (LMG, MD), pp. 252–260.
ICMLICML-1995-Heckerman #learning #network
Learning With Bayesian Networks (Abstract) (DH), p. 588.
ICMLICML-1995-Hekanaho #concept #learning #multimodal
Symbiosis in Multimodal Concept Learning (JH), pp. 278–285.
ICMLICML-1995-KimuraYK #learning #probability
Reinforcement Learning by Stochastic Hill Climbing on Discounted Reward (HK, MY, SK), pp. 295–303.
ICMLICML-1995-KrishnanLV #learning
Learning to Make Rent-to-Buy Decisions with Systems Applications (PK, PML, JSV), pp. 233–330.
ICMLICML-1995-Lang #learning #named
NewsWeeder: Learning to Filter Netnews (KL), pp. 331–339.
ICMLICML-1995-Littlestone #algorithm #learning
Comparing Several Linear-threshold Learning Algorithms on Tasks Involving Superfluous Attributes (NL), pp. 353–361.
ICMLICML-1995-LittmanCK #learning #policy #scalability
Learning Policies for Partially Observable Environments: Scaling Up (MLL, ARC, LPK), pp. 362–370.
ICMLICML-1995-MaassW #learning #performance
Efficient Learning with Virtual Threshold Gates (WM, MKW), pp. 378–386.
ICMLICML-1995-McCallum #learning
Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State (AM), pp. 387–395.
ICMLICML-1995-MoriartyM #evolution #learning #performance
Efficient Learning from Delayed Rewards through Symbiotic Evolution (DEM, RM), pp. 396–404.
ICMLICML-1995-Niyogi #complexity #learning
Free to Choose: Investigating the Sample Complexity of Active Learning of Real Valued Functions (PN), pp. 405–412.
ICMLICML-1995-NockG #learning #on the
On Learning Decision Committees (RN, OG), pp. 413–420.
ICMLICML-1995-Pomerleau #learning
Learning for Automotive Collision Avoidance and Autonomous Control (DP), p. 589.
ICMLICML-1995-SalganicoffU #learning #multi #using
Active Exploration and Learning in real-Valued Spaces using Multi-Armed Bandit Allocation Indices (MS, LHU), pp. 480–487.
ICMLICML-1995-SquiresS #automation #machine learning #recognition
Automatic Speaker Recognition: An Application of Machine Learning (BS, CS), pp. 515–521.
ICMLICML-1995-StreetMW #approach #induction #learning #predict
An Inductive Learning Approach to Prognostic Prediction (WNS, OLM, WHW), pp. 522–530.
ICMLICML-1995-TowellVGJ #information retrieval #learning
Learning Collection FUsion Strategies for Information Retrieval (GGT, EMV, NKG, BJL), pp. 540–548.
ICMLICML-1995-Wang #approach #incremental #learning
Learning by Observation and Practice: An Incremental Approach for Planning Operator Acquisition (XW), pp. 549–557.
ICMLICML-1995-Weiss #learning
Learning with Rare Cases and Small Disjuncts (GMW), pp. 558–565.
ICMLICML-1995-YamazakiPM #ambiguity #learning #natural language
Learning Hierarchies from Ambiguous Natural Language Data (TY, MJP, CJM), pp. 575–583.
KDDKDD-1995-AugierVK #algorithm #first-order #learning #logic #search-based
Learning First Order Logic Rules with a Genetic Algorithm (SA, GV, YK), pp. 21–26.
KDDKDD-1995-ChanS #machine learning #scalability
Learning Arbiter and Combiner Trees from Partitioned Data for Scaling Machine Learning (PKC, SJS), pp. 39–44.
KDDKDD-1995-CortesJC #learning #quality
Limits on Learning Machine Accuracy Imposed by Data Quality (CC, LDJ, WPC), pp. 57–62.
KDDKDD-1995-HuC #database #learning #set #similarity
Rough Sets Similarity-Based Learning from Databases (XH, NC), pp. 162–167.
KDDKDD-1995-SpirtesM #learning #network
Learning Bayesian Networks with Discrete Variables from Data (PS, CM), pp. 294–299.
SEKESEKE-1995-LiangT #domain model #learning #modelling
Apprenticeship Learning of Domain Models (YL, GT), pp. 54–62.
SEKESEKE-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.
SIGIRSIGIR-1995-VoorheesGJ #learning
Learning Collection Fusion Strategies (EMV, NKG, BJL), pp. 172–179.
SACSAC-1995-GuzdialRC #collaboration #education #interactive #learning #multi
Collaborative and multimedia interactive learning environment for engineering education (MG, NR, DC), pp. 5–9.
SACSAC-1995-StearnsC #concept #machine learning #rule-based
Rule-based machine learning of spatial data concepts (SS, DCSC), pp. 242–247.
SACSAC-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.
ICSEICSE-1995-HenningerLR #analysis #approach #learning
An Organizational Learning Approach to Domain Analysis (SH, KL, AR), pp. 95–104.
ICLPICLP-1995-Sato #learning #logic programming #semantics #source code #statistics
A Statistical Learning Method for Logic Programs with Distribution Semantics (TS), pp. 715–729.
ASEKBSE-1994-MintonW #machine learning #source code #using
Using Machine Learning to Synthesize Search Programs (SM, SRW), pp. 31–38.
SIGMODSIGMOD-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.
CSEETCSEE-1994-MooreP #experience #learning #re-engineering
Learning by Doing: Goals & Experience of Two Software Engineering Project Courses (MMM, CP), pp. 151–164.
STOCSTOC-1994-ApsitisFS #approach #learning
Choosing a learning team: a topological approach (KA, RF, CHS), pp. 283–289.
STOCSTOC-1994-AuerL #learning #simulation
Simulating access to hidden information while learning (PA, PML), pp. 263–272.
STOCSTOC-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.
STOCSTOC-1994-Sitharam #algorithm #generative #learning #pseudo
Pseudorandom generators and learning algorithms for AC (MS), pp. 478–486.
CHICHI-1994-KurtenbachB94a #learning #performance
User learning and performance with marking menus (GK, WB), pp. 258–264.
CSCWCSCW-1994-WanJ #approach #collaboration #learning #using
Computer Supported Collaborative Learning Using CLARE: The Approach and Experimental Findings (DW, PMJ), pp. 187–198.
AdaTRI-Ada-1994-Pena #bibliography #implementation #lessons learnt #process
Lessons Learned in Implementing a Team Review Process (RP), pp. 24–28.
CIKMCIKM-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.
ICMLICML-1994-AhaLLM #learning #recursion #set
Learning Recursive Relations with Randomly Selected Small Training Sets (DWA, SL, CXL, SM), pp. 12–18.
ICMLICML-1994-DruckerCJCV #algorithm #machine learning
Boosting and Other Machine Learning Algorithms (HD, CC, LDJ, YL, VV), pp. 53–61.
ICMLICML-1994-Elomaa #learning
In Defense of C4.5: Notes Learning One-Level Decision Trees (TE), pp. 62–69.
ICMLICML-1994-GervasioD #approach #incremental #learning
An Incremental Learning Approach for Completable Planning (MTG, GD), pp. 78–86.
ICMLICML-1994-Gil #incremental #learning #refinement
Learning by Experimentation: Incremental Refinement of Incomplete Planning Domains (YG), pp. 87–95.
ICMLICML-1994-GiordanaSZ #algorithm #concept #learning #search-based
Learning Disjunctive Concepts by Means of Genetic Algorithms (AG, LS, FZ), pp. 96–104.
ICMLICML-1994-Heger #learning
Consideration of Risk in Reinformance Learning (MH), pp. 105–111.
ICMLICML-1994-LewisC #learning #nondeterminism
Heterogenous Uncertainty Sampling for Supervised Learning (DDL, JC), pp. 148–156.
ICMLICML-1994-Littman #framework #game studies #learning #markov #multi
Markov Games as a Framework for Multi-Agent Reinforcement Learning (MLL), pp. 157–163.
ICMLICML-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.
ICMLICML-1994-Mataric #learning
Reward Functions for Accelerated Learning (MJM), pp. 181–189.
ICMLICML-1994-PengW #incremental #multi
Incremental Multi-Step Q-Learning (JP, RJW), pp. 226–232.
ICMLICML-1994-Pereira #bias #machine learning #natural language #problem
Frequencies vs. Biases: Machine Learning Problems in Natural Language Processing — Abstract (FCNP), p. 380.
ICMLICML-1994-SchapireW #algorithm #analysis #learning #on the #worst-case
On the Worst-Case Analysis of Temporal-Difference Learning Algorithms (RES, MKW), pp. 266–274.
ICMLICML-1994-SinghJJ #learning #markov #process
Learning Without State-Estimation in Partially Observable Markovian Decision Processes (SPS, TSJ, MIJ), pp. 284–292.
ICMLICML-1994-TchoumatchenkoG #framework #learning
A Baysian Framework to Integrate Symbolic and Neural Learning (IT, JGG), pp. 302–308.
ICMLICML-1994-ThamP #architecture #composition
A Modular Q-Learning Architecture for Manipulator Task Decomposition (CKT, RWP), pp. 309–317.
ICMLICML-1994-ZuckerG #concept #learning
Selective Reformulation of Examples in Concept Learning (JDZ, JGG), pp. 352–360.
KDDKDD-1994-AronisP #induction #machine learning #relational
Efficiently Constructing Relational Features from Background Knowledge for Inductive Machine Learning (JMA, FJP), pp. 347–358.
KDDKDD-1994-Furnkranz #comparison #concept #learning #relational
A Comparison of Pruning Methods for Relational Concept Learning (JF), pp. 371–382.
KDDKDD-1994-HeckermanGC #learning #network #statistics
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data (DH, DG, DMC), pp. 85–96.
KDDKDD-1994-HuCX #database #learning
Learning Data Trend Regularities From Databases in a Dynamic Environment (XH, NC, JX), pp. 323–334.
KDDKDD-1994-Kaufman #development #learning #multi #tool support #using
Comparing International Development Patterns Using Multi-Operator Learning and Discovery Tools (KAK), pp. 431–440.
KDDKDD-1994-SasisekharanSW #machine learning #maintenance #network #using
Proactive Network Maintenance Using Machine Learning (RS, VS, SMW), pp. 453–462.
KDDKDD-1994-ShenMOZ #database #deduction #induction #learning #using
Using Metagueries to Integrate Inductive Learning and Deductive Database Technology (WMS, BGM, KO, CZ), pp. 335–346.
KRKR-1994-Carbonell #information management #learning #representation
Knowledge Representation Issues in Integrated Planning and Learning Systems (Abstract) (JGC), p. 633.
KRKR-1994-CohenH #learning #logic
Learning the Classic Description Logic: Theoretical and Experimental Results (WWC, HH), pp. 121–133.
SEKESEKE-1994-AbranDMMS #analysis #hypermedia #learning #using
Structured hypertext for using and learning function point analysis (AA, JMD, DM, MM, DSP), pp. 164–171.
SEKESEKE-1994-ReynoldsZ #algorithm #learning #using
Learning to understand software from examples using cultural algorithms (RGR, EZ), pp. 188–192.
SIGIRSIGIR-1994-Allen #information retrieval #learning #performance
Perceptual Speed, Learning and Information Retrieval Performance (BA), pp. 71–80.
SIGIRSIGIR-1994-ApteDW #automation #categorisation #independence #learning #modelling #towards
Towards Language Independent Automated Learning of Text Categorisation Models (CA, FD, SMW), pp. 23–30.
SIGIRSIGIR-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.
OOPSLAOOPSLA-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.
LOPSTRLOPSTR-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.
SACSAC-1994-Chen #learning
Application of Boolean expression minimization to learning via hierarchical generalization (JC), pp. 303–307.
SACSAC-1994-HughesWK #learning
Virtual space learning: creating text-based learning environments (BH, JW, BK), pp. 578–582.
SACSAC-1994-Janikow #algorithm #fuzzy #learning #search-based
A genetic algorithm for learning fuzzy controllers (CZJ), pp. 232–236.
SACSAC-1994-RosenG #network #using
Training hard to learn networks using advanced simulated annealing methods (BER, JMG), pp. 256–260.
SACSAC-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.
SACSAC-1994-WongM #learning #specification #verification
Specification and verification of learning (KWW, RAM), pp. 6–9.
ICDARICDAR-1993-Dengel #documentation #learning
Initial learning of document structure (AD), pp. 86–90.
ICDARICDAR-1993-Ho #independence #learning #recognition
Recognition of handwritten digits by combining independent learning vector quantizations (TKH), pp. 818–821.
ICDARICDAR-1993-Kawatani #learning #polynomial #recognition
Handprinted numeral recognition with the learning quadratic discriminant function (TK), pp. 14–17.
ICDARICDAR-1993-KuritaK #database #image #learning #visual notation
Learning of personal visual impression for image database systems (TK, TK), pp. 547–552.
ICDARICDAR-1993-LebourgeoisH #learning
A contextual processing for an OCR system, based on pattern learning (FL, JLH), pp. 862–865.
ICDARICDAR-1993-SatohMS #comprehension #image #learning
Drawing image understanding system with capability of rule learning (SS, HM, MS), pp. 119–124.
STOCSTOC-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.
STOCSTOC-1993-Kearns #learning #performance #query #statistics
Efficient noise-tolerant learning from statistical queries (MJK), pp. 392–401.
STOCSTOC-1993-Kharitonov #encryption #learning
Cryptographic hardness of distribution-specific learning (MK), pp. 372–381.
STOCSTOC-1993-Maass #bound #complexity #learning
Bounds for the computational power and learning complexity of analog neural nets (WM), pp. 335–344.
FMFME-1993-OwreRSH #architecture #fault tolerance #lessons learnt #verification
Formal Verification for Fault-Tolerant Architectures: Some Lessons Learned (SO, JMR, NS, FWvH), pp. 482–500.
HCIHCI-SHI-1993-HutchingsHC #hypermedia #learning
A Model of Learning with Hypermedia Systems (GH, WH, CJC), pp. 494–499.
HCIHCI-SHI-1993-LeclercM #learning #natural language
Natural Language as Object and Medium in Computer-Based Learning (SL, SdM), pp. 373–378.
HCIHCI-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.
HCIHCI-SHI-1993-RizzoPCB #learning
Control of Complex System by Situated Knowledge: The Role of Implicit Learning (AR, OP, CC, SB), pp. 855–860.
HCIHCI-SHI-1993-YoungM #approach #assessment #learning #problem
A Situated Cognition Approach to Problem Solving with Implications for Computer-Based Learning and Assessment (MFY, MDM), pp. 825–830.
CHIINTERCHI-1993-NilsenJOBRM #learning #performance
The growth of software skill: a longitudinal look at learning & performance (EN, HSJ, JSO, KB, HHR, SM), pp. 149–156.
CHIINTERCHI-1993-StaskoBL #algorithm #analysis #animation #empirical #learning
Do algorithm animations assist learning?: an empirical study and analysis (JTS, ANB, CL), pp. 61–66.
CIKMCIKM-1993-ChanS #learning #multi
Experiments on Multi-Strategy Learning by Meta-Learning (PKC, SJS), pp. 314–323.
CIKMCIKM-1993-EickJ #algorithm #classification #learning #search-based
Learning Bayesian Classification Rules through Genetic Algorithms (CFE, DJ), pp. 305–313.
ICMLICML-1993-BrezellecS #bottom-up #learning #named
ÉLÉNA: A Bottom-Up Learning Method (PB, HS), pp. 9–16.
ICMLICML-1993-Cardie #learning #using
Using Decision Trees to Improve Case-Based Learning (CC), pp. 25–32.
ICMLICML-1993-Caruana #bias #induction #knowledge-based #learning #multi
Multitask Learning: A Knowledge-Based Source of Inductive Bias (RC), pp. 41–48.
ICMLICML-1993-ClarkM #induction #learning #modelling #using
Using Qualitative Models to Guide Inductive Learning (PC, SM), pp. 49–56.
ICMLICML-1993-CravenS #learning #network #using
Learning Symbolic Rules Using Artificial Neural Networks (MC, JWS), pp. 73–80.
ICMLICML-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.
ICMLICML-1993-DattaK #concept #learning #multi
Concept Sharing: A Means to Improve Multi-Concept Learning (PD, DFK), pp. 89–96.
ICMLICML-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.
ICMLICML-1993-FrazierP #learning
Learning From Entailment: An Application to Propositional Horn Sentences (MF, LP), pp. 120–127.
ICMLICML-1993-GratchCD #learning #network #scheduling
Learning Search Control Knowledge for Deep Space Network Scheduling (JG, SAC, GD), pp. 135–142.
ICMLICML-1993-HuffmanL #interactive #learning #natural language
Learning Procedures from Interactive Natural Language Instructions (SBH, JEL), pp. 143–150.
ICMLICML-1993-JordanJ #approach #divide and conquer #learning #statistics
Supervised Learning and Divide-and-Conquer: A Statistical Approach (MIJ, RAJ), pp. 159–166.
ICMLICML-1993-Kaelbling #learning #probability
Hierarchical Learning in Stochastic Domains: Preliminary Results (LPK), pp. 167–173.
ICMLICML-1993-KimR #learning
Constraining Learning with Search Control (JK, PSR), pp. 174–181.
ICMLICML-1993-Lin #learning #scalability
Scaling Up Reinforcement Learning for Robot Control (LJL), pp. 182–189.
ICMLICML-1993-MitchellT #comparison #learning #network
Explanation Based Learning: A Comparison of Symbolic and Neural Network Approaches (TMM, ST), pp. 197–204.
ICMLICML-1993-Mladenic #combinator #concept #induction #learning #optimisation
Combinatorial Optimization in Inductive Concept Learning (DM), pp. 205–211.
ICMLICML-1993-NortonH #learning #probability
Learning DNF Via Probabilistic Evidence Combination (SWN, HH), pp. 220–227.
ICMLICML-1993-Quinlan #learning #modelling
Combining Instance-Based and Model-Based Learning (JRQ), pp. 236–243.
ICMLICML-1993-RagavanR #concept #learning #lookahead
Lookahead Feature Construction for Learning Hard Concepts (HR, LAR), pp. 252–259.
ICMLICML-1993-Salganicoff #adaptation #learning
Density-Adaptive Learning and Forgetting (MS), pp. 276–283.
ICMLICML-1993-Schwartz #learning
A Reinforcement Learning Method for Maximizing Undiscounted Rewards (AS), pp. 298–305.
ICMLICML-1993-SuttonW #learning #online #random
Online Learning with Random Representations (RSS, SDW), pp. 314–321.
ICMLICML-1993-Tadepalli #bias #learning #query
Learning from Queries and Examples with Tree-structured Bias (PT), pp. 322–329.
ICMLICML-1993-Tan #independence #learning #multi
Multi-Agent Reinforcement Learning: Independent versus Cooperative Agents (MT), pp. 330–337.
SEKESEKE-1993-EspositoMS #information management #machine learning #refinement
Machine Learning Techniques for Knowledge Acquisition and Refinement (FE, DM, GS), pp. 319–323.
SEKESEKE-1993-WillisP #machine learning #program transformation #reuse
Machine Learning for Program Transformations in Software Reuse (CPW, DJP), pp. 275–277.
SACSAC-1993-GallionSCB #algorithm #learning
Dynamic ID3: A Symbolic Learning Algorithm for Many-Valued Attribute Domains (RG, CLS, DCSC, WEB), pp. 14–20.
SACSAC-1993-KountanisS #concept #graph #learning
Graphs as a Language to Describe Learning System Concepts (DIK, ES), pp. 469–475.
SACSAC-1993-VaidyanathanL #analysis #bound #learning
Analysis of Upper Bound in Valiant’s Model for Learning Bounded CNF Expressions (SV, SL), pp. 754–761.
FSEFSE-1993-Bergadano #generative #learning #testing
Test Case Generation by Means of Learning Techniques (FB), pp. 149–162.
HPDCHPDC-1993-FletcherO #distributed #learning #network #parallel
Parallel and Distributed Systems for Constructive Neural Network Learning (JF, ZO), pp. 174–178.
HTHT-ECHT-1992-Colorni #hypermedia #learning #research
A Hypertext for Learning Operational Research (Demonstration) (AC), p. 291.
HTHT-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.
PODSPODS-1992-Greiner #learning #performance #query
Learning Efficient Query Processing Strategies (RG), pp. 33–46.
STOCSTOC-1992-Angluin #bibliography #learning
Computational Learning Theory: Survey and Selected Bibliography (DA), pp. 351–369.
STOCSTOC-1992-BlumR #learning #performance #query
Fast Learning of k-Term DNF Formulas with Queries (AB, SR), pp. 382–389.
STOCSTOC-1992-BshoutyHH #learning
Learning Arithmetic Read-Once Formulas (NHB, TRH, LH), pp. 370–381.
CHICHI-1992-Clancey #bibliography #learning #research
Overview of the Institute for Research on Learning (WJC), pp. 571–572.
CHICHI-1992-Spohrer #case study #experience #learning #prototype
Simulation-based learning systems: prototypes and experiences (AJ, JCS), pp. 523–524.
CSCWCSCW-1992-BerlinJ #collaboration #learning #problem
Consultants and Apprentices: Observations about Learning and Collaborative Problem Solving (LMB, RJ), pp. 130–137.
CSCWCSCW-1992-Orlikowski #implementation #learning
Learning from Notes: Organizational Issues in Groupware Implementation (WJO), pp. 362–369.
AdaTRI-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.
CAiSECAiSE-1992-FouqueV #analysis #approach #machine learning
Building a Tool for Software Code Analysis: A Machine Learning Approach (GF, CV), pp. 278–289.
KRKR-1992-GreinerS #approximate #learning
Learning Useful Horn Approximations (RG, DS), pp. 383–392.
ICMLML-1992-AlmuallimD #concept #learning #on the
On Learning More Concepts (HA, TGD), pp. 11–19.
ICMLML-1992-Bhatnagar #learning
Learning by Incomplete Explanation-Based Learning (NB), pp. 37–42.
ICMLML-1992-Chen #learning
Improving Path Planning with Learning (PCC), pp. 55–61.
ICMLML-1992-Christiansen #learning #nondeterminism #predict
Learning to Predict in Uncertain Continuous Tasks (ADC), pp. 72–81.
ICMLML-1992-ClouseU #education #learning
A Teaching Method for Reinforcement Learning (JAC, PEU), pp. 92–110.
ICMLML-1992-ConverseH #learning
Learning to Satisfy Conjunctive Goals (TMC, KJH), pp. 117–122.
ICMLML-1992-CoxR #learning #multi
Multistrategy Learning with Introspective Meta-Explanations (MTC, AR), pp. 123–128.
ICMLML-1992-Etzioni #analysis #learning
An Asymptotic Analysis of Speedup Learning (OE), pp. 129–136.
ICMLML-1992-GiordanaS #algorithm #concept #learning #search-based #using
Learning Structured Concepts Using Genetic Algorithms (AG, CS), pp. 169–178.
ICMLML-1992-GratchD #analysis #learning #problem
An Analysis of Learning to Plan as a Search Problem (JG, GD), pp. 179–188.
ICMLML-1992-GrefenstetteR #approach #learning
An Approach to Anytime Learning (JJG, CLR), pp. 189–195.
ICMLML-1992-Hickey #algorithm #approach #evaluation #towards
Artificial Universes — Towards a Systematic Approach to Evaluation Algorithms which Learn form Examples (RJH), pp. 196–205.
ICMLML-1992-HirschbergP #analysis #concept #learning
Average Case Analysis of Learning κ-CNF Concepts (DSH, MJP), pp. 206–211.
ICMLML-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.
ICMLML-1992-Janikow #contest #induction #learning
Combining Competition and Cooperation in Supervised Inductive Learning (CZJ), pp. 241–248.
ICMLML-1992-KononenkoK #generative #learning #multi #optimisation #probability
Learning as Optimization: Stochastic Generation of Multiple Knowledge (IK, MK), pp. 257–262.
ICMLML-1992-Mahadevan #learning #modelling #probability
Enhancing Transfer in Reinforcement Learning by Building Stochastic Models of Robot Actions (SM), pp. 290–299.
ICMLML-1992-Mao #learning #named
THOUGHT: An Integrated Learning System for Acquiring Knowledge Structure (CM), pp. 300–309.
ICMLML-1992-Markov #approach #concept #learning
An Approach to Concept Learning Based on Term Generalization (ZM), pp. 310–315.
ICMLML-1992-McCallum #learning #performance #proximity #using
Using Transitional Proximity for Faster Reinforcement Learning (AM), pp. 316–321.
ICMLML-1992-Merckt #concept #flexibility #named
NFDT: A System that Learns Flexible Concepts Based on Decision Trees for Numerical Attributes (TVdM), pp. 322–331.
ICMLML-1992-RubyK #learning #optimisation
Learning Episodes for Optimization (DR, DFK), pp. 379–384.
ICMLML-1992-SammutHKM #learning
Learning to Fly (CS, SH, DK, DM), pp. 385–393.
ICMLML-1992-Singh #algorithm #learning #modelling #scalability
Scaling Reinforcement Learning Algorithms by Learning Variable Temporal Resolution Models (SPS), pp. 406–415.
ICMLML-1992-Tesauro #difference #learning
Temporal Difference Learning of Backgammon Strategy (GT), pp. 451–457.
ICMLML-1992-Zhang #learning
Selecting Typical Instances in Instance-Based Learning (JZ), pp. 470–479.
OOPSLAOOPSLA-1992-LiuGG #learning #object-oriented #question #what
What Contributes to Successful Object-Oriented Learning? (CL, SG, BG), pp. 77–86.
ASEKBSE-1991-BailinGT #re-engineering
A Learning-Based Software Engineering Environment (SCB, RHG, WT), pp. 198–206.
ASEKBSE-1991-HarandiL #design #machine learning #perspective
Acquiring Software Design Schemas: A Machine Learning Perspective (MTH, HYL), pp. 188–197.
VLDBVLDB-1991-PalmerZ #named
Fido: A Cache That Learns to Fetch (MP, SBZ), pp. 255–264.
CSEETSEI-1991-RiedlWFKM #re-engineering #what
What We Have Learned About Software Engineering Expertise (TRR, JSW, JTF, GAK, JDM), pp. 261–270.
STOCSTOC-1991-KushilevitzM #fourier #learning #using
Learning Decision Trees Using the Fourier Sprectrum (Extended Abstract) (EK, YM), pp. 455–464.
STOCSTOC-1991-LittlestoneLW #learning #linear #online
On-Line Learning of Linear Functions (NL, PML, MKW), pp. 465–475.
SASWSA-1991-Breuer #analysis #learning #synthesis
An Analysis/Synthesis Language with Learning Strategies (PTB), pp. 202–209.
CHICHI-1991-PalmiterE #evaluation #learning
An evaluation of animated demonstrations of learning computer-based tasks (SP, JE), pp. 257–263.
KDDKDD-1991-BergadanoGSBM #learning
Integrated Learning in a Real Domain (FB, AG, LS, FB, DDM), pp. 277–288.
KDDKDD-1991-UthurusamyFS #learning
Learning Useful Rules from Inconclusive Data (RU, UMF, WSS), pp. 141–158.
KRKR-1991-ChalasaniEM #algorithm #performance #permutation #problem
Integrating Efficient Model-Learning and Problem-Solving Algorithms in Permutation Environments (PC, OE, JM), pp. 89–98.
ICMLML-1991-Bain #learning
Experiments in Non-Monotonic Learning (MB), pp. 380–384.
ICMLML-1991-Berenji #approximate #learning #refinement
Refinement of Approximate Reasoning-based Controllers by Reinforcement Learning (HRB), pp. 475–479.
ICMLML-1991-BottaRSS #abduction #learning #using
Improving Learning Using Causality and Abduction (MB, SR, LS, SBS), pp. 480–484.
ICMLML-1991-Brand #learning
Decision-Theoretic Learning in an Action System (MB), pp. 283–287.
ICMLML-1991-BratkoMV #learning #modelling
Learning Qualitative Models of Dynamic Systems (IB, SM, AV), pp. 385–388.
ICMLML-1991-BrunkP #algorithm #concept #learning #relational
An Investigation of Noise-Tolerant Relational Concept Learning Algorithms (CB, MJP), pp. 389–393.
ICMLML-1991-ChienGD #learning #on the
On Becoming Decreasingly Reactive: Learning to Deliberate Minimally (SAC, MTG, GD), pp. 288–292.
ICMLML-1991-ChienWDDFGL #automation #machine learning
Machine Learning in Engineering Automation (SAC, BLW, TGD, RJD, BF, JG, SCYL), pp. 577–580.
ICMLML-1991-CobbG #learning #persistent
Learning the Persistence of Actions in Reactive Control Rules (HGC, JJG), pp. 292–297.
ICMLML-1991-Day #csp #heuristic #learning #problem
Learning Variable Descriptors for Applying Heuristics Across CSP Problems (DSD), pp. 127–131.
ICMLML-1991-desJardins #bias #learning #probability
Probabilistic Evaluating of Bias for Learning Systems (Md), pp. 495–499.
ICMLML-1991-DzeroskiL #comparison #empirical #learning
Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL (SD, NL), pp. 399–402.
ICMLML-1991-Goel #formal method #incremental #learning
Model Revision: A Theory of Incremental Model Learning (AKG), pp. 605–609.
ICMLML-1991-GokerM #incremental #information retrieval #learning
Incremental Learning in a Probalistic Information Retrieval System (AG, TLM), pp. 255–259.
ICMLML-1991-HastingsLL #learning #word
Learning Words From Context (PMH, SLL, RKL), pp. 55–59.
ICMLML-1991-Herrmann #learning
Learning Analytical Knowledge About VLSI-Design from Observation (JH), pp. 610–614.
ICMLML-1991-HirakiGYA #image #learning
Learning Spatial Relations from Images (KH, JHG, YY, YA), pp. 407–411.
ICMLML-1991-HsuS #evaluation #learning
Learning Football Evaluation for a Walking Robot (GTH, RGS), pp. 303–307.
ICMLML-1991-HummeS #using
Using Inverse Resolution to Learn Relations from Experiments (DH, CS), pp. 412–416.
ICMLML-1991-JordanR #learning #modelling
Internal World Models and Supervised Learning (MIJ, DER), pp. 70–74.
ICMLML-1991-Kadie #induction #learning
Quantifying the Value of Constructive Induction, Knowledge, and Noise Filtering on Inductive Learning (CMK), pp. 153–157.
ICMLML-1991-Kadie91a #concept #learning #set
Continous Conceptual Set Covering: Learning Robot Operators From Examples (CMK), pp. 615–619.
ICMLML-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.
ICMLML-1991-KokarR #learning
Learning to Select a Model in a Changing World (MMK, SAR), pp. 313–317.
ICMLML-1991-Krulwich #learning
Learning from Deliberated Reactivity (BK), pp. 318–322.
ICMLML-1991-Kwok #adaptation #architecture #learning #query #using
Query Learning Using an ANN with Adaptive Architecture (KLK), pp. 260–264.
ICMLML-1991-LeckieZ #approach #induction #learning
Learning Search Control Rules for Planning: An Inductive Approach (CL, IZ), pp. 422–426.
ICMLML-1991-Lewis #information retrieval #learning
Learning in Intelligent Information Retrieval (DDL), pp. 235–239.
ICMLML-1991-Lin #education #learning #self
Self-improvement Based on Reinforcement Learning, Planning and Teaching (LJL), pp. 323–327.
ICMLML-1991-MahadevanC #architecture #learning #scalability
Scaling Reinforcement Learning to Robotics by Exploiting the Subsumption Architecture (SM, JC), pp. 328–332.
ICMLML-1991-MartinB #bias #learning #variability
Variability Bias and Category Learning (JDM, DB), pp. 90–94.
ICMLML-1991-Maza #concept #learning #prototype
A Prototype Based Symbolic Concept Learning System (MdlM), pp. 41–45.
ICMLML-1991-MillanT #learning
Learning to Avoid Obstacles Through Reinforcement (JdRM, CT), pp. 298–302.
ICMLML-1991-OliveiraS #concept #learning #network
Learning Concepts by Synthesizing Minimal Threshold Gate Networks (ALO, ALSV), pp. 193–197.
ICMLML-1991-ORorkeMABC #evaluation #machine learning
Machine Learning for Nondestructive Evaluation (PO, SM, MA, WB, DCSC), pp. 620–624.
ICMLML-1991-PageF #learning
Learning Constrained Atoms (CDPJ, AMF), pp. 427–431.
ICMLML-1991-PazzaniBS #approach #concept #learning #relational
A Knowledge-intensive Approach to Learning Relational Concepts (MJP, CB, GS), pp. 432–436.
ICMLML-1991-Pierce #learning #set
Learning a Set of Primitive Actions with an Uninterpreted Sensorimotor Apparatus (DRP), pp. 338–342.
ICMLML-1991-RaedtBM #concept #constraints #interactive
Integrity Constraints and Interactive Concept-Learning (LDR, MB, BM), pp. 394–398.
ICMLML-1991-RagavanR #empirical #learning
Relations, Knowledge and Empirical Learning (HR, LAR), pp. 188–192.
ICMLML-1991-Reich #design #learning
Design Integrated Learning Systems for Engineering Design (YR), pp. 635–639.
ICMLML-1991-Schlimmer #consistency #database #induction #learning
Database Consistency via Inductive Learning (JCS), pp. 640–644.
ICMLML-1991-SilversteinP #induction #learning #relational
Relational Clichés: Constraining Induction During Relational Learning (GS, MJP), pp. 203–207.
ICMLML-1991-Singh #composition #learning
Transfer of Learning Across Compositions of Sequentail Tasks (SPS), pp. 348–352.
ICMLML-1991-SuttonM #learning #polynomial
Learning Polynomial Functions by Feature Construction (RSS, CJM), pp. 208–212.
ICMLML-1991-Tadepalli #learning
Learning with Incrutable Theories (PT), pp. 544–548.
ICMLML-1991-Tan #learning #representation
Learning a Cost-Sensitive Internal Representation for Reinforcement Learning (MT), pp. 358–362.
ICMLML-1991-TecuciM #adaptation #learning #multi
A Method for Multistrategy Task-Adaptive Learning Based on Plausible Justifications (GT, RSM), pp. 549–553.
ICMLML-1991-Thompson #approach #information retrieval #machine learning
Machine Learning in the Combination of Expert Opinion Approach to IR (PT), pp. 270–274.
ICMLML-1991-VanLehnJ #correctness #learning #physics
Learning Physics Via Explanation-Based Learning of Correctness and Analogical Search Control (KV, RMJ), pp. 110–114.
ICMLML-1991-WhitehallL #case study #how #knowledge-based #learning
A Study of How Domain Knowledge Improves Knowledge-Based Learning Systems (BLW, SCYL), pp. 559–563.
ICMLML-1991-Whitehead #complexity
Complexity and Cooperation in Q-Learning (SDW), pp. 363–367.
ICMLML-1991-Wixson #composition #learning #scalability
Scaling Reinforcement Learning Techniques via Modularity (LEW), pp. 3368–372.
ICMLML-1991-YamanishiK #learning #probability #search-based #sequence
Learning Stochastic Motifs from Genetic Sequences (KY, AK), pp. 467–471.
ECOOPECOOP-1991-BergsteinL #incremental #learning #optimisation #taxonomy
Incremental Class Dictionary Learning and Optimization (PLB, KJL), pp. 377–396.
LOPSTRLOPSTR-1991-Eusterbrock #abstraction #learning #logic programming #source code
Speed-up Transformations of Logic Programs by Abstraction and Learning (JE), pp. 167–182.
STOCSTOC-1990-Blum #infinity #learning
Learning Boolean Functions in an Infinite Atribute Space (Extended Abstract) (AB), pp. 64–72.
ICALPICALP-1990-JainS #learning
Language Learning by a “Team” (Extended Abstract) (SJ, AS), pp. 153–166.
ICALPICALP-1990-Watanabe #formal method #learning #query
A Formal Study of Learning via Queries (OW0), pp. 139–152.
CHICHI-1990-CarrollSBA #learning #smalltalk
A view matcher for learning Smalltalk (JMC, JAS, RKEB, SRA), pp. 431–437.
CHICHI-1990-HowesP #analysis #learning #semantics
Semantic analysis during exploratory learning (AH, SJP), pp. 399–406.
CSCWCSCW-1990-BullenB #experience #learning #user interface
Learning from User Experience with Groupware (CVB, JLB), pp. 291–302.
ICMLML-1990-ArunkumarY #information management #learning #representation #using
Knowledge Acquisition from Examples using Maximal Representation Learning (SA, SY), pp. 2–8.
ICMLML-1990-BergadanoGSMB #learning
Integrated Learning in a real Domain (FB, AG, LS, DDM, FB), pp. 322–329.
ICMLML-1990-ChanW #analysis #induction #learning #performance #probability
Performance Analysis of a Probabilistic Inductive Learning System (KCCC, AKCW), pp. 16–23.
ICMLML-1990-Cohen #analysis #concept #learning #representation
An Analysis of Representation Shift in Concept Learning (WWC), pp. 104–112.
ICMLML-1990-Cohen90a #approximate #learning
Learning Approximate Control Rules of High Utility (WWC), pp. 268–276.
ICMLML-1990-Epstein #learning
Learning Plans for Competitive Domains (SLE), pp. 190–197.
ICMLML-1990-Flann #abstraction
Applying Abstraction and Simplification to Learn in Intractable Domains (NSF), pp. 277–285.
ICMLML-1990-GenestMP #approach #learning
Explanation-Based Learning with Incomplete Theories: A Three-step Approach (JG, SM, BP), pp. 286–294.
ICMLML-1990-Hammond #learning #process
Learning and Enforcement: Stabilizing Environments to Facilitate Activity (KJH), pp. 204–210.
ICMLML-1990-Hirsh #bound #consistency #learning #nondeterminism
Learning from Data with Bounded Inconsistency (HH), pp. 32–39.
ICMLML-1990-Holder #machine learning #problem
The General Utility Problem in Machine Learning (LBH), pp. 402–410.
ICMLML-1990-Hume #induction #learning
Learning Procedures by Environment-Driven Constructive Induction (DVH), pp. 113–121.
ICMLML-1990-Kaelbling #learning
Learning Functions in k-DNF from Reinforcement (LPK), pp. 162–169.
ICMLML-1990-KoMT #learning #string
Learning String Patterns and Tree Patterns from Examples (KIK, AM, WGT), pp. 384–391.
ICMLML-1990-Lehman #learning
A General Method for Learning Idiosyncratic Grammars (JFL), pp. 368–376.
ICMLML-1990-LytinenM #comparison #learning
A Comparison of Learning Techniques in Second Language Learning (SLL, CEM), pp. 377–383.
ICMLML-1990-McCallumS #algorithm #search-based #using
Using Genetic Algorithms to Learn Disjunctive Rules from Examples (AM, KAS), pp. 149–152.
ICMLML-1990-ObradovicP #learning #multi
Learning with Discrete Multi-Valued Neurons (ZO, IP), pp. 392–399.
ICMLML-1990-PazzaniS #algorithm #analysis #learning
Average Case Analysis of Conjunctive Learning Algorithms (MJP, WS), pp. 339–347.
ICMLML-1990-Ram #incremental #learning
Incremental Learning of Explanation Patterns and Their Indices (AR), pp. 313–320.
ICMLML-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.
ICMLML-1990-SammutC #learning #performance #question
Is Learning Rate a Good Performance Criterion for Learning? (CS, JC), pp. 170–178.
ICMLML-1990-SchoenauerS #incremental #learning
Incremental Learning of Rules and Meta-rules (MS, MS), pp. 49–57.
ICMLML-1990-Segen #clustering #graph #learning
Graph Clustering and Model Learning by Data Compression (JS), pp. 93–101.
ICMLML-1990-SilverFIVB #framework #learning #multi
A Framework for Multi-Paradigmatic Learning (BS, WJF, GAI, JV, KB), pp. 348–356.
ICMLML-1990-Sutton #approximate #architecture #learning #programming
Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming (RSS), pp. 216–224.
ICMLML-1990-WhiteheadB #learning
Active Perception and Reinforcement Learning (SDW, DHB), pp. 179–188.
SEKESEKE-1990-EstevaR #induction #learning #reuse
Learning to Recognize Reusable Software by Induction (JCE, RGR), pp. 19–24.
SEKESEKE-1990-Mazurov #learning #parallel #process
Parallel Processes of Decision Making and Multivalued Interpretation of Contradictory Data by Learning Neuron Machines (VDM), p. 165.
SEKESEKE-1990-VolovikMT #re-engineering #what
What Software Engineering Can Learn From Practitioners (DV, RM, WTT), pp. 216–221.
SIGIRSIGIR-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.
HTHT-1989-RousSYY #hypermedia #lessons learnt
Lessons Learned from the ACM Hypertext on Hypertext Project (BR, BS, NY, EAY), pp. 385–386.
STOCSTOC-1989-KearnsV #automaton #encryption #finite #learning
Cryptographic Limitations on Learning Boolean Formulae and Finite Automata (MJK, LGV), pp. 433–444.
CHICHI-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.
CHICHI-1989-LeePB #learning #metric
Learning and transfer of measurement tasks (AYL, PGP, WAB), pp. 115–120.
ICMLML-1989-Aha #concept #incremental #independence #learning
Incremental, Instance-Based Learning of Independent and Graded Concept Descriptions (DWA), pp. 387–391.
ICMLML-1989-Anderson #learning #network
Tower of Hanoi with Connectionist Networks: Learning New Features (CWA), pp. 345–349.
ICMLML-1989-BarlettaK #empirical #learning
Improving Explanation-Based Indexing with Empirical Learning (RB, RK), pp. 84–86.
ICMLML-1989-BergadanoGP #deduction #induction #learning #top-down
Deduction in Top-Down Inductive Learning (FB, AG, SP), pp. 23–25.
ICMLML-1989-Buntine #classification #learning #using
Learning Classification Rules Using Bayes (WLB), pp. 94–98.
ICMLML-1989-Chan #induction #learning
Inductive Learning with BCT (PKC), pp. 104–108.
ICMLML-1989-ChaseZPBMH #approximate
Approximating Learned Search Control Knowledge (MPC, MZ, RLP, JDB, PPM, HH), pp. 218–220.
ICMLML-1989-Chien #learning
Learning by Analyzing Fortuitous Occurrences (SAC), pp. 249–251.
ICMLML-1989-Chrisman #bias
Evaluating Bias During Pac-Learning (LC), pp. 469–471.
ICMLML-1989-ClearwaterCHB #incremental #learning
Incremental Batch Learning (SHC, TPC, HH, BGB), pp. 366–370.
ICMLML-1989-ConverseHM #learning
Learning from Opportunity (TMC, KJH, MM), pp. 246–248.
ICMLML-1989-Cornuejols #incremental #learning
An Exploration Into Incremental Learning: the INFLUENCE System (AC), pp. 383–386.
ICMLML-1989-Diederich #learning
“Learning by Instruction” in connectionist Systems (JD), pp. 66–68.
ICMLML-1989-Dietterich #induction #learning
Limitations on Inductive Learning (TGD), pp. 124–128.
ICMLML-1989-Fawcett #learning
Learning from Plausible Explanations (TF), pp. 37–39.
ICMLML-1989-FisherMMST #learning
Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems (DHF, KBM, RJM, JWS, GGT), pp. 169–173.
ICMLML-1989-Flann #abstraction #learning #problem
Learning Appropriate Abstractions for Planning in Formation Problems (NSF), pp. 235–239.
ICMLML-1989-Fogarty #algorithm #incremental #learning #realtime #search-based
An Incremental Genetic Algorithm for Real-Time Learning (TCF), pp. 416–419.
ICMLML-1989-FriedrichN #algorithm #induction #learning #using
Using Domain Knowledge to Improve Inductive Learning Algorithms for Diagnosis (GF, WN), pp. 75–77.
ICMLML-1989-GamsK #empirical #learning
New Empirical Learning Mechanisms Perform Significantly Better in Real Life Domains (MG, AK), pp. 99–103.
ICMLML-1989-GervasioD #learning
Explanation-Based Learning of Reactive Operations (MTG, GD), pp. 252–254.
ICMLML-1989-Grefenstette #algorithm #incremental #learning #search-based
Incremental Learning of Control Strategies with Genetic algorithms (JJG), pp. 340–344.
ICMLML-1989-Haines #learning
Explanation Based Learning as Constrained Search (DH), pp. 43–45.
ICMLML-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.
ICMLML-1989-Hirsh #empirical #learning
Combining Empirical and Analytical Learning with Version Spaces (HH), pp. 29–33.
ICMLML-1989-Jones #learning #problem
Learning to Retrieve Useful Information for Problem Solving (RMJ), pp. 212–214.
ICMLML-1989-Kaelbling #embedded #framework #learning
A Formal Framework for Learning in Embedded Systems (LPK), pp. 350–353.
ICMLML-1989-Katz #learning #network
Integrating Learning in a Neural Network (BFK), pp. 69–71.
ICMLML-1989-Keller #compilation #learning #performance
Compiling Learning Vocabulary from a Performance System Description (RMK), pp. 482–495.
ICMLML-1989-Knoblock #abstraction #learning
Learning Hierarchies of Abstraction Spaces (CAK), pp. 241–245.
ICMLML-1989-LambertTL #algorithm #concept #hybrid #learning #recursion
Generalized Recursive Splitting Algorithms for Learning Hybrid Concepts (BLL, DKT, SCYL), pp. 496–498.
ICMLML-1989-Langley #empirical #learning
Unifying Themes in Empirical and Explanation-Based Learning (PL), pp. 2–4.
ICMLML-1989-LeviPS #learning
Learning Tactical Plans for Pilot Aiding (KRL, DLP, VLS), pp. 191–193.
ICMLML-1989-Marie #bias #dependence #learning
Building A Learning Bias from Perceived Dependencies (CdSM), pp. 501–502.
ICMLML-1989-Martin #learning
Reducing Redundant Learning (JDM), pp. 396–399.
ICMLML-1989-MasonCM #learning
Experiments in Robot Learning (MTM, ADC, TMM), pp. 141–145.
ICMLML-1989-MatwinM #learning
Learning Procedural Knowledge in the EBG Context (SM, JM), pp. 197–199.
ICMLML-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.
ICMLML-1989-Morris #learning
Reducing Search and Learning Goal Preferences (SM), pp. 46–48.
ICMLML-1989-MuggletonBMM #comparison #machine learning
An Experimental Comparison of Human and Machine Learning Formalisms (SM, MB, JHM, DM), pp. 113–118.
ICMLML-1989-NumaoS #learning #similarity
Explanation-Based Acceleration of Similarity-Based Learning (MN, MS), pp. 58–60.
ICMLML-1989-ORorkeCO #learning
Learning to Recognize Plans Involving Affect (PO, TC, AO), pp. 209–211.
ICMLML-1989-PagalloH #algorithm
Two Algorithms That Learn DNF by Discovering Relevant Features (GP, DH), pp. 119–123.
ICMLML-1989-Paredis #behaviour #learning
Learning the Behavior of Dynamical Systems form Examples (JP), pp. 137–140.
ICMLML-1989-Pazzani #learning
Explanation-Based Learning with Week Domain Theories (MJP), pp. 72–74.
ICMLML-1989-Puget #invariant #learning
Learning Invariants from Explanations (JFP), pp. 200–204.
ICMLML-1989-RasZ #concept #learning
Imprecise Concept Learning within a Growing Language (ZWR, MZ), pp. 314–319.
ICMLML-1989-Redmond #learning #reasoning
Combining Case-Based Reasoning, Explanation-Based Learning, and Learning form Instruction (MR), pp. 20–22.
ICMLML-1989-RudyK #learning
Learning to Plan in Complex Domains (DR, DFK), pp. 180–182.
ICMLML-1989-SarrettP #algorithm #empirical #learning
One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning (WS, MJP), pp. 26–28.
ICMLML-1989-ScottM #case study #experience #learning #nondeterminism
Uncertainty Based Selection of Learning Experiences (PDS, SM), pp. 358–361.
ICMLML-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.
ICMLML-1989-Shavlik #analysis #empirical #learning
An Empirical Analysis of EBL Approaches for Learning Plan Schemata (JWS), pp. 183–187.
ICMLML-1989-ShavlikT #learning #network
Combining Explanation-Based Learning and Artificial Neural Networks (JWS, GGT), pp. 90–93.
ICMLML-1989-SobekL #learning #using
Using Learning to Recover Side-Effects of Operators in Robotics (RPS, JPL), pp. 205–208.
ICMLML-1989-Spackman #detection #induction #learning #tool support
Signal Detection Theory: Valuable Tools for Evaluating Inductive Learning (KAS), pp. 160–163.
ICMLML-1989-Subramanian #machine learning
Representational Issues in Machine Learning (DS), pp. 426–429.
ICMLML-1989-TanS #approach #concept #learning #recognition
Cost-Sensitive Concept Learning of Sensor Use in Approach ad Recognition (MT, JCS), pp. 392–395.
ICMLML-1989-TecuciK #learning #multi
Multi-Strategy Learning in Nonhomongeneous Domain Theories (GT, YK), pp. 14–16.
ICMLML-1989-Utgoff #incremental #learning
Improved Training Via Incremental Learning (PEU), pp. 362–365.
ICMLML-1989-WefaldR #adaptation #learning
Adaptive Learning of Decision-Theoretic Search Control Knowledge (EW, SJR), pp. 408–411.
ICMLML-1989-WhiteheadB
A Role for Anticipation in Reactive Systems that Learn (SDW, DHB), pp. 354–357.
ICMLML-1989-Widmer #deduction #integration #learning
A Tight Integration of Deductive Learning (GW), pp. 11–13.
ICMLML-1989-Wollowski #learning
A Schema for an Integrated Learning System (MW), pp. 87–89.
ICMLML-1989-YagerF #learning
Participatory Learning: A Constructivist Model (RRY, KMF), pp. 420–425.
ICMLML-1989-ZhangM #learning
A Description of Preference Criterion in Constructive Learning: A Discussion of Basis Issues (JZ, RSM), pp. 17–19.
SIGIRSIGIR-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.
ICLPNACLP-1989-MarkovitchS #approach #automation #machine learning
Automatic Ordering of Subgoals — A Machine Learning Approach (SM, PDS), pp. 224–240.
DACDAC-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.
CSEETSEI-1988-Stevens #learning
SEI Demonstration: Advanced Learning Technologies Project (SS), p. 120.
STOCSTOC-1988-KearnsL #fault #learning
Learning in the Presence of Malicious Errors (Extended Abstract) (MJK, ML), pp. 267–280.
PLDIBest-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.
CSCWCSCW-1988-Hiltz #collaboration #learning
Collaborative Learning in a Virtual Classroom: Highlights of Findings (SRH), pp. 282–290.
ICMLML-1988-Amsterdam #learning
Extending the Valiant Learning Model (JA), pp. 381–394.
ICMLML-1988-Carpineto #approach #generative #learning
An Approach Based on Integrated Learning to Generating Stories (CC), pp. 298–304.
ICMLML-1988-Cohen #learning #multi
Generalizing Number and Learning from Multiple Examples in Explanation Based Learning (WWC), pp. 256–269.
ICMLML-1988-Etzioni #approach #learning #reliability
Hypothesis Filtering: A Practical Approach to Reliable Learning (OE), pp. 416–429.
ICMLML-1988-Gross #concept #incremental #learning #multi #using
Incremental Multiple Concept Learning Using Experiments (KPG), pp. 65–72.
ICMLML-1988-Helft #first-order #learning
Learning Systems of First-Order Rules (NH), pp. 395–401.
ICMLML-1988-Hirsh #learning #reasoning
Reasoning about Operationality for Explanation-Based Learning (HH), pp. 214–220.
ICMLML-1988-IbaWL #concept #incremental #learning
Trading Off Simplicity and Coverage in Incremental concept Learning (WI, JW, PL), pp. 73–79.
ICMLML-1988-JongS #game studies #learning #using
Using Experience-Based Learning in Game Playing (KADJ, ACS), pp. 284–290.
ICMLML-1988-Kadie #learning #named
Diffy-S: Learning Robot Operator Schemata from Examples (CMK), pp. 430–436.
ICMLML-1988-Kerber #using
Using a Generalization Hierarchy to Learn from Examples (RK), pp. 1–7.
ICMLML-1988-Lebowitz
Deferred Commitment in UNIMEM: Waiting to Learn (ML), pp. 80–86.
ICMLML-1988-Lynne #learning
Competitive Reinforcement Learning (KJL), pp. 188–199.
ICMLML-1988-MahadevanT #learning #on the
On the Tractability of Learning from Incomplete Theories (SM, PT), pp. 235–241.
ICMLML-1988-MarkovitchS #learning
The Role of Forgetting in Learning (SM, PDS), pp. 459–465.
ICMLML-1988-NatarajanT #framework #learning
Two New Frameworks for Learning (BKN, PT), pp. 402–415.
ICMLML-1988-Pazzani #learning
Integrated Learning with Incorrect and Incomplete Theories (MJP), pp. 291–297.
ICMLML-1988-Sammut #algorithm #evaluation
Experimental Results from an Evaluation of Algorithms that Learn to Control Dynamic Systems (CS), pp. 437–443.
ICMLML-1988-Segen #graph #learning #modelling
Learning Graph Models of Shape (JS), pp. 29–35.
ICMLML-1988-Spackman #category theory #learning
Learning Categorical Decision Criteria in Biomedical Domains (KAS), pp. 36–46.
ICMLML-1988-Tesauro #learning
Connectionist Learning of Expert Backgammon Evaluations (GT), pp. 200–206.
ICMLML-1988-Williams #learning
Learning to Program by Examining and Modifying Cases (RSW), pp. 318–324.
ICMLML-1988-WisniewskiA #induction #learning
Some Interesting Properties of a Connectionist Inductive Learning System (EJW, JAA), pp. 181–187.
SIGIRSIGIR-1988-YuM #information retrieval #learning
Two Learning Schemes in Information Retrieval (CTY, HM), pp. 201–218.
PPoPPPPEALS-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.
CADECADE-1988-DonatW #higher-order #learning #using
Learning and Applying Generalised Solutions using Higher Order Resolution (MRD, LAW), pp. 41–60.
STOCSTOC-1987-Natarajan #learning #on the
On Learning Boolean Functions (BKN), pp. 296–304.
ICALPICALP-1987-PittS #learning #probability
Probability and Plurality for Aggregations of Learning Machines (LP, CHS), pp. 1–10.
ICALPICALP-1987-Valiant #formal method #learning
Recent Developments in the Theory of Learning (Abstract) (LGV), p. 563.
HCIHCI-CE-1987-Bosser #evaluation #learning
The Evaluation of Learning Requirement of IT Systems (TB), pp. 45–52.
SIGIRSIGIR-1987-OommenM #automaton #clustering #learning #performance #probability #using
Fast Object Partitioning Using Stochastic Learning Automata (BJO, DCYM), pp. 111–122.
ICSEICSE-1987-Boehm #lessons learnt #process
Software Process Management: Lessons Learned from History (BWB), pp. 296–298.
CSLCSL-1987-RinnS #fault #learning
Learning by Teams from Examples with Errors (RR, BS), pp. 223–234.
SIGIRSIGIR-1986-DeogunR #clustering #documentation #framework #information retrieval #learning
User-Oriented Document Clustering: A Framework for Learning in Information Retrieval (JSD, VVR), pp. 157–163.
SIGIRSIGIR-1986-WongZ #approach #information retrieval #machine learning
A Machine Learning Approach to Information Retrieval (SKMW, WZ), pp. 228–233.
VLDBVLDB-1985-BorgidaW #database #exception #learning
Accommodating Exceptions in Databases, and Refining the Schema by Learning from them (AB, KEW), pp. 72–81.
SIGIRSIGIR-1985-Gordon #algorithm #documentation #learning
A Learning Algorithm Applied to Document Description (MG), pp. 179–186.
SIGIRSIGIR-1984-Allan #information retrieval #learning
Computerised Information Retrieval Systems for Open Learning (BA), pp. 325–341.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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