Pat Langley
Proceedings of the 17th International Conference on Machine Learning
ICML, 2000.

@proceedings{ICML-2000,
	address       = "Stanford, California, USA",
	editor        = "Pat Langley",
	isbn          = "1-55860-707-2",
	publisher     = "{Morgan Kaufmann}",
	title         = "{Proceedings of the 17th International Conference on Machine Learning}",
	year          = 2000,
}

Contents (151 items)

70 ×#learning
18 ×#classification
15 ×#multi
15 ×#using
11 ×#algorithm
10 ×#approach
9 ×#performance
7 ×#modelling
7 ×#problem
6 ×#clustering

ICML-2000-AlerBI #information management #learning #representation

Knowledge Representation Issues in Control Knowledge Learning (RA, DB, PI), pp. 1–8.

ICML-2000-AllweinSS #approach #classification #multi

Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers (ELA, RES, YS), pp. 9–16.

ICML-2000-AndersonMC #approach #optimisation #parametricity

A Nonparametric Approach to Noisy and Costly Optimization (BSA, AWM, DC), pp. 17–24.

ICML-2000-AndersonDP #behaviour #composition #network #student

Behavioral Cloning of Student Pilots with Modular Neural Networks (CWA, BAD, DAP), pp. 25–32.

ICML-2000-BanerjeeDS #multi

Combining Multiple Perspectives (BB, SD, SS), pp. 33–40.

ICML-2000-BaxterB #learning

Reinforcement Learning in POMDP’s via Direct Gradient Ascent (JB, PLB), pp. 41–48.

ICML-2000-BayP #difference

Characterizing Model Erros and Differences (SDB, MJP), pp. 49–56.

ICML-2000-BennettB #classification #geometry

Duality and Geometry in SVM Classifiers (KPB, EJB), pp. 57–64.

ICML-2000-BennetDS #algorithm #generative

A Column Generation Algorithm For Boosting (KPB, AD, JST), pp. 65–72.

ICML-2000-BoicuTMBSCL #education #named #programming

Disciple-COA: From Agent Programming to Agent Teaching (MB, GT, DM, MB, PS, FC, CL), pp. 73–80.

ICML-2000-BowersGL #classification

Classification of Individuals with Complex Structure (AFB, CGGC, JWL), pp. 81–88.

ICML-2000-Bowling #convergence #learning #multi #problem

Convergence Problems of General-Sum Multiagent Reinforcement Learning (MHB), pp. 89–94.

ICML-2000-Brand #optimisation

Finding Variational Structure in Data by Cross-Entropy Optimization (MB), pp. 95–102.

ICML-2000-BrutlagM #challenge #classification #email

Challenges of the Email Domain for Text Classification (JDB, CM), pp. 103–110.

ICML-2000-CampbellCS #classification #learning #query #scalability

Query Learning with Large Margin Classifiers (CC, NC, AJS), pp. 111–118.

ICML-2000-CampbellTB #network #polynomial #reduction

Dimension Reduction Techniques for Training Polynomial Networks (WMC, KT, SVB), pp. 119–126.

ICML-2000-ChangCM #learning

Learning to Create Customized Authority Lists (HC, DC, AM), pp. 127–134.

ICML-2000-ChoiY #database #learning

Learning to Select Text Databases with Neural Nets (YSC, SIY), pp. 135–142.

ICML-2000-ChownD #approach #divide and conquer #information management #learning

A Divide and Conquer Approach to Learning from Prior Knowledge (EC, TGD), pp. 143–150.

ICML-2000-CoelhoG #approach #learning

Learning in Non-stationary Conditions: A Control Theoretic Approach (JACJ, RAG), pp. 151–158.

ICML-2000-Cohen #automation #concept #learning #web

Automatically Extracting Features for Concept Learning from the Web (WWC), pp. 159–166.

ICML-2000-CohnC #documentation #identification #learning

Learning to Probabilistically Identify Authoritative Documents (DC, HC), pp. 167–174.

ICML-2000-Collins #natural language #parsing #ranking

Discriminative Reranking for Natural Language Parsing (MC), pp. 175–182.

ICML-2000-ColtonBW #automation #concept #identification

Automatic Identification of Mathematical Concepts (SC, AB, TW), pp. 183–190.

ICML-2000-ConradtTVS #learning #online

On-line Learning for Humanoid Robot Systems (JC, GT, SV, SS), pp. 191–198.

ICML-2000-CravenPSBG #coordination #learning #multi #using

Using Multiple Levels of Learning and Diverse Evidence to Uncover Coordinately Controlled Genes (MC, DP, JWS, JB, JDG), pp. 199–206.

ICML-2000-FariasR #approximate #fixpoint #learning

Fixed Points of Approximate Value Iteration and Temporal-Difference Learning (DPdF, BVR), pp. 207–214.

ICML-2000-DeJong #empirical #learning

Hidden Strengths and Limitations: An Empirical Investigation of Reinforcement Learning (GD), pp. 215–222.

ICML-2000-Domingos #classification #problem

Bayesian Averaging of Classifiers and the Overfitting Problem (PMD), pp. 223–230.

ICML-2000-Domingos00a #composition

A Unifeid Bias-Variance Decomposition and its Applications (PMD), pp. 231–238.

ICML-2000-DrummondH

Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria (CD, RCH), pp. 239–246.

ICML-2000-DyB #identification #learning #order #set

Feature Subset Selection and Order Identification for Unsupervised Learning (JGD, CEB), pp. 247–254.

ICML-2000-Eskin #detection #probability #semistructured data #using

Anomaly Detection over Noisy Data using Learned Probability Distributions (EE), pp. 255–262.

ICML-2000-EspositoFFS #refinement

Ideal Theory Refinement under Object Identity (FE, NF, SF, GS), pp. 263–270.

ICML-2000-EvgeniouPPP #bound #kernel #performance

Bounds on the Generalization Performance of Kernel Machine Ensembles (TE, LPB, MP, TP), pp. 271–278.

ICML-2000-FernG #empirical #learning #online

Online Ensemble Learning: An Empirical Study (AF, RG), pp. 279–286.

ICML-2000-FiechterR #learning #scalability

Learning Subjective Functions with Large Margins (CNF, SR), pp. 287–294.

ICML-2000-ForsterW #bound #learning

Relative Loss Bounds for Temporal-Difference Learning (JF, MKW), pp. 295–302.

ICML-2000-Ghani #classification #using

Using Error-Correcting Codes for Text Classification (RG), pp. 303–310.

ICML-2000-GiordanaSSB #framework #learning #relational

Analyzing Relational Learning in the Phase Transition Framework (AG, LS, MS, MB), pp. 311–318.

ICML-2000-GoldbergM #learning #modelling #multi

Learning Multiple Models for Reward Maximization (DG, MJM), pp. 319–326.

ICML-2000-GoldmanZ #learning

Enhancing Supervised Learning with Unlabeled Data (SAG, YZ), pp. 327–334.

ICML-2000-GordonM #learning

Learning Filaments (GJG, AM), pp. 335–342.

ICML-2000-GrudicU #policy

Localizing Policy Gradient Estimates to Action Transition (GZG, LHU), pp. 343–350.

ICML-2000-HallH #information retrieval #learning #multi #natural language

Learning Curved Multinomial Subfamilies for Natural Language Processing and Information Retrieval (KBH, TH), pp. 351–358.

ICML-2000-Hall #feature model #machine learning

Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning (MAH), pp. 359–366.

ICML-2000-Heskes #empirical #learning

Empirical Bayes for Learning to Learn (TH), pp. 367–374.

ICML-2000-HosteDSG #corpus

Meta-Learning for Phonemic Annotation of Corpora (VH, WD, EFTKS, SG), pp. 375–382.

ICML-2000-HougenGS #approach #learning

An Integrated Connectionist Approach to Reinforcement Learning for Robotic Control (DFH, MLG, JRS), pp. 383–390.

ICML-2000-Howe #comparison #representation

Data as Ensembles of Records: Representation and Comparison (NRH), pp. 391–398.

ICML-2000-HsuHW #classification #naive bayes #why

Why Discretization Works for Naive Bayesian Classifiers (CNH, HJH, TTW), pp. 399–406.

ICML-2000-HuW #game studies #probability

Experimental Results on Q-Learning for General-Sum Stochastic Games (JH, MPW), pp. 407–414.

ICML-2000-HuangSK #constraints #declarative #learning

Learning Declarative Control Rules for Constraint-BAsed Planning (YCH, BS, HAK), pp. 415–422.

ICML-2000-JiangL #approximate #information retrieval

Approximate Dimension Equalization in Vector-based Information Retrieval (FJ, MLL), pp. 423–430.

ICML-2000-Joachims #performance

Estimating the Generalization Performance of an SVM Efficiently (TJ), pp. 431–438.

ICML-2000-JuKS #classification #gesture

State-based Classification of Finger Gestures from Electromyographic Signals (PJ, LPK, YS), pp. 439–446.

ICML-2000-KatayamaKK #learning #using

A Universal Generalization for Temporal-Difference Learning Using Haar Basis Functions (SK, HK, SK), pp. 447–454.

ICML-2000-KaynakA #classification #multi

MultiStage Cascading of Multiple Classifiers: One Man’s Noise is Another Man’s Data (CK, EA), pp. 455–462.

ICML-2000-KephartT #pseudo

Pseudo-convergent Q-Learning by Competitive Pricebots (JOK, GT), pp. 463–470.

ICML-2000-Khardon #learning

Learning Horn Expressions with LogAn-H (RK), pp. 471–478.

ICML-2000-KimN #learning #network #set

Learning Bayesian Networks for Diverse and Varying numbers of Evidence Sets (ZWK, RN), pp. 479–486.

ICML-2000-KlinkenbergJ #concept #detection

Detecting Concept Drift with Support Vector Machines (RK, TJ), pp. 487–494.

ICML-2000-KomarekM #adaptation #machine learning #performance #scalability #set

A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets (PK, AWM), pp. 495–502.

ICML-2000-KubatC #classification #nearest neighbour #subclass

Voting Nearest-Neighbor Subclassifiers (MK, MCJ), pp. 503–510.

ICML-2000-LagoudakisL #algorithm #learning #using

Algorithm Selection using Reinforcement Learning (MGL, MLL), pp. 511–518.

ICML-2000-LaneB #interface #learning #reduction

Data Reduction Techniques for Instance-Based Learning from Human/Computer Interface Data (TL, CEB), pp. 519–526.

ICML-2000-LauDW #algebra #programming

Version Space Algebra and its Application to Programming by Demonstration (TAL, PMD, DSW), pp. 527–534.

ICML-2000-LauerR #algorithm #distributed #learning #multi

An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems (ML, MAR), pp. 535–542.

ICML-2000-LiB #approach #clustering #markov #modelling #using

A Bayesian Approach to Temporal Data Clustering using Hidden Markov Models (CL, GB), pp. 543–550.

ICML-2000-LiRD #algorithm #incremental #maintenance

The Space of Jumping Emerging Patterns and Its Incremental Maintenance Algorithms (JL, KR, GD), pp. 551–558.

ICML-2000-Li #learning #online

Selective Voting for Perception-like Online Learning (YL), pp. 559–566.

ICML-2000-Maloof #adaptation

An Initial Study of an Adaptive Hierarchical Vision System (MAM), pp. 567–574.

ICML-2000-MamitsukaA #database #learning #mining #performance #query #scalability

Efficient Mining from Large Databases by Query Learning (HM, NA), pp. 575–582.

ICML-2000-MargineantuD #classification #evaluation

Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers (DDM, TGD), pp. 583–590.

ICML-2000-McCallumFP #information management #markov #modelling #segmentation

Maximum Entropy Markov Models for Information Extraction and Segmentation (AM, DF, FCNP), pp. 591–598.

ICML-2000-McLachlanP

Mixtures of Factor Analyzers (GJM, DP), pp. 599–606.

ICML-2000-Mitchell

“Boosting” a Positive-Data-Only Learner (ARM), pp. 607–614.

ICML-2000-MollPB #machine learning #problem

Machine Learning for Subproblem Selection (RM, TJP, AGB), pp. 615–622.

ICML-2000-MorimotoD #behaviour #learning #using

Acquisition of Stand-up Behavior by a Real Robot using Hierarchical Reinforcement Learning (JM, KD), pp. 623–630.

ICML-2000-MuggletonBS #biology #learning #product line #sequence

Learning Chomsky-like Grammars for Biological Sequence Families (SM, CHB, AS), pp. 631–638.

ICML-2000-MullinS #classification #nearest neighbour

Complete Cross-Validation for Nearest Neighbor Classifiers (MDM, RS), pp. 639–646.

ICML-2000-MunosM #convergence

Rates of Convergence for Variable Resolution Schemes in Optimal Control (RM, AWM), pp. 647–654.

ICML-2000-MyersKSW #approach #topic

A Boosting Approach to Topic Spotting on Subdialogues (KM, MJK, SPS, MAW), pp. 655–662.

ICML-2000-NgR #algorithm #learning

Algorithms for Inverse Reinforcement Learning (AYN, SJR), pp. 663–670.

ICML-2000-NikovskiN #learning #mobile #modelling #navigation #probability

Learning Probabilistic Models for Decision-Theoretic Navigation of Mobile Robots (DN, IRN), pp. 671–678.

ICML-2000-NiyogiK #approach #clustering #reduction

An Approach to Data Reduction and Clustering with Theoretical Guarantees (PN, NK), pp. 679–686.

ICML-2000-NomotoM #analysis #automation

Comparing the Minimum Description Length Principle and Boosting in the Automatic Analysis of Discourse (TN, YM), pp. 687–694.

ICML-2000-OkamotoY #algorithm #analysis #nearest neighbour

Generalized Average-Case Analyses of the Nearest Neighbor Algorithm (SO, NY), pp. 695–702.

ICML-2000-OSullivanLCB #algorithm #named #robust

FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness (JO, JL, RC, AB), pp. 703–710.

ICML-2000-PaccanaroH #concept #distributed #learning #linear

Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space (AP, GEH), pp. 711–718.

ICML-2000-PaliourasPKS #clustering #community #scalability #web

Clustering the Users of Large Web Sites into Communities (GP, CP, VK, CDS), pp. 719–726.

ICML-2000-PellegM #clustering #estimation #named #performance

X-means: Extending K-means with Efficient Estimation of the Number of Clusters (DP, AWM), pp. 727–734.

ICML-2000-PennockMGH #algorithm #learning

A Normative Examination of Ensemble Learning Algorithms (DMP, PMRI, CLG, EH), pp. 735–742.

ICML-2000-PfahringerBG #algorithm #learning

Meta-Learning by Landmarking Various Learning Algorithms (BP, HB, CGGC), pp. 743–750.

ICML-2000-PiaterG #development #learning #visual notation

Constructive Feature Learning and the Development of Visual Expertise (JHP, RAG), pp. 751–758.

ICML-2000-PrecupSS #evaluation #policy

Eligibility Traces for Off-Policy Policy Evaluation (DP, RSS, SPS), pp. 759–766.

ICML-2000-Randlov #learning #physics #problem

Shaping in Reinforcement Learning by Changing the Physics of the Problem (JR), pp. 767–774.

ICML-2000-RandlovBR #algorithm #learning

Combining Reinforcement Learning with a Local Control Algorithm (JR, AGB, MTR), pp. 775–782.

ICML-2000-Reynolds #adaptation #bound #clustering #learning

Adaptive Resolution Model-Free Reinforcement Learning: Decision Boundary Partitioning (SIR), pp. 783–790.

ICML-2000-RichterS #learning #modelling

Knowledge Propagation in Model-based Reinforcement Learning Tasks (CR, JS), pp. 791–798.

ICML-2000-Rosenberg #image #statistics #using

Image Color Constancy Using EM and Cached Statistics (CRR), pp. 799–806.

ICML-2000-RyanR #learning

Learning to Fly: An Application of Hierarchical Reinforcement Learning (MRKR, MDR), pp. 807–814.

ICML-2000-RychetskySG

Direct Bayes Point Machines (MR, JST, MG), pp. 815–822.

ICML-2000-SannerALL #learning #performance

Achieving Efficient and Cognitively Plausible Learning in Backgammon (SS, JRA, CL, MCL), pp. 823–830.

ICML-2000-Scheffer #performance #predict

Predicting the Generalization Performance of Cross Validatory Model Selection Criteria (TS), pp. 831–838.

ICML-2000-SchohnC #learning #less is more

Less is More: Active Learning with Support Vector Machines (GS, DC), pp. 839–846.

ICML-2000-SchuurmansS #adaptation #learning

An Adaptive Regularization Criterion for Supervised Learning (DS, FS), pp. 847–854.

ICML-2000-SebbanN #problem

Instance Pruning as an Information Preserving Problem (MS, RN), pp. 855–862.

ICML-2000-SegalK #incremental #learning

Incremental Learning in SwiftFile (RS, JOK), pp. 863–870.

ICML-2000-ShultzR #comparison #knowledge-based #learning #multi #using

Using Knowledge to Speed Learning: A Comparison of Knowledge-based Cascade-correlation and Multi-task Learning (TRS, FR), pp. 871–878.

ICML-2000-SilvaL #hybrid #learning

Obtaining Simplified Rule Bases by Hybrid Learning (RBdAeS, TBL), pp. 879–886.

ICML-2000-SingerV #learning #modelling #performance #predict

Learning to Predict Performance from Formula Modeling and Training Data (BS, MMV), pp. 887–894.

ICML-2000-SlatteryM #relational #testing

Discovering Test Set Regularities in Relational Domains (SS, TMM), pp. 895–902.

ICML-2000-SmartK #learning

Practical Reinforcement Learning in Continuous Spaces (WDS, LPK), pp. 903–910.

ICML-2000-SmolaS #approximate #machine learning #matrix

Sparse Greedy Matrix Approximation for Machine Learning (AJS, BS), pp. 911–918.

ICML-2000-SohT #image #learning #using

Using Learning by Discovery to Segment Remotely Sensed Images (LKS, CT), pp. 919–926.

ICML-2000-SridharanT #automation #multi

Multi-agent Q-learning and Regression Trees for Automated Pricing Decisions (MS, GT), pp. 927–934.

ICML-2000-Stone #network

TPOT-RL Applied to Network Routing (PS), pp. 935–942.

ICML-2000-Strens #framework #learning

A Bayesian Framework for Reinforcement Learning (MJAS), pp. 943–950.

ICML-2000-Talavera #concept #feature model #incremental #learning #probability

Feature Selection and Incremental Learning of Probabilistic Concept Hierarchies (LT), pp. 951–958.

ICML-2000-TellerV #evolution #learning #performance #programming

Efficient Learning Through Evolution: Neural Programming and Internal Reinforcement (AT, MMV), pp. 959–966.

ICML-2000-TeowL #kernel #parametricity

Selection of Support Vector Kernel Parameters for Improved Generalization (LNT, KFL), pp. 967–974.

ICML-2000-ThollardDH #automaton #probability #using

Probabilistic DFA Inference using Kullback-Leibler Divergence and Minimality (FT, PD, CdlH), pp. 975–982.

ICML-2000-Ting #algorithm #case study #comparative

A Comparative Study of Cost-Sensitive Boosting Algorithms (KMT), pp. 983–990.

ICML-2000-TodorovskiDSWG #behaviour #difference #equation

Discovering the Structure of Partial Differential Equations from Example Behaviour (LT, SD, AS, JPW, DG), pp. 991–998.

ICML-2000-TongK #classification #learning

Support Vector Machine Active Learning with Application sto Text Classification (ST, DK), pp. 999–1006.

ICML-2000-Torgo #linear

Partial Linear Trees (LT), pp. 1007–1014.

ICML-2000-TorkkolaC #learning

Mutual Information in Learning Feature Transformations (KT, WMC), pp. 1015–1022.

ICML-2000-Towell #detection

Local Expert Autoassociators for Anomaly Detection (GGT), pp. 1023–1030.

ICML-2000-TowellPM #learning

Learning Priorities From Noisy Examples (GGT, TP, MRM), pp. 1031–1038.

ICML-2000-VaithyanathanD #learning

Hierarchical Unsupervised Learning (SV, BD), pp. 1039–1046.

ICML-2000-AllenG #comparison #empirical #learning

Model Selection Criteria for Learning Belief Nets: An Empirical Comparison (TVA, RG), pp. 1047–1054.

ICML-2000-BoschZ #in memory #learning #multi

Unpacking Multi-valued Symbolic Features and Classes in Memory-Based Language Learning (AvdB, JZ), pp. 1055–1062.

ICML-2000-Zaanen #learning #recursion #syntax #using

Bootstrapping Syntax and Recursion using Alginment-Based Learning (MvZ), pp. 1063–1070.

ICML-2000-Veeser #approach #automaton #finite #learning

An Evolutionary Approach to Evidence-Based Learning of Deterministic Finite Automata (SV), pp. 1071–1078.

ICML-2000-VijayakumarS #incremental #learning #realtime

Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space (SV, SS), pp. 1079–1086.

ICML-2000-VilaltaO #bias #classification #distance #evaluation #metric #quantifier

A Quantification of Distance Bias Between Evaluation Metrics In Classification (RV, DO), pp. 1087–1094.

ICML-2000-VuceticO #contest

Discovering Homogeneous Regions in Spatial Data through Competition (SV, ZO), pp. 1095–1102.

ICML-2000-WagstaffC #clustering #constraints

Clustering with Instance-level Constraints (KW, CC), pp. 1103–1110.

ICML-2000-WalkerWL #comprehension #fault #identification #natural language #using

Using Natural Language Processing and discourse Features to Identify Understanding Errors (MAW, JHW, IL), pp. 1111–1118.

ICML-2000-WnagZ #approach #lazy evaluation #learning #multi #problem

Solving the Multiple-Instance Problem: A Lazy Learning Approach (JW, JDZ), pp. 1119–1126.

ICML-2000-WashioMN #equation

Enhancing the Plausibility of Law Equation Discovery (TW, HM, YN), pp. 1127–1134.

ICML-2000-WeissI #induction #lightweight

Lightweight Rule Induction (SMW, NI), pp. 1135–1142.

ICML-2000-WesterdijkW #classification #modelling #multi #using

Classification with Multiple Latent Variable Models using Maximum Entropy Discrimination (MW, WW), pp. 1143–1150.

ICML-2000-Wiering #multi

Multi-Agent Reinforcement Leraning for Traffic Light Control (MW), pp. 1151–1158.

ICML-2000-WilliamsS #classification #kernel

The Effect of the Input Density Distribution on Kernel-based Classifiers (CKIW, MWS), pp. 1159–1166.

ICML-2000-YangAP #effectiveness #learning #multi #validation

Combining Multiple Learning Strategies for Effective Cross Validation (YY, TA, TP), pp. 1167–1174.

ICML-2000-YildizA #linear

Linear Discriminant Trees (OTY, EA), pp. 1175–1182.

ICML-2000-ZelikovitzH #classification #problem #using

Improving Short-Text Classification using Unlabeled Data for Classification Problems (SZ, HH), pp. 1191–1198.

ICML-2000-ZupanBBD #concept #induction #semistructured data

Induction of Concept Hierarchies from Noisy Data (BZ, IB, MB, JD), pp. 1199–1206.

ICML-2000-Langley #machine learning

Crafting Papers on Machine Learning (PL), pp. 1207–1216.