Table of Contents

• Knowledge Representation Issues in Control Knowledge LearningRicardo Aler, Daniel Borrajo, Pedro Isasi. 1-8
• Reducing Multiclass to Binary: A Unifying Approach for Margin ClassifiersErin L. Allwein, Robert E. Schapire, Yoram Singer. 9-16
• A Nonparametric Approach to Noisy and Costly OptimizationBrigham S. Anderson, Andrew W. Moore, David Cohn. 17-24
• Behavioral Cloning of Student Pilots with Modular Neural NetworksCharles W. Anderson, Bruce A. Draper, David A. Peterson. 25-32
• Combining Multiple PerspectivesBikramjit Banerjee, Sandip Debnath, Sandip Sen. 33-40
• Reinforcement Learning in POMDP s via Direct Gradient AscentJonathan Baxter, Peter L. Bartlett. 41-48
• Characterizing Model Erros and DifferencesStephen D. Bay, Michael J. Pazzani. 49-56
• Duality and Geometry in SVM ClassifiersKristin P. Bennett, Erin J. Bredensteiner. 57-64
• A Column Generation Algorithm For BoostingKristin P. Bennett, Ayhan Demiriz, John Shawe-Taylor. 65-72
• Disciple-COA: From Agent Programming to Agent TeachingMihai Boicu, Gheorghe Tecuci, Dorin Marcu, Michael Bowman, Ping Shyr, Florin Ciucu, Cristian Levcovici. 73-80
• Classification of Individuals with Complex StructureAntony F. Bowers, Christophe G. Giraud-Carrier, John W. Lloyd. 81-88
• Convergence Problems of General-Sum Multiagent Reinforcement LearningMichael H. Bowling. 89-94
• Finding Variational Structure in Data by Cross-Entropy OptimizationMatthew Brand. 95-102
• Challenges of the Email Domain for Text ClassificationJake D. Brutlag, Christopher Meek. 103-110
• Query Learning with Large Margin ClassifiersColin Campbell, Nello Cristianini, Alex J. Smola. 111-118
• Dimension Reduction Techniques for Training Polynomial NetworksWilliam M. Campbell, Kari Torkkola, Sreeream V. Balakrishnan. 119-126
• Learning to Create Customized Authority ListsHuan Chang, David Cohn, Andrew McCallum. 127-134
• Learning to Select Text Databases with Neural NetsYong S. Choi, Suk I. Yoo. 135-142
• A Divide and Conquer Approach to Learning from Prior KnowledgeEric Chown, Thomas G. Dietterich. 143-150
• Learning in Non-stationary Conditions: A Control Theoretic ApproachJefferson A. Coelho Jr., Roderic A. Grupen. 151-158
• Automatically Extracting Features for Concept Learning from the WebWilliam W. Cohen. 159-166
• Learning to Probabilistically Identify Authoritative DocumentsDavid Cohn, Huan Chang. 167-174
• Discriminative Reranking for Natural Language ParsingMichael Collins. 175-182
• Automatic Identification of Mathematical ConceptsSimon Colton, Alan Bundy, Toby Walsh. 183-190
• On-line Learning for Humanoid Robot SystemsJörg Conradt, Gaurav Tevatia, Sethu Vijayakumar, Stefan Schaal. 191-198
• Using Multiple Levels of Learning and Diverse Evidence to Uncover Coordinately Controlled GenesMark Craven, David Page, Jude W. Shavlik, Joseph Bockhorst, Jeremy D. Glasner. 199-206
• Fixed Points of Approximate Value Iteration and Temporal-Difference LearningDaniela Pucci de Farias, Benjamin Van Roy. 207-214
• Hidden Strengths and Limitations: An Empirical Investigation of Reinforcement LearningGerald DeJong. 215-222
• Bayesian Averaging of Classifiers and the Overfitting ProblemPedro Domingos. 223-230
• A Unifeid Bias-Variance Decomposition and its ApplicationsPedro Domingos. 231-238
• Exploiting the Cost (In)sensitivity of Decision Tree Splitting CriteriaChris Drummond, Robert C. Holte. 239-246
• Feature Subset Selection and Order Identification for Unsupervised LearningJennifer G. Dy, Carla E. Brodley. 247-254
• Anomaly Detection over Noisy Data using Learned Probability DistributionsEleazar Eskin. 255-262
• Ideal Theory Refinement under Object IdentityFloriana Esposito, Nicola Fanizzi, Stefano Ferilli, Giovanni Semeraro. 263-270
• Bounds on the Generalization Performance of Kernel Machine EnsemblesTheodoros Evgeniou, Luis Pérez-Breva, Massimiliano Pontil, Tomaso Poggio. 271-278
• Online Ensemble Learning: An Empirical StudyAlan Fern, Robert Givan. 279-286
• Learning Subjective Functions with Large MarginsClaude-Nicolas Fiechter, Seth Rogers. 287-294
• Relative Loss Bounds for Temporal-Difference LearningJürgen Forster, Manfred K. Warmuth. 295-302
• Using Error-Correcting Codes for Text ClassificationRayid Ghani. 303-310
• Analyzing Relational Learning in the Phase Transition FrameworkAttilio Giordana, Lorenza Saitta, Michèle Sebag, Marco Botta. 311-318
• Learning Multiple Models for Reward MaximizationDani Goldberg, Maja J. Mataric. 319-326
• Enhancing Supervised Learning with Unlabeled DataSally A. Goldman, Yan Zhou. 327-334
• Learning FilamentsGeoffrey J. Gordon, Andrew Moore. 335-342
• Localizing Policy Gradient Estimates to Action TransitionGregory Z. Grudic, Lyle H. Ungar. 343-350
• Learning Curved Multinomial Subfamilies for Natural Language Processing and Information RetrievalKeith Hall, Thomas Hofmann. 351-358
• Correlation-based Feature Selection for Discrete and Numeric Class Machine LearningMark A. Hall. 359-366
• Empirical Bayes for Learning to LearnTom Heskes. 367-374
• Meta-Learning for Phonemic Annotation of CorporaVéronique Hoste, Walter Daelemans, Erik F. Tjong Kim Sang, Steven Gillis. 375-382
• An Integrated Connectionist Approach to Reinforcement Learning for Robotic ControlDean F. Hougen, Maria L. Gini, James R. Slagle. 383-390
• Data as Ensembles of Records: Representation and ComparisonNicholas R. Howe. 391-398
• Why Discretization Works for Naive Bayesian ClassifiersChun-Nan Hsu, Hung-Ju Huang, Tzu-Tsung Wong. 399-406
• Experimental Results on Q-Learning for General-Sum Stochastic GamesJunling Hu, Michael P. Wellman. 407-414
• Learning Declarative Control Rules for Constraint-BAsed PlanningYi-Cheng Huang, Bart Selman, Henry A. Kautz. 415-422
• Approximate Dimension Equalization in Vector-based Information RetrievalFan Jiang, Michael L. Littman. 423-430
• Estimating the Generalization Performance of an SVM EfficientlyThorsten Joachims. 431-438
• State-based Classification of Finger Gestures from Electromyographic SignalsPeter Ju, Leslie Pack Kaelbling, Yoram Singer. 439-446
• A Universal Generalization for Temporal-Difference Learning Using Haar Basis FunctionsSusumu Katayama, Hajime Kimura, Shigenobu Kobayashi. 447-454
• MultiStage Cascading of Multiple Classifiers: One Man s Noise is Another Man s DataCenk Kaynak, Ethem Alpaydin. 455-462
• Pseudo-convergent Q-Learning by Competitive PricebotsJeffrey O. Kephart, Gerald Tesauro. 463-470
• Learning Horn Expressions with LogAn-HRoni Khardon. 471-478
• Learning Bayesian Networks for Diverse and Varying numbers of Evidence SetsZu Whan Kim, Ramakant Nevatia. 479-486
• Detecting Concept Drift with Support Vector MachinesRalf Klinkenberg, Thorsten Joachims. 487-494
• A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data SetsPaul Komarek, Andrew W. Moore. 495-502
• Voting Nearest-Neighbor SubclassifiersMiroslav Kubat, Martin Cooperson Jr.. 503-510
• Algorithm Selection using Reinforcement LearningMichail G. Lagoudakis, Michael L. Littman. 511-518
• Data Reduction Techniques for Instance-Based Learning from Human/Computer Interface DataTerran Lane, Carla E. Brodley. 519-526
• Version Space Algebra and its Application to Programming by DemonstrationTessa A. Lau, Pedro Domingos, Daniel S. Weld. 527-534
• An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent SystemsMartin Lauer, Martin A. Riedmiller. 535-542
• A Bayesian Approach to Temporal Data Clustering using Hidden Markov ModelsCen Li, Gautam Biswas. 543-550
• The Space of Jumping Emerging Patterns and Its Incremental Maintenance AlgorithmsJinyan Li, Kotagiri Ramamohanarao, Guozhu Dong. 551-558
• Selective Voting for Perception-like Online LearningYi Li. 559-566
• An Initial Study of an Adaptive Hierarchical Vision SystemMarcus A. Maloof. 567-574
• Efficient Mining from Large Databases by Query LearningHiroshi Mamitsuka, Naoki Abe. 575-582
• Bootstrap Methods for the Cost-Sensitive Evaluation of ClassifiersDragos D. Margineantu, Thomas G. Dietterich. 583-590
• Maximum Entropy Markov Models for Information Extraction and SegmentationAndrew McCallum, Dayne Freitag, Fernando C. N. Pereira. 591-598
• Mixtures of Factor AnalyzersGeoffrey J. McLachlan, David Peel. 599-606
• Boosting a Positive-Data-Only LearnerAndrew R. Mitchell. 607-614
• Machine Learning for Subproblem SelectionRobert Moll, Theodore J. Perkins, Andrew G. Barto. 615-622
• Acquisition of Stand-up Behavior by a Real Robot using Hierarchical Reinforcement LearningJun Morimoto, Kenji Doya. 623-630
• Learning Chomsky-like Grammars for Biological Sequence FamiliesStephen Muggleton, Christopher H. Bryant, Ashwin Srinivasan. 631-638
• Complete Cross-Validation for Nearest Neighbor ClassifiersMatthew D. Mullin, Rahul Sukthankar. 639-646
• Rates of Convergence for Variable Resolution Schemes in Optimal ControlRémi Munos, Andrew W. Moore. 647-654
• A Boosting Approach to Topic Spotting on SubdialoguesKary Myers, Michael J. Kearns, Satinder P. Singh, Marilyn A. Walker. 655-662
• Algorithms for Inverse Reinforcement LearningAndrew Y. Ng, Stuart J. Russell. 663-670
• Learning Probabilistic Models for Decision-Theoretic Navigation of Mobile RobotsDaniel Nikovski, Illah R. Nourbakhsh. 671-678
• An Approach to Data Reduction and Clustering with Theoretical GuaranteesPartha Niyogi, Narendra Karmarkar. 679-686
• Comparing the Minimum Description Length Principle and Boosting in the Automatic Analysis of DiscourseTadashi Nomoto, Yuji Matsumoto. 687-694
• Generalized Average-Case Analyses of the Nearest Neighbor AlgorithmSeishi Okamoto, Nobuhiro Yugami. 695-702
• FeatureBoost: A Meta-Learning Algorithm that Improves Model RobustnessJoseph O Sullivan, John Langford, Rich Caruana, Avrim Blum. 703-710
• Learning Distributed Representations by Mapping Concepts and Relations into a Linear SpaceAlberto Paccanaro, Geoffrey E. Hinton. 711-718
• Clustering the Users of Large Web Sites into CommunitiesGeorgios Paliouras, Christos Papatheodorou, Vangelis Karkaletsis, Constantine D. Spyropoulos. 719-726
• X-means: Extending K-means with Efficient Estimation of the Number of ClustersDan Pelleg, Andrew W. Moore. 727-734
• A Normative Examination of Ensemble Learning AlgorithmsDavid M. Pennock, Pedrito Maynard-Reid II, C. Lee Giles, Eric Horvitz. 735-742
• Meta-Learning by Landmarking Various Learning AlgorithmsBernhard Pfahringer, Hilan Bensusan, Christophe G. Giraud-Carrier. 743-750
• Constructive Feature Learning and the Development of Visual ExpertiseJustus H. Piater, Roderic A. Grupen. 751-758
• Eligibility Traces for Off-Policy Policy EvaluationDoina Precup, Richard S. Sutton, Satinder P. Singh. 759-766
• Shaping in Reinforcement Learning by Changing the Physics of the ProblemJette Randløv. 767-774
• Combining Reinforcement Learning with a Local Control AlgorithmJette Randløv, Andrew G. Barto, Michael T. Rosenstein. 775-782
• Adaptive Resolution Model-Free Reinforcement Learning: Decision Boundary PartitioningStuart I. Reynolds. 783-790
• Knowledge Propagation in Model-based Reinforcement Learning TasksCorinna Richter, Jörg Stachowiak. 791-798
• Image Color Constancy Using EM and Cached StatisticsCharles R. Rosenberg. 799-806
• Learning to Fly: An Application of Hierarchical Reinforcement LearningMalcolm R. K. Ryan, Mark D. Reid. 807-814
• Direct Bayes Point MachinesMatthias Rychetsky, John Shawe-Taylor, Manfred Glesner. 815-822
• Achieving Efficient and Cognitively Plausible Learning in BackgammonScott Sanner, John R. Anderson, Christian Lebiere, Marsha C. Lovett. 823-830
• Predicting the Generalization Performance of Cross Validatory Model Selection CriteriaTobias Scheffer. 831-838
• Less is More: Active Learning with Support Vector MachinesGreg Schohn, David Cohn. 839-846
• An Adaptive Regularization Criterion for Supervised LearningDale Schuurmans, Finnegan Southey. 847-854
• Instance Pruning as an Information Preserving ProblemMarc Sebban, Richard Nock. 855-862
• Incremental Learning in SwiftFileRichard Segal, Jeffrey O. Kephart. 863-870
• Using Knowledge to Speed Learning: A Comparison of Knowledge-based Cascade-correlation and Multi-task LearningThomas R. Shultz, François Rivest. 871-878
• Obtaining Simplified Rule Bases by Hybrid LearningRicardo Bezerra de Andrade e Silva, Teresa Bernarda Ludermir. 879-886
• Learning to Predict Performance from Formula Modeling and Training DataBryan Singer, Manuela M. Veloso. 887-894
• Discovering Test Set Regularities in Relational DomainsSeán Slattery, Tom M. Mitchell. 895-902
• Practical Reinforcement Learning in Continuous SpacesWilliam D. Smart, Leslie Pack Kaelbling. 903-910
• Sparse Greedy Matrix Approximation for Machine LearningAlex J. Smola, Bernhard Schölkopf. 911-918
• Using Learning by Discovery to Segment Remotely Sensed ImagesLeen-Kiat Soh, Costas Tsatsoulis. 919-926
• Multi-agent Q-learning and Regression Trees for Automated Pricing DecisionsManu Sridharan, Gerald Tesauro. 927-934
• TPOT-RL Applied to Network RoutingPeter Stone. 935-942
• A Bayesian Framework for Reinforcement LearningMalcolm J. A. Strens. 943-950
• Feature Selection and Incremental Learning of Probabilistic Concept HierarchiesLuis Talavera. 951-958
• Efficient Learning Through Evolution: Neural Programming and Internal ReinforcementAstro Teller, Manuela M. Veloso. 959-966
• Selection of Support Vector Kernel Parameters for Improved GeneralizationLoo-Nin Teow, Kia-Fock Loe. 967-974
• Probabilistic DFA Inference using Kullback-Leibler Divergence and MinimalityFranck Thollard, Pierre Dupont, Colin de la Higuera. 975-982
• A Comparative Study of Cost-Sensitive Boosting AlgorithmsKai Ming Ting. 983-990
• Discovering the Structure of Partial Differential Equations from Example BehaviourLjupco Todorovski, Saso Dzeroski, Ashwin Srinivasan, Jonathan Whiteley, David Gavaghan. 991-998
• Support Vector Machine Active Learning with Application sto Text ClassificationSimon Tong, Daphne Koller. 999-1006
• Partial Linear TreesLuís Torgo. 1007-1014
• Mutual Information in Learning Feature TransformationsKari Torkkola, William M. Campbell. 1015-1022
• Local Expert Autoassociators for Anomaly DetectionGeoffrey G. Towell. 1023-1030
• Learning Priorities From Noisy ExamplesGeoffrey G. Towell, Thomas Petsche, Michael R. Miller. 1031-1038
• Hierarchical Unsupervised LearningShivakumar Vaithyanathan, Byron Dom. 1039-1046
• Model Selection Criteria for Learning Belief Nets: An Empirical ComparisonTim Van Allen, Russell Greiner. 1047-1054
• Unpacking Multi-valued Symbolic Features and Classes in Memory-Based Language LearningAntal van den Bosch, Jakub Zavrel. 1055-1062
• Bootstrapping Syntax and Recursion using Alginment-Based LearningMenno van Zaanen. 1063-1070
• An Evolutionary Approach to Evidence-Based Learning of Deterministic Finite AutomataStefan Veeser. 1071-1078
• Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional SpaceSethu Vijayakumar, Stefan Schaal. 1079-1086
• A Quantification of Distance Bias Between Evaluation Metrics In ClassificationRicardo Vilalta, Daniel Oblinger. 1087-1094
• Discovering Homogeneous Regions in Spatial Data through CompetitionSlobodan Vucetic, Zoran Obradovic. 1095-1102
• Clustering with Instance-level ConstraintsKiri Wagstaff, Claire Cardie. 1103-1110
• Using Natural Language Processing and discourse Features to Identify Understanding ErrorsMarilyn A. Walker, Jeremy H. Wright, Irene Langkilde. 1111-1118
• Solving the Multiple-Instance Problem: A Lazy Learning ApproachJun Wang, Jean-Daniel Zucker. 1119-1126
• Enhancing the Plausibility of Law Equation DiscoveryTakashi Washio, Hiroshi Motoda, Yuji Niwa. 1127-1134
• Lightweight Rule InductionSholom M. Weiss, Nitin Indurkhya. 1135-1142
• Classification with Multiple Latent Variable Models using Maximum Entropy DiscriminationMachiel Westerdijk, Wim Wiegerinck. 1143-1150
• Multi-Agent Reinforcement Leraning for Traffic Light ControlMarco Wiering. 1151-1158
• The Effect of the Input Density Distribution on Kernel-based ClassifiersChristopher K. I. Williams, Matthias Seeger. 1159-1166
• Combining Multiple Learning Strategies for Effective Cross ValidationYiming Yang, Tom Ault, Thomas Pierce. 1167-1174
• Linear Discriminant TreesOlcay Taner Yildiz, Ethem Alpaydin. 1175-1182
• Improving Short-Text Classification using Unlabeled Data for Classification ProblemsSarah Zelikovitz, Haym Hirsh. 1191-1198
• Induction of Concept Hierarchies from Noisy DataBlaz Zupan, Ivan Bratko, Marko Bohanec, Janez Demsar. 1199-1206
• Crafting Papers on Machine LearningPat Langley. 1207-1216