Table of Contents

• Query Learning Strategies Using Boosting and BaggingNaoki Abe, Hiroshi Mamitsuka. 1-9
• Genetic Programming and Deductive-Inductive Learning: A Multi-Strategy ApproachRicardo Aler, Daniel Borrajo, Pedro Isasi. 10-18
• An Experimental Evaluation of Coevolutive Concept LearningCosimo Anglano, Attilio Giordana, Giuseppe Lo Bello, Lorenza Saitta. 19-27
• KnightCap: A Chess Programm That Learns by Combining TD(lambda) with Game-Tree SearchJonathan Baxter, Andrew Tridgell, Lex Weaver. 28-36
• Combining Nearest Neighbor Classifiers Through Multiple Feature SubsetsStephen D. Bay. 37-45
• Learning Collaborative Information FiltersDaniel Billsus, Michael J. Pazzani. 46-54
• Top-Down Induction of Clustering TreesHendrik Blockeel, Luc De Raedt, Jan Ramon. 55-63
• A Supra-Classifier Architecture for Scalable Knowledge ReuseKurt D. Bollacker, Joydeep Ghosh. 64-72
• Learning Sorting and Decision Trees with POMDPsBlai Bonet, Hector Geffner. 73-81
• Feature Selection via Concave Minimization and Support Vector MachinesPaul S. Bradley, Olvi L. Mangasarian. 82-90
• Refining Initial Points for K-Means ClusteringPaul S. Bradley, Usama M. Fayyad. 91-99
• Finite-Time Regret Bounds for the Multiarmed Bandit ProblemNicolò Cesa-Bianchi, Paul Fischer. 100-108
• Bayesian Classifiers Are Large Margin Hyperplanes in a Hilbert SpaceNello Cristianini, John Shawe-Taylor, Peter Sykacek. 109-117
• The MAXQ Method for Hierarchical Reinforcement LearningThomas G. Dietterich. 118-126
• A Process-Oriented Heuristic for Model SelectionPedro Domingos. 127-135
• Relational Reinforcement LearningSaso Dzeroski, Luc De Raedt, Hendrik Blockeel. 136-143
• Generating Accurate Rule Sets Without Global OptimizationEibe Frank, Ian H. Witten. 144-151
• Using a Permutation Test for Attribute Selection in Decision TreesEibe Frank, Ian H. Witten. 152-160
• Multistrategy Learning for Information ExtractionDayne Freitag. 161-169
• An Efficient Boosting Algorithm for Combining PreferencesYoav Freund, Raj D. Iyer, Robert E. Schapire, Yoram Singer. 170-178
• Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric FittingNir Friedman, Moisés Goldszmidt, Thomas J. Lee. 179-187
• The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector MachinesThilo-Thomas Frieß, Nello Cristianini, Colin Campbell. 188-196
• Multi-criteria Reinforcement LearningZoltán Gábor, Zsolt Kalmár, Csaba Szepesvári. 197-205
• Local Cascade GeneralizationJoao Gama. 206-214
• A Learning Rate Analysis of Reinforcement Learning Algorithms in Finite-HorizonFrédérick Garcia, Seydina M. Ndiaye. 215-223
• Well-Behaved Borgs, Bolos, and BerserkersDiana F. Gordon. 224-232
• Solving a Huge Number of Similar Tasks: A Combination of Multi-Task Learning and a Hierarchical Bayesian ApproachTom Heskes. 233-241
• Multiagent Reinforcement Learning: Theoretical Framework and an AlgorithmJunling Hu, Michael P. Wellman. 242-250
• Coevolutionary Learning: A Case StudyHugues Juillé, Jordan B. Pollack. 251-259
• Near-Optimal Reinforcement Learning in Polynominal TimeMichael J. Kearns, Satinder P. Singh. 260-268
• A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal GeneralizationMichael J. Kearns, Yishay Mansour. 269-277
• An Analysis of Actor/Critic Algorithms Using Eligibility Traces: Reinforcement Learning with Imperfect Value FunctionHajime Kimura, Shigenobu Kobayashi. 278-286
• Using Learning for Approximation in Stochastic ProcessesDaphne Koller, Raya Fratkina. 287-295
• An Information-Theoretic Definition of SimilarityDekang Lin. 296-304
• Structural Machine Learning with Galois Lattice and GraphsMichel Liquiere, Jean Sallantin. 305-313
• Learning a Language-Independent Representation for Terms from a Partially Aligned CorpusMichael L. Littman, Fan Jiang, Greg A. Keim. 314-322
• Using Eligibility Traces to Find the Best Memoryless Policy in Partially Observable Markov Decision ProcessesJohn Loch, Satinder P. Singh. 323-331
• Learning to Locate an Object in 3D Space from a Sequence of Camera ImagesDimitris Margaritis, Sebastian Thrun. 332-340
• Multiple-Instance Learning for Natural Scene ClassificationOded Maron, Aparna Lakshmi Ratan. 341-349
• Employing EM and Pool-Based Active Learning for Text ClassificationAndrew McCallum, Kamal Nigam. 350-358
• Improving Text Classification by Shrinkage in a Hierarchy of ClassesAndrew McCallum, Ronald Rosenfeld, Tom M. Mitchell, Andrew Y. Ng. 359-367
• A Case Study in the Use of Theory Revision in Requirements ValidationT. L. McCluskey, Margaret Mary West. 368-376
• Stochastic Resonance with Adaptive Fuzzy SystemsSanya Mitaim, Bart Kosko. 377-385
• Q2: Memory-Based Active Learning for Optimizing Noisy Continuous FunctionsAndrew W. Moore, Jeff G. Schneider, Justin A. Boyan, Mary S. Lee. 386-394
• Collaborative Filtering Using Weighted Majority Prediction AlgorithmsAtsuyoshi Nakamura, Naoki Abe. 395-403
• On Feature Selection: Learning with Exponentially Many Irrelevant Features as Training ExamplesAndrew Y. Ng. 404-412
• On the Power of Decision ListsRichard Nock, Pascal Jappy. 413-420
• An Analysis of Direct Reinforcement Learning in Non-Markovian DomainsMark D. Pendrith, Michael McGarity. 421-429
• A Randomized ANOVA Procedure for Comparing Performance CurvesJustus H. Piater, Paul R. Cohen, Xiaoqin Zhang, Michael Atighetchi. 430-438
• Classification Using Phi-Machines and Constructive Function ApproximationDoina Precup, Paul E. Utgoff. 439-444
• The Case against Accuracy Estimation for Comparing Induction AlgorithmsFoster J. Provost, Tom Fawcett, Ron Kohavi. 445-453
• Theory Refinement of Bayesian Networks with Hidden VariablesSowmya Ramachandran, Raymond J. Mooney. 454-462
• Learning to Drive a Bicycle Using Reinforcement Learning and ShapingJette Randløv, Preben Alstrøm. 463-471
• Learning First-Order Acyclic Horn Programs from EntailmentChandra Reddy, Prasad Tadepalli. 472-480
• RL-TOPS: An Architecture for Modularity and Re-Use in Reinforcement LearningMalcolm R. K. Ryan, Mark D. Pendrith. 481-487
• Evolving Structured Programs with Hierarchical Instructions and Skip NodesRafal Salustowicz, Jürgen Schmidhuber. 488-496
• An Investigation of Transformation-Based Learning in DiscourseKen Samuel, Sandra Carberry, K. Vijay-Shanker. 497-505
• Automatic Segmentation of Continuous Trajectories with Invariance to Nonlinear Warpings of TimeLawrence K. Saul. 506-514
• Ridge Regression Learning Algorithm in Dual VariablesCraig Saunders, Alexander Gammerman, Volodya Vovk. 515-521
• Value Function Based Production SchedulingJeff G. Schneider, Justin A. Boyan, Andrew W. Moore. 522-530
• Heading in the Right DirectionHagit Shatkay, Leslie Pack Kaelbling. 531-539
• A Neural Network Model for Prognostic PredictionW. Nick Street. 540-546
• Learning the Grammar of DanceJoshua M. Stuart, Elizabeth Bradley. 547-555
• Intra-Option Learning about Temporally Abstract ActionsRichard S. Sutton, Doina Precup, Satinder P. Singh. 556-564
• Teaching an Agent to Test StudentsGheorghe Tecuci, Harry Keeling. 565-573
• The Problem with Noise and Small DisjunctsGary M. Weiss, Haym Hirsh. 574