Table of Contents

• Multiple-Instance Learning of Real-Valued DataRobert A. Amar, Daniel R. Dooly, Sally A. Goldman, Qi Zhang. 3-10
• Efficient algorithms for decision tree cross-validationHendrik Blockeel, Jan Struyf. 11-18
• Learning from Labeled and Unlabeled Data using Graph MincutsAvrim Blum, Shuchi Chawla. 19-26
• Convergence of Gradient Dynamics with a Variable Learning RateMichael H. Bowling, Manuela M. Veloso. 27-34
• Learning an Agent s Utility Function by Observing BehaviorUrszula Chajewska, Daphne Koller, Dirk Ormoneit. 35-42
• A Generalized Kalman Filter for Fixed Point Approximation and Efficient Temporal Difference LearningDavid Choi, Benjamin Van Roy. 43-50
• A Unified Loss Function in Bayesian Framework for Support Vector RegressionWei Chu, S. Sathiya Keerthi, Chong Jin Ong. 51-58
• Boosting with Confidence InformationCraig W. Codrington. 59-65
• Latent Semantic KernelsNello Cristianini, John Shawe-Taylor, Huma Lodhi. 66-73
• Filters, Wrappers and a Boosting-Based Hybrid for Feature SelectionSanmay Das. 74-81
• Structured Prioritised SweepingRichard Dearden. 82-89
• Bias Correction in Classification Tree ConstructionAlin Dobra, Johannes Gehrke. 90-97
• An Efficient Approach for Approximating Multi-dimensional Range Queries and Nearest Neighbor Classification in Large DatasetsCarlotta Domeniconi, Dimitrios Gunopulos. 98-105
• A General Method for Scaling Up Machine Learning Algorithms and its Application to ClusteringPedro Domingos, Geoff Hulten. 106-113
• Visual Development and the Acquisition of Binocular Disparity SensitivitiesMelissa Dominguez, Robert A. Jacobs. 114-121
• Relevance Feedback using Support Vector MachinesHarris Drucker, Behzad Shahraray, David C. Gibbon. 122-129
• A Theory-Refinement Approach to Information ExtractionTina Eliassi-Rad, Jude W. Shavlik. 130-137
• Learning Embedded Maps of Markov ProcessesYaakov Engel, Shie Mannor. 138-145
• Round Robin Rule LearningJohannes Fürnkranz. 146-153
• WBCsvm: Weighted Bayesian Classification based on Support Vector MachinesThomas Gärtner, Peter A. Flach. 154-161
• Reinforcement Learning with Bounded RiskPeter Geibel. 162-169
• Learning Probabilistic Models of Relational StructureLise Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar. 170-177
• Hypertext Categorization using Hyperlink Patterns and Meta DataRayid Ghani, Seán Slattery, Yiming Yang. 178-185
• Continuous-Time Hierarchical Reinforcement LearningMohammad Ghavamzadeh, Sridhar Mahadevan. 186-193
• Evolutionary Search, Stochastic Policies with Memory, and Reinforcement Learning with Hidden StateMatthew R. Glickman, Katia P. Sycara. 194-201
• Bayesian approaches to failure prediction for disk drivesGreg Hamerly, Charles Elkan. 202-209
• General Loss Bounds for Universal Sequence PredictionMarcus Hutter. 210-217
• Expectation Maximization for Weakly Labeled DataYuri A. Ivanov, Bruce Blumberg, Alex Pentland. 218-225
• On No-Regret Learning, Fictitious Play, and Nash EquilibriumAmir Jafari, Amy R. Greenwald, David Gondek, Gunes Ercal. 226-233
• Some Theoretical Aspects of Boosting in the Presence of Noisy DataWenxin Jiang. 234-241
• Learning to Select Good Title Words: An New Approach based on Reverse Information RetrievalRong Jin, Alexander G. Hauptmann. 242-249
• Composite Kernels for Hypertext CategorisationThorsten Joachims, Nello Cristianini, John Shawe-Taylor. 250-257
• Feature Construction with Version Spaces for Biochemical ApplicationsStefan Kramer, Luc De Raedt. 258-265
• Pairwise Comparison of Hypotheses in Evolutionary LearningKrzysztof Krawiec. 266-273
• Boosting Noisy DataAbba Krieger, Chuan Long, Abraham Wyner. 274-281
• Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence DataJohn D. Lafferty, Andrew McCallum, Fernando C. N. Pereira. 282-289
• An Improved Predictive Accuracy Bound for Averaging ClassifiersJohn Langford, Matthias Seeger, Nimrod Megiddo. 290-297
• Adjusting the Outputs of a Classifier to New a Priori Probabilities May Significantly Improve Classification Accuracy: Evidence from a multi-class problem in remote sensingPatrice Latinne, Marco Saerens, Christine Decaestecker. 298-305
• Estimating a Kernel Fisher Discriminant in the Presence of Label NoiseNeil D. Lawrence, Bernhard Schölkopf. 306-313
• Collaborative Learning and Recommender SystemsWee Sun Lee. 314-321
• Friend-or-Foe Q-learning in General-Sum GamesMichael L. Littman. 322-328
• Using EM to Learn 3D Models of Indoor Environments with Mobile RobotsYufeng Liu, Rosemary Emery, Deepayan Chakrabarti, Wolfram Burgard, Sebastian Thrun. 329-336
• Inducing Partially-Defined Instances with Evolutionary AlgorithmsXavier Llorà, Josep Maria Garrell i Guiu. 337-344
• Learning with the Set Covering MachineMario Marchand, John Shawe-Taylor. 345-352
• Coupled Clustering: a Method for Detecting Structural CorrespondenceZvika Marx, Ido Dagan, Joachim M. Buhmann. 353-360
• Automatic Discovery of Subgoals in Reinforcement Learning using Diverse DensityAmy McGovern, Andrew G. Barto. 361-368
• Some Greedy Algorithms for Sparse Nonlinear RegressionPrasanth B. Nair, Arindam Choudhury, Andy J. Keane. 369-376
• Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selectionAndrew Y. Ng, Michael I. Jordan. 377-384
• Ridge Regression Confidence MachineIlia Nouretdinov, Thomas Melluish, Volodya Vovk. 385-392
• Breeding Decision Trees Using Evolutionary TechniquesAthanassios Papagelis, Dimitrios Kalles. 393-400
• Mixtures of Rectangles: Interpretable Soft ClusteringDan Pelleg, Andrew W. Moore. 401-408
• Lyapunov-Constrained Action Sets for Reinforcement LearningTheodore J. Perkins, Andrew G. Barto. 409-416
• Off-Policy Temporal Difference Learning with Function ApproximationDoina Precup, Richard S. Sutton, Sanjoy Dasgupta. 417-424
• Multiple Instance RegressionSoumya Ray, David Page. 425-432
• Comprehensible Interpretation of Relief s EstimatesMarko Robnik-Sikonja, Igor Kononenko. 433-440
• Toward Optimal Active Learning through Sampling Estimation of Error ReductionNicholas Roy, Andrew McCallum. 441-448
• Using the Genetic Algorithm to Reduce the Size of a Nearest-Neighbor Classifier and to Select Relevant AttributesAntonin Rozsypal, Miroslav Kubat. 449-456
• Repairing Faulty Mixture Models using Density EstimationPeter Sand, Andrew W. Moore. 457-464
• Application of Fuzzy Similarity-Based Fractal Dimensions to Characterize Medical Time SeriesManish Sarkar, Tze-Yun Leong. 465-472
• Average-Reward Reinforcement Learning for Variance Penalized Markov Decision ProblemsMakoto Sato, Shigenobu Kobayashi. 473-480
• Incremental Maximization of Non-Instance-Averaging Utility Functions with Applications to Knowledge Discovery ProblemsTobias Scheffer, Stefan Wrobel. 481-488
• Discovering Communicable Scientific Knowledge from Spatio-Temporal DataMark Schwabacher, Pat Langley. 489-496
• Clustering Continuous Time SeriesPaola Sebastiani, Marco Ramoni. 497-504
• Boosting Neighborhood-Based ClassifiersMarc Sebban, Richard Nock, Stéphane Lallich. 505-512
• Unsupervised Sequence Segmentation by a Mixture of Switching Variable Memory Markov SourcesYevgeny Seldin, Gill Bejerano, Naftali Tishby. 513-520
• Smoothed Bootstrap and Statistical Data Cloning for Classifier EvaluationGregory Shakhnarovich, Ran El-Yaniv, Yoram Baram. 521-528
• Learning to Generate Fast Signal Processing ImplementationsBryan Singer, Manuela M. Veloso. 529-536
• Scaling Reinforcement Learning toward RoboCup SoccerPeter Stone, Richard S. Sutton. 537-544
• Direct Policy Search using Paired Statistical TestsMalcolm J. A. Strens, Andrew W. Moore. 545-552
• A Multi-Agent Policy-Gradient Approach to Network RoutingNigel Tao, Jonathan Baxter, Lex Weaver. 553-560
• Improving Probabilistic Grammatical Inference Core Algorithms with Post-processing TechniquesFranck Thollard. 561-568
• A procedure for unsupervised lexicon learningAnand Venkataraman. 569-576
• Constrained K-means Clustering with Background KnowledgeKiri Wagstaff, Claire Cardie, Seth Rogers, Stefan Schrödl. 577-584
• Reinforcement Learning in Dynamic Environments using Instantiated InformationMarco Wiering. 585-592
• Exploration Control in Reinforcement Learning using Optimistic Model SelectionJeremy L. Wyatt. 593-600
• Feature selection for high-dimensional genomic microarray dataEric P. Xing, Michael I. Jordan, Richard M. Karp. 601-608
• Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiersBianca Zadrozny, Charles Elkan. 609-616
• Learnability of Augmented Naive Bayes in Nonimal DomainsHuajie Zhang, Charles X. Ling. 617-623
• Some Sparse Approximation Bounds for Regression ProblemsTong Zhang. 624-631
• Symmetry in Markov Decision Processes and its Implications for Single Agent and Multiagent LearningMartin Zinkevich, Tucker R. Balch. 632