Abstract is missing.
- Scalable Internal-State Policy-Gradient Methods for POMDPsDouglas Aberdeen, Jonathan Baxter. 3-10
- Feature Subset Selection and Inductive Logic ProgrammingÉrick Alphonse, Stan Matwin. 11-18
- Pruning Improves Heuristic Search for Cost-Sensitive LearningValentina Bayer Zubek, Thomas G. Dietterich. 19-26
- Semi-supervised Clustering by SeedingSugato Basu, Arindam Banerjee, Raymond J. Mooney. 27-34
- Constraint-based Learning of Long Relational ConceptsJacques Ales Bianchetti, Céline Rouveirol, Michèle Sebag. 35-42
- Exploiting Relations Among Concepts to Acquire Weakly Labeled Training DataJoseph Bockhorst, Mark Craven. 43-50
- An epsilon-Optimal Grid-Based Algorithm for Partially Observable Markov Decision ProcessesBlai Bonet. 51-58
- Transformation-Based RegressionBjörn Bringmann, Stefan Kramer, Friedrich Neubarth, Hannes Pirker, Gerhard Widmer. 59-66
- A New Statistical Approach to Personal Name ExtractionZheng Chen, Liu Wenyin, Feng Zhang. 67-74
- Learning Decision Rules by Randomized Iterative Local SearchMichael Chisholm, Prasad Tadepalli. 75-82
- IEMS - The Intelligent Email SorterElisabeth Crawford, Judy Kay, Eric McCreath. 83-90
- Exact model averaging with naive Bayesian classifiersDenver Dash, Gregory F. Cooper. 91-98
- Anytime Interval-Valued Outputs for Kernel Machines: Fast Support Vector Machine Classification via Distance GeometryDennis DeCoste. 99-106
- Action Refinement in Reinforcement Learning by Probability SmoothingThomas G. Dietterich, Dídac Busquets, Ramon López de Mántaras, Carles Sierra. 107-114
- Integrating Experimentation and Guidance in Relational Reinforcement LearningKurt Driessens, Saso Dzeroski. 115-122
- Is Combining Classifiers Better than Selecting the Best OneSaso Dzeroski, Bernard Zenko. 123-130
- Fast Minimum Training Error DiscretizationTapio Elomaa, Juho Rousu. 131-138
- Learning Decision Trees Using the Area Under the ROC CurveCésar Ferri, Peter A. Flach, José Hernández-Orallo. 139-146
- Univariate Polynomial Inference by Monte Carlo Message Length ApproximationLeigh J. Fitzgibbon, David L. Dowe, Lloyd Allison. 147-154
- An Analysis of Functional TreesJoao Gama. 155-162
- Descriptive Induction through Subgroup Discovery: A Case Study in a Medical DomainDragan Gamberger, Nada Lavrac. 163-170
- On generalization bounds, projection profile, and margin distributionAshutosh Garg, Sariel Har-Peled, Dan Roth. 171-178
- Multi-Instance KernelsThomas Gärtner, Peter A. Flach, Adam Kowalczyk, Alex J. Smola. 179-186
- Combining Labeled and Unlabeled Data for MultiClass Text CategorizationRayid Ghani. 187-194
- Hierarchically Optimal Average Reward Reinforcement LearningMohammad Ghavamzadeh, Sridhar Mahadevan. 195-202
- Sufficient Dimensionality Reduction - A novel Analysis MethodAmir Globerson, Naftali Tishby. 203-210
- A Unified Decomposition of Ensemble Loss for Predicting Ensemble PerformanceMichael Goebel, Patricia J. Riddle, Mike Barley. 211-218
- Graph-Based Relational Concept LearningJesus A. Gonzalez, Lawrence B. Holder, Diane J. Cook. 219-226
- Coordinated Reinforcement LearningCarlos Guestrin, Michail G. Lagoudakis, Ronald Parr. 227-234
- Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPsCarlos Guestrin, Relu Patrascu, Dale Schuurmans. 235-242
- Discovering Hierarchy in Reinforcement Learning with HEXQBernhard Hengst. 243-250
- Classification Value GroupingColin K. M. Ho. 251-258
- Linkage and Autocorrelation Cause Feature Selection Bias in Relational LearningDavid Jensen, Jennifer Neville. 259-266
- Approximately Optimal Approximate Reinforcement LearningSham Kakade, John Langford. 267-274
- An Alternate Objective Function for Markovian FieldsSham Kakade, Yee Whye Teh, Sam T. Roweis. 275-282
- Interpreting and Extending Classical Agglomerative Clustering Algorithms using a Model-Based approachSepandar D. Kamvar, Dan Klein, Christopher D. Manning. 283-290
- Kernels for Semi-Structured DataHisashi Kashima, Teruo Koyanagi. 291-298
- A Fast Dual Algorithm for Kernel Logistic RegressionS. Sathiya Keerthi, Kaibo Duan, Shirish Krishnaj Shevade, Aun Neow Poo. 299-306
- From Instance-level Constraints to Space-Level Constraints: Making the Most of Prior Knowledge in Data ClusteringDan Klein, Sepandar D. Kamvar, Christopher D. Manning. 307-314
- Diffusion Kernels on Graphs and Other Discrete Input SpacesRisi Imre Kondor, John D. Lafferty. 315-322
- Learning the Kernel Matrix with Semi-Definite ProgrammingGert R. G. Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent El Ghaoui, Michael I. Jordan. 323-330
- Combining Trainig Set and Test Set BoundsJohn Langford. 331-338
- Competitive Analysis of the Explore/Exploit TradeoffJohn Langford, Martin Zinkevich, Sham Kakade. 339-346
- Inducing Process Models from Continuous DataPat Langley, Javier Nicolás Sánchez, Ljupco Todorovski, Saso Dzeroski. 347-354
- Reinforcement Learning and Shaping: Encouraging Intended BehaviorsAdam Laud, Gerald DeJong. 355-362
- Cranking: Combining Rankings Using Conditional Probability Models on PermutationsGuy Lebanon, John D. Lafferty. 363-370
- Learning to Share Distributed Probabilistic BeliefsChristopher Leckie, Kotagiri Ramamohanarao. 371-378
- The Perceptron Algorithm with Uneven MarginsYaoyong Li, Hugo Zaragoza, Ralf Herbrich, John Shawe-Taylor, Jaz S. Kandola. 379-386
- Partially Supervised Classification of Text DocumentsBing Liu, Wee Sun Lee, Philip S. Yu, Xiaoli Li. 387-394
- Feature Selection with Selective SamplingHuan Liu, Hiroshi Motoda, Lei Yu. 395-402
- Investigating the Maximum Likelihood Alternative to TD(lambda)Fletcher Lu, Relu Patrascu, Dale Schuurmans. 403-410
- A Necessary Condition of Convergence for Reinforcement Learning with Function ApproximationArtur Merke, Ralf Schoknecht. 411-418
- Towards Large Margin Speech Recognizers by Boosting and Discriminative TrainingCarsten Meyer, Peter Beyerlein. 419-426
- Learning word normalization using word suffix and context from unlabeled dataDunja Mladenic. 427-434
- Active + Semi-supervised Learning = Robust Multi-View LearningIon Muslea, Steven Minton, Craig A. Knoblock. 435-442
- Adaptive View Validation: A First Step Towards Automatic View DetectionIon Muslea, Steven Minton, Craig A. Knoblock. 443-450
- Stock Trading System Using Reinforcement Learning with Cooperative AgentsJangmin O, Jae-Won Lee, Byoung-Tak Zhang. 451-458
- Learning k-Reversible Context-Free Grammars from Positive Structural ExamplesTim Oates, Devina Desai, Vinay Bhat. 459-465
- MMIHMM: Maximum Mutual Information Hidden Markov ModelsNuria Oliver, Ashutosh Garg. 466-473
- Learning Spatial and Temporal Correlation for Navigation in a 2-Dimensional Continuous WorldAnand Panangadan, Michael G. Dyer. 474-481
- A Boosted Maximum Entropy Model for Learning Text ChunkingSeong-Bae Park, Byoung-Tak Zhang. 482-489
- On the Existence of Fixed Points for Q-Learning and Sarsa in Partially Observable DomainsTheodore J. Perkins, Mark D. Pendrith. 490-497
- Learning from Scarce ExperienceLeonid Peshkin, Christian R. Shelton. 498-505
- PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement LearningMarc Pickett, Andrew G. Barto. 506-513
- Using Unlabelled Data for Text Classification through Addition of Cluster ParametersBhavani Raskutti, Herman L. Ferrá, Adam Kowalczyk. 514-521
- Using Abstract Models of Behaviours to Automatically Generate Reinforcement Learning HierarchiesMalcolm R. K. Ryan. 522-529
- Syllables and other String Kernel ExtensionsCraig Saunders, Hauke Tschach, John Shawe-Taylor. 530-537
- Incorporating Prior Knowledge into BoostingRobert E. Schapire, Marie Rochery, Mazin G. Rahim, Narendra Gupta. 538-545
- Modeling Auction Price Uncertainty Using Boosting-based Conditional Density EstimationRobert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, János A. Csirik. 546-553
- How to Make Stacking Better and Faster While Also Taking Care of an Unknown WeaknessAlexander K. Seewald. 554-561
- Model-based Hierarchical Average-reward Reinforcement LearningSandeep Seri, Prasad Tadepalli. 562-569
- Separating Skills from Preference: Using Learning to Program by RewardDaniel G. Shapiro, Pat Langley. 570-577
- Discriminative Feature Selection via Multiclass Variable Memory Markov ModelNoam Slonim, Gill Bejerano, Shai Fine, Naftali Tishby. 578-585
- Learning to Fly by Controlling Dynamic InstabilitiesDavid Stirling. 586-593
- Randomized Variable EliminationDavid J. Stracuzzi, Paul E. Utgoff. 594-601
- Markov Chain Monte Carlo Sampling using Direct Search OptimizationMalcolm J. A. Strens, Mark Bernhardt, Nicholas Everett. 602-609
- Qualitative reverse engineeringDorian Suc, Ivan Bratko. 610-617
- Finding an Optimal Gain-Ratio Subset-Split Test for a Set-Valued Attribute in Decision Tree InductionFumio Takechi, Einoshin Suzuki. 618-625
- Refining the Wrapper Approach - Smoothed Error Estimates for Feature SelectionLoo-Nin Teow, Haifeng Liu, Hwee Tou Ng, Eric Yap. 626-633
- Sparse Bayesian Learning for Regression and Classification using Markov Chain Monte CarloShien-Shin Tham, Arnaud Doucet, Kotagiri Ramamohanarao. 634-641
- Issues in Classifier Evaluation using Optimal Cost CurvesKai Ming Ting. 642-649
- Modeling for Optimal Probability PredictionYong Wang, Ian H. Witten. 650-657
- Mining Both Positive and Negative Association RulesXindong Wu, Chengqi Zhang, Shichao Zhang. 658-665
- Non-Disjoint Discretization for Naive-Bayes ClassifiersYing Yang, Geoffrey I. Webb. 666-673
- Representational Upper Bounds of Bayesian NetworksHuajie Zhang, Charles X. Ling. 674-681
- Content-Based Image Retrieval Using Multiple-Instance LearningQi Zhang, Sally A. Goldman, Wei Yu, Jason E. Fritts. 682-689
- Statistical Behavior and Consistency of Support Vector Machines, Boosting, and BeyondTong Zhang. 690-700