Abstract is missing.
- Associative Reinforcement Learning using Linear Probabilistic ConceptsNaoki Abe, Philip M. Long. 3-11
- Learning to Optimally Schedule Internet Banner AdvertisementsNaoki Abe, Atsuyoshi Nakamura. 12-21
- A Minimum Risk Metric for Nearest Neighbor ClassificationEnrico Blanzieri, Francesco Ricci. 22-31
- Local Learning for Iterated Time-Series PredictionGianluca Bontempi, Mauro Birattari, Hugues Bersini. 32-38
- Instance-Family Abstraction in Memory-Based Language LearningAntal van den Bosch. 39-48
- Least-Squares Temporal Difference LearningJustin A. Boyan. 49-56
- Learning to Ride a Bicycle using Iterated Phantom InductionMark Brodie, Gerald DeJong. 57-66
- Sonar-Based Mapping of Large-Scale Mobile Robot Environments using EMWolfram Burgard, Dieter Fox, Hauke Jans, Christian Matenar, Sebastian Thrun. 67-76
- Hierarchical Models for Screening of Iron Deficiency AnemiaIgor V. Cadez, Christine E. McLaren, Padhraic Smyth, Geoffrey J. McLachlan. 77-86
- Combining Error-Driven Pruning and Classification for Partial ParsingClaire Cardie, Scott Mardis, David R. Pierce. 87-96
- AdaCost: Misclassification Cost-Sensitive BoostingWei Fan, Salvatore J. Stolfo, Junxin Zhang, Philip K. Chan. 97-105
- Abstracting from Robot Sensor Data using Hidden Markov ModelsLaura Firoiu, Paul R. Cohen. 106-114
- Making Better Use of Global DiscretizationEibe Frank, Ian H. Witten. 115-123
- The Alternating Decision Tree Learning AlgorithmYoav Freund, Llew Mason. 124-133
- Discriminant TreesJoao Gama. 134-142
- Experiments with Noise Filtering in a Medical DomainDragan Gamberger, Nada Lavrac, Ciril Groselj. 143-151
- Learning User Evaluation Functions for Adaptive Scheduling AssistanceMelinda T. Gervasio, Wayne Iba, Pat Langley. 152-161
- On Some Misbehaviour of Back-Propagation with Non-Normalized RBFNs and a SolutionAttilio Giordana, Roberto Piola. 162-170
- Boosting a Strong Learner: Evidence Against the Minimum MarginMichael Bonnell Harries. 171-180
- Detecting Motifs from SequencesYuh-Jyh Hu, Suzanne B. Sandmeyer, Dennis F. Kibler. 181-190
- Distributed Robotic Learning: Adaptive Behavior Acquisition for Distributed Autonomous Swimming Robot in Real WorldDaisuke Iijima, Wenwei Yu, Hiroshi Yokoi, Yukinori Kakazu. 191-199
- Transductive Inference for Text Classification using Support Vector MachinesThorsten Joachims. 200-209
- Efficient Non-Linear Control by Combining Q-learning with Local Linear ControllersHajime Kimura, Shigenobu Kobayashi. 210-219
- Tractable Average-Case Analysis of Naive Bayesian ClassifiersPat Langley, Stephanie Sage. 220-228
- Learning Hierarchical Performance Knowledge by ObservationMichael van Lent, John E. Laird. 229-238
- Correcting Noisy DataChoh-Man Teng. 239-248
- An Accelerated Chow and Liu Algorithm: Fitting Tree Distributions to High-Dimensional Sparse DataMarina Meila. 249-257
- Feature Selection for Unbalanced Class Distribution and Naive BayesDunja Mladenic, Marko Grobelnik. 258-267
- Combining Statistical Learning with a Knowledge-Based Approach - A Case Study in Intensive Care MonitoringKatharina Morik, Peter Brockhausen, Thorsten Joachims. 268-277
- Policy Invariance Under Reward Transformations: Theory and Application to Reward ShapingAndrew Y. Ng, Daishi Harada, Stuart J. Russell. 278-287
- Learning Discriminatory and Descriptive Rules by an Inductive Logic Programming SystemMaziar Palhang, Arcot Sowmya. 288-297
- Simple DFA are Polynomially Probably Exactly Learnable from Simple ExamplesRajesh Parekh, Vasant Honavar. 298-306
- Learning Policies with External MemoryLeonid Peshkin, Nicolas Meuleau, Leslie Pack Kaelbling. 307-314
- Noise-Tolerant Recursive Best-First InductionUros Pompe. 315-324
- Implicit Imitation in Multiagent Reinforcement LearningBob Price, Craig Boutilier. 325-334
- Using Reinforcement Learning to Spider the Web EfficientlyJason Rennie, Andrew McCallum. 335-343
- Attribute Dependencies, Understandability and Split Selection in Tree Based ModelsMarko Robnik-Sikonja, Igor Kononenko. 344-353
- GA-based Learning of Context-Free Grammars using Tabular RepresentationsYasubumi Sakakibara, Mitsuhiro Kondo. 354-360
- Expected Error Analysis for Model SelectionTobias Scheffer, Thorsten Joachims. 361-370
- Distributed Value FunctionsJeff G. Schneider, Weng-Keen Wong, Andrew W. Moore, Martin A. Riedmiller. 371-378
- Feature Engineering for Text ClassificationSam Scott, Stan Matwin. 379-388
- Feature Selection as a Preprocessing Step for Hierarchical ClusteringLuis Talavera. 389-397
- OPT-KD: An Algorithm for Optimizing Kd-TreesDouglas A. Talbert, Douglas H. Fisher. 398-405
- Active Learning for Natural Language Parsing and Information ExtractionCynthia A. Thompson, Mary Elaine Califf, Raymond J. Mooney. 406-414
- Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic ProcessesSebastian Thrun, John Langford, Dieter Fox. 415-424
- Approximation Via Value UnificationPaul E. Utgoff, David J. Stracuzzi. 425-432
- Model Selection in Unsupervised Learning with Applications To Document ClusteringShivakumar Vaithyanathan, Byron Dom. 433-443
- Machine-Learning Applications of Algorithmic RandomnessVolodya Vovk, Alexander Gammerman, Craig Saunders. 444-453
- Learning Comprehensible Descriptions of Multivariate Time SeriesMohammed Waleed Kadous. 454-463
- Hierarchical Optimization of Policy-Coupled Semi-Markov Decision ProcessesGang Wang, Sridhar Mahadevan. 464-473
- Large Margin Trees for Induction and TransductionDonghui Wu, Kristin P. Bennett, Nello Cristianini, John Shawe-Taylor. 474-483
- An Region-Based Learning Approach to Discovering Temporal Structures in DataWei Zhang. 484-492
- Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision TreesZijian Zheng, Geoffrey I. Webb, Kai Ming Ting. 493-502
- A Hybrid Lazy-Eager Approach to Reducing the Computation and Memory Requirements of Local Parametric Learning AlgorithmsYuanhui Zhou, Carla E. Brodley. 503