Abstract is missing.
- Feature Engineering and Classifier Selection: A Case Study in Venusian Volcano DetectionLars Asker, Richard Maclin. 3-11
- Robot Learning From DemonstrationChristopher G. Atkeson, Stefan Schaal. 12-20
- On Learning From Multi-Instance Examples: Empirical Evaluation of a Theoretical ApproachPeter Auer. 21-29
- Using Optimal Dependency-Trees for Combinational OptimizationShumeet Baluja, Scott Davies. 30-38
- The Canonical Distortion Measure for Vector Quantization and Function ApproximationJonathan Baxter. 39-47
- FONN: Combining First Order Logic with Connectionist LearningMarco Botta, Attilio Giordana, Roberto Piola. 46-56
- Improving Minority Class Prediction Using Case-Specific Feature WeightsClaire Cardie, Nicholas Nowe. 57-65
- A Comparative Study of Inductive Logic Programming Methods for Software Fault PredictionWilliam W. Cohen, Premkumar T. Devanbu. 66-74
- Learning Symbolic PrototypesPiew Datta, Dennis F. Kibler. 75-82
- PAC Learning with Constant-Partition Classification Noise and Applications to Decision Tree InductionScott E. Decatur. 83-91
- Efficient Feature Selection in Conceptual ClusteringMark Devaney, Ashwin Ram. 92-97
- Knowledge Acquisition form Examples Vis Multiple ModelsPedro Domingos. 98-106
- Improving Regressors using Boosting TechniquesHarris Drucker. 107-115
- Expected Mistake Bound Model for On-Line Reinforcement LearningClaude-Nicolas Fiechter. 116-124
- Learning Belief Networks in the Presence of Missing Values and Hidden VariablesNir Friedman. 125-133
- Probabilistic Linear TreeJoao Gama. 134-142
- A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text CategorizationThorsten Joachims. 143-151
- Reinforcement Learning in POMDPs with Function ApproximationHajime Kimura, Kazuteru Miyazaki, Shigenobu Kobayashi. 152-160
- Option Decision Trees with Majority VotesRon Kohavi, Clayton Kunz. 161-169
- Hierarchically Classifying Documents Using Very Few WordsDaphne Koller, Mehran Sahami. 170-178
- Addressing the Curse of Imbalanced Training Sets: One-Sided SelectionMiroslav Kubat, Stan Matwin. 179-186
- Automatic Rule Acquisition for Spelling CorrectionLidia Mangu, Eric Brill. 187-194
- Pessimistic decision tree pruning based Continuous-timeYishay Mansour. 202-210
- Pruning Adaptive BoostingDragos D. Margineantu, Thomas G. Dietterich. 211-218
- On the Decomposition of Polychotomies into DichotomiesEddy Mayoraz, Miguel Moreira. 219-226
- ARCCHNID: Adaptive Retrieval Agents Choosing Heuristic NeighborhoodsFilippo Menczer. 227-235
- Efficient Locally Weighted Polynomial Regression PredictionsAndrew W. Moore, Jeff G. Schneider, Kan Deng. 236-244
- Preventing Overfitting of Cross-Validation DataAndrew Y. Ng. 245-253
- The Effects of Training Set Size on Decision Tree ComplexityTim Oates, David Jensen. 254-262
- The Effective Size of a Neural Network: A Principal Component ApproachDavid W. Opitz. 263-271
- Exponentiated Gradient Methods for Reinforcement LearningDoina Precup, Richard S. Sutton. 272-277
- Learning Goal-Decomposition Rules using ExercisesChandra Reddy, Prasad Tadepalli. 278-286
- Learning String Edit DistanceEric Sven Ristad, Peter N. Yianilos. 287-295
- An adaptation of Relief for attribute estimation in regressionMarko Robnik-Sikonja, Igor Kononenko. 296-304
- Predicting Multiprocessor Memory Access Patterns with Learning ModelsM. F. Sakr, Steven P. Levitan, Donald M. Chiarulli, Bill G. Horne, C. Lee Giles. 305-312
- Using output codes to boost multiclass learning problemsRobert E. Schapire. 313-321
- Boosting the margin: A new explanation for the effectiveness of voting methodsRobert E. Schapire, Yoav Freund, Peter Barlett, Wee Sun Lee. 322-330
- Why Experimentation can be better than Perfect Guidance Tobias Scheffer, Russell Greiner, Christian Darken. 331-339
- Characterizing the generalization performance of model selection strategiesDale Schuurmans, Lyle H. Ungar, Dean P. Foster. 340-348
- A Bayesian Approach to Model Learning in Non-Markovian EnvironmentsNobuo Suematsu, Akira Hayashi, Shigang Li. 349-357
- Hierarchical Explanation-Based Reinforcement LearningPrasad Tadepalli, Thomas G. Dietterich. 358-366
- Stacking Bagged and Dagged ModelsKai Ming Ting, Ian H. Witten. 367-375
- Declarative Bias in Equation DiscoveryLjupco Todorovski, Saso Dzeroski. 376-384
- Functional Models for Regression Tree LeavesLuís Torgo. 385-393
- Integrating Feature Construction with Multiple Classifiers in Decision Tree InductionRicardo Vilalta, Larry A. Rendell. 394-402
- Instance Pruning TechniquesD. Randall Wilson, Tony R. Martinez. 403-411
- A Comparative Study on Feature Selection in Text CategorizationYiming Yang, Jan O. Pedersen. 412-420
- Machine Learning by Function DecompositionBlaz Zupan, Marko Bohanec, Ivan Bratko, Janez Demsar. 421-429