Abstract is missing.
- A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree GrammarsNaoki Abe, Hiroshi Mamitsuka. 3-11
- Improving Accuracy of Incorrect Domain TheoriesLars Asker. 19-27
- Greedy Attribute SelectionRich Caruana, Dayne Freitag. 28-36
- Using Sampling and Queries to Extract Rules from Trained Neural NetworksMark Craven, Jude W. Shavlik. 37-45
- The Generate, Test, and Explain Discovery System ArchitectureMichael de la Maza. 46-52
- In Defense of C4.5: Notes Learning One-Level Decision TreesTapio Elomaa. 62-69
- Incremental Reduced Error PruningJohannes Fürnkranz, Gerhard Widmer. 70-77
- An Incremental Learning Approach for Completable PlanningMelinda T. Gervasio, Gerald DeJong. 78-86
- Learning by Experimentation: Incremental Refinement of Incomplete Planning DomainsYolanda Gil. 87-95
- Learning Disjunctive Concepts by Means of Genetic AlgorithmsAttilio Giordana, Lorenza Saitta, Floriano Zini. 96-104
- Consideration of Risk in Reinformance LearningMatthias Heger. 105-111
- Rule Introduction for Semantic Query OptimizationChun-Nan Hsu, Craig A. Knoblock. 112-120
- Irrelevant Features and the Subset Selection ProblemGeorge H. John, Ron Kohavi, Karl Pfleger. 121-129
- An Efficient Subsumption Algorithm for Inductive Logic ProgrammingJörg-Uwe Kietz, Marcus Lübbe. 130-138
- Getting the Most from Flawed TheoriesMoshe Koppel, Alberto Maria Segre, Ronen Feldman. 139-147
- Heterogenous Uncertainty Sampling for Supervised LearningDavid D. Lewis, Jason Catlett. 148-156
- Markov Games as a Framework for Multi-Agent Reinforcement LearningMichael L. Littman. 157-163
- To Discount or Not to Discount in Reinforcement Learning: A Case Study Comparing R Learning and Q LearningSridhar Mahadevan. 164-172
- Comparing Methods for Refining Certainty-Factor Rule-BasesJ. Jeffrey Mahoney, Raymond J. Mooney. 173-180
- Efficient Algorithms for Minimizing Cross Validation ErrorAndrew W. Moore, Mary S. Lee. 190-198
- Revision of Production System Rule-BasesPatrick M. Murphy, Michael J. Pazzani. 199-207
- Using Genetic Search to Refine Knowledge-based Neural NetworksDavid W. Opitz, Jude W. Shavlik. 208-216
- Reducing Misclassification CostsMichael J. Pazzani, Christopher J. Merz, Patrick M. Murphy, Kamal Ali, Timothy Hume, Clifford Brunk. 217-225
- Incremental Multi-Step Q-LearningJing Peng, Ronald J. Williams. 226-232
- Towards a Better Understanding of Memory-based Reasoning SystemsJohn Rachlin, Simon Kasif, Steven Salzberg, David W. Aha. 242-250
- On the Worst-Case Analysis of Temporal-Difference Learning AlgorithmsRobert E. Schapire, Manfred K. Warmuth. 266-274
- Learning Without State-Estimation in Partially Observable Markovian Decision ProcessesSatinder P. Singh, Tommi Jaakkola, Michael I. Jordan. 284-292
- Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing AlgorithmsDavid B. Skalak. 293-301
- A Baysian Framework to Integrate Symbolic and Neural LearningIrina Tchoumatchenko, Jean-Gabriel Ganascia. 302-308
- A Modular Q-Learning Architecture for Manipulator Task DecompositionChen K. Tham, Richard W. Prager. 309-317
- An Improved Algorithm for Incremental Induction of Decision TreesPaul E. Utgoff. 318-325
- A Powerful Heuristic for the Discovery of Complex Patterned BehaviourRaúl E. Valdés-Pérez, Aurora Pérez. 326-334
- Small Sample Decision tree PruningSholom M. Weiss, Nitin Indurkhya. 335-342
- Combining Top-down and Bottom-up Techniques in Inductive Logic ProgrammingJohn M. Zelle, Raymond J. Mooney, Joshua B. Konvisser. 343-351
- Selective Reformulation of Examples in Concept LearningJean-Daniel Zucker, Jean-Gabriel Ganascia. 352-360
- A Statistical Approach to Decision Tree ModelingMichael I. Jordan. 363-370
- Bayesian Inductive Logic ProgrammingStephen Muggleton. 371-379
- Frequencies vs. Biases: Machine Learning Problems in Natural Language Processing - AbstractFernando C. N. Pereira. 380