Top
| Preface | |
| Workshops | |
| Tutorials | |
| Organizing Committee | |
| Program Committee | |
| Schedule | |
| A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars | p. 3 |
| Learning Recursive Relations with Randomly Selected Small Training Sets | p. 12 |
| Improving Accuracy of Incorrect Domain Theories | p. 19 |
| Greedy Attribute Selection | p. 28 |
| Using Sampling and Queries to Extract Rules from Trained Neural Networks | p. 37 |
| The Generate, Test, and Explain Discovery System Architecture | p. 46 |
| Boosting and Other Machine Learning Algorithms | p. 53 |
| In Defense of C4.5: Notes on Learning One-Level Decision Trees | p. 62 |
| Incremental Reduced Error Pruning | p. 70 |
| An Incremental Learning Approach for Completable Planning | p. 78 |
| Learning by Experimentation: Incremental Refinement of Incomplete Planning Domains | p. 87 |
| Learning Disjunctive Concepts by Means of Genetic Algorithms | p. 96 |
| Consideration of Risk in Reinforcement Learning | p. 105 |
| Rule Induction for Semantic Query Optimization | p. 112 |
| Irrelevant Features and the Subset Selection Problem | p. 121 |
| An Efficient Subsumption Algorithm for Inductive Logic Programming | p. 130 |
| Getting the Most from Flawed Theories | p. 139 |
| Heterogeneous Uncertainty Sampling for Supervised Learning | p. 148 |
| Markov Games as a Framework for Multi-Agent Reinforcement Learning | p. 157 |
| To Discount or Not to Discount in Reinforcement Learning: A Case Study Comparing R Learning and Q Learning | p. 164 |
| Comparing Methods for Refining Certainty-Factor Rule-Bases | p. 173 |
| Reward Functions for Accelerated Learning | p. 181 |
| Efficient Algorithms for Minimizing Cross Validation Error | p. 190 |
| Revision of Production System Rule-Bases | p. 199 |
| Using Genetic Search to Refine Knowledge-based Neural Networks | p. 208 |
| Reducing Misclassification Costs | p. 217 |
| Incremental Multi-Step Q-Learning | p. 226 |
| The Minimum Description Length Principle and Categorical Theories | p. 233 |
| Towards a Better Understanding of Memory-based Reasoning Systems | p. 242 |
| Hierarchical Self-Organization in Genetic Programming | p. 251 |
| A Conservation Law for Generalization Performance | p. 259 |
| On the Worst-Case Analysis of Temporal-Difference Learning Algorithms | p. 266 |
| A Constraint-based Induction Algorithm in FOL | p. 275 |
| Learning Without State-Estimation in Partially Observable Markovian Decision Processes | p. 284 |
| Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms | p. 293 |
| A Baysian Framework to Integrate Symbolic and Neural Learning | p. 302 |
| A Modular Q-Learning Architecture for Manipulator Task Decomposition | p. 309 |
| An Improved Algorithm for Incremental Induction of Decision Trees | p. 318 |
| A Powerful Heuristic for the Discovery of Complex Patterned Behavior | p. 326 |
| Small Sample Decision Tree Pruning | p. 335 |
| Combining Top-down and Bottom-up Techniques in Inductive Logic Programming | p. 343 |
| Selective Reformulation of Examples in Concept Learning | p. 352 |
| A Statistical Approach to Decision Tree Modeling | p. 363 |
| Bayesian Inductive Logic Programming | p. 371 |
| Frequencies vs Biases: Machine Learning Problems in Natural Language Processing - Abstract | p. 380 |
| Author Index | p. 381 |
| Table of Contents provided by Blackwell. All Rights Reserved. |
