Machine Learning Proceedings 1994

¿PrefaceWorkshopsTutorialsOrganizing CommitteeProgram CommitteeScheduleContributed Papers A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars Learning Recursive Relations with Randomly Selected Small Training Sets Improving Accuracy of Incorrect Domain Theories Greedy Attribute Selection Using Sampling and Queries to Extract Rules from Trained Neural Networks The Generate, Test, and Explain Discovery System Architecture Boosting and Other Machine Learning Algorithms In Defense of C4.5: Notes on Learning One-Level Decision Trees Incremental Reduced Error Pruning An Incremental Learning Approach for Completable Planning Learning by Experimentation: Incremental Refinement of Incomplete Planning Domains Learning Disjunctive Concepts by Means of Genetic Algorithms Consideration of Risk in Reinforcement Learning Rule Induction for Semantic Query Optimization Irrelevant Features and the Subset Selection Problem An Efficient Subsumption Algorithm for Inductive Logic Programming Getting the Most from Flawed Theories Heterogeneous Uncertainty Sampling for Supervised Learning Markov Games as a Framework for Multi-Agent Reinforcement Learning To Discount or Not to Discount in Reinforcement Learning: A Case Study Comparing R Learning and Q Learning Comparing Methods for Refining Certainty-Factor Rule-Bases Reward Functions for Accelerated Learning Efficient Algorithms for Minimizing Cross Validation Error Revision of Production System Rule-Bases Using Genetic Search to Refine Knowledge-Based Neural Networks Reducing Misclassification Costs Incremental Multi-Step Q-Learning The Minimum Description Length Principle and Categorical Theories Towards a Better Understanding of Memory-Based Reasoning Systems Hierarchical Self-Organization in Genetic Programming A Conservation Law for Generalization Performance On the Worst-Case Analysis of Temporal-Difference Learning Algorithms A Constraint-Based Induction Algorithm in FOL Learning Without State-Estimation in Partially Observable Markovian Decision Processes Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms A Baysian Framework to Integrate Symbolic and Neural Learning A Modular Q-Learning Architecture for Manipulator Task Decomposition An Improved Algorithm for Incremental Induction of Decision Trees A Powerful Heuristic for the Discovery of Complex Patterned Behavior Small Sample Decision Tree Pruning Combining Top-Down and Bottom-Up Techniques in Inductive Logic Programming Selective Reformulation of Examples in Concept LearningInvited Talks A Statistical Approach to Decision Tree Modeling Bayesian Inductive Logic Programming Frequencies Vs Biases: Machine Learning Problems in Natural Language Processing-AbstractAuthor Index