Machine Learning Proceedings 1994

Preface

Workshops

Tutorials

Organizing Committee

Program Committee

Schedule

Contributed 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 Learning

Invited Talks

A Statistical Approach to Decision Tree Modeling

Bayesian Inductive Logic Programming

Frequencies Vs Biases: Machine Learning Problems in Natural Language Processing—Abstract

Author Index