Machine Learning Proceedings 1995

Machine Learning Proceedings 1995

¿PrefaceAdvisory CommitteeProgram CommitteeAuxiliary ReviewersWorkshopsTutorialsScheduleContributed Papers On-Line Learning of Binary Lexical Relations Using Two-Dimensional Weighted Majority Algorithms On Handling Tree-Structured Attributes in Decision Tree Learning Theory and Applications of Agnostic PAC-Learning with Small Decision Trees Residual Algorithms: Reinforcement Learning with Function Approximation Removing the Genetics from the Standard Genetic Algorithm Inductive Learning of Reactive Action Models Visualizing High-Dimensional Structure with the Incremental Grid Growing Neural Network Empirical Support for Winnow and Weighted-Majority Based Algorithms: Results on a Calendar Scheduling Domain Automatic Selection of Split Criterion During Tree Growing Based on Node Location A Lexically Based Semantic Bias for Theory Revision A Comparative Evaluation of Voting and Meta-Learning on Partitioned Data Fast and Efficient Reinforcement Learning with Truncated Temporal Differences K*: An Instance-Based Learner Using an Entropic Distance Measure Fast Effective Rule Induction Text Categorization and Relational Learning Protein Folding: Symbolic Refinement Competes with Neural Networks A Bayesian Analysis of Algorithms for Learning Finite Functions Committee-Based Sampling For Training Probabilistic Classifiers Learning Prototypical Concept Descriptions A Case Study of Explanation-Based Control Explanation-Based Learning and Reinforcement Learning: A Unified View Lessons from Theory Revision Applied to Constructive Induction Supervised and Unsupervised Discretization of Continuous Features Bounds on the Classification Error of the Nearest Neighbor Rule Q-Learning for Bandit Problems Distilling Reliable Information From Unreliable Theories A Quantitative Study of Hypothesis Selection Learning Proof Heuristics by Adapting Parameters Efficient Algorithms for Finding Multi-Way Splits for Decision Trees Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem Stable Function Approximation in Dynamic Programming The Challenge of Revising an Impure Theory Symbiosis in Multimodal Concept Learning Tracking the Best Expert Reinforcement Learning by Stochastic Hill Climbing on Discounted Reward Automatic Parameter Selection by Minimizing Estimated Error Error-Correcting Output Coding Corrects Bias and Variance Learning to Make Rent-to-Buy Decisions with Systems Applications NewsWeeder: Learning to Filter Netnews Hill Climbing Beats Genetic Search on a Boolean Circuit Synthesis Problem of Koza's Case-Based Acquisition of Place Knowledge Comparing Several Linear-Threshold Learning Algorithms on Tasks Involving Superfluous Attributes Learning Policies for Partially Observable Environments: Scaling Up Increasing the Performance and Consistency of Classification Trees by Using the Accuracy Criterion at the Leaves Efficient Learning with Virtual Threshold Gates Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State Efficient Learning from Delayed Rewards through Symbiotic Evolution Free to Choose: Investigating the Sample Complexity of Active Learning of Real Valued Functions On Learning Decision Committees Inferring Reduced Ordered Decision Graphs of Minimum Description Length On Pruning and Averaging Decision Trees Efficient Memory-Based Dynamic Programming Using Multidimensional Projection to Find Relations Compression-Based Discretization of Continuous Attributes MDL and Categorical Theories (Continued) For Every Generalization Action, Is There Really an Equal and Opposite Reaction? Analysis of the Conservation Law for Generalization Performance Active Exploration and Learning in Real-Valued Spaces Using Multi-Armed Bandit Allocation Indices Discovering Solutions with Low Kolmogorov Complexity and High Generalization Capability A Comparison of Induction Algorithms for Selective and Non-Selective Bayesian Classifiers Retrofitting Decision Tree Classifiers Using Kernel Density Estimation Automatic Speaker Recognition: An Application of Machine Learning An Inductive Learning Approach to Prognostic Prediction TD Models: Modeling the World at a Mixture of Time Scales Learning Collection Fusion Strategies for Information Retrieval Learning by Observation and Practice: An Incremental Approach for Planning Operator Acquisition Learning with Rare Cases and Small Disjuncts Horizontal Generalization Learning Hierarchies from Ambiguous Natural Language DataInvited Talks (Abstracts Only) Machine Learning and Information Retrieval Learning With Bayesian Networks Learning for Automotive Collision Avoidance and Autonomous ControlAuthor Index