Machine Learning Proceedings 1990

"Chapter 1 Empirical Learning Knowledge Acquisition from Examples Using Maximal Representation Learning KBG: A Knowledge Based Generalizer Performance Analysis of A Probabilistic Inductive Learning System A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping Learning from Data with Bounded Inconsistency Conceptual Set Covering: Improving Fit-And-Split Algorithms Incremental Learning of Rules and Meta-Rules An Incremental Method for Finding Multivariate Splits for Decision Trees Incremental Induction of Topologically Minimal TreesChapter 2 Conceptual Clustering A Rational Analysis of Categorization Search Control, Utility, and Concept Induction Graph Clustering and Model Learning by Data CompressionChapter 3 Constructive Induction and Reformulation An Analysis of Representation Shift In Concept Learning Learning Procedures by Environment-Driven Constructive Induction Beyond Inversion of ResolutionChapter 4 Genetic Algorithms Genetic Programming Improving the Performance of Genetic Algorithms in Automated Discovery of Parameters Using Genetic Algorithms to Learn Disjunctive Rules from Examples NEWBOOLE: A Fast GBML SystemChapter 5 Neural Network & Reinforcement Learning Learning Functions in k-DNF from Reinforcement Is Learning Rate a Good Performance Criterion for Learning? Active Perception and Reinforcement LearningChapter 6 Learning and Planning Learning Plans for Competitive Domains Explanations of Empirically Derived Reactive Plans Learning and Enforcement: Stabilizing Environments to Facilitate Activity Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment Integrated Architecture for Learning, Planning, and Reacting Based on Approximating Dynamic ProgrammingChapter 7 Robot Learning Reducing Real-World Failures of Approximate Explanation-Based Rules Correcting and Extending Domain Knowledge Using Outside Guidance Acquisition of Dynamic Control Knowledge for a Robotic Manipulator Feature Extraction and Clustering of Tactile Impressions with Connectionist ModelsChapter 8 Explanation-Based Learning Generalizing the Order of Goals as an Approach to Generalizing Number Learning Approximate Control Rules of High Utility Applying Abstraction and Simplification to Learn in Intractable Domains Explanation-Based Learning with Incomplete Theories: A Three-Step Approach Using Abductive Recovery of Failed Proof s for Problem Solving by Analogy Issues in the Design of Operator Composition Systems Incremental Learning of Explanation Patterns and Their IndicesChapter 9 Explanation-Based and Empirical Learning Integrated Learning in a Real Domain Incremental Version-Space Merging Average Case Analysis of Conjunctive Learning Algorithms ILS: A Framework for Multi-Paradigmatic Learning An Integrated Framework of Inducing Rules from ExamplesChapter 10 Language Learning Adaptive Parsing: A General Method for Learning Idiosyncratic Grammars A Comparison of Learning Techniques in Second Language Learning Learning String Patterns and Tree Patterns from Examples Learning with Discrete Multi-Valued NeuronsChapter 11 Other Topics The General Utility Problem in Machine Learning A Robust Approach to Numeric Discovery More Results on the Complexity of Knowledge Base Refinement: Belief NetworksIndex