Proceedings of the Fourth International Workshop on MACHINE LEARNING
June 22–25, 1987 University of California, Irvine
- 1st Edition - September 1, 1998
- Editor: Pat Langley
- Language: English
- Paperback ISBN:9 7 8 - 0 - 9 3 4 6 1 3 - 4 1 - 5
- eBook ISBN:9 7 8 - 1 - 4 8 3 2 - 8 2 8 5 - 5
Proceedings of the Fourth International Workshop on Machine Learning provides careful theoretical analyses that make clear contact with traditional problems in machine learning.… Read more
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Request a sales quoteProceedings of the Fourth International Workshop on Machine Learning provides careful theoretical analyses that make clear contact with traditional problems in machine learning. This book discusses the key role of learning in cognition. Organized into 39 chapters, this book begins with an overview of pattern recognition systems of necessity that incorporate an approximate-matching process to determine the degree of similarity between an unknown input and all stored references. This text then describes the rationale in the Protos system for relegating inductive learning and deductive problem solving to minor roles in support of retaining, indexing and matching exemplars. Other chapters consider the power as well as the appropriateness of exemplar-based representations and their associated acquisition methods. This book discusses as well the extensions to the way a case is classified by a decision tree that address shortcomings. The final chapter deals with the advances in machine learning research. This book is a valuable resource for psychologists, scientists, theorists, and research workers.
Preface: The Emerging Science of Machine LearningLearning and Classification Exemplar-Based Approaches Learning About Speech Sounds: The NEXUS Project PROTOS: An Exemplar-Based Learning Apprentice Learning Representative Exemplars of Concepts: An Initial Case StudyProbabilistic Approaches Decision Trees as Probabilistic Classifiers Conceptual Clustering, Learning from Examples, and Inference How to Learn Imprecise Concepts: A Method for Employing a Two-Tiered Knowledge Representation in Learning Quasi-Darwinian Learning in a Classifier SystemConcept Learning and Bias More Robust Concept Learning Using Dynamically-Variable Bias Incremental Adjustment of Representations for LearningLearning, Problem Solving, and Planning Heuristic Search Approaches Concept Learning in Context Strategy Learning with Multilayer Connectionist Representations Learning a Preference PredicatePlanning Approaches Acquiring Effective Search Control Rules: Explanation-Based Learning in the PRODIGY System The Anatomy of a Weak Learning Method for Use in Goal Directed Search Learning and Reusing ExplanationsProblem Reduction Approaches LT Revisited: Experimental Results of Applying Explanation-Based Learning to the Logic of Principia Mathematica What is an Explanation in DISCIPLE? Extending Problem Solver Capabilities Through Case-Based InferenceLearning and Natural Language Learning to Integrate Syntax and Semantics How Do Machine-Learning Paradigms Fare in Language Acquisition? The Acquisition of PolysemyMachine Discovery Observational Discovery Cirrus: An Automated Protocol Analysis Tool Scientific Theory Formation Through Analogical Inference Inducing Causal and Social Theories: A Prerequisite for Explanation-based Learning The Role of Abstractions in Learning Qualitative ModelsDiscovery and Experimentation Learning by Experimentation Observation and Generalisation in a Simulated Robot World Empirical and Analytic Discovery in IL Combining Many Searches in the FAHRENHEIT Discovery SystemCognitive Architectures for Learning Causal Analysis and Inductive Learning Varieties of Learning in Soar: 1987 Hill-Climbing Theories of LearningOverviews Bias, Version Spaces and Valiant's Learning Framework Recent Results on Boolean Concept Learning Machine Learning from Structured Objects A New Approach to Unsupervised Learning in Deterministic Environments Searching for Operational Concept Descriptions in BAR, MetaLEX, and EBG Explanation-Based Generalization as Resolution Theorem Proving Analogy and Single-Instance GeneralizationIndex
- No. of pages: 410
- Language: English
- Edition: 1
- Published: September 1, 1998
- Imprint: Morgan Kaufmann
- Paperback ISBN: 9780934613415
- eBook ISBN: 9781483282855
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