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Proceedings 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 Learning
Learning 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 Study
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 System
Concept Learning and Bias
More Robust Concept Learning Using Dynamically-Variable Bias
Incremental Adjustment of Representations for Learning
Learning, Problem Solving, and Planning Heuristic Search Approaches
Concept Learning in Context
Strategy Learning with Multilayer Connectionist Representations
Learning a Preference Predicate
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 Explanations
Problem 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 Inference
Learning and Natural Language
Learning to Integrate Syntax and Semantics
How Do Machine-Learning Paradigms Fare in Language Acquisition?
The Acquisition of Polysemy
Machine 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 Models
Discovery 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 System
Cognitive Architectures for Learning
Causal Analysis and Inductive Learning
Varieties of Learning in Soar: 1987
Hill-Climbing Theories of Learning
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 Generalization
- No. of pages:
- © Morgan Kaufmann 1987
- 1st September 1998
- Morgan Kaufmann
- eBook ISBN:
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