Promotional Information

Springer Book Archives

Table of Contents

One General Issues in Machine Learning.- 1 An Overview of Machine Learning.- 2 Why Should Machines Learn?.- Two Learning from Examples.- 3 A Comparative Review of Selected Methods for Learning from Examples.- 4 A Theory and Methodology of Inductive Learning.- Three Learning in Problem-Solving and Planning.- 5 Learning by Analogy: Formulating and Generalizing Plans from Past Experience.- 6 Learning by Experimentation: Acquiring and Refining Problem-Solving Heuristics.- 7 Acquisition of Proof Skills in Geometry.- 8 Using Proofs and Refutations to Learn from Experience.- Four Learning from Observation and Discovery.- 9 The Role of Heuristics in Learning by Discovery: Three Case Studies.- 10 Rediscovering Chemistry With the BACON System.- 11 Learning From Observation: Conceptual Clustering.- Five Learning from Instruction.- 12 Machine Transformation of Advice into a Heuristic Search Procedure.- 13 Learning by Being Told: Acquiring Knowledge for Information Management.- 14 The Instructible Production System: A Retrospective Analysis.- Six Applied Learning Systems.- 15 Learning Efficient Classification Procedures and their Application to Chess End Games.- 16 Inferring Student Models for Intelligent Computer-Aided Instruction.- Comprehensive Bibliography of Machine Learning.- Glossary of Selected Terms In Machine Learning.- About the Authors.- Author Index.