Machine Learning An Artificial Intelligence Approach
General Material Designation
[Book]
First Statement of Responsibility
R S Michalski
.PUBLICATION, DISTRIBUTION, ETC
Name of Publisher, Distributor, etc.
Berlin Springer Berlin
Date of Publication, Distribution, etc.
2013
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
XI, 572 Seiten in 1 Teil XI, 572 Seiten, 25 schw.-w. Illustrationen 244 x 170 mm
SERIES
Series Title
Symbolic Computation
CONTENTS NOTE
Text of Note
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.