Systems that learn : an introduction to learning theory for cognitive and computer scientists

Title:

Personal Author:

Osherson, Daniel N.

Publication Information:

Cambridge, Mass : MIT Press, 1986

ISBN:

9780262150309

Subject Term:

Learning -- Mathematical models

Learning, Psychology of

Human information processing -- Mathematical models

Added Author:

Stob, Michael

Weinstein, Scott

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000000001366	BF318 O83 1986	Open Access Book	Book	Searching... Unknown

Systems That Learn presents a mathematical framework for the study of learning in a variety of domains. It provides the basic concepts and techniques of learning theory as well as a comprehensive account of what is currently known about a variety of learning paradigms. Daniel N. Osherson and Scott Weinstein are at MIT, and Michael Stob at Calvin College.

Series Foreword	p. xi
Preface	p. xiii
Acknowledgments	p. xv
How to Use This Book	p. xvii
Introduction	p. 1
I Identification
1 Fundamentals of Learning Theory	p. 7
2 Central Theorems on Identification	p. 25
3 Learning Theory and Natural Language	p. 34
II Identification Generalized
4 Strategies	p. 45
5 Environments	p. 96
6 Criteria of Learning	p. 119
7 Exact Learning	p. 152
III Other Paradigms Of Learning
8 Efficient Learning	p. 167
9 Sufficient Input for Learning	p. 178
10 Topological Perspective on Learning	p. 182
Bibliography	p. 195
List of Symbols	p. 198
Name Index	p. 201
Subject Index	p. 203

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Summary

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Table of Contents