Systems That Learn: An Introduction to Learning Theory / Edition 2

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Overview

Formal learning theory is one of several mathematical approaches to the study of intelligent adaptation to the environment. The analysis developed in this book is based on a number theoretical approach to learning and uses the tools of recursive-function theory to understand how learners come to an accurate view of reality. This revised and expanded edition of a successful text provides a comprehensive, self-contained introduction to the concepts and techniques of the theory.
Exercises throughout the text provide experience in the use of computational arguments to prove facts about learning.

The MIT Press

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Product Details

  • ISBN-13: 9780262100779
  • Publisher: MIT Press
  • Publication date: 2/12/1999
  • Series: Learning, Development, and Conceptual Change
  • Edition description: second edition
  • Edition number: 2
  • Pages: 329
  • Product dimensions: 7.30 (w) x 9.00 (h) x 1.00 (d)

Table of Contents

Series Foreword
Preface
I Fundamentals of Learning Theory 1
1 Introduction 3
2 Formalities 15
3 Identification 27
4 Identification by Computable Scientists 61
II Fundamental Paradigms Generalized 89
5 Strategies for Learning 91
6 Criteria of Learning 127
7 Inference of Approximations 151
8 Environments 167
III Part III: Additional Topics 195
9 Team and Probabilistic Learning 197
10 Learning with Additional Information 221
11 Learning with Oracles 251
12 Complexity Issues in Identification 261
13 Beyond Identification by Enumeration 281
Bibliography 289
Notation Index 303
Author and Subject Index 309
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