Machine Learning: A Theoretical Approach

by Balas K. Natarajan

Overview

This is the first comprehensive introduction to computational learning theory. The author's uniform presentation of fundamental results and their applications offers AI researchers a theoretical perspective on the problems they study. The book presents tools for the analysis of probabilistic models of learning, tools that crisply classify what is and is not efficiently learnable. After a general introduction to Valiant's PAC paradigm and the important notion of the Vapnik-Chervonenkis dimension, the author explores specific topics such as finite automata and neural networks. The presentation is intended for a broad audience—the author's ability to motivate and pace discussions for beginners has been praised by reviewers. Each chapter contains numerous examples and exercises, as well as a useful summary of important results. An excellent introduction to the area, suitable either for a first course, or as a component in general machine learning and advanced AI courses. Also an important reference for AI researchers.

Product Details

ISBN-13:	9781558601482
Publisher:	Elsevier Science
Publication date:	07/01/1991
Pages:	217
Product dimensions:	0.69(w) x 6.00(h) x 9.00(d)

Table of Contents

Chapter 1 Introduction
Chapter 2 Learning Concept on Countable Domains
Chapter 3 Time Complexity of Concept Learning
Chapter 4 Learning Concepts on Uncoutable Domains
Chapter 5 Learning Functions
Chapter 6 Finite Automata
Chapter 7 Neural Networks
Chapter 8 Generalizing the Learning Model
Chapter 9 Conclusion

Editorial Reviews

A comprehensive introduction to computational learning theory--a new subfield of machine learning. Suitable either for a first course or as a component in general machine learning and advanced AI courses. Each chapter contains numerous examples and exercises. Annotation c. Book News, Inc., Portland, OR (booknews.com)

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