Computational Learning Theory (Cambridge Tracts in Theoretical...

Features

• Cover Type: Paperback with 171 pages
• Published by: Cambridge University Press March 13, 1997
• Written in: English
• ISBN 10 Number: 0521599229
• ISBN 13 Number: 978-0521599221
• Book Dimensions: 9.5 x 6.7 x 0.4 inches
• Weighs: 12 ounces

Product Description
Computational learning theory is one of the first attempts to construct a mathematical theory of a cognitive process. It has been a field of much interest and rapid growth in recent years. This text provides a framework for studying a variety of algorithmic processes, such as those currently in use for training artificial neural networks. The authors concentrate on an approximate model for learning and gradually develop the ideas of efficiency considerations. Finally, they consider applications of the theory to artificial neural networks. An abundance of exercises and an extensive list of references round out the text. This volume provides a comprehensive review of the topic, including information drawn from logic, probability, and complexity theory. It forms a solid introduction to the theory of comptutational learning suitable for a broad spectrum of graduate students from theoretical computer science to mathematics.

Book Description
Computational learning theory is a subject which has been advancing rapidly in the last few years. The authors concentrate on the probably approximately correct model of learning, and gradually develop the ideas of efficiency considerations. Finally, applications of the theory to artificial neural networks are considered. Many exercises are included throughout, and the list of references is extensive. This volume is relatively self contained as the necessary background material from logic, probability and complexity theory is included. It will therefore form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretical computer science and mathematics.

Reader Reviews
This book gives a good introduction to the mathematical modeling of cognition and does so with a level of mathematics that is very accessible to a typical graduate student in computer science or psychology. The book could have been written using tools from measure theory but luckily it was not for a book at an introductory level. The concept of probably approximately correct is introduced early on in the third chapter of the book with efficient learning given later on in Chapter 5. Chapter 7, the best chapter of the book, discusses the idea of VC dimension, which has had many applications, such as network stability and optimization. VC dimension plays the pre-dominant theme in the rest of the book, with the book ending with an application to neural networks. There are short problem sets at the end of the chapters, and these are useful for more understanding of the concepts in the book. A very interesting book and worth the price.