Computational Learning Theory

Anthony, Martin H. G.; Biggs, Norman

ISBN 10: 0521416035 / ISBN 13: 9780521416030

Published by Cambridge University Press, New York

About this Item

VG condition. Lower front corners are a bit worn and there is a narrow three inch mark on the rear cover, otherwise the covers look great. The binding is tight. Interior pages are clean and unmarked. Small scuff on front flyleaf. USPS electronic tracking number issued free of charge.

Seller Inventory # 11092

Bibliographic Details

• Title: Computational Learning Theory
• Publisher: Cambridge University Press, New York
• Language: English
• ISBN 10: 0521416035
• ISBN 13: 9780521416030
• Binding: Hardcover
• Edition: First Edition.
• Condition: Very Good
• Dust Jacket Condition: No Jacket
• Seller catalogs:
  • Computers & Technology

About this title

Synopsis

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.