Neural Network Learning: Theoretical Foundations

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Martin Anthony (London School of Economics and Political Science) Peter L. Bartlett (Australian National University, Canberra)

Neural Network Learning

Theoretical Foundations

Martin Anthony (London School of Economics and Political Science), Peter L. Bartlett (Australian National University, Canberra)

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English

Cambridge University Press
20 August 2009

Machine learning; Neural networks & fuzzy systems

This book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Research on pattern classification with binary-output networks is surveyed, including a discussion of the relevance of the Vapnik–Chervonenkis dimension, and calculating estimates of the dimension for several neural network models. A model of classification by real-output networks is developed, and the usefulness of classification with a 'large margin' is demonstrated. The authors explain the role of scale-sensitive versions of the Vapnik–Chervonenkis dimension in large margin classification, and in real prediction. They also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient constructive learning algorithms. The book is self-contained and is intended to be accessible to researchers and graduate students in computer science, engineering, and mathematics.

By: Martin Anthony (London School of Economics and Political Science), Peter L. Bartlett (Australian National University, Canberra)
Imprint: Cambridge University Press
Country of Publication: United Kingdom
Dimensions: Height: 229mm, Width: 152mm, Spine: 23mm
Weight: 590g
ISBN: 9780521118620
ISBN 10: 052111862X
Pages: 404
Publication Date: 20 August 2009
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

Reviews for Neural Network Learning: Theoretical Foundations

'The book is a useful and readable mongraph. For beginners it is a nice introduction to the subject, for experts a valuable reference.' Zentralblatt MATH