Neural Network Learning: Theoretical Foundations - Softcover

Anthony, Martin

Softcover

ISBN 10: 052111862X ISBN 13: 9780521118620

Publisher: Cambridge University Press, 2009

Synopsis

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.

About this title

Product Description

Neural Network Learning This book describes theoretical advances in the study of artificial neural networks. Full description

Review

'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

Edition details

• Publisher: Cambridge University Press
• Publication date: 2009
• ISBN 10: 052111862X
• ISBN 13: 9780521118620
• Binding: Paperback
• Language: English
• Edition number: 1
• Number of pages: 404