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neural network learning theoretical foundations

statistical

learning,

theory

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This is one of the most classical books on statistical learning theory.

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Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work.

Neural Network Learning:

Theoretical Foundations

This book describes recent 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. Estimates of this dimension are

calculated 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 estimation. 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.

Martin Anthony is Reader in Mathematics and Executive Director of

the Centre for Discrete and Applicable Mathematics at the London

School of Economics and Political Science.

Peter Bartlett is a Senior Fellow st the Research School of Information

Sciences and Engineering at the Australian National University.

Neural Network Learning:

Theoretical Foundations

Martin Anthony and Peter L. Bartlett

CAMBRIDGE

UNIVERSITY PRESS

CAMBRIDGE UNIVERSITY PRESS

Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, Sao Paulo, Delhi

Cambridge University Press

The Edinburgh Building, Cambridge CB2 8RU, UK

Published in the United States of America by Cambridge University Press, New York

www. Cambridge. org

Information on this title: www.cambridge.org/9780521118620

This publication is in copyright. Subject to statutory exception

and to the provisions of relevant collective licensing agreements,

no reproduction of

any

part may take place without the written

permission of Cambridge University Press.

First published 1999

Reprinted

2001,

2002

This digitally printed version 2009

catalogue

record for

this publication

is available from

the British Library

Library of

Congress Cataloguing

Publication

data

Anthony, Martin.

Learning in neural networks : theoretical foundations /

Martin Anthony and Peter L. Bartlett.

cm.

Includes bibliographical references.

ISBN 0 521 57353 X (hardcover)

Neural networks (Computer science). I. Bartlett, Peter L.,

1966-

. II. Title.

QA76.87.A58 1999

006.3'2-dc21 98-53260 CIP

ISBN 978-0-521-57353-5 hardback

ISBN 978-0-521-11862-0 paperback

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Ameoi

2017-02-11

是比较有用的
saf11

2016-03-18

非常好，需要认真阅读
qq_33596512

2016-01-02

Martin Anthony and Peter Bartlett的经典书籍。统计学习理论的大牛之一。
Copper_PKU

2015-07-30

非常好的资料
focus8889

2015-11-03

原创性的理论。神经网络技术文献的经典。

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