Learning and Generalisation With Applications to Neural Networks
Learning and Generalization provides a formal mathematical theory for addressing intuitive questions such as: • How does a machine learn a new concept on the basis of examples? • How can a neural network, after sufficient training, correctly predict the o
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M. Vidyasagar
Learning and Generalisation With Applications to Neural Networks With 38 Figures
Springer
M. Vidyasagar, PhD Executive Vice President, Tata Consultancy Services, 1-2-10 Sardar Patel Road, Secunderabad 500 003, India
Series Editors
E.D. Sontag eM. Thoma e A. Isidori e J. van Schuppen
British Library Cataloguing in Publication Data Vidyasagar, M. (Mathukumalli), 1947Learning and generalisation : with applications to neural networks. - 2nd ed. - (Communications and control engineering) 1.Machine learning 2.Neural networks (Computer science) 1.Title 006.3'1 Library of Congress Cataloging-in-Publication Data Vidyasagar, M. (Mathukumalli), 1947Learning and generalisation : with applications to neural networks / M. Vidyasagar.-2nded. p. cm. -- (Communications and control engineering, ISSN 0178-5354) 1. Machine learning. 2. Control theory. 3. Neural networks (Computer science) 1. Title. II. Series. Q325.5 .V53 2002 006.3'1--dc21 2002070674 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms oflicences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. Communications and Control Engineering Series ISSN 0178-5354 ISBN 978-1-84996-867-6 ISBN 978-1-4471-3748-1 (eBook) DOI 10.1007/978-1-4471-3748-1 © Springer-Verlag London 2003
