Neural Networks and Statistical Learning
This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical l
- PDF / 23,770,990 Bytes
- 996 Pages / 453.544 x 683.151 pts Page_size
- 70 Downloads / 315 Views
al Networks and Statistical Learning Second Edition
Neural Networks and Statistical Learning
Ke-Lin Du M. N. S. Swamy •
Neural Networks and Statistical Learning Second Edition
123
Ke-Lin Du Department of Electrical and Computer Engineering Concordia University Montreal, QC, Canada
M. N. S. Swamy Department of Electrical and Computer Engineering Concordia University Montreal, QC, Canada
Xonlink Inc. Hangzhou, China
ISBN 978-1-4471-7451-6 ISBN 978-1-4471-7452-3 https://doi.org/10.1007/978-1-4471-7452-3
(eBook)
1st edition: © Springer-Verlag London 2014 2nd edition: © Springer-Verlag London Ltd., part of Springer Nature 2019 The author(s) has/have asserted their right(s) to be identified as the author(s) of this work in accordance with the Copyright, Designs and Patents Act 1988. This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer-Verlag London Ltd. part of Springer Nature. The registered company address is: The Campus, 4 Crinan Street, London, N1 9XW, United Kingdom
To Falong Xing and Jie Zeng —Ke-Lin Du To my teachers and my students —M. N. S. Swamy
Preface to the Second Edition
Since the publication of the first edition in December 2013, the rapid rise of deep learning and AI has resulted in a wave of research activities and numerous new results. During the past few years, there have been several breakthroughs in deep learning and AI. At the same time, research and application of big data are widespread. Machine learning has become the brains behind big data. In such a setting, this book has become one of the best sellers of Springer books. Under the suggestion of Anthony Doyle at Springer London Ltd., we decided to publish this second edition. In this second edition, we will add six new chapters to the first edition: • Chapter 3 focuses on computation learning theory. Part of its con
