Reinforcement Learning
Reinforcement learning has its origin in the psychology of animal learning. It awards the learner (agent) for correct actions, and punishes for wrong actions. In the mammalian brain, learning by reinforcement is a function of brain nuclei known as the bas
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ural Networks and Statistical Learning
Neural Networks and Statistical Learning
Ke-Lin Du M. N. S. Swamy •
Neural Networks and Statistical Learning
123
M. N. S. Swamy Department of Electrical and Computer Engineering Concordia University Montreal, QC Canada
Ke-Lin Du Enjoyor Labs Enjoyor Inc. Hangzhou China and Department of Electrical and Computer Engineering Concordia University Montreal, QC Canada
Additional material to this book can be downloaded from http://extras.springer.com/
ISBN 978-1-4471-5570-6 DOI 10.1007/978-1-4471-5571-3
ISBN 978-1-4471-5571-3
(eBook)
Springer London Heidelberg New York Dordrecht Library of Congress Control Number: 2013948860 Springer-Verlag London 2014 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. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. 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. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
In memory of my grandparents K.-L. Du To my family M. N. S. Swamy To all the researchers with original contributions to neural networks and machine learning K.-L. Du, and M. N. S. Swamy
Preface
The human brain, consisting of nearly 1011 neurons, is the center of human intelligence. Human intelligence has been simulated in various ways. Artificial intelligence (AI) pursues exact logical reasoning based on symbol manipulation. Fuzzy logics model the highly uncertain behavior of decision making. Neural networks model the hi
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