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Moamar Sayed-Mouchaweh

Learning From Data Streams in Dynamic Environments

123

SpringerBriefs in Applied Sciences and Technology

More information about this series at http://www.springer.com/series/8884

Moamar Sayed-Mouchaweh

Learning from Data Streams in Dynamic Environments

Moamar Sayed-Mouchaweh Computer Science and Automatic Control Department High National Engineering School of Mines Douai, France

ISSN 2191-530X ISSN 2191-5318 (electronic) SpringerBriefs in Applied Sciences and Technology ISBN 978-3-319-25665-8 ISBN 978-3-319-25667-2 (eBook) DOI 10.1007/978-3-319-25667-2 Library of Congress Control Number: 2015956612 Springer Cham Heidelberg New York Dordrecht London © The Author 2016 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, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www.springer.com)

Preface

This book treats the problem of learning from data streams generated by time-based and evolving nonstationary processes. It presents major and well-known techniques, methods, and tools able to manage, to exploit, and to interpret correctly the increasing amount of data in environments that are continuously changing. The goal is to build a predictor (classifier, learner), about the future system behavior, able to tackle and to govern the high variability of evolving and nonstationary systems. This book addresses the problems of modeling, prediction, classification, data understanding, and processing in nonstationary and unpredictable environments. It presents some major and well-known methods for the design of systems able to learn and to fully adapt their structure and to adjust their parameters according to the changes in their environments. In summary, this book aims at (1) defining the problem of learning from data streams in evolving and nonstationary environments, its interests, its applications, and its challenges, (2) providing a general scheme and principals of methods and techn