Search Techniques in Intelligent Classification Systems
A unified methodology for categorizing various complex objects is presented in this book. Through probability theory, novel asymptotically minimax criteria suitable for practical applications in imaging and data analysis are examined including the special
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Andrey V. Savchenko
Search Techniques in Intelligent Classification Systems 123
SpringerBriefs in Optimization Series Editors Sergiy Butenko Mirjam Dür Panos M. Pardalos János D. Pintér Stephen M. Robinson Tamás Terlaky My T. Thai
SpringerBriefs in Optimization showcases algorithmic and theoretical techniques, case studies, and applications within the broad-based field of optimization. Manuscripts related to the ever-growing applications of optimization in applied mathematics, engineering, medicine, economics, and other applied sciences are encouraged.
More information about this series at http://www.springer.com/series/8918
Andrey V. Savchenko
Search Techniques in Intelligent Classification Systems
123
Andrey V. Savchenko Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics Nizhny Novgorod, Russia
ISSN 2190-8354 ISSN 2191-575X (electronic) SpringerBriefs in Optimization ISBN 978-3-319-30513-4 ISBN 978-3-319-30515-8 (eBook) DOI 10.1007/978-3-319-30515-8 Library of Congress Control Number: 2016935506 Mathematics Subject Classification (2010): 68T10, 68T20, 68T45 © The Author(s) 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 This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG Switzerland
To my wife Liudmila and children Vladislav and Irina for their love and support
Preface
Modern intelligent classification systems are characterized with an insufficient performance in the case of large databases. Various search techniques have been proposed to speedup the search procedures for such tasks as image analysis, speech recognition, etc. However, the features, the classifiers, and the structural scheme of decision-making are individually designed for each specific domain. The purpose of this monograph is to describe the unified methodology for the classification of audiovisual data. By using probability th
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