Domain Knowledge-Based Compaction
Domain knowledge about the problem on hand always leads to an effective solution. In this chapter, we discuss ways to make use of domain knowledge in generating abstraction. We consider binary classifiers such as support vector machine (SVM) and adaptive
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T. Ravindra Babu M. Narasimha Murty S.V. Subrahmanya
Compression Schemes for Mining Large Datasets A Machine Learning Perspective
Advances in Computer Vision and Pattern Recognition
For further volumes: www.springer.com/series/4205
T. Ravindra Babu r M. Narasimha Murty S.V. Subrahmanya
r
Compression Schemes for Mining Large Datasets A Machine Learning Perspective
T. Ravindra Babu Infosys Technologies Ltd. Bangalore, India
S.V. Subrahmanya Infosys Technologies Ltd. Bangalore, India
M. Narasimha Murty Indian Institute of Science Bangalore, India Series Editors Prof. Sameer Singh Rail Vision Europe Ltd. Castle Donington Leicestershire, UK
Dr. Sing Bing Kang Interactive Visual Media Group Microsoft Research Redmond, WA, USA
ISSN 2191-6586 ISSN 2191-6594 (electronic) Advances in Computer Vision and Pattern Recognition ISBN 978-1-4471-5606-2 ISBN 978-1-4471-5607-9 (eBook) DOI 10.1007/978-1-4471-5607-9 Springer London Heidelberg New York Dordrecht Library of Congress Control Number: 2013954523 © Springer-Verlag London 2013 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)
Preface
We come across a number of celebrated text books on Data Mining covering multiple aspects of the topic since its early development, such as those on databases, pattern recognition,
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