Entropy Guided Transformation Learning: Algorithms and Applications
Entropy Guided Transformation Learning: Algorithms and Applications (ETL) presents a machine learning algorithm for classification tasks. ETL generalizes Transformation Based Learning (TBL) by solving the TBL bottleneck: the construction of good template
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Series Editors Stan Zdonik Peng Ning Shashi Shekhar Jonathan Katz Xindong Wu Lakhmi C. Jain David Padua Xuemin Shen Borko Furht V. S. Subrahmanian
For further volumes: http://www.springer.com/series/10028
Cícero Nogueira dos Santos Ruy Luiz Milidiú
Entropy Guided Transformation Learning: Algorithms and Applications
123
Cícero Nogueira dos Santos Research, IBM Research Brazil Av. Pasteur 146 Rio de Janeiro, RJ 22296-903 Brazil
ISSN 2191-5768 ISBN 978-1-4471-2977-6 DOI 10.1007/978-1-4471-2978-3
Ruy Luiz Milidiú Departamento de Informática (DI) Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio) Rio de Janeiro, RJ Brazil
e-ISSN 2191-5776 e-ISBN 978-1-4471-2978-3
Springer London Heidelberg New York Dordrecht British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Control Number: 2012933839 Ó The Author(s) 2012 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
This book presents entropy guided transformation learning (ETL), a machine learning algorithm for classification tasks. ETL generalizes transformation based learning (TBL) by automatically solving the TBL bottleneck: the construction of good template sets. ETL uses the Information Gain measure, through De
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