Multiple Instance Learning Foundations and Algorithms
This book provides a general overview of multiple instance learning (MIL), defining the framework and covering the central paradigms. The authors discuss the most important algorithms for MIL such as classification, regression and clustering. With a focus
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ce Learning Foundations and Algorithms
Multiple Instance Learning
Francisco Herrera Sebastián Ventura Rafael Bello Chris Cornelis Amelia Zafra Dánel Sánchez-Tarragó Sarah Vluymans •
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Multiple Instance Learning Foundations and Algorithms
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Francisco Herrera Department of Computer Science and Artificial Intelligence University of Granada Granada Spain
Amelia Zafra Department of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain
Sebastián Ventura Department of Computer Sciences University of Córdoba Córdoba Spain
Dánel Sánchez-Tarragó Central University “Marta Abreu” of Las Villas Santa Clara, Villa Clara Cuba
Rafael Bello Center of Information Studies Central University “Marta Abreu” of Las Villas Santa Clara, Villa Clara Cuba
Sarah Vluymans Department of Applied Mathematics, Computer Science and Statistics Ghent University Ghent Belgium
Chris Cornelis Department of Applied Mathematics, Computer Science and Statistics Ghent University Ghent Belgium
ISBN 978-3-319-47758-9 DOI 10.1007/978-3-319-47759-6
ISBN 978-3-319-47759-6
(eBook)
Library of Congress Control Number: 2016954601 © Springer International Publishing AG 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 The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
Multiple instance learning (MIL) is a recent learning framework that has become very popular lately. In this framework, objects are represented as sets of feature vectors (or bags, in MIL terminology). This kind of representation is well suited for certain problems, such as the prediction of structure–activity relationships, image classification, document categorization or the prediction of protein binding sites. In fact, MIL provides a much more natural representation than the one used in classical machine learning, where a single feature vect
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