Boosted Statistical Relational Learners From Benchmarks to Data-Driv
This SpringerBrief addresses the challenges of analyzing multi-relational and noisy data by proposing several Statistical Relational Learning (SRL) methods. These methods combine the expressiveness of first-order logic and the ability of probability theor
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Sriraam Natarajan • Kristian Kersting Tushar Khot • Jude Shavlik
Boosted Statistical Relational Learners From Benchmarks to Data-Driven Medicine
Sriraam Natarajan Indiana University Bloomington, Indiana USA
Tushar Khot Indiana University Bloomington, Indiana USA
Kristian Kersting TU Dortmund University Dortmund Germany
Jude Shavlik University of Wisconsin Madison, Wisconsin USA
ISSN 2191-5768 SpringerBriefs in Computer Science ISBN 978-3-319-13643-1 DOI 10.1007/978-3-319-13644-8
ISSN 2191-5776 (electronic) ISBN 978-3-319-13644-8 (eBook)
Library of Congress Control Number: 2014960238 Springer Cham Heidelberg New York Dordrecht London © The Author(s) 2014 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 is part of Springer Science+Business Media (www.springer.com)
Acknowledgements
We gratefully acknowledge the contributions of all the co-authors of the several papers written on this topic. Specifically, we are thankful to Bernd Guttmann, Prasad Tadepalli, Saket Joshi, Gautam Kunapuli, Phillip Odom, Jeremy Weiss, David Page, Jose Picado, Baidya Saha, Adam Edwards, Chris Whitlow, Joe Maldjian, Jeff Carr for their contributions and discussions on several topics in this research. Sriraam Natarajan, Jude Shavlik and Tushar Khot acknowledge DARPA Machine Reading Program and Deep Exploration and Filtering of Text Program under the Air Force Research Laboratory (AFRL) prime contract no. FA8750-09-C-0181 and FA8750-13-2-0039 respectively. Kristian Kersting’s research leading to this monograph was partly supported by the Fraunhofer ATTRACT fellowship STREAM and by the European Commission under contract number FP7-248258-First-MM.
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Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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2 Statistical Relational Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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