Joint Learning of Distance Metric and Kernel Classifier via Multiple Kernel Learning
Both multiple kernel learning (MKL) and support vector metric learning (SVML) were developed to adaptively learn kernel function from training data, and have been proved to be effective in many challenging applications. Actually, many MKL formulations are
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ommunications in Computer and Information Science
Pattern Recognition 7th Chinese Conference, CCPR 2016 Chengdu, China, November 5–7, 2016 Proceedings, Part I
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Communications in Computer and Information Science
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Commenced Publication in 2007 Founding and Former Series Editors: Alfredo Cuzzocrea, Dominik Ślęzak, and Xiaokang Yang
Editorial Board Simone Diniz Junqueira Barbosa Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil Phoebe Chen La Trobe University, Melbourne, Australia Xiaoyong Du Renmin University of China, Beijing, China Joaquim Filipe Polytechnic Institute of Setúbal, Setúbal, Portugal Orhun Kara TÜBİTAK BİLGEM and Middle East Technical University, Ankara, Turkey Igor Kotenko St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg, Russia Ting Liu Harbin Institute of Technology (HIT), Harbin, China Krishna M. Sivalingam Indian Institute of Technology Madras, Chennai, India Takashi Washio Osaka University, Osaka, Japan
More information about this series at http://www.springer.com/series/7899
Tieniu Tan Xuelong Li Xilin Chen Jie Zhou Jian Yang Hong Cheng (Eds.) •
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Pattern Recognition 7th Chinese Conference, CCPR 2016 Chengdu, China, November 5–7, 2016 Proceedings, Part I
123
Editors Tieniu Tan Chinese Academy of Sciences Institute of Automation Beijing China Xuelong Li Xi’an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi’an China Xilin Chen Chinese Academy of Sciences Institute of Computing Technology Beijing China
Jie Zhou Tsinghua University Beijing China Jian Yang Nanjing University of Science and Technology Nanjing China Hong Cheng University of Electronic Science and Technology Chengdu, Sichuan China
ISSN 1865-0929 ISSN 1865-0937 (electronic) Communications in Computer and Information Science ISBN 978-981-10-3001-7 ISBN 978-981-10-3002-4 (eBook) DOI 10.1007/978-981-10-3002-4 Library of Congress Control Number: 2016950420 © Springer Nature Singapore Pte Ltd. 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
