Soft-Constrained Nonparametric Density Estimation with Artificial Neural Networks
The estimation of probability density functions (pdf) from unlabeled data samples is a relevant (and, still open) issue in pattern recognition and machine learning. Statistical parametric and nonparametric approaches present severe drawbacks. Only a few i
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Friedhelm Schwenker · Hazem M. Abbas Neamat El Gayar · Edmondo Trentin (Eds.)
Artificial Neural Networks in Pattern Recognition 7th IAPR TC3 Workshop, ANNPR 2016 Ulm, Germany, September 28–30, 2016 Proceedings
123
Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science
LNAI Series Editors Randy Goebel University of Alberta, Edmonton, Canada Yuzuru Tanaka Hokkaido University, Sapporo, Japan Wolfgang Wahlster DFKI and Saarland University, Saarbrücken, Germany
LNAI Founding Series Editor Joerg Siekmann DFKI and Saarland University, Saarbrücken, Germany
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More information about this series at http://www.springer.com/series/1244
Friedhelm Schwenker Hazem M. Abbas Neamat El Gayar Edmondo Trentin (Eds.) •
•
Artificial Neural Networks in Pattern Recognition 7th IAPR TC3 Workshop, ANNPR 2016 Ulm, Germany, September 28–30, 2016 Proceedings
123
Editors Friedhelm Schwenker Ulm University Ulm Germany
Neamat El Gayar Cairo University Giza Egypt
Hazem M. Abbas Ain Shams University Cairo Egypt
Edmondo Trentin Università di Siena Siena Italy
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Artificial Intelligence ISBN 978-3-319-46181-6 ISBN 978-3-319-46182-3 (eBook) DOI 10.1007/978-3-319-46182-3 Library of Congress Control Number: 2016950420 LNCS Sublibrary: SL7 – Artificial Intelligence © 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
This volume contains the papers presented at the 7th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR 2016), held at Ulm University, Ulm, Germany, during September 28–30, 2016. ANNPR 2016 followed the success of the ANNPR workshops of 2003 (Florence), 2006 (Ulm), 2008 (Paris), 2010 (Cairo), 2012 (Trento),
