Artificial Neural Networks in Pattern Recognition

6th IAPR TC 3 International Workshop, ANNPR 2014, Montreal, QC, Canada, October 6-8, 2014. Proceedings

  • Neamat El Gayar
  • Friedhelm Schwenker
  • Cheng Suen
Conference proceedings ANNPR 2014
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8774)

Table of contents

  1. Front Matter
  2. Invited Paper

    1. Zhi-Hua Zhou
      Pages 1-11
  3. Learning Algorithms and Architectures

    1. Mohamed Farouk Abdel Hady, Abubakrelsedik Karali, Eslam Kamal, Rania Ibrahim
      Pages 23-34
    2. Alexander Bernstein, Alexander Kuleshov
      Pages 47-58
    3. Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti
      Pages 59-70
    4. Markus Kächele, Patrick Thiam, Günther Palm, Friedhelm Schwenker
      Pages 83-92
    5. Tawfik A. Moahmed, Neamat El Gayar, Amir F. Atiya
      Pages 93-104
    6. Christophe Pagano, Eric Granger, Robert Sabourin, Gian Luca Marcialis, Fabio Roli
      Pages 105-116
    7. Kaspar Riesen, Andreas Fischer, Horst Bunke
      Pages 117-128
    8. Kaspar Riesen, Andreas Fischer, Horst Bunke
      Pages 129-140
    9. Florian Schmid, Ludwig Lausser, Hans A. Kestler
      Pages 141-152
    10. Friedhelm Schwenker, Markus Frey, Michael Glodek, Markus Kächele, Sascha Meudt, Martin Schels et al.
      Pages 153-164
    11. Patrick Thiam, Viktor Kessler, Friedhelm Schwenker
      Pages 165-170
    12. Stephan Tschechne, Roman Sailer, Heiko Neumann
      Pages 171-182
  4. Applications

    1. Sukalpa Chanda, Guoping Bu, Hong Guan, Jun Jo, Umapada Pal, Yew-Chaye Loo et al.
      Pages 193-203
    2. Bo-Yuan Feng, Mingwu Ren, Xu-Yao Zhang, Ching Y. Suen
      Pages 204-215

About these proceedings

Introduction

This book constitutes the refereed proceedings of the 6th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2014, held in Montreal, QC, Canada, in October 2014. The 24 revised full papers presented were carefully reviewed and selected from 37 submissions for inclusion in this volume. They cover a large range of topics in the field of learning algorithms and architectures and discussing the latest research, results, and ideas in these areas.

Keywords

classification feature selection information extraction kernel methods learning algorithms machine learning meta-learning multiple classifier systems neural networks optimal network architecture pattern classification statistical learning support vector machines time series prediction unsupervised active learning

Editors and affiliations

  • Neamat El Gayar
    • 1
  • Friedhelm Schwenker
    • 2
  • Cheng Suen
    • 3
  1. 1.Faculty of Computers and Information, OrmanCairo UniversityGizaEgypt
  2. 2.Institute for Neural Information ProcessingUniversity of UlmUlmGermany
  3. 3.Department of Computer Science and Software EngineeringConcordia UniversityMonralCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-11656-3
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-11655-6
  • Online ISBN 978-3-319-11656-3
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book