Artificial Neural Networks and Machine Learning – ICANN 2014

24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings

  • Stefan Wermter
  • Cornelius Weber
  • Włodzisław Duch
  • Timo Honkela
  • Petia Koprinkova-Hristova
  • Sven Magg
  • Günther Palm
  • Alessandro E. P. Villa

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8681)

Table of contents

  1. Theory

    1. Optimization

    2. Layered Networks

      1. Iakov Karandashev, Boris Kryzhanovsky
        Pages 323-330
      2. Věra Kůrková, Marcello Sanguineti
        Pages 331-338
      3. Pitoyo Hartono, Paul Hollensen, Thomas Trappenberg
        Pages 339-346
  2. Reinforcement Learning and Action

    1. Nathan Sprague
      Pages 347-354
    2. Sotirios P. Chatzis, Dimitrios Kosmopoulos
      Pages 355-362
    3. Kuniyuki Takahashi, Tetsuya Ogata, Hadi Tjandra, Shingo Murata, Hiroaki Arie, Shigeki Sugano
      Pages 363-370
    4. Mohsen Firouzi, Saeed Bagheri Shouraki, Jörg Conradt
      Pages 379-386
  3. Vision

    1. Detection and Recognition

      1. Pablo Barros, Sven Magg, Cornelius Weber, Stefan Wermter
        Pages 403-410
      2. Marvin Struwe, Stephan Hasler, Ute Bauer-Wersing
        Pages 411-418
      3. Hanchen Xiong, Sandor Szedmak, Antonio Rodríguez-Sánchez, Justus Piater
        Pages 419-426
    2. Invariances and Shape Recovery

    3. Attention and Pose Estimation

      1. Oliver Lomp, Kasim Terzić, Christian Faubel, J. M. H. du Buf, Gregor Schöner
        Pages 451-458
      2. Frederik Beuth, Amirhossein Jamalian, Fred H. Hamker
        Pages 459-466
      3. Yoshihiro Nagano, Norifumi Watanabe, Atsushi Aoyama
        Pages 467-474

About these proceedings


The book constitutes the proceedings of the 24th International Conference on Artificial Neural Networks, ICANN 2014, held in Hamburg, Germany, in September 2014.
The 107 papers included in the proceedings were carefully reviewed and selected from 173 submissions. The focus of the papers is on following topics: recurrent networks; competitive learning and self-organisation; clustering and classification; trees and graphs; human-machine interaction; deep networks; theory; reinforcement learning and action; vision; supervised learning; dynamical models and time series; neuroscience; and applications.


computational neuroscience distributed computation dynamical systems ensemble methods evolving systems machine learning neural networks parallel distributed system particle swarm optimization reinforcement learning robust pattern recognition self-organizing maps speech recognition support vector machines swarm intelligence turing machines unsupervised learning

Editors and affiliations

  • Stefan Wermter
    • 1
  • Cornelius Weber
    • 1
  • Włodzisław Duch
    • 2
  • Timo Honkela
    • 3
  • Petia Koprinkova-Hristova
    • 4
  • Sven Magg
    • 1
  • Günther Palm
    • 5
  • Alessandro E. P. Villa
    • 6
  1. 1.Department of InformaticsUniversity of HamburgHamburgGermany
  2. 2.Department of InformaticsNicolaus Compernicus UniversityTorunPoland
  3. 3.Department of Modern LanguagesUniversity of HelsinkiHelsinkiFinland
  4. 4.Institute of Information and Communication TechnologiesBulgarian Academy of SciencesSofiaBulgaria
  5. 5.Institute of Neural Information ProcessingUniversity of UlmOberer EselsbergGermany
  6. 6.Department of Information Systems, Quartier UNIL-Dorigny, Bâtiment InternefUniversity of LausanneLausanneSwitzerland

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-11178-0
  • Online ISBN 978-3-319-11179-7
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book