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. Front Matter
  2. Recurrent Networks

    1. Sequence Learning

      1. Shingo Murata, Hiroaki Arie, Tetsuya Ogata, Jun Tani, Shigeki Sugano
        Pages 9-16
      2. Sigurd Spieckermann, Siegmund Düll, Steffen Udluft, Thomas Runkler
        Pages 17-24
    2. Echo State Networks

    3. Recurrent Network Theory

      1. Jérémie Cabessa, Alessandro E. P. Villa
        Pages 57-64
      2. Claudius Gros, Mathias Linkerhand, Valentin Walther
        Pages 65-72
      3. Hiroki Yamaoka, Narutoshi Horimoto, Toshimichi Saito
        Pages 73-80
  3. Competitive Learning and Self-Organisation

    1. Frank-Michael Schleif
      Pages 81-88
    2. German Ignacio Parisi, Cornelius Weber, Stefan Wermter
      Pages 89-96
    3. Nicolai Waniek, Simon Bremer, Jörg Conradt
      Pages 97-104
    4. Ricardo Sousa, Ajalmar R. da Rocha Neto, Jaime S. Cardoso, Guilherme A. Barreto
      Pages 105-112
  4. Clustering and Classification

    1. Hideitsu Hino, Noboru Murata
      Pages 113-120
    2. Jens Hocke, Thomas Martinetz
      Pages 129-135
    3. Daniel Lückehe, Oliver Kramer
      Pages 137-144
  5. Trees and Graphs

    1. Hideitsu Hino, Atsushi Noda, Masami Tatsuno, Shotaro Akaho, Noboru Murata
      Pages 145-152

About these proceedings

Introduction

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.

Keywords

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 https://doi.org/10.1007/978-3-319-11179-7
  • 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