Artificial Neural Networks – ICANN 2007

17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part I

  • Editors
  • Joaquim Marques de Sá
  • Luís A. Alexandre
  • Włodzisław Duch
  • Danilo Mandic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4668)

Table of contents

  1. Front Matter
  2. Learning Theory

    1. Shinichi Nakajima, Sumio Watanabe
      Pages 1-10
    2. Yu Nishiyama, Sumio Watanabe
      Pages 29-38
    3. Héctor F. Satizábal M., Andres Pérez-Uribe
      Pages 39-48
    4. Feng Liu, Fengzhan Tian, Qiliang Zhu
      Pages 49-57
    5. Rowland R. Sillito, Robert B. Fisher
      Pages 58-67
    6. M. A. H. Akhand, Kazuyuki Murase
      Pages 98-108
  3. Advances in Neural Network Learning Methods

    1. Anton Schwaighofer, Mathäus Dejori, Volker Tresp, Martin Stetter
      Pages 119-128
    2. Thorsten Suttorp, Christian Igel
      Pages 139-148
    3. Koichiro Yamauchi, Masayoshi Sato
      Pages 149-158
    4. Daniel García, Ana González, José R. Dorronsoro
      Pages 159-168

Other volumes

  1. Artificial Neural Networks – ICANN 2007
    17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part I
  2. 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part II

About these proceedings

Introduction

This two volume set LNCS 4668 and LNCS 4669 constitutes the refereed proceedings of the 17th International Conference on Artificial Neural Networks, ICANN 2007, held in Porto, Portugal, in September 2007.

The 197 revised full papers presented were carefully reviewed and selected from 376 submissions. The 98 papers of the first volume are organized in topical sections on learning theory, advances in neural network learning methods, ensemble learning, spiking neural networks, advances in neural network architectures neural network technologies, neural dynamics and complex systems, data analysis, estimation, spatial and spatio-temporal learning, evolutionary computing, meta learning, agents learning, complex-valued neural networks, as well as temporal synchronization and nonlinear dynamics in neural networks.

Keywords

Boolean function algorithmic learning algorithms bioinspired computing biomedical data analysis classification clutering cognition cognitive science complexity computer vision fuzzy logic genetic programming modeling multi-agent system

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-74690-4
  • Copyright Information Springer-Verlag Berlin Heidelberg 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-74689-8
  • Online ISBN 978-3-540-74690-4
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book