Artificial Neural Networks – ICANN 2010

20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part I

  • Konstantinos Diamantaras
  • Wlodek Duch
  • Lazaros S. Iliadis

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6352)

Table of contents

  1. Front Matter
  2. ANN Applications

  3. Bayesian ANN

    1. Yasuhiro Sogawa, Shohei Shimizu, Aapo Hyvärinen, Takashi Washio, Teppei Shimamura, Seiya Imoto
      Pages 67-76
    2. Dimitris Tzikas, Aristidis Likas
      Pages 87-96
    3. Kobra Etminani, Mahmoud Naghibzadeh, Amir Reza Razavi
      Pages 101-106
  4. Bio Inspired – Spiking ANN

    1. Boudjelal Meftah, Olivier Lezoray, Michel Lecluse, Abdelkader Benyettou
      Pages 117-126
    2. Masumi Kogure, Shuichi Matsuzaki, Yasuhiro Wada
      Pages 127-134
    3. Vladyslav Shaposhnyk, Pierre Dutoit, Stephen Perrig, Alessandro E. P. Villa
      Pages 135-144

Other volumes

  1. Artificial Neural Networks – ICANN 2010
    20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part I
  2. 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part II
  3. 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part III

About these proceedings

Introduction

th This volume is part of the three-volume proceedings of the 20 International Conference on Arti?cial Neural Networks (ICANN 2010) that was held in Th- saloniki, Greece during September 15–18, 2010. ICANN is an annual meeting sponsored by the European Neural Network Society (ENNS) in cooperation with the International Neural Network So- ety (INNS) and the Japanese Neural Network Society (JNNS). This series of conferences has been held annually since 1991 in Europe, covering the ?eld of neurocomputing, learning systems and other related areas. As in the past 19 events, ICANN 2010 provided a distinguished, lively and interdisciplinary discussion forum for researches and scientists from around the globe. Ito?eredagoodchanceto discussthe latestadvancesofresearchandalso all the developments and applications in the area of Arti?cial Neural Networks (ANNs). ANNs provide an information processing structure inspired by biolo- cal nervous systems and they consist of a large number of highly interconnected processing elements (neurons). Each neuron is a simple processor with a limited computing capacity typically restricted to a rule for combining input signals (utilizing an activation function) in order to calculate the output one. Output signalsmaybesenttootherunitsalongconnectionsknownasweightsthatexcite or inhibit the signal being communicated. ANNs have the ability “to learn” by example (a large volume of cases) through several iterations without requiring a priori ?xed knowledge of the relationships between process parameters.

Keywords

CUDA EEG Hidden Markov Model Markov Model algorithms artificial intelligence bayesian neural networks biomedical neural networks biometrics brain-computer interfaces classification computational neuroscience dimensionality reduction evolutionary algorithm filtering

Editors and affiliations

  • Konstantinos Diamantaras
    • 1
  • Wlodek Duch
    • 2
  • Lazaros S. Iliadis
    • 3
  1. 1.Department of InformaticsTEI of ThessalonikiSindosGreece
  2. 2.School of Physics, Astronomy, and Informatics, Department of InformaticsNicolaus Copernicus UniversityTorunPoland
  3. 3.Department of Forestry and Management of the Environment and Natural ResourcesDemocritus University of ThraceOrestiadaGreece

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-15819-3
  • Copyright Information Springer-Verlag Berlin Heidelberg 2010
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-15818-6
  • Online ISBN 978-3-642-15819-3
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book