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Artificial Neural Networks - ICANN 2010

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

  • Conference proceedings
  • © 2010

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Editors:
  1. Konstantinos Diamantaras

    1. Department of Informatics, TEI of Thessaloniki, Sindos, Greece

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  2. Wlodek Duch

    1. Department of Informatics, Nicolaus Copernicus University, School of Physics, Astronomy, and Informatics, Torun, Poland

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  3. Lazaros S. Iliadis

    1. Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, Orestiada Thrace, Greece

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 6354)

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

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Conference proceedings info: ICANN 2010.

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About this book

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.

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Table of contents (78 papers)

  1. Front Matter

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  2. Classification – Pattern Recognition

    1. Deep Bottleneck Classifiers in Supervised Dimension Reduction

      • Elina Parviainen
      Pages 1-10

    2. Local Modeling Classifier for Microarray Gene-Expression Data

      • Iago Porto-Díaz, Verónica Bolón-Canedo, Amparo Alonso-Betanzos, Óscar Fontenla-Romero
      Pages 11-20

    3. Learning of Lateral Connections for Representational Invariant Recognition

      • Christian Keck, Jörg Lücke
      Pages 21-30

    4. Computational Properties of Probabilistic Neural Networks

      • Jiří Grim, Jan Hora
      Pages 31-40

    5. Local Minima of a Quadratic Binary Functional with a Quasi-Hebbian Connection Matrix

      • Yakov Karandashev, Boris Kryzhanovsky, Leonid Litinskii
      Pages 41-51

    6. A Learned Saliency Predictor for Dynamic Natural Scenes

      • Eleonora Vig, Michael Dorr, Thomas Martinetz, Erhardt Barth
      Pages 52-61

    7. Learning a Combination of Heterogeneous Dissimilarities from Incomplete Knowledge

      • Manuel Martín-Merino
      Pages 62-71

    8. A Bilinear Model for Consistent Topographic Representations

      • Urs Bergmann, Christoph von der Malsburg
      Pages 72-81

    9. Accelerating Large-Scale Convolutional Neural Networks with Parallel Graphics Multiprocessors

      • Dominik Scherer, Hannes Schulz, Sven Behnke
      Pages 82-91

    10. Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition

      • Dominik Scherer, Andreas Müller, Sven Behnke
      Pages 92-101

    11. Visual Shape Recognition Neural Network Using BESOM Model

      • Hiroaki Hasegawa, Masafumi Hagiwara
      Pages 102-105

    12. Comparing Feature Extraction Techniques and Classifiers in the Handwritten Letters Classification Problem

      • Antonio García-Manso, Carlos J. García-Orellana, Horacio M. González-Velasco, Miguel Macías-Macías, Ramón Gallardo-Caballero
      Pages 106-109

    13. The Parameter Optimization of the Pulse Coupled Neural Network for the Pattern Recognition

      • Masato Yonekawa, Hiroaki Kurokawa
      Pages 110-113

    14. The Use of Feed Forward Neural Network for Recognizing Characters of Dactyl Alphabet

      • Roman Záluský, Emil Raschman, Mário Krajmer, Daniela Ďuračková
      Pages 114-117

    15. Detecting DDoS Attack towards DNS Server Using a Neural Network Classifier

      • Jun Wu, Xin Wang, Xiaodong Lee, Baoping Yan
      Pages 118-123

    16. Classification Based on Multiple-Resolution Data View

      • Mateusz Kobos, Jacek Mańdziuk
      Pages 124-129

    17. Identification of the Head-and-Shoulders Technical Analysis Pattern with Neural Networks

      • Achilleas Zapranis, Prodromos Tsinaslanidis
      Pages 130-136

    18. Analyzing Classification Methods in Multi-label Tasks

      • Araken M. Santos, Laura E. A. Santana, Anne M. Canuto
      Pages 137-142

    19. Learning Bimanual Coordination Patterns for Rhythmic Movements

      • Rikke Amilde Løvlid, Pinar Öztürk
      Pages 143-148
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Other volumes

  1. Artificial Neural Networks – ICANN 2010

  2. Artificial Neural Networks – ICANN 2010

  3. Artificial Neural Networks – ICANN 2010

Editors and Affiliations

  • Department of Informatics, TEI of Thessaloniki, Sindos, Greece

    Konstantinos Diamantaras

  • Department of Informatics, Nicolaus Copernicus University, School of Physics, Astronomy, and Informatics, Torun, Poland

    Wlodek Duch

  • Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, Orestiada Thrace, Greece

    Lazaros S. Iliadis

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