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Artificial Neural Networks – ICANN 2009

19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, Part I

  • Conference proceedings
  • © 2009

Overview

Editors:
  1. Cesare Alippi

    1. Dipartimento di Elettronica, Politecnico di Milano, Milano, Italy

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  2. Marios Polycarpou

    1. Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus

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  3. Christos Panayiotou

    1. Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus

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  4. Georgios Ellinas

    1. Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 5768)

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

Included in the following conference series:

Conference proceedings info: ICANN 2009.

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Table of contents (104 papers)

  1. Front Matter

    Download chapter PDF

  2. Learning Algorithms

    1. Mutual Information Based Initialization of Forward-Backward Search for Feature Selection in Regression Problems

      • Alberto Guillén, Antti Sorjamaa, Gines Rubio, Amaury Lendasse, Ignacio Rojas
      Pages 1-9

    2. Kernel Learning for Local Learning Based Clustering

      • Hong Zeng, Yiu-ming Cheung
      Pages 10-19

    3. Projective Nonnegative Matrix Factorization with α-Divergence

      • Zhirong Yang, Erkki Oja
      Pages 20-29

    4. Active Generation of Training Examples in Meta-Regression

      • Ricardo B. C. Prudêncio, Teresa B. Ludermir
      Pages 30-39

    5. A Maximum-Likelihood Connectionist Model for Unsupervised Learning over Graphical Domains

      • Edmondo Trentin, Leonardo Rigutini
      Pages 40-49

    6. Local Feature Selection for the Relevance Vector Machine Using Adaptive Kernel Learning

      • Dimitris Tzikas, Aristidis Likas, Nikolaos Galatsanos
      Pages 50-59

    7. MINLIP: Efficient Learning of Transformation Models

      • Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel
      Pages 60-69

    8. Efficient Uncertainty Propagation for Reinforcement Learning with Limited Data

      • Alexander Hans, Steffen Udluft
      Pages 70-79

    9. Optimal Training Sequences for Locally Recurrent Neural Networks

      • Krzysztof Patan, Maciej Patan
      Pages 80-89

    10. Statistical Instance-Based Ensemble Pruning for Multi-class Problems

      • Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato, Alberto Suárez
      Pages 90-99

    11. Robustness of Kernel Based Regression: A Comparison of Iterative Weighting Schemes

      • Kris De Brabanter, Kristiaan Pelckmans, Jos De Brabanter, Michiel Debruyne, Johan A. K. Suykens, Mia Hubert et al.
      Pages 100-110

    12. Mixing Different Search Biases in Evolutionary Learning Algorithms

      • Kristina Davoian, Wolfram-M. Lippe
      Pages 111-120

    13. Semi-supervised Learning for Regression with Co-training by Committee

      • Mohamed Farouk Abdel Hady, Friedhelm Schwenker, Günther Palm
      Pages 121-130

    14. An Analysis of Meta-learning Techniques for Ranking Clustering Algorithms Applied to Artificial Data

      • Rodrigo G. F. Soares, Teresa B. Ludermir, Francisco A. T. De Carvalho
      Pages 131-140

    15. Probability-Based Distance Function for Distance-Based Classifiers

      • Cezary Dendek, Jacek Mańdziuk
      Pages 141-150

    16. Constrained Learning Vector Quantization or Relaxed k-Separability

      • Marek Grochowski, Włodzisław Duch
      Pages 151-160

    17. Minimization of Quadratic Binary Functional with Additive Connection Matrix

      • Leonid Litinskii
      Pages 161-170

    18. Mutual Learning with Many Linear Perceptrons: On-Line Learning Theory

      • Kazuyuki Hara, Yoichi Nakayama, Seiji Miyoshi, Masato Okada
      Pages 171-180

  3. Computational Neuroscience

    1. Synchrony State Generation in Artificial Neural Networks with Stochastic Synapses

      • Karim El-Laithy, Martin Bogdan
      Pages 181-190
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Other volumes

  1. Artificial Neural Networks – ICANN 2009

  2. Artificial Neural Networks – ICANN 2009

Keywords

About this book

This volume is part of the two-volume proceedings of the 19th International Conf- ence on Artificial Neural Networks (ICANN 2009), which was held in Cyprus during September 14–17, 2009. The ICANN conference is an annual meeting sp- sored by the European Neural Network Society (ENNS), in cooperation with the - ternational Neural Network Society (INNS) and the Japanese Neural Network Society (JNNS). ICANN 2009 was technically sponsored by the IEEE Computational Intel- gence Society. This series of conferences has been held annually since 1991 in various European countries and covers the field of neurocomputing, learning systems and related areas. Artificial neural networks provide an information-processing structure inspired by biological nervous systems. They consist of a large number of highly interconnected processing elements, with the capability of learning by example. The field of artificial neural networks has evolved significantly in the last two decades, with active partici- tion from diverse fields, such as engineering, computer science, mathematics, artificial intelligence, system theory, biology, operations research, and neuroscience. Artificial neural networks have been widely applied for pattern recognition, control, optimization, image processing, classification, signal processing, etc.

Editors and Affiliations

  • Dipartimento di Elettronica, Politecnico di Milano, Milano, Italy

    Cesare Alippi

  • Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus

    Marios Polycarpou, Christos Panayiotou, Georgios Ellinas

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