Artificial Neural Networks – ICANN 2009

1. Front Matter

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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

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.

• Controller Area Network
• Support Vector Machine
• algorithms
• autonomous vehicles
• bioinspired computing
• cognitive systems
• computational neuroscience
• computer vision
• evolutionary learning
• learning theory
• machine learning
• natural language processing
• neural gas
• neural networks
• neuro-fuzzy systems

• 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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