Artificial Neural Networks - ICANN 2001

1. Front Matter

   Pages I-XXII

   PDF

2. Invited Papers

   1. The Complementary Brain (Abstract)

      • Stephen Grossberg

      Pages 3-4

   2. Neural Networks for Adaptive Processing of Structured Data

      • Alessandro Sperduti

      Pages 5-12

   3. Bad Design and Good Performance: Strategies of the Visual System for Enhanced Scene Analysis

      • Florentin Wörgötter

      Pages 13-15

3. Data Analysis and Pattern Recognition

   1. Fast Curvature Matrix-Vector Products

      • Nicol N. Schraudolph

      Pages 19-26

   2. Architecture Selection in NLDA Networks

      • José R. Dorronsoro, Ana M. González, Carlos Santa Cruz

      Pages 27-32

   3. Neural Learning Invariant to Network Size Changes

      • Vicente Ruiz de Angulo, Carme Torras

      Pages 33-40

   4. Boosting Mixture Models for Semi-supervised Learning

      • Yves Grandvalet, Florence d’Alché-Buc, Christophe Ambroise

      Pages 41-48

   5. Bagging Can Stabilize without Reducing Variance

      • Yves Grandvalet

      Pages 49-56

   6. Symbolic Prosody Modeling by Causal Retro-causal NNs with Variable Context Length

      • Achim F. Müller, Hans Georg Zimmermann

      Pages 57-64

   7. Discriminative Dimensionality Reduction Based on Generalized LVQ

      • Atsushi Sato

      Pages 65-72

   8. A Computational Intelligence Approach to Optimization with Unknown Objective Functions

      • Hirotaka Nakayama, Masao Arakawa, Rie Sasaki

      Pages 73-80

   9. Clustering Gene Expression Data by Mutual Information with Gene Function

      • Samuel Kaski, Janne Sinkkonen, Janne Nikkilä

      Pages 81-87

   10. Learning to Learn Using Gradient Descent

       • Sepp Hochreiter, A. Steven Younger, Peter R. Conwell

       Pages 87-94

   11. A Variational Approach to Robust Regression

       • Anita C. Faul, Michael E. Tipping

       Pages 95-102

   12. Minimum-Entropy Data Clustering Using Reversible Jump Markov Chain Monte Carlo

       • Stephen J. Roberts, Christopher Holmes, Dave Denison

       Pages 103-110

   13. Behavioral Market Segmentation of Binary Guest Survey Data with Bagged Clustering

       • Sara Dolničar, Friedrich Leisch

       Pages 111-118

   14. Direct Estimation of Polynomial Densities in Generalized RBF Networks Using Moments

       • Evangelos Dermatas

       Pages 119-126

   15. Generalisation Improvement of Radial Basis Function Networks Based on Qualitative Input Conditioning for Financial Credit Risk Prediction

       • Xavier Parra, Núria Agell, Xari Rovira

       Pages 127-134

   16. Approximation of Bayesian Discriminant Function by Neural Networks in Terms of Kullback-Leibler Information

       • Yoshifusa Ito, Cidambi Srinivasan

       Pages 135-140

This book is based on the papers presented at the International Conference on Arti?cial Neural Networks, ICANN 2001, from August 21–25, 2001 at the - enna University of Technology, Austria. The conference is organized by the A- trian Research Institute for Arti?cal Intelligence in cooperation with the Pattern Recognition and Image Processing Group and the Center for Computational - telligence at the Vienna University of Technology. The ICANN conferences were initiated in 1991 and have become the major European meeting in the ?eld of neural networks. From about 300 submitted papers, the program committee selected 171 for publication. Each paper has been reviewed by three program committee m- bers/reviewers. We would like to thank all the members of the program comm- tee and the reviewers for their great e?ort in the reviewing process and helping us to set up a scienti?c program of high quality. In addition, we have invited eight speakers; three of their papers are also included in the proceedings. We would like to thank the European Neural Network Society (ENNS) for their support. We acknowledge the ?nancial support of Austrian Airlines, A- trian Science Foundation (FWF) under the contract SFB 010, Austrian Society ¨ for Arti?cial Intelligence (OGAI), Bank Austria, and the Vienna Convention Bureau. We would like to express our sincere thanks to A. Flexer, W. Horn, K. Hraby, F. Leisch, C. Schittenkopf, and A. Weingessel. The conference and the proceedings would not have been possible without their enormous contri- tion.

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• Dept. of Mecidal Cybernetics and Artificial Intelligence, University of Vienna, Vienna, Austria

  Georg Dorffner

• Institute for Computer Aided Automation Pattern Recognition and Image Processing Group, Technical University of Vienna, Vienna, Austria

  Horst Bischof

• Institut für Statistik, Wirtschaftsuniversität Wien, Wien, Austria

  Kurt Hornik

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