Titlebook: Artificial Neural Networks - ICANN 2001; International Confer Georg Dorffner,Horst Bischof,Kurt Hornik Conference proceedings 2001 Springer

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Generalisation Improvement of Radial Basis Function Networks Based on Qualitative Input Conditioningthe problem is strongly recommended when using learning techniques. The paper aims at conditioning the input information in order to enhance the neural network generalisation by adding qualitative expert information on orders of magnitude. An example of this method applied to some industrial firms is given.

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Conference proceedings 2001enna 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 conf

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Brent Kawahara,Hector Estrada,Luke S. LeeFisher information matrices with arbitrary vectors, using techniques similar to but even cheaper than the fast Hessian-vector product [.]. The stability of SMD [.,.,.,.], a learning rate adaptation method that uses curvature matrix-vector products, improves when the extended Gauss-Newton matrix is substituted for the Hessian.

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Optical Waveguide Modulation Techniques,n large systems feasible by using recurrent neural networks with their attendant learning routines as meta-learning systems. Our system derived complex well performing learning algorithms from scratch. In this paper we also show that our approach performs non-stationary time series prediction.

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0302-9743 ngs. 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 c978-3-540-42486-4978-3-540-44668-2Series ISSN 0302-9743 Series E-ISSN 1611-3349

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https://doi.org/10.1057/978-1-349-93268-9he advantage that they are free from such assumptions. They can be, however, sensitive to noise and computationally intensive in high-dimensional spaces. In this paper we re-consider the issue of data partitioning from an information-theoretic viewpoint and show that minimisation of partition entrop