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Direct Estimation of Polynomial Densities in Generalized RBF Networks Using Moments

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Artificial Neural Networks — ICANN 2001 (ICANN 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2130))

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Abstract

We present a direct estimation method of the output layer weights in a polynomial extension of the generalized radial-basis-function networks when used in pattern classification problems. The estimation is based on the L2-distance minimization of the density and the population moments. Each synaptic weight in the output layer is derived as a nonlinear function of the training data moments. The experimental results, using one- and two-dimensional simulated data and different polynomial orders, show that the classification rate of the polynomial densities is very close to the optimum rate.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Technology, USA

    Evangelos Dermatas

Authors
  1. Evangelos Dermatas
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Editors and Affiliations

  1. Dept. of Mecidal Cybernetics and Artificial Intelligence, University of Vienna, Freyung 6/2, 1010, Vienna, Austria

    Georg Dorffner

  2. Institute for Computer Aided Automation Pattern Recognition and Image Processing Group, Technical University of Vienna, Favoritenstr. 9/1832, 1040, Vienna, Austria

    Horst Bischof

  3. Institut für Statistik, Wirtschaftsuniversität Wien, Augasse 2-6, 1090, Wien, Austria

    Kurt Hornik

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© 2001 Springer-Verlag Berlin Heidelberg

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Dermatas, E. (2001). Direct Estimation of Polynomial Densities in Generalized RBF Networks Using Moments. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_17

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  • DOI: https://doi.org/10.1007/3-540-44668-0_17

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42486-4

  • Online ISBN: 978-3-540-44668-2

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