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Training Radial Basis Functions by Gradient Descent

  • Mercedes Fernández-Redondo
  • Carlos Hernández-Espinosa
  • Mamen Ortiz-Gómez
  • Joaquín Torres-Sospedra
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3070)

Abstract

In this paper we present experiments comparing different training algorithms for Radial Basis Functions (RBF) neural networks. In particular we compare the classical training which consist of a unsupervised training of centers followed by a supervised training of the weights at the output, with the full supervised training by gradient descent proposed recently in same papers. We conclude that a fully supervised training performs generally better. We also compare Batch training with Online training and we conclude that Online training suppose a reduction in the number of iterations.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Mercedes Fernández-Redondo
    • 1
  • Carlos Hernández-Espinosa
    • 1
  • Mamen Ortiz-Gómez
    • 1
  • Joaquín Torres-Sospedra
    • 1
  1. 1.D. de Ingeniería y Ciencia de los ComputadoresUniversidad Jaume ICastellónSpain

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