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Gradient Descent and Radial Basis Functions

  • Mercedes Fernández-Redondo
  • Joaquín Torres-Sospedra
  • Carlos Hernández-Espinosa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)

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 consists of an 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.

Keywords

Radial Basis Function Gradient Descent Training Algorithm Radial Basis Function Neural Network Radial Basis Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mercedes Fernández-Redondo
    • 1
  • Joaquín Torres-Sospedra
    • 1
  • Carlos Hernández-Espinosa
    • 1
  1. 1.Departamento de Ingenieria y Ciencia de los ComputadoresUniversitat Jaume ICastellonSpain

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