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Ensembles of RBFs Trained by Gradient Descent

  • Carlos Hernández-Espinosa
  • Mercedes Fernández-Redondo
  • Joaquín Torres-Sospedra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3173)

Abstract

Building an ensemble of classifiers is an useful way to improve the performance. In the case of neural networks the bibliography has centered on the use of Multilayer Feedforward (MF). However, there are other interesting networks like Radial Basis Functions (RBF) that can be used as elements of the ensemble. Furthermore, as pointed out recently the network RBF can also be trained by gradient descent, so all the methods of constructing the ensemble designed for MF are also applicable to RBF. In this paper we present the results of using eleven methods to construct a ensemble of RBF networks. The results show that the best method is in general the Simple Ensemble.

Keywords

Radial Basis Function Gradient Descent Radial Basis Function Neural Network Ensemble Method Error Reduction 
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 2004

Authors and Affiliations

  • Carlos Hernández-Espinosa
    • 1
  • Mercedes Fernández-Redondo
    • 1
  • Joaquín Torres-Sospedra
    • 1
  1. 1.Dept. de Ingeniería y Ciencia de los ComputadoresUniversidad Jaume ICastellonSpain

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