First Experiments on Ensembles of Radial Basis Functions

  • Carlos Hernández-Espinosa
  • Mercedes Fernández-Redondo
  • Joaquín Torres-Sospedra
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3077)


Building an ensemble of classifiers is an useful way to improve the performance with respect to a single classifier. In the case of neural networks the bibliography has centered on the use of Multilayer Feedforward. 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 Multilayer Feedforward are also applicable to RBF. In this paper we present the results of using eleven methods to construct an ensemble of RBF networks. We have trained ensembles of a reduced number of networks (3 and 9) to keep the computational cost low. The results show that the best method is in general the Simple Ensemble.


Radial Basis Function Radial Basis Function Neural Network Ensemble Method Error Reduction Training Pattern 
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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