Training RBFs networks: a comparison among supervised and not supervised algorithms

Authors:
Mercedes Fernández-Redondo;Joaquín Torres-Sospedra;Carlos Hernández-Espinosa
Affiliations:
Departamento de Ingenieria y Ciencia de los Computadores, Universitat Jaume I, Castellon, Spain;Departamento de Ingenieria y Ciencia de los Computadores, Universitat Jaume I, Castellon, Spain;Departamento de Ingenieria y Ciencia de los Computadores, Universitat Jaume I, Castellon, Spain
Venue:
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Year:
2006

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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 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.