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.
This research was supported by project MAPACI TIC2002-02273 of CICYT in Spain.
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© 2004 Springer-Verlag Berlin Heidelberg
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Fernández-Redondo, M., Hernández-Espinosa, C., Ortiz-Gómez, M., Torres-Sospedra, J. (2004). Training Radial Basis Functions by Gradient Descent. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_23
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DOI: https://doi.org/10.1007/978-3-540-24844-6_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22123-4
Online ISBN: 978-3-540-24844-6
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