Abstract
In this paper we present a review and comparison of five different algorithms for training a RBF network. The algorithms are compared using nine databases. Our results show that the simplest algorithm, k-means clustering, may be the best alternative. The results of RBF are also compared with the results of Multilayer Feedforward with Backpropagation, the performance of a RBF network trained with k-means clustering is slightly better and the computational cost considerably lower. So we think that RBF may be a better alternative.
This work was supported by a Proyect of Generalitat Valenciana number GV01-14
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Ortiz-Gómez, M., Hernández-Espinosa, C., Fernández-Redondo, M. (2003). An Empirical Comparison of Training Algorithms for Radial Basis Functions. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_17
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DOI: https://doi.org/10.1007/3-540-44869-1_17
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