Algorithmes d'entraînement local de RBF
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The aim of this work is to study the effect of locality in classification tasks with radial basis function neural networks (RBFNN). The networks are trained in a three stage process. Firstly, the data are decomposed in their natural clusters, using clustering algorithms of different complexity. Secondly, a local RBFNN is fit to each cluster. These RBFNNs are local in the sense that they are modeling only a part of the problem, as given by the previous stage. Any RBFNN training algorithm can be used here. Thirdly, the local networks are fused together. We propose several simple techniques to do so. The results are analyzed in light of the following aspects: overall feasibility of the idea, influence of clustering algorithm complexity, influence of specific training algorithms, and selection of the fusing method.
CitationQuartier, B., Belanche, Ll. "Algorithmes d'entraînement local de RBF". 2001.
Is part ofLSI-01-42-R