Abstract:
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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. |