Abstract
A novel approach based on the construction of a Voronoi diagram is proposed to determine the number of layers, the number of neurons in each layer, and their connection weights for a particular implementation of a neural network. The neural network has a multilayer feedforward topology, and is designed to classify patterns in the multidimensional feature space. To illustrate the procedure, an example is given of the classification of patterns that are not linearly separable in feature space. >
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