Size reduction of a radial basis function network

We present a method of reducing the size of a radial basis function (RBF) network. The reduction is issued from the parallelism between RBF-networks and Parzen Windows estimator. We validate the method on a handwritten digit classification task. The resulting network is compared with the k-nearest neighbour statistical method. The results appear better for the network at a much lower computational cost.