An RBF network with tunable function shape

In this paper we consider a radial basis function (RBF) network with tunable shape and spreadout parameters of the activation function. We argue that fewer hidden nodes with different RBF shapes can better match the classification regions still preserving the context of the probabilistic semiparametric approximation of the conditional probability density functions (pdf). Instead of squared Euclidean norm (L₂ norm) in the power of the exponent, a weighted L_p norm is used with variable p. In order to demonstrate the advantage of the proposed scheme some experimental results on the 2-spirals data set are reported