In this paper we apply normalized radial basis function networks to function learning and in nonparametric classification. A simple parameter learning technique is proposed and convergence and the rates of convergence of the empirically trained networks are studied theoretically and in computer experiments.

This paper presents a novel technique which may be used to determine an appropriate threshold for interpreting the outputs of a trained Radial Basis Function (RBF) classifier. Results from two experiments demonstrate that this method can be used to improve the performance of RBF classifiers in practical applications.

We review characterizations of (conditional) positive definiteness and show how they apply to the theory of radial basis functions. We then give complete proofs for the (conditional) positive definiteness of all practically relevant basis functions. Furthermore, we show how some of these characterizations may lead to construction tools for positive definite functions. Finally, we give new const...

We present techniques for modeling Variational Hermite Radial Basis Function (VHRBF) Implicits using a set of sketch-based interface and modeling (SBIM) operators. VHRBF Implicits is a simple and compact representation well suited for SBIM. It provides quality reconstructions, preserving the intended shape from a coarse and nonuniform number of point-normal samples extracted directly from the i...

Article history: Received 13 April 2012 Received in revised form 10 October 2012 Accepted 27 October 2012 Available online 8 December 2012