This paper exhaustively discusses and compares the performance differences between radial basis probabilistic neural networks (RBPNN) and radial basis function neural networks (RBF...
In this paper a neural network for approximating function is described. The activation functions of the hidden nodes are the Radial Basis Functions (RBF) whose parameters are learn...
In this paper we apply the method of complexity regularization to derive estimation bounds for nonlinear function estimation using a single hidden layer radial basis function netwo...
There has been recently a lot of interest for functional data analysis [1] and extensions of well-known methods to functional inputs (clustering algorithm [2], non-parametric model...
Nicolas Delannay, Fabrice Rossi, Brieuc Conan-Guez...
In the present paper, Wavelet Networks, are proven to be, as well as many other neural paradigms, a speci c case of the generic paradigm named Weighted Radial Basis Functions Netw...
Mirko Sgarbi, Valentina Colla, Leonardo Maria Reyn...