Flexible kernels for RBF networks

In this paper we propose a novel approach for modeling kernels in Radial Basis Function networks. The method provides an extra degree of flexibility to the kernel structure. This flexibility comes through the use of modifier functions applied to the distance computation procedure, essential for all kernel evaluations. Initially the classifier uses an unsupervised method to construct the network topology, where most parameters of the network are defined without any customization from the user. During the second phase only one parameter per kernel is estimated. Experimental evidence on four datasets shows that the algorithm is robust and competitive.