On the training of radial basis function classifiers

An approach for the implementation of the Radial Basis Function (RBF) technique is presented and applied to a network of the appropriate architecture. The paper explores a methodology for selecting kernel function parameters and the extent to which the number of RBF nodes can be reduced without significantly affecting the overall training error. These objectives are accomplished through algorithms that shall be described in detail. Emphasis is also placed on the problems faced by a technique that has been proved superior to the more traditional training algorithms, particularly in terms of processing speed and solvability of nonlinear patterns. Solutions are consequently proposed in view of making RBF a more efficient method for interpolation and classification purposes.

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