A strategy for an efficient training of radial basis function networks for classification applications

Radial basis function (RBF) networks are used in many applications, e.g. for pattern classification or nonlinear regression. For a given application, parameters of an RBF network such as centers and radii of basis functions or weights must be adapted. Typically, either stochastic, iterative training algorithms (e.g. gradient-based methods such as backpropagation or second-order techniques such as scaled conjugate gradients) or clustering methods in combination with a linear optimization technique (e.g. c-means and singular value decomposition for a linear least-squares problem) are used for this task. The article shows that a combination of the two approaches leads to significant improvements concerning the training time as well as the approximation and generalization properties of the networks. In the particular marketing application investigated here (prediction of customer behavior), the overall training time could be reduced compared to backpropagation and the prediction accuracy could be increased compared to c-means plus singular value decomposition. The article also describes a new idea for the initialization of basis function centers. Basically, this approach is a modification of the standard c-means algorithm that leads to a linear least-squares problem for which solvability can be guaranteed. This idea raises the reliability of the training procedure without additional costs regarding the run time as well as the quality of results.