Efficient training of RBF networks for classification

Radial basis function networks with linear outputs are often used in regression problems because they can be substantially faster to train than multilayer perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. We show how RBFs with logistic and softmax outputs can be trained efficiently using algorithms derived from generalised linear models. This approach is compared with standard nonlinear optimisation algorithms on a number of datasets.