Abstract

An extended self-organising learning scheme is proposed, namely the Bayesian self-organising map (BSOM), in which both the distance measure and neighbourhood function have been replaced by the neuron's `on-line' estimated posterior probabilities. Such posteriors, in a Bayesian inference sense, will then contribute to gradually sharpening the estimation for input distributions and model parameters for which generally there is little prior knowledge. The BSOM has been successfully used to team the underlying mixture distribution of input data, and hence form an optimal pattern classifier