Maximum Likelihood Topology Preserving Ensembles

Abstract

Statistical re-sampling techniques have been used extensively and successfully in the machine learning approaches for generations of classifier and predictor ensembles. It has been frequently shown that combining so called unstable predictors has a stabilizing effect on and improves the performance of the prediction system generated in this way. In this paper we use the re-sampling techniques in the context of a topology preserving map which can be used for scale invariant classification, taking into account the fact that it models the residual after feedback with a family of distributions and finds filters which make the residuals most likely under this model. This model is applied to artificial data sets and compared with a similar version based on the Self Organising Map (SOM).