Model clustering for neural network ensembles

Abstract

We show that large ensembles of (neural network) models, obtainede.g. in bootstrapping or sampling from (Bayesian) probability distributions, can be effectively summarized by a relatively small number of representative models. We present a method to find representative models through clustering based on the models' outputs on a data set. We apply the method on models obtained through bootstrapping (Boston housing) andon a multitask learning example. © Springer-Verlag Berlin Heidelberg 2002.