Machine Learning - Special issue on inductive transfer
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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We show that large ensembles of (neural network) models, obtained e.g. in bootstrapping or sampling from (Bayesian) probability distributions, can be effectively summarized by a relatively small number of representative models. We present a methodto findrepresen tative models through clustering based on the models' outputs on a data set. We apply the methodon models obtainedthrough bootstrapping (Boston housing) and on a multitask learning example.