%0 Journal Article %1 bakker02 %A Bakker, Bert %A Heskes, Tom %D 2002 %J Springer Lecture Notes in Computer Science %K Blueprint KDubiq kdubiq %P p. 383 f %T Model Clustering for Neural Network Ensembles %V LNCS 2415 %X Abstract.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 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) and on a multitask learning example.LNCS 2415, p. 383 ff.

@article{bakker02, abstract = {Abstract.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 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) and on a multitask learning example.LNCS 2415, p. 383 ff.}, added-at = {2008-04-30T12:59:47.000+0200}, author = {Bakker, Bert and Heskes, Tom}, biburl = {https://www.bibsonomy.org/bibtex/2fc788bfc8f277d9c2f67e5a7fb8a5387/kdubiq}, description = {KDubiq Blueprint}, groupsearch = {0}, interhash = {5ebdeedc08e5589fa49fdbbb657036d1}, intrahash = {fc788bfc8f277d9c2f67e5a7fb8a5387}, journal = {Springer Lecture Notes in Computer Science}, keywords = {Blueprint KDubiq kdubiq}, pages = {p. 383 f}, timestamp = {2008-05-07T11:22:31.000+0200}, title = {Model Clustering for Neural Network Ensembles}, volume = {LNCS 2415}, year = 2002 }