@article{bakker02,
	title = {Model Clustering for Neural Network Ensembles},
	author = {Bert Bakker and Tom Heskes},
	journal = {Springer Lecture Notes in Computer Science},
	pages = {p. 383 f},
	volume = {LNCS 2415},
	year = {2002},
	biburl = {http://www.bibsonomy.org/bibtex/2fc788bfc8f277d9c2f67e5a7fb8a5387/kdubiq},
	description = {KDubiq Blueprint},
	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.},
	groupsearch = {0},
	keywords = {Blueprint KDubiq kdubiq }
}