@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},
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 }
}