When Networks Disagree: Ensemble Methods for Hybrid Neural Networks

When Networks Disagree: Ensemble Methods for Hybrid Neural Networks (1993)

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by Michael P. Perrone , Leaon N. Cooper

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Datum	Value	Source
TITLE	When Networks Disagree: Ensemble Methods for Hybrid Neural Networks	user correction - Legacy Corrections
AUTHOR NAME	Michael P. Perrone	user correction - Legacy Corrections
AUTHOR AFFIL	Physics Department; Neuroscience Department; Institute for Brain and Neural Systems	user correction - Legacy Corrections
AUTHOR ADDR	Box 1843, Brown University; Providence, RI 02912	user correction - Legacy Corrections
AUTHOR NAME	Leaon N. Cooper	user correction - Legacy Corrections
AUTHOR AFFIL	Physics Department; Neuroscience Department; Institute for Brain and Neural Systems	user correction - Legacy Corrections
AUTHOR ADDR	Box 1843, Brown University; Providence, RI 02912	user correction - Legacy Corrections
ABSTRACT	This paper presents a general theoretical framework for ensemble methods of constructing significantly improved regression estimates. Given a population of regression estimators, we construct a hybrid estimator which is as good or better in the MSE sense than any estimator in the population. We argue that the ensemble method presented has several properties: 1) It efficiently uses all the networks of a population - none of the networks need be discarded. 2) It efficiently uses all the available data for training without over-fitting. 3) It inherently performs regularization by smoothing in functional space which helps to avoid over-fitting. 4) It utilizes local minima to construct improved estimates whereas other neural network algorithms are hindered by local minima. 5) It is ideally suited for parallel computation. 6) It leads to a very useful and natural measure of the number of distinct estimators in a population. 7) The optimal parameters of the ensemble estimator are given in clo...	user correction - Legacy Corrections
YEAR	1993	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	126--142	INFERENCE
CITATIONS	22 found	ParsCit 1.0