Improving Model Accuracy using Optimal Linear Combinations of Trained Neural Networks

Improving Model Accuracy using Optimal Linear Combinations of Trained Neural Networks (1992)

Cached

Download Links

[www.emsl.pnl.gov:2080]

by Sherif Hashem , Bruce Schmeiser

Venue:	IEEE Transactions on Neural Networks
Citations:	30 - 3 self

Versions

Version History

Metadata Version 3

User correction supplied by SystemCorrections

Datum	Value	Source
TITLE	Improving Model Accuracy using Optimal Linear Combinations of Trained Neural Networks	user correction - Legacy Corrections
AUTHOR NAME	Sherif Hashem	SVM HeaderParse 0.1
AUTHOR AFFIL	y	SVM HeaderParse 0.2
AUTHOR NAME	Bruce Schmeiser	SVM HeaderParse 0.1
AUTHOR AFFIL	z	SVM HeaderParse 0.2
ABSTRACT	Neural network (NN) based modeling often requires trying multiple networks with different architectures and training parameters in order to achieve an acceptable model accuracy. Typically, only one of the trained networks is selected as "best" and the rest are discarded. We propose using optimal linear combinations (OLCs) of the corresponding outputs of a set of NNs as an alternative to using a single network. Modeling accuracy is measured by mean squared error (MSE) with respect to the distribution of random inputs. Optimality is defined by minimizing the MSE, with the resultant combination referred to as MSE-OLC. We formulate the MSE-OLC problem for trained NNs and derive two closed-form expressions for the optimal combination-weights. An example that illustrates significant improvement in model accuracy as a result of using MSE-OLCs of the trained networks is included. I. INTRODUCTION Constructing neural network (NN) based models often involves training a number of networks. The cr...	user correction - Legacy Corrections
YEAR	1992	user correction - Legacy Corrections
VENUE	IEEE Transactions on Neural Networks	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	792--794	INFERENCE
VOLUME	6	INFERENCE
CITATIONS	9 found	ParsCit 1.0