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research article
Two neural network construction methods
Thimm, Georg
•
Fiesler, Emile
Two low complexity methods for neural network construction, that are applicable to various neural network models, are introduced and evaluated for high order perceptrons. The methods are based on a Boolean approximation of real-valued data. This approximation is used to construct an initial neural network topology which is subsequently trained on the original (real-valued) data. The methods are evaluated for their effectiveness in reducing the network size and increasing the network's generalization capabilities in comparison to fully connected high order perceptrons.
Type
research article
Authors
Thimm, Georg
•
Fiesler, Emile
Publication date
1997
Published in
Volume
6
Issue
1-2
Start page
25
End page
31
Peer reviewed
REVIEWED
EPFL units
Available on Infoscience
March 10, 2006
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