Electronic Resource
Springer
Neural processing letters
6 (1997), S. 25-31
ISSN:
1573-773X
Keywords:
Boolean logic
;
connectionism
;
high order neural network
;
high order perceptron
;
ontogenic neural network
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
Abstract 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 of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1009632505828
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