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.
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Thimm, G., Fiesler, E. Two Neural Network Construction Methods. Neural Processing Letters 6, 25–31 (1997). https://doi.org/10.1023/A:1009632505828
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DOI: https://doi.org/10.1023/A:1009632505828