On the performance of single-layered neural networks.
This paper studies the performance of single-layered neural networks. This study begins with the performance of single-layered neural networks trained using the outer-product rule. The outer-product rule is a suboptimal learning scheme, resulting under certain assumptions from optimal least-squares training of single-layered neural networks with respect to their analog output. Extensive analysis reveals the improvement on the network performance caused by its optimal least-squares training. The effect of the training scheme on the performance of single-layered neural networks with binary output is exhibited by experimentally comparing the performance of single-layered neural networks trained with respect to their analog and binary output.
Karayiannis
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NB
Department of Electrical Engineering, University of Houston, TX 77204-4793.
Venetsanopoulos
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AN
D008433:Mathematics; D016571:Neural Networks, Computer