How Neural Networks for Pattern Recognition Can Be Synthesized

概要

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This paper discusses synthesis of neural networks for pattern recognition. First the saturation characteristics of the sigmoid function are shown to be essential for pattern recognition. Then neural networks for pattern recognition are proved to be synthesized by the following steps: (1) If the input data are separated by k hyperplanes, take k hidden neurons setting the weights between the input and hidden neurons as the coefficients of linear equations that define the hyperplanes. (2) If a class is separated into a single region by hyperplanes selected from the k hyperplanes, a three-layered neural network is synthesized to recognize that class; if not, a four-layered neural network is synthesized. Finally, a method to accelerate learning is proposed and is verified for a parity circuit, classification of two-dimensional patterns, and number recognition.
一般社団法人情報処理学会の論文
1991-12-31