Learning method of neural network

Abstract

The present invention is a learning method of a neural network for identifying N category using a data set consisted of N categories, in which one learning sample is extracted from a learning sample set in step SP1, and the distances between the sample and all the learning samples are obtained in step SP2. The closest n samples are obtained for each category in step SP3, and similarity for each category is obtained using the distances from the samples and a similarity conversion function f(d)=exp (-.alpha..multidot.d.sup.2). In step SP4, the similarity for each category is used as a target signal for the extracted learning sample, and it returns to an initial state until target signals for all the learning samples are determined. When target signals are determined for all the learning samples, in step SP5, the neural network is subjected to learning by the back-propagation using the learning samples and the obtained target signals.