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Feature Extraction of Letter Using Pattern Classifier Neural Network  

Ryoo Young-Jae (목포대 제어시스템공학과)
Publication Information
The Transactions of the Korean Institute of Electrical Engineers D / v.52, no.2, 2003 , pp. 102-106 More about this Journal
Abstract
This paper describes a new pattern classifier neural network to extract the feature from a letter. The proposed pattern classifier is based on relative distance, which is measure between an input datum and the center of cluster group. So, the proposed classifier neural network is called relative neural network(RNN). According to definitions of the distance and the learning rule, the structure of RNN is designed and the pseudo code of the algorithm is described. In feature extraction of letter, RNN, in spite of deletion of learning rate, resulted in the identical performance with those of winner-take-all(WTA), and self-organizing-map(SOM) neural network. Thus, it is shown that RNN is suitable to extract the feature of a letter.
Keywords
pattern classifier; relative neural network; feature extraction;
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