Efficiency of self-generating neural networks applied to pattern recognition

Authors:
Hirotaka Inoue;Hiroyuki Narihisa
Affiliations:
Graduate School of Engineering Okayama University of Science 1-1 Ridai-cho, Okayama, 700-0005, Japan;Department of Information and Computer Engineering Okayama University of Science 1-1 Ridai-cho, Okayama, 700-0005, Japan
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2003

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Pattern Recognition

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Abstract

Self-generating neural networks (SGNNs) have been in the spotlight of the fields of neural networks algorithm research for the sake of their efficiency. In practice, this neural network is implemented as a self-generating neural tree (SGNT) which is based on a hierarchical clustering algorithm. In this paper, we present the superior performance of the SGNT when it is applied to character recognition problems. Basically, the SGNT algorithm is generated as a kind of competitive learning algorithm. Therefore, it is natural to have a competent performance at the area of clustering or classification. However, our experimental results show that the SGNN method is very efficient to solve even pattern recognition problems, especially when they include a noisy signal problem.