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A self-organizing HCMAC neural-network classifier (2003)
| Venue: | IEEE Transactions on neural networks |
| Citations: | 9 - 1 self |
BibTeX
@ARTICLE{Lee03aself-organizing,
author = {Hahn-ming Lee and Chih-ming Chen and Yung-feng Lu},
title = {A self-organizing HCMAC neural-network classifier},
journal = {IEEE Transactions on neural networks},
year = {2003},
volume = {14},
pages = {15--27}
}
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Abstract
Abstract—This paper presents a self-organizing hierarchical cerebellar model arithmetic computer (HCMAC) neural-network classifier, which contains a self-organizing input space module and an HCMAC neural network. The conventional CMAC can be viewed as a basis function network (BFN) with supervised learning, and performs well in terms of its fast learning speed and local generalization capability for approximating nonlinear functions. However, the conventional CMAC has an enormous memory requirement for resolving high-dimensional classification problems, and its performance heavily depends on the approach of input space quantization. To solve these problems, this paper presents a novel supervised HCMAC neural network capable of resolving high-dimensional classification problems well. Also, in order to reduce what is often trial-and-error parameters searching for constructing memory allocation automatically, proposed herein is a self-organizing input space module that uses Shannon’s entropy measure and the golden-section search method to appropriately determine the input space quantization according to the various distributions of training data sets. Experimental results indicate that the self-organizing HCMAC indeed has a fast learning ability and low memory requirement. It is a better performing network than the conventional CMAC for resolving high-dimensional classification problems. Furthermore, the self-organizing HCMAC classifier has a better classification ability than other compared classifiers. Index Terms—Cerebellar model arithmetic computer (CMAC), golden-section search method, self-organizing hierarchical CMAC (HCMAC) classifier, Shannon’s entropy measure. I.
Keyphrases
high-dimensional classification problem conventional cmac self-organizing hcmac neural-network classifier self-organizing input space module hcmac neural network golden-section search method input space quantization low memory requirement neural-network classifier fast learning ability self-organizing hcmac classifier data set various distribution entropy measure self-organizing hcmac experimental result classification ability performing network basis function network self-organizing hierarchical cmac enormous memory requirement nonlinear function trial-and-error parameter shannon entropy measure local generalization capability fast learning speed memory allocation
