A pruning structure of self-organizing HCMAC neural network classifier

Chih Ming Chen; Chin Ming Hong; Yung Feng Lu

doi:10.1109/IJCNN.2004.1380042

A pruning structure of self-organizing HCMAC neural network classifier

Chih Ming Chen^*, Chin Ming Hong, Yung Feng Lu

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

A self-organizing HCMAC neural network was proposed to solve high dimensional pattern classification problems well in our previous work. However, a large amount of redundant GCMAC nodes might be constructed due to the expansion approach of full binary tree topology. Therefore, this study presents a pruning structure of self-organizing HCMAC neural network to solve this problem. Experimental results show the proposed pruning structure not only can largely reduce memory requirement, but also keep fast training speed and has higher pattern classification accuracy rate than the original self-organizing HCMAC neural network does in the most testing benchmark data sets.

Original language	English
Title of host publication	2004 IEEE International Joint Conference on Neural Networks - Proceedings
Pages	861-866
Number of pages	6
DOIs	https://doi.org/10.1109/IJCNN.2004.1380042
Publication status	Published - 2004
Event	2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary Duration: 2004 Jul 25 → 2004 Jul 29

Publication series

Name	IEEE International Conference on Neural Networks - Conference Proceedings
Volume	2
ISSN (Print)	1098-7576

Conference

Conference	2004 IEEE International Joint Conference on Neural Networks - Proceedings
Country/Territory	Hungary
City	Budapest
Period	2004/07/25 → 2004/07/29

ASJC Scopus subject areas

Software

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10.1109/IJCNN.2004.1380042

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A pruning structure of self-organizing HCMAC neural network classifier. / Chen, Chih Ming; Hong, Chin Ming; Lu, Yung Feng.
2004 IEEE International Joint Conference on Neural Networks - Proceedings. 2004. p. 861-866 (IEEE International Conference on Neural Networks - Conference Proceedings; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, CM, Hong, CM & Lu, YF 2004, A pruning structure of self-organizing HCMAC neural network classifier. in 2004 IEEE International Joint Conference on Neural Networks - Proceedings. IEEE International Conference on Neural Networks - Conference Proceedings, vol. 2, pp. 861-866, 2004 IEEE International Joint Conference on Neural Networks - Proceedings, Budapest, Hungary, 2004/07/25. https://doi.org/10.1109/IJCNN.2004.1380042

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abstract = "A self-organizing HCMAC neural network was proposed to solve high dimensional pattern classification problems well in our previous work. However, a large amount of redundant GCMAC nodes might be constructed due to the expansion approach of full binary tree topology. Therefore, this study presents a pruning structure of self-organizing HCMAC neural network to solve this problem. Experimental results show the proposed pruning structure not only can largely reduce memory requirement, but also keep fast training speed and has higher pattern classification accuracy rate than the original self-organizing HCMAC neural network does in the most testing benchmark data sets.",

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AB - A self-organizing HCMAC neural network was proposed to solve high dimensional pattern classification problems well in our previous work. However, a large amount of redundant GCMAC nodes might be constructed due to the expansion approach of full binary tree topology. Therefore, this study presents a pruning structure of self-organizing HCMAC neural network to solve this problem. Experimental results show the proposed pruning structure not only can largely reduce memory requirement, but also keep fast training speed and has higher pattern classification accuracy rate than the original self-organizing HCMAC neural network does in the most testing benchmark data sets.

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M3 - Conference contribution

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A pruning structure of self-organizing HCMAC neural network classifier

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