A pruning structure of self-organizing HCMAC neural network classifier

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

*此作品的通信作者

研究成果: 書貢獻/報告類型會議論文篇章

2 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
主出版物標題2004 IEEE International Joint Conference on Neural Networks - Proceedings
頁面861-866
頁數6
DOIs
出版狀態已發佈 - 2004
事件2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, 匈牙利
持續時間: 2004 七月 252004 七月 29

出版系列

名字IEEE International Conference on Neural Networks - Conference Proceedings
2
ISSN(列印)1098-7576

會議

會議2004 IEEE International Joint Conference on Neural Networks - Proceedings
國家/地區匈牙利
城市Budapest
期間2004/07/252004/07/29

ASJC Scopus subject areas

  • 軟體

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