期刊
NEUROCOMPUTING
卷 51, 期 -, 页码 87-103出版社
ELSEVIER
DOI: 10.1016/S0925-2312(02)00599-4
关键词
on-line learning; self-organizing; clustering; classification
Many real world data processing tasks demand intelligent computational models with good efficiency and adaptability in their on-line operations. Consequently, neural algorithms with constructive network structure and incremental learning ability are of increasing interest. In this paper we present an algorithm of evolving self-organizing map (ESOM), which features an evolving network structure and fast on-line learning. Experiments have been carried out on some benchmark data sets for vector quantisation and classification tasks. Compared with other methods, ESOM achieved better or comparable performance with a much shorter learning process. Our results show that ESOM is a promising computational model for on-line pattern analysis in real world problems. (C) 2002 Elsevier Science B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据