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On-line pattern analysis by evolving self-organizing maps
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AI
DOI：10.1016/S0925-2312(02)00599-4.png)
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En 中文
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.
Keyword:
on-line learning
self-organizing
clustering
classification
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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