Self-organizing maps of symbol strings

被引：139

作者：

Kohonen, T ^{[1
]}

Somervuo, P ^{[1
]}

机构：

[1] Helsinki Univ Technol, Neural Networks Res Ctr, FIN-02015 Helsinki, Finland

来源：

NEUROCOMPUTING | 1998年 / 21卷 / 1-3期

关键词：

self-organizing map; learning vector quantization; string clustering;

D O I：

10.1016/S0925-2312(98)00031-9

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Unsupervised self-organizing maps (SOMs), as well as supervised learning by Learning Vector Quantization (LVQ) can be defined for string variables, too. Their computing becomes possible when the SOM and the LVQ algorithms are expressed as batch versions, and when the average over a list of symbol strings is defined to be the string that has the smallest sum of generalized distance functions from all the other strings. (C) 1998 Elsevier Science B.V. All rights reserved.

引用

页码：19 / 30

页数：12

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