Developments and applications of the self-organizing map and related algorithms

被引:47
|
作者
Kangas, J
Kohonen, T
机构
[1] Helsinki University of Technology, Neural Networks Research Centre, FIN-02150, Espoo
关键词
D O I
10.1016/0378-4754(96)88223-1
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this paper the basic principles and developments of an unsupervised learning algorithm, the self-organizing map (SOM) and a supervised learning algorithm, the learning vector quantization (LVQ) are explained. Some practical applications of the algorithms in data analysis, data visualization and pattern recognition tasks are mentioned. At the end of the paper new results are reported about increased error tolerance in the transmission of vector quantized images, provided by the topological ordering of codewords by the SOM algorithm.
引用
收藏
页码:3 / 12
页数:10
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