A Survey of Some Classic Self-organizing Maps with Incremental Learning

摘要：

Kohonens Self-Organizing Maps (SOM) is a class of typical artificial neural networks (ANN) with unsupervised learning which has been widely used in clustering tasks, dimensionality reduction, data mining, information extraction, density approximation, data compression, etc. A basic principle of unsupervised learning is the competition mechanism, in which the output neurons compete for activation. In most competitive learning algorithms only one output neuron is activated at any given time. This is realized by means of the so-called winner- takes-all mode. Another mode is winner-takes-more. In this paper, the competitive learning is firstly introduced, the SOM topology and leaning mechanism are then illustrated. Thirdly, some selforganizing maps with incremental learning (SOMIL), such as self organizing surfaces, evolve self-organizing maps, incremental grid growing and growing hierarchical self-organizing map, are outlined Finally, the new development of SOMIL is reviewed. Some conclusions are given at the end of the paper.

关键词： artificial neural networks competitive learning self-organizing maps incremental learning

作者: Xinjian Qiang Guojian Cheng Zhen Li

作者单位: School of Computer Science Xian Shiyou University Xian, Shaanxi, P.R.China

会议类型: 国际会议

会议名称: 2010 2nd International Conference on Signal Processing System(2010年信号处理系统国际会议 ICSPS 2010)

会议地点: 大连

会议语种:英文

页码: 804-809

在线出版日期: 2010-07-05（万方平台首次上网日期，不代表论文的发表时间）