Self -Organizing Feature Map is a layered neural network consisting of an input layer and a competitive layer
For the data visualization and vector quantization・The accuracy of SOM vector quantization depends on the number of competitive layer’s neurons. Therefore, when an unknown data set is given, it is difficult to decide the sufficient Competitive layer size. In order to find the sufficient number of neurons, We propose a Tree－Structured SOM(TS－SOM)Variant that adds and deletes neurons using the means error and frequency in use among neighboring neurons. In this paper, We proposed a visualization method for our growing type TS－SOM.

開催日 : 2011年3月18日-19日　会場 : 首都大学東京