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Home Multivariate Data Modeling Classification and Discrimination Self Organizing Maps  
See also: ANN  Introduction  
Kohonen Networks
The Kohonen network (or "selforganizing map" or SOM, for short) has been developed by Teuvo Kohonen. The basic idea behind the Kohonen network is to set up a structure of interconnected processing units ("neurons") which compete for the signal. While the structure of the map may be quite arbitrary, most implementations support only rectangular and linear maps. Each node of the map is defined by a vector w_{ij} which is
adjusted during the training. The basic training algorithm is quite simple:
2) find the node which is closest to the selected data (i.e. the distance between w_{ij} and the training data is a minimum) 3) adjust the weight vectors of the closest node and the nodes around it in such a way that the wij move towards the training data 4) repeat from step 1) for a fixed number of repetitions Kohonen maps may be arranged in any neighborhood relationship. A simple
but interesting application is the usage of Kohonen maps to solve the travelling salesman problem. Start the interactive example to see how it works. You can also go to the DataLab to perform some further experiments with Kohonen networks.


Home Multivariate Data Modeling Classification and Discrimination Self Organizing Maps 
Last Update: 20121008