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2005
Springer

A Game-Theoretic Approach to Competitive Learning in Self-Organizing Maps

9 years 1 months ago
A Game-Theoretic Approach to Competitive Learning in Self-Organizing Maps
Abstract. Self-Organizing Maps (SOM) is a powerful tool for clustering and discovering patterns in data. Competitive learning in the SOM training process focusses on finding a neuron that is most similar to that of an input vector. Since an update of a neuron only benefits part of the feature map, it can be thought of as a local optimization problem. The ability to move away from a local optimization model into a global optimization model requires the use of game theory techniques to analyze overall quality of the SOM. A new algorithm GTSOM is introduced to take into account cluster quality measurements and dynamically modify learning rates to ensure improved quality through successive iterations.
Joseph P. Herbert, Jingtao Yao
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ICNC
Authors Joseph P. Herbert, Jingtao Yao
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