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Erschienen in:
2005 | OriginalPaper | Buchkapitel
A Game-Theoretic Approach to Competitive Learning in Self-Organizing Maps
verfasst von : Joseph Herbert, JingTao Yao
Erschienen in: Advances in Natural Computation
Verlag: Springer Berlin Heidelberg
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Self-Organizing Maps (SOM) is a powerful tool for clustering and discovering patterns in data. Competitive learning in the SOM training process focuses 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.