Neural maps and topographic vector quantization
Neural Networks
Self-Organizing Maps
Self-Organization in Biological Systems
Self-Organization in Biological Systems
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Determining an optimal seismic network configuration using self-organizing maps
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
Self-Organizing Map Formation with a Selectively Refractory Neighborhood
Neural Processing Letters
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The training scheme in self-organizing maps consists of two phases: i) competition, in which all units intend to become the best matching unit (BMU), and ii) cooperation, in which the BMU allows its neighbor units to adapt their weight vector. In order to study the relevance of cooperation, we present a model in which units do not necessarily cooperate with their neighbors, but follow some strategy. The strategy concept is inherited from game theory, and it establishes whether the BMU will allow or not their neighbors to learn the input stimulus. Different strategies are studied, including unconditional cooperation as in the original model, unconditional defection, and several history-based schemes. Each unit is allowed to change its strategy in accordance with some heuristics. We give evidence of the relevance of non-permanent cooperators units in order to achieve good maps, and we show that self-organization is possible when cooperation is not a constraint.