Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Self-organizing maps with recursive neighborhood adaptation
Neural Networks - New developments in self-organizing maps
Residual activity in the neurons allows SOMs to learn temporal order
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Decreasing Neighborhood Revisited in Self-Organizing Maps
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Self-Organizing Map Formation with a Selectively Refractory Neighborhood
Neural Processing Letters
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In the traditional self-organizing map (SOM) the best matching unit (BMU) affects other neurons, through the learning rule, as a function of distance. Here, we propose a new parameter in the learning rule so neurons are not only affected by BMU as a function of distance, but as a function of the frequency of activation from both, the BMU and input vectors, to the affected neurons. This frequency parameter allows non radial neighborhoods and the quality of the formed maps is improved with respect to those formed by traditional SOM, as we show by comparing several error measures and five data sets.