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A neural network algorithm based on a soft-max adaptation rule is presented. This algorithm exhibits good performance in reaching the optimum minimization of a cost function for vector quantization data compression. The soft-max rule employed is an extension of the standard K-means clustering procedure and takes into account a neighborhood ranking of the(More)
Figure 3: Comparison between original Growing Neural-Gas (a) and Recruiting Growing Neural-Gas (b) algorithms. We have presented a new method for function approximation with Neural-Gas networks. This method combines closely both supervised and unsupervised learning to gather the neurons according to the error density made in the output space approximating a(More)
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