Growing kernel-based self-organized maps trained with supervised bias

Most unsupervised learning algorithms ignore prior application knowledge. Also, Self Orgnanized Maps (SOM) based approaches usually develop topographic maps with disjoint and uniform activation regions that correspond to a hard clustering of the patterns at their nodes. We present a novel Self-Organizing map that adapts its parameters in kernel space, grows dynamically up to a size defined with statistical criteria and is capable of incorporating a priori information in the form of a supervised bias at the cluster formation.

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