A K Nearest Classifier design

This paper presents a multi-classifier system design controlled by the topology of the learning data. Our work also introduces a training algorithm for an incremental self-organizing map (SOM). This SOM is used to distribute classification tasks to a set of classifiers. Thus, the useful classifiers are activated when new data arrives. Comparative results are given for synthetic problems, for an image segmentation problem from the UCI repository and for a handwritten digit recognition problem.

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