Pruning a classifier based on a self-organizing map using Boolean function formalization

An algorithm is presented to minimize the number of neurons needed for a classifier based on Kohonens self-organizing maps (SOM), or on any other "code-book type" (or "prototype based") classifier such as Kohonens linear vector quantization (LVQ), K-means or nearest neighbor. The neuron minimization problem is formalized as a problem of simplification of Boolean functions, and a geometric interpretation of this simplification is provided. A step by step example with an illustrative classification problem is given.