Neural minimal distance methods

Minimal distance methods are simple and in some circumstances highly accurate. In this paper relations between neural and minimal distance methods are investigated. Neural realization facilitates new versions of minimal distance methods. Parametrization of distance functions, distance-based weighting of neighbors, active selection of reference vectors from the training set and relations to the case-based reasoning are discussed.