Learning vector quantization

In computer science, learning vector quantization (LVQ) is a prototype-based supervised classification algorithm.

LVQ can be understood as a special case of an artificial neural network, more precisely, it applies a winner-take-all Hebbian learning-based approach.

It is a precursor to self-organizing maps (SOM) and related to neural gas and the k-nearest neighbor algorithm (k-NN).

In winner-take-all training algorithms one determines, for each data point, the prototype which is closest to the input according to a given distance measure.

Recently, techniques have been developed which adapt a parameterized distance measure in the course of training the system, see e.g. (Schneider, Biehl, and Hammer, 2009)[3] and references therein.