Learning vector quantization: Difference between revisions

Revision as of 19:25, 18 July 2022

Revision as of 23:52, 21 July 2022

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An advantage of LVQ is that it creates prototypes that are easy to interpret for experts in the respective application ___domain.<ref>{{citation|author=T. Kohonen|contribution=Learning vector quantization|editor=M.A. Arbib|title=The Handbook of Brain Theory and Neural Networks|pages=537–540|publisher=MIT Press|___location=Cambridge, MA|year=1995}}</ref>

LVQ systems can be applied to [[multi-class classification]] problems in a natural way.

A key issue in LVQ is the choice of an appropriate measure of distance or similarity for training and classification. 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)<ref>{{cite journal|authors=P. Schneider, B. Hammer, and M. Biehl|title=Adaptive Relevance Matrices in Learning Vector Quantization|journal= Neural Computation|volume=21|issue=10|pages=3532–3561|year=2009|doi=10.1162/neco.2009.10-08-892|pmid=19635012|citeseerx=10.1.1.216.1183|s2cid=17306078}}</ref> and references therein.