Learning vector quantization: Difference between revisions

Revision as of 19:24, 18 July 2022

Revision as of 19:25, 18 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.

It is used in a variety of practical applications. See the [http://liinwww.ira.uka.de/bibliography/Neural/SOM.LVQ.html 'Bibliography on the Self-Organizing Map (SOM) and Learning Vector Quantization (LVQ)]'.

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.

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# While there are vectors left in <math> L </math> go to step 1, else terminate.

Note: <math>\vec{w_i}</math> and <math>\vec{x}</math> are [[vector space|vectors]] in feature space.<br>

A more formal description can be found here: http://jsalatas.ictpro.gr/implementation-of-competitive-learning-networks-for-weka/

== References ==