Neural Computing and Applications

, Volume 25, Issue 3–4, pp 511–524 | Cite as

A review of learning vector quantization classifiers

  • David Nova
  • Pablo A. Estévez
Invited Review


In this work, we present a review of the state of the art of learning vector quantization (LVQ) classifiers. A taxonomy is proposed which integrates the most relevant LVQ approaches to date. The main concepts associated with modern LVQ approaches are defined. A comparison is made among eleven LVQ classifiers using one real-world and two artificial datasets.


Learning vector quantization Supervised learning Neural networks Margin maximization Likelihood ratio maximization 



This work was funded by CONICYT-CHILE under Grant FONDECYT 1110701.


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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Department of Electrical Engineering, Faculty of Physical and Mathematical SciencesUniversity of ChileSantiagoChile
  2. 2.Department of Electrical Engineering and Advanced Mining Technology Center, Faculty of Physical and Mathematical SciencesUniversity of ChileSantiagoChile

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