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A review of learning vector quantization classifiers

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Abstract

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.

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Acknowledgments

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

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Physical and Mathematical Sciences, University of Chile, Santiago, Chile

    David Nova

  2. Department of Electrical Engineering and Advanced Mining Technology Center, Faculty of Physical and Mathematical Sciences, University of Chile, Santiago, Chile

    Pablo A. Estévez

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  1. David Nova
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  2. Pablo A. Estévez
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Correspondence to Pablo A. Estévez.

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Nova, D., Estévez, P.A. A review of learning vector quantization classifiers. Neural Comput & Applic 25, 511–524 (2014). https://doi.org/10.1007/s00521-013-1535-3

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