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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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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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