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.
Cite
@article{arxiv.1509.07093,
title = {A review of learning vector quantization classifiers},
author = {David Nova and Pablo A. Estevez},
journal= {arXiv preprint arXiv:1509.07093},
year = {2015}
}