A review of learning vector quantization classifiers

TL;DR

This paper reviews the latest developments in Learning Vector Quantization classifiers, proposing a taxonomy, defining key concepts, and comparing eleven classifiers on real and artificial datasets.

Contribution

It introduces a comprehensive taxonomy of LVQ approaches, clarifies core concepts, and provides an empirical comparison of multiple classifiers.

Findings

Eleven LVQ classifiers compared on datasets

Taxonomy integrates recent LVQ approaches

Insights into classifier performance and distinctions

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.