A review of learning vector quantization classifiers

A review of learning vector quantization classifiers by David Nova, Pablo A. Estevez.


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.

From the introduction:

Learning Vector Quantization (LVQ) is a family of algorithms for statistical pattern classification, which aims at learning prototypes (codebook vectors) representing class regions. The class regions are defined by hyperplanes between prototypes, yielding Voronoi partitions. In the late 80’s Teuvo Kohonen introduced the algorithm LVQ1 [36, 38], and over the years produced several variants. Since their inception LVQ algorithms have been researched by a small but active community. A search on the ISI Web of Science in November, 2013, found 665 journal articles with the keywords “Learning Vector Quantization” or “LVQ” in their titles or abstracts. This paper is a review of the progress made in the field during the last 25 years.

Heavy sledding but if you want to review the development of a classification algorithm with a manageable history, this is a likely place to start.


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