Learning vector quantization: Difference between revisions

Revision as of 16:43, 25 August 2022

Revision as of 17:20, 5 May 2023

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LVQ systems can be applied to [[multi-class classification]] problems in a natural way.

A key issue in LVQ is the choice of an appropriate measure of distance or similarity for training and classification. Recently, techniques have been developed which adapt a parameterized distance measure in the course of training the system, see e.g. (Schneider, Biehl, and Hammer, 2009)<ref>{{cite journal|authorsauthor1=P. Schneider, |author2=B. Hammer, and |author3=M. Biehl |title=Adaptive Relevance Matrices in Learning Vector Quantization|journal= Neural Computation|volume=21|issue=10|pages=3532–3561|year=2009|doi=10.1162/neco.2009.10-08-892|pmid=19635012|citeseerx=10.1.1.216.1183|s2cid=17306078}}</ref> and references therein.

LVQ can be a source of great help in classifying text documents.{{Citation needed|date=December 2019|reason=removed citation to predatory publisher content}}