Learning vector quantization: Difference between revisions

Revision as of 15:12, 18 July 2018

Revision as of 06:10, 16 February 2019

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for an extensive bibliography.

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|authors=P. Schneider, B. Hammer, and M. Biehl|title=Adaptive Relevance Matrices in Learning Vector Quantization|journal= Neural Computation|volume=21|issue=10|pages=3532–3561|year=2009|urldoi=http://www.mitpressjournals.org/doi/abs/10.1162/neco.2009.10-08-892|doipmid=19635012|citeseerx=10.1162/neco1.20091.10-08-892216.1183}}</ref> and references therein.

LVQ can be a source of great help in classifying text documents.<ref>{{cite journal|authors=Fahad and Sikander|title=Classification of textual documents using learning vector quantization|journal=Information Technology Journal|volume=6|issue=1|year=2007|pages=154–159|url=http://198.170.104.138/itj/2007/154-159.pdf|archive-url=https://web.archive.org/web/20140809110450/http://198.170.104.138/itj/2007/154-159.pdf|dead-url=yes|archive-date=2014-08-09|doi=10.3923/itj.2007.154.159}}</ref>