Learning vector quantization: Difference between revisions

Revision as of 06:10, 16 February 2019

Revision as of 08:51, 8 September 2019

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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|doi=10.1162/neco.2009.10-08-892|pmid=19635012|citeseerx=10.1.1.216.1183}}</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-status=yesdead|archive-date=2014-08-09|doi=10.3923/itj.2007.154.159}}</ref>

==Algorithm==

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== References ==

<references />

== Further reading ==

* [http://www.cis.hut.fi/panus/papers/dtwsom.pdf Self-Organizing Maps and Learning Vector Quantization for Feature Sequences, Somervuo and Kohonen. 2004] (pdf)