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Proceedings Paper

Soft learning vector quantization
Author(s): James C. Bezdek; Nikhil R. Pal
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Paper Abstract

Learning vector quantization (LVQ) often requires extensive experimentation with the learning rate distribution and update neighborhood used during iteration towards good prototypes. A single winner prototype controls the updates. This paper discusses two soft relatives of LVQ: the soft competition scheme (SCS) of Yair et al. and fuzzy LVQ equals FLVQ. These algorithms both extend the update rates that are partially based on posterior probabilities. FLVQ is a batch algorithm whose learning rates are derived from fuzzy memberships. We show several relationships between SCS and FLVQ; and we show that SCS learning rates can be interpreted in terms of statistical decision theory. Finally, we show the relationship between FLVQ, fuzzy c-means, hard c-means, a batch version of LVQ, and SCS.

Paper Details

Date Published: 13 June 1995
PDF: 14 pages
Proc. SPIE 2493, Applications of Fuzzy Logic Technology II, (13 June 1995); doi: 10.1117/12.211799
Show Author Affiliations
James C. Bezdek, Univ. of West Florida (United States)
Nikhil R. Pal, Indian Statistical Institute (India)

Published in SPIE Proceedings Vol. 2493:
Applications of Fuzzy Logic Technology II
Bruno Bosacchi; James C. Bezdek, Editor(s)

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