DocumentCode :
327707
Title :
A formulation of learning vector quantization using a new misclassification measure
Author :
SATo, Atsushi ; Yamada, Keiji
Author_Institution :
C&C Media Res. Labs., NEC Corp., Kawasaki, Japan
Volume :
1
fYear :
1998
fDate :
16-20 Aug 1998
Firstpage :
322
Abstract :
This paper reports a formulation of learning vector quantization (LVQ) using a new misclassification measure based on minimum classification error (MCE). We show that the convergence property of reference vectors depends on the definition of the misclassification measure, and show that our definition guarantees the convergence, unlike LVQ1.1 or Juan and Katagiri´s formulation based on MCE (1992). Experimental results for handwritten digit recognition reveal that the proposed method is superior to LVQ algorithms in recognition capability
Keywords :
convergence; learning (artificial intelligence); pattern classification; vector quantisation; LVQ; MCE; convergence; handwritten digit recognition; learning vector quantization; minimum classification error; misclassification measure; reference vectors; Convergence; Electronic mail; Handwriting recognition; Laboratories; National electric code; Nearest neighbor searches; Vector quantization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on
Conference_Location :
Brisbane, Qld.
ISSN :
1051-4651
Print_ISBN :
0-8186-8512-3
Type :
conf
DOI :
10.1109/ICPR.1998.711145
Filename :
711145
Link To Document :
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