VECTOR QUANTIZATION TECHNIQUE FOR NONPARAMETRIC CLASSIFIER DESIGN

被引：47
|
作者：
XIE, QB
LASZLO, CA
WARD, RK
机构：
[1] Department of Electrical Engineering, University of British Columbia, Vancouver
来源：
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 1993年 / 15卷 / 12期
关键词：
CONDENSING ALGORITHMS; DATA REDUCTION; KAPPA-NEAREST-NEIGHBOR (KAPPA-NN) CLASSIFIER; NONPARAMETRIC CLASSIFICATION; PARZEN KERNEL CLASSIFIER; VECTOR QUANTIZATION;
D O I：
10.1109/34.250849
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
An effective data reduction technique based on vector quantization is introduced for nonparametric classifier design. Two new nonparametric classifiers are developed, and their performance is evaluated using various examples. The new methods maintain a classification accuracy that is competitive with that of classical methods but, at the same time, yields very high data reduction rates.
引用
收藏
页码：1326 / 1330
页数：5
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