Weighted Parzen windows for pattern classification

被引：73

作者：

Babich, GA ^{[1
]}

Camps, OI ^{[1
]}

机构：

[1] PENN STATE UNIV, DEPT COMP SCI & ENGN, DEPT ELECT ENGN, UNIVERSITY PK, PA 16802 USA

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 1996年 / 18卷 / 05期

关键词：

nonparametric classifiers; Parzen-windows; kernel estimator; clustering; training samples; discriminant analysis; Bayes error; leave-one-out; holdout;

D O I：

10.1109/34.494647

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This correspondence introduces the weighted-Parzen-window classifier. The proposed technique uses a clustering procedure to find a set of reference Vectors and weights which are used to approximate the Parzen-window (kernel-estimator) classifier. The weighted-Parzen-window classifier requires less computation and storage than the full Parzen-window classifier. Experimental results showed that significant savings could be achieved with only minimal, if any, error rate degradation for synthetic and real data sets.

引用

页码：567 / 570

页数：4

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