A learning scheme for a fuzzy k-NN rule

被引：75
|
作者：
Jozwik, Adam [1 ]
机构：
[1] Polish Acad Sci, Inst Biocybernet & Biomed Engn, PL-00818 Warsaw, Poland
来源：
PATTERN RECOGNITION LETTERS | 1983年 / 1卷 / 5-6期
关键词：
NN rules; learning procedure; fuzzy decisions; probability of misclassification;
D O I：
10.1016/0167-8655(83)90064-8
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The performance of a fuzzy k-NN rule depends on the number k and a fuzzy membership- array W[l, m(R)], where l and m(R) denote the number of classes and the number of elements in the reference set X-R respectively. The proposed learning procedure consists in iterative finding such k and W which minimize the error rate estimated by the 'leaving one out' method.
引用
收藏
页码：287 / 289
页数：3
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