Validation of k-Nearest Neighbor Classifiers

被引：11
|
作者：
Bax, Eric [1 ]
机构：
[1] Yahoo, Pasadena, CA 91116 USA
来源：
IEEE TRANSACTIONS ON INFORMATION THEORY | 2012年 / 58卷 / 05期
关键词：
Learning systems; machine learning; nearest neighbor; statistical learning; supervised learning; CLASSIFICATION; APPROXIMATE;
D O I：
10.1109/TIT.2011.2180887
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a method to compute probably approximately correct error bounds for k-nearest neighbor classifiers. The method withholds some training data as a validation set to bound the error rate of the holdout classifier that is based on the remaining training data. Then, the method uses the validation set to bound the difference in error rates between the holdout classifier and the classifier based on all training data. The result is a bound on the out-of-sample error rate for the classifier based on all training data.
引用
收藏
页码：3225 / 3234
页数：10
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