Modernizing k-nearest neighbors

被引：2

作者：

Elizabeth Yancey, Robin ^{[1
]}

Xin, Bochao ^{[1
]}

Matloff, Norm ^{[1
]}

机构：

[1] Univ Calif Davis, Dept Comp Sci, Davis, CA 95616 USA

来源：

STAT | 2021年 / 10卷 / 01期

关键词：

k-nearest neighbor; local bandwidth; local linear; mahalanobis distance;

D O I：

10.1002/sta4.335

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

The k-nearest neighbors (k-NN) method is one of the oldest statistical/machine learning techniques. It is included in virtually every major package, such as caret, parsnip, mlr3 and scikit-learn. Yet those packages do not go beyond the basics. With today's high-speed computation capability, k-NN can be made much more powerful. Here, we present directions in which that can be done.

引用

页数：8

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