NEAREST NEIGHBOR-BASED IMPORTANCE WEIGHTING

被引：0
|
作者：
Loog, Marco [1 ]
机构：
[1] Delft Univ Technol, Pattern Recognit Lab, NL-2600 AA Delft, Netherlands
来源：
2012 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP) | 2012年
关键词：
COVARIATE SHIFT;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Importance weighting is widely applicable in machine learning in general and in techniques dealing with data co-variate shift problems in particular. A novel, direct approach to determine such importance weighting is presented. It relies on a nearest neighbor classification scheme and is relatively straightforward to implement. Comparative experiments on various classification tasks demonstrate the effectiveness of our so-called nearest neighbor weighting (NNeW) scheme. Considering its performance, our procedure can act as a simple and effective baseline method for importance weighting.
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页数：6
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