Log-penalized linear regression

被引：0
作者：
Sweetkind-Singer, JA [1 ]
机构：
[1] Stanford Univ, Elect Engn Dept, Stanford, CA 94305 USA
来源：
2003 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY - PROCEEDINGS | 2003年
关键词：
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：
Regularization penalties are commonly used in linear regression to reduce overfitting [1]. We introduce a log regularization penalty, motivated by a minimum-description-length (MDL) perspective [2] and from ideas in algorithmic complexity [3], and compare it to the more commonly used penalties known as ridge regression and the lasso [1].
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收藏
页码：286 / 286
页数：1
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