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].

引用

页码：286 / 286

页数：1

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