Bregman Distance to L1 Regularized Logistic Regression

被引：0

作者：

Das Gupta, Mithun ^{[1
]}

Huang, Thomas S. ^{[1
]}

机构：

[1] Univ Illinois, Urbana, IL USA

来源：

19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6 | 2008年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this work we investigate the relationship between Bregman distances and regularized Logistic Regression model. We convert L1-regularized logistic regression (LR) into more general Bregman divergence framework and propose a primal-dual method based algorithm for learning the parameters of the model. The proposed method utilizes L1 regularization to incorporate parameter sparsity into the divergence minimization scheme. We perform tests on public domain data sets and produce results which are amongst the best reported.

引用

页码：2577 / 2580

页数：4

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