<em>v</em>-软间隔罗杰斯特回归分类机

doi:10.11999/JEIT150769

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Abstract

Coordinate Descent (CD) is a promising method for large scale pattern classification issues with straightforward operation and fast convergence speed. In this paper, inspired by v-soft margin Support Vector Machine (v-SVM) for pattern classification, a new v-Soft Margin Logistic Regression Classifier (v-SMLRC) is proposed for pattern classification to enhance the generalization performance of Logistic Regression Classifier (LRC). The dual of v-SMLRC can be regarded as CDdual problem with equality constraint and then a new large scale pattern classification method called v-SMLRC-CDdual is proposed. The proposed v-SMLRC-CDdual can maximize the inner-class margin and effectively enhance the generalization performance of LRC. Empirical results conducted with large scale document datasets demonstrate that the proposed method is effective and comparable to other related methods.

Key words： Logistic regression Generalization Coordinate Descent (CD) Dual Coordinate Descent (CDdual)

Received: 29 June 2015 Published: 22 January 2016

PACS:

TP391.4

Fund:

The National Natural Science Foundation of China (61272210, 61202311), The Natural Science Foundation of Jiangsu Province (BK2012552), The Science and Technology Foundation of Guizhou Province ([2013]2136)

Corresponding Authors: HUANG Chengquan E-mail: hcq863@163.com

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	HUANG Chengquan
	WANG Shitong
	JIANG Yizhang
	DONG Aimei

Cite this article:

HUANG Chengquan,WANG Shitong,JIANG Yizhang等. v-Soft Margin Logistic Regression Classifier[J]. JEIT, 2016, 38(4): 985-992.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT150769 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I4/985

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