v-软间隔罗杰斯特回归分类机

坐标下降(Coordinate Descent, CD)方法是求解大规模数据分类问题的有效方法，具有简单操作流程和快速收敛速率。为了提高罗杰斯特回归分类器(Logistic Regression Classifier, LRC)的泛化性能，受v-软间隔支持向量机的启发，该文提出一种v-软间隔罗杰斯特回归分类机(v-Soft Margin Logistic Regression Classifier, v-SMLRC)，证明了v-SMLRC对偶为一等式约束对偶坐标下降CDdual并由此提出了适合于大规模数据的v-SMLRC-CDdual。所提出的v-SMLRC-CDdual既能最大化类间间隔，又能有效提高LRC的泛化性能。大规模文本数据集实验表明，v-SMLRC-CDdual分类性能优于或等同于相关方法。

v-Soft Margin Logistic Regression Classifier

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.