Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院
Journal:Proc. SIAM Int. Conf. Data Min., SDM
Abstract:Linear classifier is an essential part of machine learning, and improving its robustness has attracted much effort. Logistic regression (LR) is one of the most widely used linear classifier for its simplicity and probabilistic output. To reduce the risk of overfitting, LR was enhanced by introducing a generalized logistic loss (GLL) with a L2-norm regularization, aiming to maximize the minimum margin. However, the strategy of maximizing minimal margin is less robust to noisy data. In this paper, we incorporate GLL with margin distribution to exploit the statistical information from the training data, and propose a margin distribution logistic machine (MDLM) for better generalization performance and robustness. Furthermore, we extend MDLM to a multi-class version and learn different classes simultaneously by utilizing more information shared across these classes. Extensive experimental results validate the effectiveness of MDLM on both binary classification and multi-class classification. Copyright © by SIAM.
Translation or Not:no
Date of Publication:2017-01-01
Co-author:Sheng Jun Huang,XT20075,Zhang Daoqiang
Correspondence Author:dy