文献类型：期刊文献

英文题名：Least Absolute Deviation Support Vector Regression

作者：Wang, Kuaini[1];Zhang, Jingjing[1];Chen, Yanyan[1,2];Zhong, Ping[1]

第一作者：Wang, Kuaini

通讯作者：Zhong, P[1]

机构：[1]China Agr Univ, Coll Sci, Beijing 100083, Peoples R China;[2]Beijing Union Univ, Coll Appl Sci & Technol, Beijing 102200, Peoples R China

第一机构：China Agr Univ, Coll Sci, Beijing 100083, Peoples R China

通讯机构：[1]corresponding author), China Agr Univ, Coll Sci, Beijing 100083, Peoples R China.

年份：2014

卷号：2014

外文期刊名：MATHEMATICAL PROBLEMS IN ENGINEERING

收录：;EI(收录号：20143117998184);Scopus(收录号：2-s2.0-84904681145);WOS:【SCI-EXPANDED(收录号:WOS:000339192400001)】；

基金：The work is supported by the National Natural Science Foundation of China under Grant no. 11171346 and Chinese Universities Scientific Fund no. 2013YJ010.

语种：英文

外文关键词：Numerical methods - Statistics - Support vector machines

摘要：Least squares support vector machine (LS-SVM) is a powerful tool for pattern classification and regression estimation. However, LS-SVM is sensitive to large noises and outliers since it employs the squared loss function. To solve the problem, in this paper, we propose an absolute deviation loss function to reduce the effects of outliers and derive a robust regression model termed as least absolute deviation support vector regression (LAD-SVR). The proposed loss function is not differentiable. We approximate it by constructing a smooth function and develop a Newton algorithm to solve the robust model. Numerical experiments on both artificial datasets and benchmark datasets demonstrate the robustness and effectiveness of the proposed method.