标题:Least absolute deviation-based robust support vector regression
作者:Chen, Chuanfa; Li, Yanyan; Yan, Changqing; Guo, Jinyun; Liu, Guolin
作者机构:[Chen, Chuanfa; Guo, Jinyun] Shandong Univ Sci & Technol, State Key Lab Min Disaster Prevent & Control Cofo, Qingdao 266590, Peoples R China.; [Chen 更多
通讯作者地址:[Chen, CF]Shandong Univ Sci & Technol, Coll Geomat, Qingdao 266590, Peoples R China.
来源:KNOWLEDGE-BASED SYSTEMS
出版年:2017
卷:131
页码:183-194
DOI:10.1016/j.knosys.2017.06.009
关键词:Support vector regression; Robust; Outlier; Least absolute deviation
摘要:To suppress the influence of outliers on function estimation, we propose a least absolute deviation (LAD) based robust support vector regression (SVR). Furthermore, an efficient algorithm based on the split-Bregman iteration is introduced to solve the optimization problem of the proposed algorithm. Both artificial and benchmark datasets are employed to compare the performance of the proposed algorithm with those of least squares SVR (LS-SVR), and two weighted versions of LS-SVR with the weight functions of Hampel and Logistic, respectively. Experiments demonstrate the superiority of the proposed algorithm. (C) 2017 Elsevier B.V. All rights reserved.
收录类别:EI;SCIE
WOS核心被引频次:1
资源类型:期刊论文
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