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Least Absolute Deviation Support Vector Regression
Joint Authors
Wang, Kuaini
Zhong, Ping
Zhang, Jingjing
Chen, Yanyan
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-07
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wang, Kuaini& Zhang, Jingjing& Chen, Yanyan& Zhong, Ping. 2014. Least Absolute Deviation Support Vector Regression. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-451424
Modern Language Association (MLA)
Wang, Kuaini…[et al.]. Least Absolute Deviation Support Vector Regression. Mathematical Problems in Engineering No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-451424
American Medical Association (AMA)
Wang, Kuaini& Zhang, Jingjing& Chen, Yanyan& Zhong, Ping. Least Absolute Deviation Support Vector Regression. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-451424
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-451424