Abstract
In this paper, we propose a robust scheme for least squares support vector regression (LS-SVR), termed as RLS-SVR, which employs non-convex least squares loss function to overcome the limitation of LS-SVR that it is sensitive to outliers. Non-convex loss gives a constant penalty for any large outliers. The proposed loss function can be expressed by a difference of convex functions (DC). The resultant optimization is a DC program. It can be solved by utilizing the Concave–Convex Procedure (CCCP). RLS-SVR iteratively builds the regression function by solving a set of linear equations at one time. The proposed RLS-SVR includes the classical LS-SVR as its special case. Numerical experiments on both artificial datasets and benchmark datasets confirm the promising results of the proposed algorithm.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
