Robustε-Support Vector Regression | Zendy

Yuan Lv | Zendy; Zhong Gan | Zendy

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Robustε-Support Vector Regression

Author(s) -

Yuan Lv,

Zhong Gan

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/373571

Subject(s) - robust regression , support vector machine , regression , regression analysis , domain (mathematical analysis) , nonlinear regression , proper linear model , computer science , bayesian multivariate linear regression , linear regression , mathematics , dual (grammatical number) , mathematical optimization , algorithm , data mining , machine learning , statistics , mathematical analysis , art , literature

Spheroid disturbance of input data brings great challenges to support vector regression; thus it is essential to study the robust regression model. This paper is dedicated to establish a robust regression model which makes the regression function robust against disturbance of data and system parameter. Firstly, two theorems have been given to show that the robust linear e-support vector regression problem could be settled by solving the dual problems. Secondly, it has been focused on the development of robust support vector regression algorithm which is extended from linear domain to nonlinear domain. Finally, the numerical experiments result demonstrates the effectiveness of the models and algorithms proposed in this paper.

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