scholarly journals Performance Analysis of a Novel Robust Support Vector Regression Algorithm

Author(s):  
YUAN LV ◽  
CHAO XU ◽  
BIN ZHONG
2017 ◽  
Vol 11 (8) ◽  
pp. 92
Author(s):  
Waleed Dhhan ◽  
Habshah Midi ◽  
Thaera Alameer

Support vector regression is used to evaluate the linear and non-linear relationships among variables. Although it is non-parametric technique, it is still affected by outliers, because the possibility to select them as support vectors. In this article, we proposed a robust support vector regression for linear and nonlinear target functions. In order to carry out this goal, the support vector regression model with fixed parameters is used to detect and minimize the effects of abnormal points in the data set. The efficiency of the proposed method is investigated by using real and simulation examples.


2017 ◽  
Vol 131 ◽  
pp. 183-194 ◽  
Author(s):  
Chuanfa Chen ◽  
Yanyan Li ◽  
Changqing Yan ◽  
Jinyun Guo ◽  
Guolin Liu

Author(s):  
Lemuel Clark P. Velasco ◽  
Daisy Lou L. Polestico ◽  
Dominique Michelle M. Abella ◽  
Genesis T. Alegata ◽  
Gabrielle C. Luna

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