Least absolute deviation-based robust support vector regression

2017 ◽  
Vol 131 ◽  
pp. 183-194 ◽  
Author(s):  
Chuanfa Chen ◽  
Yanyan Li ◽  
Changqing Yan ◽  
Jinyun Guo ◽  
Guolin Liu
Keyword(s):  
Support Vector Regression ◽  
Support Vector ◽  
Least Absolute Deviation ◽  
Absolute Deviation ◽  
Robust Support Vector Regression

scholarly journals Least Absolute Deviation Support Vector Regression

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Kuaini Wang ◽  
Jingjing Zhang ◽  
Yanyan Chen ◽  
Ping Zhong
Keyword(s):  
Support Vector Regression ◽  
Loss Function ◽  
Robust Regression ◽  
Support Vector ◽  
Regression Estimation ◽  
Least Absolute Deviation ◽  
Absolute Deviation ◽  
Robust Model ◽  
Benchmark Datasets ◽  
Classification And Regression

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.

scholarly journals Robust Support Vector Regression Model in the Presence of Outliers and Leverage Points

2017 ◽  
Vol 11 (8) ◽  
pp. 92
Author(s):  
Waleed Dhhan ◽  
Habshah Midi ◽  
Thaera Alameer
Keyword(s):  
Regression Model ◽  
Support Vector Regression ◽  
Support Vector ◽  
Data Set ◽  
Support Vector Regression Model ◽  
Leverage Points ◽  
Linear Relationships ◽  
Linear And Nonlinear ◽  
Abnormal Points ◽  
Robust Support Vector Regression

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.

A robust support vector regression with a linear-log concave loss function

2016 ◽  
Vol 67 (5) ◽  
pp. 735-742 ◽  
Author(s):  
Dohyun Kim ◽  
Chungmok Lee ◽  
Sangheum Hwang ◽  
Myong K Jeong
Keyword(s):  
Support Vector Regression ◽  
Loss Function ◽  
Support Vector ◽  
Robust Support Vector Regression
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