Sparse support vector regression algorithm with piecewise loss function

2010 ◽  
Author(s):  
Gensheng Hu
Keyword(s):  
Support Vector Regression ◽  
Loss Function ◽  
Support Vector

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.

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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