Sparse support vector machine with pinball loss

Author(s):  
M. Tanveer ◽  
S. Sharma ◽  
R. Rastogi ◽  
P. Anand
Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1652
Author(s):  
Wanida Panup ◽  
Rabian Wangkeeree

In this paper, we propose a stochastic gradient descent algorithm, called stochastic gradient descent method-based generalized pinball support vector machine (SG-GPSVM), to solve data classification problems. This approach was developed by replacing the hinge loss function in the conventional support vector machine (SVM) with a generalized pinball loss function. We show that SG-GPSVM is convergent and that it approximates the conventional generalized pinball support vector machine (GPSVM). Further, the symmetric kernel method was adopted to evaluate the performance of SG-GPSVM as a nonlinear classifier. Our suggested algorithm surpasses existing methods in terms of noise insensitivity, resampling stability, and accuracy for large-scale data scenarios, according to the experimental results.


2017 ◽  
Vol 68 ◽  
pp. 199-210 ◽  
Author(s):  
Xin Shen ◽  
Lingfeng Niu ◽  
Zhiquan Qi ◽  
Yingjie Tian

2018 ◽  
Vol 35 (1) ◽  
pp. 52-70 ◽  
Author(s):  
Wenxin Zhu ◽  
Yunyan Song ◽  
Yingyuan Xiao

2021 ◽  
Vol 106 ◽  
pp. 104458
Author(s):  
Yunhao Zhang ◽  
Jiajun Yu ◽  
Xinyi Dong ◽  
Ping Zhong

2021 ◽  
Vol 98 ◽  
pp. 106840
Author(s):  
Ming-Zeng Liu ◽  
Yuan-Hai Shao ◽  
Chun-Na Li ◽  
Wei-Jie Chen

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