Stochastic Subgradient for Large-Scale Support Vector Machine Using the Generalized Pinball Loss Function

Symmetry

Wanida Panup¹  Rabian Wangkeeree¹ 

[1] Department of Mathematics, Faculty of Science, Naresuan University, Phitsanulok 65000, Thailand;

关键词: support vector machine;    generalized pinball loss function;    large-scale problems;    stochastic gradient descent algorithm;    feature noise;   

DOI  :  10.3390/sym13091652

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown