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

Autor:	Wanida Panup, Rabian Wangkeeree
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	support vector machine generalized pinball loss function large-scale problems stochastic gradient descent algorithm feature noise Mathematics QA1-939
Zdroj:	Symmetry, Vol 13, Iss 9, p 1652 (2021)
Druh dokumentu:	article
ISSN:	2073-8994
DOI:	10.3390/sym13091652
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/d63bd5e9dd5a47a68554cbfb11f56e17 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.