Robust \(\epsilon\)-support vector regression
type: Item
instance of: scholarly article
title: Robust \(\epsilon\)-support vector regression
zbMATH Open document ID: 1407.62231
DOI: 10.1155/2014/373571
author: Yuan Lv
author: Zhong Gan
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: Spheroid disturbance of input data brings great challenges to support vector regression; thus it is essential to study the robust regression model. This paper is dedicated to establish a robust regression model which makes the regression function robust against disturbance of data and system parameter. Firstly, two theorems have been given to show that the robust linear \(\epsilon\)-support vector regression problem could be settled by solving the dual problems. Secondly, it has been focused on the development of robust support vector regression algorithm which is extended from linear domain to nonlinear domain. Finally, the numerical experiments result demonstrates the effectiveness of the models and algorithms proposed in this paper.
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62G35
Mathematics Subject Classification ID: 90C31
zbMATH DE Number: 7016290
Wikidata QID: Q59064773
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/373571
OpenAlex ID: W2028698999
cites work: Q4935997
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description: scientific article; zbMATH DE number 7016290