Uncertain support vector regression with imprecise observations

Autor:	Qiqi Li, Zhongfeng Qin, Zhe Liu
Rok vydání:	2022
Předmět:	Statistics and Probability Artificial Intelligence General Engineering
Zdroj:	Journal of Intelligent & Fuzzy Systems. 43:3403-3409
ISSN:	1875-8967 1064-1246
DOI:	10.3233/jifs-212156
Popis:	Traditional support vector regression dedicates to obtaining a regression function through a tube, which contains as many as precise observations. However, the data sometimes cannot be imprecisely observed, which implies that traditional support vector regression is not applicable. Motivated by this, in this paper, we employ uncertain variables to describe imprecise observations and build an optimization model, i.e., the uncertain support vector regression model. We further derive the crisp equivalent form of the model when inverse uncertainty distributions are known. Finally, we illustrate the application of the model by numerical examples.
Databáze:	OpenAIRE
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