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Support Vector Quantile Regression Using Asymmetric e-Insensitive Loss Function
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 Title & Authors
Support Vector Quantile Regression Using Asymmetric e-Insensitive Loss Function
Shim, Joo-Yong; Seok, Kyung-Ha; Hwang, Chang-Ha; Cho, Dae-Hyeon;
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 Abstract
Support vector quantile regression(SVQR) is capable of providing a good description of the linear and nonlinear relationships among random variables. In this paper we propose a sparse SVQR to overcome a limitation of SVQR, nonsparsity. The asymmetric e-insensitive loss function is used to efficiently provide sparsity. The experimental results are presented to illustrate the performance of the proposed method by comparing it with nonsparse SVQR.
 Keywords
Asymmetric e-insensitive loss function;quantile regression;support vector machine;support vector quantile regression;
 Language
English
 Cited by
 References
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