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Support Vector Quantile Regression with Weighted Quadratic Loss Function
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 Title & Authors
Support Vector Quantile Regression with Weighted Quadratic Loss Function
Shim, Joo-Yong; Hwang, Chang-Ha;
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Support vector quantile regression(SVQR) is capable of providing more complete description of the linear and nonlinear relationships among random variables. In this paper we propose an iterative reweighted least squares(IRWLS) procedure to solve the problem of SVQR with a weighted quadratic loss function. Furthermore, we introduce the generalized approximate cross validation function to select the hyperparameters which affect the performance of SVQR. Experimental results are then presented which illustrate the performance of the IRWLS procedure for SVQR.
Support vector quantile regression;iterative reweighted least squares procedure;Kernel function;quadratic loss function;generalized approximate cross validation function;
 Cited by
M-quantile regression using kernel machine technique,Hwang, Chang-Ha;

Journal of the Korean Data and Information Science Society, 2010. vol.21. 5, pp.973-981
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