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http://dx.doi.org/10.7465/jkdi.2014.25.3.677

Support vector quantile regression ensemble with bagging  

Shim, Jooyong (Department of Data Science, Inje University)
Hwang, Changha (Department of Applied Statistics, Dankook University)
Publication Information
Journal of the Korean Data and Information Science Society / v.25, no.3, 2014 , pp. 677-684 More about this Journal
Abstract
Support vector quantile regression (SVQR) is capable of providing more complete description of the linear and nonlinear relationships among random variables. To improve the estimation performance of SVQR we propose to use SVQR ensemble with bagging (bootstrap aggregating), in which SVQRs are trained independently using the training data sets sampled randomly via a bootstrap method. Then, they are aggregated to obtain the estimator of the quantile regression function using the penalized objective function composed of check functions. Experimental results are then presented, which illustrate the performance of SVQR ensemble with bagging.
Keywords
Bootstrap aggregating; check function; cross validation function; kernel function; support vector quantile regression;
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Times Cited By KSCI : 5  (Citation Analysis)
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