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SVQR with asymmetric quadratic loss function
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 Title & Authors
SVQR with asymmetric quadratic loss function
Shim, Jooyong; Kim, Malsuk; Seok, Kyungha;
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 Abstract
Support vector quantile regression (SVQR) can be obtained by applying support vector machine with a check function instead of an e-insensitive loss function into the quantile regression, which still requires to solve a quadratic program (QP) problem which is time and memory expensive. In this paper we propose an SVQR whose objective function is composed of an asymmetric quadratic loss function. The proposed method overcomes the weak point of the SVQR with the check function. We use the iterative procedure to solve the objective problem. Furthermore, we introduce the generalized cross validation function to select the hyper-parameters which affect the performance of SVQR. Experimental results are then presented, which illustrate the performance of proposed SVQR.
 Keywords
Asymmetric quadratic loss function;generalized cross validation;
 Language
English
 Cited by
1.
Robust varying coefficient model using L1 regularization, Journal of the Korean Data and Information Science Society, 2016, 27, 4, 1059  crossref(new windwow)
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