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Abstract Details
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Activity Number:
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411
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #302134 |
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Title:
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Solving Hinge-Loss Support Vector Machine with Quantile Regression
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Author(s):
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Yonggang Yao*+ and Guixian Lin
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Companies:
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SAS Institute Inc. and SAS Institute Inc.
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Address:
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100 SAS Campus Drive, Cary, NC, 27513,
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Keywords:
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Support Vector Machine ;
Quantile Regression ;
Rank Score Test ;
Likelihood Ratio Test
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Abstract:
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Hinge-loss for support vector machine (SVM) and check-loss for quantile regression (QR) are the most popular L1 loss functions that have been widely applied for respectively solving classification and regression problems. In this report, we describe how a hinge-loss SVM problem can be cast as a check-loss QR problem. We also numerically investigate several QR inference methods in order to solving SVM problems, and our simulation studys show that rank score and likelihood ratio methods are of good performance to make statistical inference for SVM classification methods.
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