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The robust and efficient adaptive normal direction support vector regression
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DOI：10.1016/j.eswa.2010.08.089.png)
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Abstract
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The recently proposed reduced convex hull support vector regression (RH-SVR) treats support vector regression (SVR) as a classification problem in the dual feature space by introducing an epsilon-tube. In this paper, an efficient and robust adaptive normal direction support vector regression (AND-SVR) is developed by combining the geometric algorithm for support vector machine (SVM) classification. AND-SVR finds a better shift direction for training samples based on the normal direction of output function in the feature space compared with RH-SVR. Numerical examples on several artificial and UCI benchmark datasets with comparisons show that the proposed AND-SVR derives good generalization performance. Crown Copyright (C) 2010 Published by Elsevier Ltd. All rights reserved.
Keywords:
Support vector regression
Geometric algorithm
Normal direction
Epsilon-tube
Sample shift
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