The robust and efficient adaptive normal direction support vector regression

Authors:
Xinjun Peng;Yifei Wang
Affiliations:
Department of Mathematics, Shanghai Normal University, Shanghai 200234, PR China and Scientific Computing Key Laboratory of Shanghai Universities, Shanghai 200234, PR China;Department of Mathematics, Shanghai University, Shanghai 200444, PR China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

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