The robust and efficient adaptive normal direction support vector regression

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

Research highlights
► The AND-SVR is not so sensitive to the epsilon-tube.
► The performance of the AND-SVR is better than the classical SVR and RH-SVR.
► The AND-SVR finds a better shift direction than the RH-SVR.
► The learning of the AND-SVR is efficient.