کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
385952 | 660876 | 2011 | 11 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: The robust and efficient adaptive normal direction support vector regression The robust and efficient adaptive normal direction support vector regression](/preview/png/385952.png)
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.
Journal: Expert Systems with Applications - Volume 38, Issue 4, April 2011, Pages 2998–3008