  Support Vector Machines (SVMs) are popular tools for data mining tasks such
as classification, regression, and density estimation. However, original SVM
(C-SVM) only considers local information of data points on or over the margin.
Therefore, C-SVM loses robustness. To solve this problem, one approach is to
translate (i.e., to move without rotation or change of shape) the hyperplane
according to the distribution of the entire data. But existing work can only be
applied for 1-D case. In this paper, we propose a simple and efficient method
called General Scaled SVM (GS-SVM) to extend the existing approach to
multi-dimensional case. Our method translates the hyperplane according to the
distribution of data projected on the normal vector of the hyperplane. Compared
with C-SVM, GS-SVM has better performance on several data sets.
