A New Method for Constructing Kernel Function of Support Vector Regression

The principle of support vector regression(SVR)is firstly discussed,then geometry of kernel function is analyzed from the viewpoint of information geometry,and the kernel function is contructed in data-dependent way by a conformal transformation,which reduces volume elements locally in neighborhoods of support vectors in feature space.This makes the performance of SVR improved.Simulation results show the effectiveness of the method.