An iterative modified kernel for support vector regression

Abstract

In order to improve the performance of a support vector regression, a new method for modified kernel function is proposed. In this method the information of whole samples is included in kernel function by conformal mapping. So the Kernel function is data-dependent. With random initial parameter of kernel function, iterative modifying is not stopped until satisfactory effect. Comparing with the conventional model, the improved approach does not need selecting parameters of kernel function. Simulation results show that the improved approach has better learning ability and forecasting precision than traditional model.