Regression based on support vector classification

Abstract

In this article, we propose a novel regression method which is based solely on Support Vector Classification. Experiments show that the new method has comparable or better generalization performance than ε-insensitive Support Vector Regression. The tests were performed on synthetic data, on various publicly available regression data sets, and on stock price data. Furthermore, we demonstrate how a priori knowledge which has been already incorporated to Support Vector Classification for predicting indicator functions, could be directly used for a regression problem. © 2011 Springer-Verlag.