Support Vector Regression as a Classification Problem with a Priori Knowledge in the Form of Detractors

In this article, we propose applying a recently proposed technique of reducing Support Vector Classification models for regression problems. The reduced method creates reduced models by removing support vectors and uses a general formulation of Support Vector Classification with a priori knowledge in the form of detractors. We apply this method for regression problems by using a reformulation of ε-insensitive Support Vector Regression as a special case of this general formulation. Indeed, the experiments show that reduced technique can be successfully applied for regression problems. The tests were performed on various regression data sets and on stock price data from public domain repositories.