A novel support vector machine algorithm for solving nonlinear regression problems based on symmetrical points

A novel support vector machine (SVM) algorithm for regression problems is proposed in this paper. Each pattern in the original training set is converted into a pair of patterns, which are labeled by 1 and −1, respectively. Therefore, the regression problem can be considered as a classification problem. By optimizing the obtained decision function, the model output of unknown samples can be estimated. Experimental results show the proposed method works well, and in many cases it produces less support vectors than the normal support vector regression (SVR) machine.