Abstract: Support vector machine (SVM) has been successfully applied to solving classification and regression problems. However, to solve the quadratic programming problem is needed for SVM training process, and the more the number of training samples, the longer the training process. A novel incremental learning method was therefore proposed combining the hypershpere approach with regressional SVM to reduce the number of training samples by using two concentric hypershperes, thus shortening the training time. Analysis results showed that the incremental learning method has lower computational complexity than the normal SVM training method. Experimental results demonstrated that the method proposed can dramatically save the training time with negaligible degradation of regression accuracy.