A New SVR Incremental Algorithm Based on Boundary Vector

In dealing with a large number of train samples, Support Vector Regression (SVR) algorithm is slow. In particular, while new sample is added, all the training samples must be re-trained. In this paper, a new SVR incremental algorithm is presented, which is based on boundary vector. The algorithm takes full advantages of the geometric information of training sample sets. The observed data of China´s GDP is used as a case study for the new algorithm. The computing results show that the new algorithm not only can guarantee the accuracy of machine learning and good generalization ability, but also can increase the learning speed of the algorithm than the classical SVR algorithm, and can be used rapid incremental learning.