Online training of Support Vector Regression

Bach implementations of Support Vector Regression (SVR) are inefficient when used in an online setting, because they must be retrained from scratch every time the training set is modified. This paper presents an online support vector regression (OSVR) for regression problems that have input data supplied in sequence rather than in batch. The OSVR has been applied to two benchmark problems shows that the OSVR algorithm has a much faster convergence and results in a smaller number of support vectors and a better generalization performance in comparison with the existing algorithms.