Online SVR training by solving the primal optimization problem

Online regression estimation becomes important in presence of drifts and rapid changes in the training data. In this article we propose a new online training algorithm for SVR, called PRIONA, which is based on the idea of computing approximate solutions to the primal optimization problem. We explore different unconstrained optimization methods for the solution of the primal SVR problem and investigate the impact of different buffering strategies. By using a line search PRIONA does not require a priori selection of a learning rate which facilitates its practical application. Further PRIONA is shown to perform better in terms of prediction accuracy on various benchmark data sets in comparison to the NORMA and SILK online SVR algorithms.