Incremental and Decremental Multi-category Classification by Support Vector Machines

In this paper we propose an online multi-category support vector classifier dedicated to non-stationary environment. Our algorithm recursively discriminates between datasets of three or more classes, one sample at a time. With its incremental and decremental procedures, it can achieve an efficient update of the decision function after the incorporation/elimination of the incoming/oldest data. The key idea is to keep the KKT conditions of one single optimization problem satisfied, while adding or eliminating data. Compared to the QP approach, our classifier is able to provide accurate results. The performance of the proposed algorithm is shown on synthetic and experimental data.