Fast Incremental SVM Learning Algorithm based on Center Convex Vector

A fast SVM learning algorithm is proposed according to incremental learning and center convex hull operator. It is established on analyzing the relevance of support vector and convex hull from the angle of calculation geometry. The convex hull of current training samples is solved in the first place. Further, Euclidean distance elimination is applied to convex hull. Meanwhile, every time when the incremental learning is going on, the training samples should contain samples violated KKT condition in previous sample set, experiment results indicate that the algorithm effectively shortens training time while classification accuracy keep a satisfied level.