Algorithms of fast SVM evaluation based on subspace projection

A fast iteration algorithm is proposed to approximate the reduced set vectors shared by each binary SVM solution for multi-class classification simultaneously. The iteration algorithm can be applied to the general kernel types such as k(/spl par/ x - x´ /spl par//sup 2/) and k(x/sup T/x´). In addition, we present a fast block algorithm in the test phase to speed up the classification further. Experimental results have shown that the classification speeds on MNIST and Hanwang handwritten digit databases on P4 1.7 Ghz were about 16,000 and 10,895 patterns per second without sacrificing the classification accuracy of the original SVM system. The speed-up factor of 110 on MNIST database has been achieved.