BISVM:block-based incremental training algorithm of SVM for very large dataset
适于大规模数据集的块增量学习算法: BISVM

This paper made a deep study on the training problems of SVM on very large data set, proposed a novel block-based incremental algorithm for solving the problem, namely BISVM, which worked like SMO. The new algorithm utilizes the increase and the decrease procedures to learn inputting data blocks one by one so that the rapidly-increased computation costs for large datasets could be avoided. Theoretical analyses show that BISVM converges to the solution of support vector machines. Experimental results on KDD dataset indicate that training time of BISVM is approximate liner to the scale of problem, while receives the comparable generalization performance as that of LIBSVM.