A divisional incremental training algorithm of support vector machine

Support vector machine (SVM) has become a popular classification tool but the main disadvantages of SVM are their large memory requirement and computation time to deal with very large datasets. Therefore we prefer to incremental learning algorithms especially when the data available are obtained at different intervals. The key of SVM to incremental training is to assure the final results consists of almost all support vectors. This paper proposes a divisional incremental training algorithm of SVM, considering the possible impact of new training data to history learning results. Training data are divided into smaller sets to decrease the computation complexity and the support vectors are obtained in a crossed way. The experiment results on the real-world test dataset show that the classification accuracy is satisfying, and the efficiency of proposed incremental algorithm is superior to that of batch SVM model.