A novel incremental SVM learning algorithm

In this paper we present a novel approach to incremental support vector machine (SVM) learning algorithm. We analyze the possible change of support vector set after new samples are added to training set. Based on the analysis result, a novel algorithm is presented. In this algorithm useless samples are discarded and knowledge is accumulated. The experiment result shows that this algorithm is more effective than traditional SVM while the classification precision is also guaranteed.