A New Algorithm for SVM Incremental Learning

Based on analyzing the relationship between the Karush-Kuhn-Tucker (KKT) conditions of support vector machine and the distribution of the training samples, the possible changes of support vector set after new samples are added to training set was analyzed, and the generalized Karush-Kuhn-Tucker conditions was defined. Based on the classification equivalence between the previous training set and the newly added training set, a new algorithm for SVM incremental learning is proposed. With the presented algorithm, the useless sample is discarded and useful information in training samples is accumulated. Experimental results with the standard datasets indicate the effectiveness of the proposed algorithm