A Fast SVM Incremental Learning Algorithm Based on the Central Convex Hulls Algorithm

How to deal with the newly added training samples, and utilize the result of the previous training effectively to get better classification result fast are the main tasks of incremental learning. A fast SVM incremental learning algorithm based on the central convex hulls algorithm is proposed in this paper. To utilize the result of the previous training and retain the useful information in the training set effectively, the relationship between the KKT conditions of support vector machine and the distribution of the training samples is analyzed. To reduce the computational cost of the incremental learning, the current training sample set is pre-extracted from the geometric point of view, and the obtained between-class convex hull vectors are used as the training samples in the SVM incremental training. Experimental results with the standard dataset indicate the feasibility and effectiveness of the proposed algorithm.