关键词: 支持向量机, 增量学习, 壳向量

Abstract: To reduce the computational cost of the incremental learning，a fast Support Vector Machine（SVM） incremental learning algorithm is proposed，and the definition of the between-class convex hull vector is given.The given algorithm is based on utilizing the result of the previous training effectively and retaining the most important samples for the incremental learning to reduce the computational cost.In the process of incremental learning，the convex hull vectors of the previous training and the newly added samples constitute the current training sample set，the current training sample set is pre-extracted from the geometric point of view by using the convex hulls algorithm，the central distance ratio method is used to obtain the between-class convex hull vectors before the SVM incremental training.Experiments prove that the given algorithm has better classification performance.

Key words: Support Vector Machine（SVM）, incremental learning, convex hull vector