关键词: 机器学习，支持向量机，增量学习，KKT条件，壳向量

Abstract: Because the classical support vector machine is difficult to realize incremental learning flectly and rapidly when the number of training samples gets larger, this thesis proposed an incremental learning algorithm based on KKT conditions and hull vectors. This algorithm first selects the hull vectors which contain all support vectors. Next, it eliminates the useless samples among newly-added ones by using KKh conditions in order to reduce the number of training samples, then starts increment learning. The experimental results show that this algorithm not only guarantees the precision and good generalization ability of the learning machine, but also faster than the classical SVM algorithm.Therefore, it can be used in incremental learning.

Key words: Machine learning, Support vector machine, Incremental learning, KKT conditions, Hull vectors