Research of Incremental Learning Algorithm Based on KKT Conditions and Hull Vectors
基于KKT条件与壳向量的增量学习算法研究

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.