Research of Incremental Learning Algorithm Based on KKT Conditions and Hull Vectors

Because the classical support vector machine is difficult to realize incremental learning fleetly 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 KKT 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.