Fast incremental learning method for one-class support vector machine

Abstract

A fast incremental learning method of one-class support vector machine(OCSVM) was proposed.A new decision function of OCSVM was constructed by adding a delta function based on the initial classifier in order to achieve the incremental learning.The objective function which had the similar form with OCSVM was constructed to solve the parameters of delta function by analyzing the geometric properties of delta function.The optimization problem can be converted into a standard quadratic programming(QP) problem,but the Karush-Kuhn-Tucker(KKT)conditions greatly changed.An improved sequential minimal optimization(SMO) method was proposed according to the new KKT condition.Directly manipulating the initial classifier and under its influence,the method only trained the new data,so saved much learning time and storage space.Experimental results show that the fast incremental learning method performs better than other incremental methods in both time and accuracy.