An Incremental SVM Learning Method Storing Support Vectors

Abstract

An incremental or iterative learning classifier based on support vector machines (SVMs) is proposed and analyzed from the geometrical viewpoint. We show that the effective examples are necessary and sufficient to obtain the SVM solution of given examples. This means that SVM can be solved online. The proposed method stores support vectors instead of effective examples since it is difficult to discriminate effective examples. Some convergence properties of the proposed method are also given.