Incremental SVM Learning: Review

Abstract

The aim of this paper is to present a review of methods for incremental Support Vector Machines (SVM) learning and their adaptation for data stream classification in evolving environments. We formalize a taxonomy of these methods based on their characteristics and the type of solution they provide. We discuss the strength and weakness of the various learning methods and also highlight some applications involving data stream, where incremental SVM learning has been used.