Incremental learning with support vector machines

Support Vector Machines (SVMs) have become a popular tool for learning with large amounts of high dimensional data. However, it may sometimes be preferable to learn incrementally from previousSVM results, as computing a SVM is very costly in terms of time and memory consumption or because the SVM may be used in an online learning setting. In this paper an approach for incremental learning with Support Vector Machines is presented, that improves existing approaches. Empirical evidence is given to prove that this approach can effectively deal with changes in the target concept that are results of the incremental learning setting.