An Effective Incremental Algorithm for ν-Support Vector Machine

The ν-support vector machine (ν-SVM) for classification has the advantage of using a parameter ν on controlling the number of support vectors. However, comparing to regular C-SVM, its formulation is more complicated because of having an additional inequality so up to now there are no exact and effective methods for incremental ν-SVM learning. In this paper, based on the truth that the additional inequality can be treated as an equality, we propose an effective and exact incremental learning algorithm for ν-SVM which conquers the difficult problem the incremental learning path may break off by the original incremental method for C-SVM.