Nonlinear optimization and support vector machines

Abstract Support vector machine (SVM) is one of the most important class learning models and algorithms, has been successfully applied in various fields. Nonlinear optimization plays a crucial role SVM methodology, both defining designing convergent efficient algorithms for large-scale training problems. In this paper we present convex programming problems underlying focusing on supervised binary classification. We analyze used methods problems, discuss how properties these can be incorporated useful algorithms.