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 bina...

A support vector machine (SVM) is an algorithm that finds a hyperplane which optimally separates labeled data points in \begin{document}$ \mathbb{R}^n $\end{document} into positive and negative classes. The on the margin of this separating are called support vectors. We connect possible configurati...

Often, when dealing with real-world recognition problems, we do not need, and often cannot have, knowledge of the entire set possible classes that might appear during operational testing. In such cases, need to think robust classification methods able deal "unknown" properly reject samples belonging never seen training. Notwithstanding, existing classifiers date were mostly developed for closed...