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...

Classification models are very sensitive to data uncertainty, and finding robust classifiers that less uncertainty has raised great interest in the machine learning literature. This paper aims construct \emph{Support Vector Machine} under feature via two probabilistic arguments. The first classifier, \emph{Single Perturbation}, reduces local effect of with respect one given acts as a test could...