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...

Online social networks usage are pervasive now a days. Mining the text present in online social networks will be useful for predictive analytic. Predicting information from unstructured data present in the social networks is a challenging research problem. Extracting, identifying or otherwise characterizing the sentiment content of the text unit using statistics and machine learning methods are...

this paper addresses the problem of minimizing the sum of maximum earliness and tardiness on identical parallel machines scheduling problem. each job has a processing time and a due date. since this problem is trying to minimize and diminish the values of earliness and tardiness, the results can be useful for just–in-time production systems. it is shown that the problem is np-hard. using effici...

In this paper, we consider the problem of semi-supervised binary classification by Support Vector Machines (SVM). This problem is explored as an unconstrained and non-smooth optimization task when part of the available data is unlabelled. We apply non-smooth optimization techniques to classification where the objective function considered is non-convex and nondifferentiable and so difficult to ...

title: Provably Fast Training Algorithms for Support Vector Machines author: Jose Balcázar1, Yang Dai2, Junichi Tanaka3, and Osamu Watanabe3 affiliation: 1. Departament de Llenguatges i Sistemes Informatics, Univ. Politecnica de Catalunya Campus Nord, Jordi Girona Salgado 1-3, 08034 Barcelona, Spain 2. Dept. of Bioengineering (MC063), Univ. Illionis at Chicago 851 S. Morgan Str, Chicago, IL 606...