A novel method for solving universum twin bounded support vector machine in the primal space

In supervised learning, the Universum, a third class that is not a part of either class in the classification task, has proven to be useful. In this study we propose (N\( \mathfrak {U} \)TBSVM), a Newton-based approach for solving in the primal space the optimization problems related to Twin Bounded Support Vector Machines with Universum data (\( \mathfrak {U} \)TBSVM). In the N\( \mathfrak {U} \)TBSVM, the constrained programming problems of \( \mathfrak {U} \)TBSVM are converted into unconstrained optimization problems, and a generalization of Newton’s method for solving the unconstrained problems is introduced. Numerical experiments on synthetic, UCI, and NDC data sets show the ability and effectiveness of the proposed N\( \mathfrak {U} \)TBSVM. We apply the suggested method for gender detection from face images, and compare it with other methods.