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

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

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  • NETL
  • NSTL
  • Springer Nature
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(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 ((U)TBSVM). In the N(U)TBSVM, the constrained programming problems of (U)TBSVM are converted into unconstrained opti-mization 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(U)TBSVM. We apply the suggested method for gender detection from face images, and compare it with other methods.

Twin bounded support vector machineUniversumNewton's methodUnconstrained optimization problem

Hossein Moosaei、Saeed Khosravi、Fatemeh Bazikar、Milan Hladik、Mario Rosario Guarracino

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Department of Informatics, Faculty of Science, Jan Evangelista Purkyne University, Usti nad Labem, Czech Republic||Department of Applied Mathematics, School of Computer Science, Faculty of Mathematics and Physics, Charles University, Prague. Czech Republic||Prague University of Economics and Business, Prague, Czech Republic

Department of Computer Science, Faculty of Science, University of Bojnord, Bojnord, Iran

Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Guilan, Rasht, Iran

Department of Applied Mathematics, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic||Department of Econometrics, University of Economics, Prague, Czech Republic

Department of Economics and Law, University of Cassino and Southern Lazio Campus Folcara, Cassino, Italy||Laboratory of Algorithms and Technologies for Networks Analysis, National Research University Higher School of Economics, Nizhny Novgorod, Russia

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2025

10.1007/s10472-023-09896-5

Annals of mathematics and artificial intelligence

Annals of mathematics and artificial intelligence

ISSN：1012-2443
年,卷(期)：2025.93(1)
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