首页|An improved multi-task least squares twin support vector machine

An improved multi-task least squares twin support vector machine

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  • NETL
  • NSTL
  • Springer Nature
In recent years, multi-task learning (MTL) has become a popular field in machine learn-ing and has a key role in various domains. Sharing knowledge across tasks in MTL can improve the performance of learning algorithms and enhance their generalization capability. A new approach called the multi-task least squares twin support vector machine (MTLS-TSVM) was recently proposed as a least squares variant of the direct multi-task twin support vector machine (DMTSVM). Unlike DMTSVM, which solves two quadratic programming problems, MTLS-TSVM solves two linear systems of equations, resulting in a reduced com-putational time. In this paper, we propose an enhanced version of MTLS-TSVM called the improved multi-task least squares twin support vector machine (IMTLS-TSVM). IMTLS-TSVM offers a significant advantage over MTLS-TSVM by operating based on the empirical risk minimization principle, which allows for better generalization performance. The model achieves this by including regularization terms in its objective function, which helps control the model's complexity and prevent overfitting. We demonstrate the effectiveness of IMTLS-TSVM by comparing it to several single-task and multi-task learning algorithms on various real-world data sets. Our results highlight the superior performance of IMTLS-TSVM in addressing multi-task learning problems.

Least squaresMulti-task learningTwin support vector machineMulti-task twin support vector machineQuadratic programming problems

Hossein Moosaei、Fatemeh Bazikar、Panos M. Pardalos

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Jan Evangelista Purkyne University in Usti nad Labem, Department of Informatics, Faculty of Science, Usti nad Labem, Czech Republic||Department of Mathematics, University of Bojnord, Bojnord, Iran

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

Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, USA

2025

10.1007/s10472-023-09877-8

Annals of mathematics and artificial intelligence

Annals of mathematics and artificial intelligence

ISSN：1012-2443
年,卷(期)：2025.93(1)
  • 58