MODIFIED NEWTON’S METHOD FOR SOLVING PARAMETRIC ν-SUPPORT VECTOR REGRESSION WITH UNIVERSUM DATA

Articles in Press

Document Type : Research Paper

Authors

1 Department of Computer Science, Faculty of Mathematical Sciences, Alzahra University, Tehran, Iran.

2 Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Abstract

Universum, representing a third category distinct from the two primary classes in classification tasks,
facilitates the incorporation of prior knowledge into the learning process. Extensive studies have
confirmed its effectiveness in improving both supervised and semi-supervised classification models.
Recently, Universum data has been introduced into parametric ν-support vector regression (UPar-
ν-SVR) to enhance generalization performance. In this paper, we present a Newton-based method
for solving UPar-ν-SVR, with the objective of further improving its efficiency and accuracy. Our
approach reformulates the problem into an unconstrained convex optimization framework and employs a generalized Newton’s method for its solution. To assess the effectiveness of our proposed method, We conduct comprehensive experiments on multiple UCI benchmark data sets. The experimental results indicate that our algorithm outperforms existing techniques, providing superior generalization capabilities and computational efficiency.

Keywords

Main Subjects

Computational Methods for Differential Equations

Articles in Press, Accepted Manuscript
Available Online from 16 July 2025

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History

  • Receive Date: 24 March 2025
  • Revise Date: 27 May 2025
  • Accept Date: 16 July 2025

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