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Bazikar, Fatemeh, Sadeghi Bigham, Bahram, Hemmati, Atefeh. (1405). Modified Newton’s method for solving parametric ν-support vector regression with Universum data. سامانه مدیریت نشریات علمی, 14(3), 1353-1367. doi: 10.22034/cmde.2025.66530.3113
Fatemeh Bazikar; Bahram Sadeghi Bigham; Atefeh Hemmati. "Modified Newton’s method for solving parametric ν-support vector regression with Universum data". سامانه مدیریت نشریات علمی, 14, 3, 1405, 1353-1367. doi: 10.22034/cmde.2025.66530.3113
Bazikar, Fatemeh, Sadeghi Bigham, Bahram, Hemmati, Atefeh. (1405). 'Modified Newton’s method for solving parametric ν-support vector regression with Universum data', سامانه مدیریت نشریات علمی, 14(3), pp. 1353-1367. doi: 10.22034/cmde.2025.66530.3113
Bazikar, Fatemeh, Sadeghi Bigham, Bahram, Hemmati, Atefeh. Modified Newton’s method for solving parametric ν-support vector regression with Universum data. سامانه مدیریت نشریات علمی, 1405; 14(3): 1353-1367. doi: 10.22034/cmde.2025.66530.3113

Modified Newton’s method for solving parametric ν-support vector regression with Universum data

Computational Methods for Differential Equations
مقاله 21، دوره 14، شماره 3، مهر 2026، صفحه 1353-1367    اصل مقاله (2.31 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22034/cmde.2025.66530.3113
نویسندگان
Fatemeh Bazikar1؛ Bahram Sadeghi Bigham* 1؛ Atefeh Hemmati2
1Department of Computer Science, Faculty of Mathematical Sciences, Alzahra University, Tehran, Iran.
2Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
چکیده
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 $\nu$-support vector regression (UPar-$\nu$-SVR) to enhance generalization performance. In this paper, we present a Newton-based method for solving UPar-$\nu$-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.
کلیدواژه‌ها
Parametric $\nu-$support vector regression؛ Univesum data؛ Newton's method
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