Regularization via generalized operational matrices: Theory and applications in machine learning classification

IF 2.4 2区数学 Q1 MATHEMATICS, APPLIED

Journal of Computational and Applied Mathematics Pub Date : 2026-10-01 Epub Date: 2026-02-19 DOI:10.1016/j.cam.2026.117459

Saba Asgarzadeh , M.R. Eslahchi

引用次数: 0

Abstract

In this research, we introduce new classes of regularization matrices constructed using generalized operational matrices of the Caputo fractional derivative. Experimental results confirm the performance and effectiveness of the proposed method. In another part of this study, the obtained matrices are applied to classification tasks in machine learning. The results demonstrate improved classification accuracy and more effective data representation.

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广义运算矩阵的正则化：机器学习分类的理论与应用

在本研究中，我们引入了用Caputo分数阶导数的广义运算矩阵构造的一类新的正则化矩阵。实验结果验证了该方法的性能和有效性。在本研究的另一部分中，将得到的矩阵应用于机器学习中的分类任务。结果表明，该方法提高了分类精度和数据表示效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computational and Applied Mathematics 数学-应用数学

CiteScore

5.40

自引率

4.20%

发文量

437

审稿时长

3.0 months

期刊介绍： The Journal of Computational and Applied Mathematics publishes original papers of high scientific value in all areas of computational and applied mathematics. The main interest of the Journal is in papers that describe and analyze new computational techniques for solving scientific or engineering problems. Also the improved analysis, including the effectiveness and applicability, of existing methods and algorithms is of importance. The computational efficiency (e.g. the convergence, stability, accuracy, ...) should be proved and illustrated by nontrivial numerical examples. Papers describing only variants of existing methods, without adding significant new computational properties are not of interest. The audience consists of: applied mathematicians, numerical analysts, computational scientists and engineers.