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arXiv 2501.01082math.OC
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拉格朗日乘子与对偶性及其在约束支持向量机中的应用

Lagrange Multipliers and Duality with Applications to Constrained Support Vector Machine

Fariborz Maseeh Department of Mathematics and Statistics, Portland State University(波特兰州立大学法里博兹·马西数学与统计系);Department of Statistics and Operations Research, The University of North Carolina at Chapel Hill(北卡罗来纳大学教堂山分校统计与运筹学系)

Nguyen Mau Nam, Gary Sandine, Quoc Tran-Dinh

AI总结:

本文提出一种改进的SVM模型,通过引入几何约束和正则化项,结合拉格朗日对偶性与凸优化技术,提升模型的泛化能力与鲁棒性。

摘要:

本文运用准相对内部的概念来分析拉格朗日乘子法,并在希尔伯特空间中建立非光滑凸优化问题的强拉格朗日对偶性。然后,我们通过在分离超平面中引入新的几何约束或正则化项来推广经典的支撑向量机(SVM)模型,作为SVM模型的正则化机制。该新的SVM模型通过新的子梯度算法以及对偶方法,在理论和数值方面利用拉格朗日对偶性和其他凸优化技术进行检验。

英文摘要:

In this paper, we employ the concept of quasi-relative interior to analyze the method of Lagrange multipliers and establish strong Lagrangian duality for nonsmooth convex optimization problems in Hilbert spaces. Then, we generalize the classical support vector machine (SVM) model by incorporating a new geometric constraint or a regularizer on the separating hyperplane, serving as a regularization mechanism for the SVM model. This new SVM model is examined using Lagrangian duality and other convex optimization techniques in both theoretical and numerical aspects via a new subgradient algorithm as well as a primal-dual method.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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