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Gradient projection algorithms for optimization problems on convex sets and application to SVM

عنوان مقاله: Gradient projection algorithms for optimization problems on convex sets and application to SVM
شناسه ملی مقاله: JR_IJNAA-14-8_019
منتشر شده در در سال 1402
مشخصات نویسندگان مقاله:

Radhia Bessi - The Laboratory of Mathematical Modelling and Numeric in Engineering Sciences, National Engineering School of Tunis, University of Tunis El Manar, Rue B\&#۰۳۹;echir Salem Belkhiria Campus Universitaire, B.P. ۳۷, ۱۰۰۲ Tunis Belvedere, Tunisia
Harouna Soumare - The Laboratory of Mathematical Modelling and Numeric in Engineering Sciences, National Engineering School of Tunis, University of Tunis El Manar, Rue Bechir Salem Belkhiria Campus universitaire, B.P. ۳۷, ۱۰۰۲ Tunis Belvédère,

خلاصه مقاله:
In this paper, we present some gradient projection algorithms for solving optimization problems with a convex-constrained set. We derive the optimality condition when the convex set is a cone and under some mild assumptions, we prove the convergence of these algorithms. Finally, we apply them to quadratic problems arising in training support vector machines for the Wisconsin Diagnostic Breast Cancer (WDBC) classification problem.

کلمات کلیدی:
Optimization on convex cones, projection algorithm, generalized gradient projection algorithm, Euler inequation, quadratic optimization problem, Lipschitz continuous gradient, soft and hard dual SVM problem, classification of breast cancer

صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1767391/