Difference of Convex programming in adversarial SVM

DOI10.1016/j.cam.2024.116201MaRDI QIDQ6593354

Benedetto Manca, Annabella Astorino, Manlio Gaudioso, Enrico Gorgone

Publication date: 26 August 2024

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

zbMATH Keywords

DC programmingsparse optimizationadversarial machine learning

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• A fixed-center spherical separation algorithm with kernel transformations for classification problems
• Global optimality conditions for nonconvex optimization
• Lagrangian relaxation for SVM feature selection
• DC formulations and algorithms for sparse optimization problems
• Minimizing nonsmooth DC functions via successive DC piecewise-affine approximations
• The DC (Difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems
• The security of machine learning
• Polyhedral separation via difference of convex (DC) programming
• Feature selection in SVM via polyhedral \(k\)-norm
• Support vector machine polyhedral separability in semisupervised learning
• Pattern separation by convex programming
• Data preprocessing in semi-supervised SVM classification
• Deforming $||.||_{1}$ into $||.||_{\infty}$ via Polyhedral Norms: A Pedestrian Approach
• Numerical Nonsmooth Optimization
• Ellipsoidal separation for classification problems
• Multisurface method of pattern separation
• Sparse optimization via vector \(k\)-norm and DC programming with an application to feature selection for support vector machines
• Ellipsoidal classification via semidefinite programming
• The descent-ascent algorithm for DC programming
• Dual formulation of the sparsity constrained optimization problem: application to classification

This page was built for publication: Difference of Convex programming in adversarial SVM