A majorization penalty method for SVM with sparse constraint

DOI10.1080/10556788.2022.2142584arXiv2105.07121MaRDI QIDQ6175561

Qing-Na Li, Unnamed Author

Publication date: 24 July 2023

Published in: Optimization Methods and Software (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2105.07121

zbMATH Keywords

conjugate gradient methodsupport vector machinesparse constraintmajorization penalty method

Mathematics Subject Classification ID

Nonlinear programming (90C30)

Cites Work

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• On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
• A QP-free constrained Newton-type method for variational inequality problems
• Support-vector networks
• Proximal gradient method for huberized support vector machine
• A semismooth Newton method for support vector classification and regression
• Support Vector Machines with the Ramp Loss and the Hard Margin Loss
• A Fast Matrix Majorization-Projection Method for Penalized Stress Minimization With Box Constraints
• Sparse Approximate Solutions to Linear Systems
• Quadratic Convergence of Smoothing Newton's Method for 0/1 Loss Optimization
• An efficient augmented Lagrangian method for support vector machine
• Robust Support Vector Machines for Classification with Nonconvex and Smooth Losses
• Estimation of Dependences Based on Empirical Data
• SSVM: A smooth support vector machine for classification

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