On the working set selection in gradient projection-based decomposition techniques for support vector machines

DOI10.1080/10556780500140714zbMath1116.90115OpenAlexW2032148848MaRDI QIDQ5717544

Thomas Serafini, Luca Zanni

Publication date: 10 January 2006

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

Full work available at URL: https://doi.org/10.1080/10556780500140714

zbMATH Keywords

support vector machineslarge-scale problemsdecomposition techniquesquadratic programsgradient projection methods

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Learning and adaptive systems in artificial intelligence (68T05) Methods of reduced gradient type (90C52)

Related Items

Binary separation and training support vector machines, On the convergence rate of scaled gradient projection method, A Reduced-Space Algorithm for Minimizing $\ell_1$-Regularized Convex Functions, Unnamed Item, An improved gradient projection-based decomposition technique for support vector machines, Nonlinear optimization and support vector machines, Efficient optimization of support vector machine learning parameters for unbalanced datasets, Nonlinear optimization and support vector machines, Decomposition algorithm model for singly linearly-constrained problems subject to lower and Upper bounds, On the convergence of inexact block coordinate descent methods for constrained optimization

Uses Software

• UCI-ml
• CONV_QP
• GPDT
• LIBSVM
• SVMTorch
• MNIST

Cites Work

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