Using an iterative linear solver in an interior-point method for generating support vector machines

DOI10.1007/s10589-008-9228-zzbMath1208.90184OpenAlexW2061219021MaRDI QIDQ616790

Joshua D. Griffin, E. Michael Gertz

Publication date: 12 January 2011

Published in: Computational Optimization and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10589-008-9228-z

zbMATH Keywords

machine learningKrylov-space methodsmatrix-free preconditioning

Mathematics Subject Classification ID

Applications of mathematical programming (90C90) Quadratic programming (90C20) Interior-point methods (90C51)

Related Items

Nonlinear optimization and support vector machines, Nonlinear optimization and support vector machines

Uses Software

• LAPACK
• CLAPACK
• BLAS
• UCI-ml
• OOQP
• LaSVM

Cites Work

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• Semismooth support vector machines.
• 10.1162/15324430260185619
• 10.1162/15324430152748218
• Interior-Point Methods for Massive Support Vector Machines
• Improvements to Platt's SMO Algorithm for SVM Classifier Design
• Remark on algorithm 659
• An updated set of basic linear algebra subprograms (BLAS)