SVM-Maj: a majorization approach to linear support vector machines with different hinge errors

DOI10.1007/s11634-008-0020-9zbMath1151.90551OpenAlexW2116915136MaRDI QIDQ107957

P. J. F. Groenen, J. C. Bioch, G. Nalbantov, G. Nalbantov, Jan C. Bioch, Patrick J. F. Groenen

Publication date: 27 March 2008

Published in: Advances in Data Analysis and Classification, Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11634-008-0020-9

zbMATH Keywords

support vector machinesoptimal scalingiterative majorizationabsolute hinge errorhuber hinge errorquadratic hinge error

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Nonlinear programming (90C30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (5)

Binary separation and training support vector machines ⋮ Algorithms for Sparse Support Vector Machines ⋮ Sharp quadratic majorization in one dimension ⋮ SVMMaj ⋮ Stream-suitable optimization algorithms for some soft-margin support vector machine variants

Uses Software

• UCI-ml
• SVMlight
• LIBSVM

Cites Work

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• Local minima in categorical multiple regression
• Quantitative analysis of qualitative data
• Regression with qualitative and quantitative variables: An alternating least squares method with optimal scaling features
• Setting up alternating least squares and iterative majorization algorithms for solving various matrix optimization problems
• Modern multidimensional scaling. Theory and applications.
• Piecewise linear regularized solution paths
• Bayesian Trigonometric Support Vector Classifier
• Robust Statistics
• The elements of statistical learning. Data mining, inference, and prediction

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