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

2008

Additional Metadata
Keywords	Absolute hinge error, Huber hinge error, Iterative majorization, Optimal scaling, Quadratic hinge error, Support vector machines
Persistent URL	doi.org/10.1007/s11634-008-0020-9, hdl.handle.net/1765/73626
Journal	Advances in Data Analysis and Classification
Organisation	Erasmus Research Institute of Management
Citation APA APA Style APA-ALL Style AAA Style Cell Style Chicago Style Harvard Style IEEE Style MLA Style Nature Style Vancouver Style American-Institute-of-Physics Style Council-of-Science-Editors Style BibTex Format Endnote Format RIS Format CSL Format DOIs only Format	Groenen, P., Nalbantov, G.& Bioch, C. (2008). SVM-Maj: A majorization approach to linear support vector machines with different hinge errors. Advances in Data Analysis and Classification, 2(1), 17–43.https://doi.org/10.1007/s11634-008-0020-9

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