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Training a Support Vector Machine in the Primal

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons83855

Chapelle,  O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Chapelle, O. (2007). Training a Support Vector Machine in the Primal. Neural Computation, 19(5), 1155-1178. doi:10.1162/neco.2007.19.5.1155.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-CE73-F
Abstract
Most literature on Support Vector Machines (SVMs) concentrate on the dual optimization problem. In this paper, we would like to point out that the primal problem can also be solved efficiently, both for linear and non-linear SVMs, and that there is no reason for ignoring this possibilty. On the contrary, from the primal point of view new families of algorithms for large scale SVM training can be investigated.