Training a Support Vector Machine in the Primal

Chapelle, O

doi:10.1162/neco.2007.19.5.1155

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Training a Support Vector Machine in the Primal

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

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https://www.mitpressjournals.org/doi/pdf/10.1162/neco.2007.19.5.1155
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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: https://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.