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Book Chapter

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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Citation

Chapelle, O. (2007). Training a Support Vector Machine in the Primal. In L. Bottou, O. Chapelle, D. DeCoste, & J. Weston (Eds.), Large Scale Kernel Machines (pp. 29-50). Cambridge, MA, USA: MIT Press.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-CC0B-D
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 to ignore this possibility.
On the contrary, from the primal point of view new families of algorithms for
large scale SVM training can be investigated.