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Training a Support Vector Machine in the Primal

2007

Book Chapter

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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.

Author(s): Chapelle, O.
Book Title: Large Scale Kernel Machines
Pages: 29-50
Year: 2007
Month: September
Day: 0

Series: Neural Information Processing
Editors: Bottou, L. , O. Chapelle, D. DeCoste, J. Weston
Publisher: MIT Press

Department(s): Empirical Inference
Bibtex Type: Book Chapter (inbook)

Address: Cambridge, MA, USA
Digital: 0
Language: en
Note: This is a slightly updated version of the Neural Computation paper
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inbook{4178,
  title = {Training a Support Vector Machine in the Primal},
  author = {Chapelle, O.},
  booktitle = {Large Scale Kernel Machines},
  pages = {29-50},
  series = {Neural Information Processing},
  editors = {Bottou, L. , O. Chapelle, D. DeCoste, J. Weston},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = sep,
  year = {2007},
  note = {This is a slightly updated version of the Neural Computation paper},
  month_numeric = {9}
}