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Training a Support Vector Machine in the Primal




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.

Author(s): Chapelle, O.
Journal: Neural Computation
Volume: 19
Number (issue): 5
Pages: 1155-1178
Year: 2007
Month: March
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1162/neco.2007.19.5.1155
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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  title = {Training a Support Vector Machine in the Primal},
  author = {Chapelle, O.},
  journal = {Neural Computation},
  volume = {19},
  number = {5},
  pages = {1155-1178},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = mar,
  year = {2007},
  month_numeric = {3}