Empirische Inferenz Article 2007

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
Bibtex Type: Article (article)
DOI: 10.1162/neco.2007.19.5.1155
Digital: 0
Electronic Archiving: grant_archive
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik
Links:

BibTex

@article{4142,
  title = {Training a Support Vector Machine in the Primal},
  journal = {Neural Computation},
  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.},
  volume = {19},
  number = {5},
  pages = {1155-1178},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = mar,
  year = {2007},
  slug = {4142},
  author = {Chapelle, O.},
  month_numeric = {3}
}