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Training Support Vector Machines with Multiple Equality Constraints

2005

Conference Paper

ei


In this paper we present a primal-dual decomposition algorithm for support vector machine training. As with existing methods that use very small working sets (such as Sequential Minimal Optimization (SMO), Successive Over-Relaxation (SOR) or the Kernel Adatron (KA)), our method scales well, is straightforward to implement, and does not require an external QP solver. Unlike SMO, SOR and KA, the method is applicable to a large number of SVM formulations regardless of the number of equality constraints involved. The effectiveness of our algorithm is demonstrated on a more difficult SVM variant in this respect, namely semi-parametric support vector regression.

Author(s): Kienzle, W. and Schölkopf, B.
Book Title: Proceedings of the 16th European Conference on Machine Learning, Lecture Notes in Computer Science, Vol. 3720
Journal: Machine Learning: ECML 2005
Pages: 182-193
Year: 2005
Month: November
Day: 0
Editors: JG Carbonell and J Siekmann
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1007/11564096_21
Event Name: ECML 2005
Event Place: Porto, Portugal

Address: Berlin, Germany
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@inproceedings{3511,
  title = {Training Support Vector Machines with Multiple Equality Constraints },
  author = {Kienzle, W. and Sch{\"o}lkopf, B.},
  journal = {Machine Learning: ECML 2005},
  booktitle = {Proceedings of the 16th European Conference on Machine Learning, Lecture Notes in Computer Science, Vol. 3720},
  pages = {182-193},
  editors = {JG Carbonell and J Siekmann},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = nov,
  year = {2005},
  month_numeric = {11}
}