Training support vector machines using Gilbert's algorithm

@article{Martin2005TrainingSV,
  title={Training support vector machines using Gilbert's algorithm},
  author={Shawn Martin},
  journal={Fifth IEEE International Conference on Data Mining (ICDM'05)},
  year={2005},
  pages={8 pp.-}
}
Support vector machines are classifiers designed around the computation of an optimal separating hyperplane. This hyperplane is typically obtained by solving a constrained quadratic programming problem, but may also be located by solving a nearest point problem. Gilbert's algorithm can be used to solve this nearest point problem but is unreasonably slow. In this paper we present a modified version of Gilbert's algorithm for the fast computation of the support vector machine hyperplane. We then… CONTINUE READING
Highly Cited
This paper has 30 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 12 extracted citations

A scalable FPGA architecture for non-linear SVM training

2008 International Conference on Field-Programmable Technology • 2008
View 4 Excerpts
Highly Influenced

Efficient FPGA mapping of Gilbert’s algorithm for SVM training on large-scale classification problems

2008 International Conference on Field Programmable Logic and Applications • 2008
View 4 Excerpts
Highly Influenced

VLSI Design of SVM-Based Seizure Detection System With On-Chip Learning Capability

IEEE Transactions on Biomedical Circuits and Systems • 2018
View 1 Excerpt

FPGA based nonlinear Support Vector Machine training using an ensemble learning

2015 25th International Conference on Field Programmable Logic and Applications (FPL) • 2015
View 3 Excerpts

FPGA Implementation of a Support Vector Machine Based Classification System and Its Potential Application in Smart Grid

2014 11th International Conference on Information Technology: New Generations • 2014
View 1 Excerpt

Novel Cascade FPGA Accelerator for Support Vector Machines Classification

IEEE Transactions on Neural Networks and Learning Systems • 2012
View 2 Excerpts

NESVM: A Fast Gradient Method for Support Vector Machines

2010 IEEE International Conference on Data Mining • 2010
View 1 Excerpt

References

Publications referenced by this paper.
Showing 1-10 of 21 references

Support-Vector Networks

Machine Learning • 2004
View 5 Excerpts
Highly Influenced

Successive over- Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM’05

O. L. Mangasarian, D. R. Musicant
IEEE Transactions on Neural Networks, • 2005
View 2 Excerpts

Geometry in learning

K. Bennett, E. J. Bredensteiner
C. Gorini, editor, Geometry at Work, pages 132–145. Mathematical Association of America, Washington, D.C. • 2000
View 1 Excerpt

SMOBR – a SMO program for SVMs

Marcelo Barros de Almeida
http://www.litc.cpdee.ufmg.br/∼barros/svm/smobr, • 2000
View 1 Excerpt

Sathiya Keerthi’s home page

S. S. Keerthi
http://guppy.mpe.nus.edu.sg/∼mpessk/npa.shtml • 1999
View 1 Excerpt

Similar Papers

Loading similar papers…