A Random Sampling Technique for Training Support Vector Machines

@inproceedings{Balczar2001ARS,
  title={A Random Sampling Technique for Training Support Vector Machines},
  author={Jos{\'e} L. Balc{\'a}zar and Yang Dai and Osamu Watanabe},
  booktitle={ALT},
  year={2001}
}
Random sampling techniques have been developed for combinatorial optimization problems. In this note, we report an application of one of these techniques for training support vector machines (more precisely, primal-form maximal-margin classifiers) that solve two-group classification problems by using hyperplane classifiers. Through this research, we are aiming (I) to design efficient and theoretically guaranteed support vector machine training algorithms, and (II) to develop systematic and… CONTINUE READING
Highly Cited
This paper has 50 citations. REVIEW CITATIONS
33 Citations
13 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 33 extracted citations

51 Citations

0510'01'04'08'12'16
Citations per Year
Semantic Scholar estimates that this publication has 51 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 13 references

On the convergence of the decomposition method for support vector machines, IEEE Trans

  • C. J. Lin
  • on Neural Networks,
  • 2001
1 Excerpt

and J

  • N. Cristianin
  • Shawe-Taylor, An Introduction to Support Vector…
  • 2000
2 Excerpts

and B

  • A. J. Smol
  • Scholkopf, A tutorial on support vector…
  • 1998
2 Excerpts

Platt , Fast training of support vector machines using sequential minimal optimization , in Advances in Kernel Methods – Support Vector Learning ( B . Scholkopf , C . J . C . Burges , and

  • A. J. Smola
  • 1997

Similar Papers

Loading similar papers…