A Random Sampling Technique for Training Support Vector Machines

  title={A Random Sampling Technique for Training Support Vector Machines},
  author={Jos{\'e} L. Balc{\'a}zar and Yang Dai and Osamu Watanabe},
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
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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

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