Data selection for support vector machine classifiers

@inproceedings{Fung2000DataSF,
  title={Data selection for support vector machine classifiers},
  author={Glenn Fung and Olvi L. Mangasarian},
  booktitle={KDD},
  year={2000}
}
The problem of extracting a minimal number of data points from a large dataset, in order to generate a support vector machine (SVM) classifier, is formulated as a concave minimization problem and solved by a finite number of linear programs. This minimal set of data points, which is the smallest number of support vectors that completely characterize a separating plane classifier, is considerably smaller than that required by a standard 1-norm support vector machine with or without feature… CONTINUE READING
45 Citations
2 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 45 extracted citations

References

Publications referenced by this paper.
Showing 1-2 of 2 references

Similar Papers

Loading similar papers…