Data selection for support vector machine classifiers

  title={Data selection for support vector machine classifiers},
  author={Glenn Fung and Olvi L. Mangasarian},
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
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