Tuning Support Vector Machines for Minimax and Neyman-Pearson Classification

  title={Tuning Support Vector Machines for Minimax and Neyman-Pearson Classification},
  author={Mark A. Davenport and Richard G. Baraniuk and Clayton D. Scott},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
This paper studies the training of support vector machine (SVM) classifiers with respect to the minimax and Neyman-Pearson criteria. In principle, these criteria can be optimized in a straightforward way using a cost-sensitive SVM. In practice, however, because these criteria require especially accurate error estimation, standard techniques for tuning SVM parameters, such as cross-validation, can lead to poor classifier performance. To address this issue, we first prove that the usual cost… CONTINUE READING
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Learning with the Neyman-Pearson and Min-Max Criteria

  • A. Cannon, J. Howse, D. Hush, C. Scovel
  • Technical Report LA-UR 02-2951, Los Alamos Nat’l…
  • 2002
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