Now showing items 1-5 of 5

    • Controlling False Alarms with Support Vector Machines 

      Davenport, Mark A.; Baraniuk, Richard G.; Scott, Clayton D. (2006-05-01)
      We study the problem of designing support vector classifiers with respect to a Neyman-Pearson criterion. Specifically, given a user-specified level alpha, 0 < alpha < 1, how can we ensure a false alarm rate no greater than ...
    • Learning minimum volume sets with support vector machines 

      Davenport, Mark A.; Baraniuk, Richard G.; Scott, Clayton D. (2006-09-01)
      Given a probability law P on d-dimensional Euclidean space, the minimum volume set (MV-set) with mass beta , 0 < beta < 1, is the set with smallest volume enclosing a probability mass of at least beta. We examine the use ...
    • Minimax support vector machines 

      Davenport, Mark A.; Baraniuk, Richard G.; Scott, Clayton D. (2007-08-01)
      We study the problem of designing support vector machine (SVM) classifiers that minimize the maximum of the false alarm and miss rates. This is a natural classification setting in the absence of prior information regarding ...
    • Regression level set estimation via cost-sensitive classification 

      Scott, Clayton D.; Davenport, Mark A. (2007-06-01)
      Regression level set estimation is an important yet understudied learning task. It lies somewhere between regression function estimation and traditional binary classification, and in many cases is a more appropriate setting ...
    • Tuning support vector machines for minimax and Neyman-Pearson classification 

      Scott, Clayton D.; Baraniuk, Richard G.; Davenport, Mark A. (2008-08-19)
      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 ...