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 ...