Now showing items 1-4 of 4

    • The 2nu-SVM: A Cost-Sensitive Extension of the nu-SVM 

      Davenport, Mark A. (2005-12-01)
      Standard classification algorithms aim to minimize the probability of making an incorrect classification. In many important applications, however, some kinds of errors are more important than others. In this report we ...
    • 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 ...
    • Sparse Signal Detection from Incoherent Projections 

      Davenport, Mark A.; Wakin, Michael B.; Duarte, Marco F.; Baraniuk, Richard G. (2006-05-01)
      The recently introduced theory of Compressed Sensing (CS) enables the reconstruction or approximation of sparse or compressible signals from a small set of incoherent projections; often the number of projections can be ...