Browsing George R. Brown School of Engineering by Author "Scott, Clayton D."
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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 ...