Browsing George R. Brown School of Engineering by Author "Davenport, Mark A."
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The 2nuSVM: A CostSensitive Extension of the nuSVM
Davenport, Mark A. (20051201)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. (20060501)We study the problem of designing support vector classifiers with respect to a NeymanPearson criterion. Specifically, given a userspecified level alpha, 0 < alpha < 1, how can we ensure a false alarm rate no greater than ... 
Detection and estimation with compressive measurements
Baraniuk, Richard G.; Davenport, Mark A.; Wakin, Michael B. (20061101)The recently introduced theory of compressed sensing enables the reconstruction of sparse or compressible signals from a small set of nonadaptive, linear measurements. If properly chosen, the number of measurements can ... 
Error control for support vector machines
Davenport, Mark A. (2007)In binary classification there are two types of errors, and in many applications these may have very different costs. We consider two learning frameworks that address this issue: minimax classification, where we seek to ... 
Learning minimum volume sets with support vector machines
Davenport, Mark A.; Baraniuk, Richard G.; Scott, Clayton D. (20060901)Given a probability law P on ddimensional Euclidean space, the minimum volume set (MVset) 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. (20070801)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 ... 
Multiscale random projections for compressive classification
Duarte, Marco F.; Davenport, Mark A.; Wakin, Michael B.; Laska, Jason N.; Takhar, Dharmpal; Kelly, Kevin F.; Baraniuk, Richard G. (20070901)We propose a framework for exploiting dimensionreducing random projections in detection and classification problems. Our approach is based on the generalized likelihood ratio test; in the case of image classification, ... 
Random observations on random observations: Sparse signal acquisition and processing
Davenport, Mark A. (2010)In recent years, signal processing has come under mounting pressure to accommodate the increasingly highdimensional raw data generated by modern sensing systems. Despite extraordinary advances in computational power, ... 
Regression level set estimation via costsensitive classification
Scott, Clayton D.; Davenport, Mark A. (20070601)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 ... 
A simple proof of the restricted isometry property for random matrices
Baraniuk, Richard G.; Davenport, Mark A.; DeVore, Ronald A.; Wakin, Michael B. (20070118)We give a simple technique for verifying the Restricted Isometry Property (as introduced by Candès and Tao) for random matrices that underlies Compressed Sensing. Our approach has two main ingredients: (i) concentration ... 
Singlepixel imaging via compressive sampling
Duarte, Marco F.; Davenport, Mark A.; Takhar, Dharmpal; Laska, Jason N.; Sun, Ting; Kelly, Kevin F.; Baraniuk, Richard G. (20080301) 
The smashed filter for compressive classification and target recognition
Davenport, Mark A.; Duarte, Marco F.; Wakin, Michael B.; Laska, Jason N.; Takhar, Dharmpal; Kelly, Kevin F.; Baraniuk, Richard G. (20070101)The theory of compressive sensing (CS) enables the reconstruction of a sparse or compressible image or signal from a small set of linear, nonadaptive (even random) projections. However, in many applications, including ... 
Sparse Signal Detection from Incoherent Projections
Davenport, Mark A.; Wakin, Michael B.; Duarte, Marco F.; Baraniuk, Richard G. (20060501)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 ... 
A Theoretical Analysis of Joint Manifolds
Davenport, Mark A.; Hegde, Chinmay; Duarte, Marco; Baraniuk, Richard G. (200901)The emergence of lowcost sensor architectures for diverse modalities has made it possible to deploy sensor arrays that capture a single event from a large number of vantage points and using multiple modalities. In many ... 
Tuning support vector machines for minimax and NeymanPearson classification
Scott, Clayton D.; Baraniuk, Richard G.; Davenport, Mark A. (20080819)This paper studies the training of support vector machine (SVM) classifiers with respect to the minimax and NeymanPearson criteria. In principle, these criteria can be optimized in a straightforward way using a costsensitive ...