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Data Selection in Binary Hypothesis Testing
, 2003
"... Traditionally, statistical signal processing algorithms are developed from probabilistic models for data. The design of the algorithms and their ultimate perfomance depend upon these assumed models. In certain situations, collecting or processing all available measurements may be inefficient or proh ..."
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hypothesis testing. We develop models for data selection in several cases, considering both random and deterministic approaches. Our considerations are divided into two classes
A Nonparametric Training Algorithm for Decentralized Binary Hypothesis Testing Networks
 Proceedings of 1993 American Control Conference
, 1993
"... We derive a nonparametric training algorithm which asymptotically achieves the minimum possible error rate, over the set of linear classifiers, for decentralized binary hypothesis testing (detection) networks. The training procedure is nonparametric in the sense that it does not require the function ..."
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Cited by 1 (0 self)
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We derive a nonparametric training algorithm which asymptotically achieves the minimum possible error rate, over the set of linear classifiers, for decentralized binary hypothesis testing (detection) networks. The training procedure is nonparametric in the sense that it does not require
Optimal Distributed Binary Hypothesis testing with Independent Identical Sensors
 Department of Computer Engineering and Informatics, University of Patras
, 2000
"... We consider the problem of distributed binary hypothesis testing with independent identical sensors. It is well known that for this problem the optimal sensor rules are a likelihood ratio threshold tests and the optimal fusion rule is a KoutofN rule [1]. Under the Bayesian criterion, we show that ..."
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Cited by 3 (1 self)
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We consider the problem of distributed binary hypothesis testing with independent identical sensors. It is well known that for this problem the optimal sensor rules are a likelihood ratio threshold tests and the optimal fusion rule is a KoutofN rule [1]. Under the Bayesian criterion, we show
1Distributed Sequential Detection for Gaussian Binary Hypothesis Testing
"... This paper studies the problem of sequential Gaussian binary hypothesis testing in a distributed multiagent network. A sequential probability ratio test (SPRT) type algorithm in a distributed framework of the consensus+innovations form is proposed, in which the agents update their decision statist ..."
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This paper studies the problem of sequential Gaussian binary hypothesis testing in a distributed multiagent network. A sequential probability ratio test (SPRT) type algorithm in a distributed framework of the consensus+innovations form is proposed, in which the agents update their decision
A Generalized Sequential Sign Detector for Binary Hypothesis Testing
, 1998
"... It is known that for fixed error probabilities sequential signal detection based on the sequential probability ratio test (SPRT) is optimum in terms of the average number of signal samples for detection. But, often suboptimal detectors like the sequential sign detector are preferred over the optima ..."
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Cited by 5 (4 self)
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, a generalized sequential sign detector for detecting binary signals in stationary, first order Markov dependent noise is studied. Under iid assumptions, this reduces to the usual sequential sign detector. The optimal decision thresholds and the average sample number for the test to terminate
Sparsity from binary hypothesis testing and application to nonparametric estimation
 in European Signal Processing Conference, EUSIPCOâ€™08
, 2008
"... This paper presents and discusses an alternative notion of sparsity. This notion derives from a theoretical result in binary hypothesis testing and slightly differs from the standard notion of sparsity introduced by Donoho and Johnstone. As an application of this alternative notion of sparsity and ..."
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Cited by 4 (3 self)
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This paper presents and discusses an alternative notion of sparsity. This notion derives from a theoretical result in binary hypothesis testing and slightly differs from the standard notion of sparsity introduced by Donoho and Johnstone. As an application of this alternative notion of sparsity
1 On Concentration and Revisited Large Deviations Analysis of Binary Hypothesis Testing
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Optimal BiLevel Quantization of i.i.d. Sensor Observations for Binary Hypothesis Testing
 IEEE Trans. Inform. Theory
, 2002
"... We consider the problem of binary hypothesis testing using binary decisions from independent and identically distributed (i.i.d). sensors. Identical likelihoodratio quantizers with threshold are used at the sensors to obtain sensor decisions. Under this condition, the optimal fusion rule is known t ..."
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Cited by 15 (2 self)
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We consider the problem of binary hypothesis testing using binary decisions from independent and identically distributed (i.i.d). sensors. Identical likelihoodratio quantizers with threshold are used at the sensors to obtain sensor decisions. Under this condition, the optimal fusion rule is known
Results 1  10
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41,490