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NeymanPearson Classification, Convexity and Stochastic Constraints
 Journal of Machine Learning Research
"... Motivated by problems of anomaly detection, this paper implements the NeymanPearson paradigm to deal with asymmetric errors in binary classification with a convex loss ϕ. Given a finite collection of classifiers, we combine them and obtain a new classifier that satisfies simultaneously the two foll ..."
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Cited by 7 (2 self)
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Motivated by problems of anomaly detection, this paper implements the NeymanPearson paradigm to deal with asymmetric errors in binary classification with a convex loss ϕ. Given a finite collection of classifiers, we combine them and obtain a new classifier that satisfies simultaneously the two
Convex Analysis
, 1970
"... In this book we aim to present, in a unified framework, a broad spectrum of mathematical theory that has grown in connection with the study of problems of optimization, equilibrium, control, and stability of linear and nonlinear systems. The title Variational Analysis reflects this breadth. For a lo ..."
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Cited by 5350 (67 self)
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long time, ‘variational ’ problems have been identified mostly with the ‘calculus of variations’. In that venerable subject, built around the minimization of integral functionals, constraints were relatively simple and much of the focus was on infinitedimensional function spaces. A major theme
GENERALIZED NEYMANPEARSON LEMMA VIA CONVEX DUALITY
, 2007
"... We extend the classical NeymanPearson theory for testing composite hypotheses versus composite alternatives, using a convex duality approach as in Witting (1985). Results of Aubin & Ekeland (1984) from nonsmooth convex analysis are employed, along with a theorem of Komlós (1967), in order to e ..."
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Cited by 13 (1 self)
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We extend the classical NeymanPearson theory for testing composite hypotheses versus composite alternatives, using a convex duality approach as in Witting (1985). Results of Aubin & Ekeland (1984) from nonsmooth convex analysis are employed, along with a theorem of Komlós (1967), in order
Optimal Noise Benefits in Neyman–Pearson and InequalityConstrained Statistical Signal Detection
"... Abstract—We present theorems and an algorithm to find optimal or nearoptimal “stochastic resonance ” (SR) noise benefits for Neyman–Pearson hypothesis testing and for more general inequalityconstrained signal detection problems. The optimal SR noise distribution is just the randomization of two no ..."
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Cited by 11 (3 self)
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Abstract—We present theorems and an algorithm to find optimal or nearoptimal “stochastic resonance ” (SR) noise benefits for Neyman–Pearson hypothesis testing and for more general inequalityconstrained signal detection problems. The optimal SR noise distribution is just the randomization of two
Batch and online learning algorithms for Nonconvex NeymanPearson classification
"... We describe and evaluate two algorithms for NeymanPearson (NP) classification problem which has been recently shown to be of a particular importance for bipartite ranking problems. NP classification is a nonconvex problem involving a constraint on false negatives rate. We investigated batch algorit ..."
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We describe and evaluate two algorithms for NeymanPearson (NP) classification problem which has been recently shown to be of a particular importance for bipartite ranking problems. NP classification is a nonconvex problem involving a constraint on false negatives rate. We investigated batch
Batch and online learning algorithms for Nonconvex NeymanPearson classification
"... We describe and evaluate two algorithms for NeymanPearson (NP) classification problem which has been recently shown to be of a particular importance for bipartite ranking problems. NP classification is a nonconvex problem involving a constraint on false negatives rate. We investigated batch algorit ..."
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We describe and evaluate two algorithms for NeymanPearson (NP) classification problem which has been recently shown to be of a particular importance for bipartite ranking problems. NP classification is a nonconvex problem involving a constraint on false negatives rate. We investigated batch
A generalized NeymanPearson lemma for hedge problems in incomplete markets
"... Some financial problems as minimizing the shortfall risk when hedging in incomplete markets lead to problems belonging to test theory. This paper considers a generalization of the NeymanPearson lemma. With methods of convex duality we deduce the structure of an optimal randomized test when testing ..."
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Some financial problems as minimizing the shortfall risk when hedging in incomplete markets lead to problems belonging to test theory. This paper considers a generalization of the NeymanPearson lemma. With methods of convex duality we deduce the structure of an optimal randomized test when
HighRate Vector Quantization for the NeymanPearson Detection of Correlated Processes
"... This paper investigates the effect of quantization on the performance of the NeymanPearson test. It is assumed that a sensing unit observes samples of a correlated stationary ergodic multivariate process. Each sample is passed through an Npoint quantizer and transmitted to a decision device which ..."
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This paper investigates the effect of quantization on the performance of the NeymanPearson test. It is assumed that a sensing unit observes samples of a correlated stationary ergodic multivariate process. Each sample is passed through an Npoint quantizer and transmitted to a decision device which
Just Relax: Convex Programming Methods for Identifying Sparse Signals in Noise
, 2006
"... This paper studies a difficult and fundamental problem that arises throughout electrical engineering, applied mathematics, and statistics. Suppose that one forms a short linear combination of elementary signals drawn from a large, fixed collection. Given an observation of the linear combination that ..."
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Cited by 496 (2 self)
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. This paper studies a method called convex relaxation, which attempts to recover the ideal sparse signal by solving a convex program. This approach is powerful because the optimization can be completed in polynomial time with standard scientific software. The paper provides general conditions which ensure
Probability Theory The Neyman–Pearson lemma under gprobability ✩,✩✩
, 2008
"... This article was published in an Elsevier journal. The attached copy is furnished to the author for noncommercial research and education use, including for instruction at the author’s institution, sharing with colleagues and providing to institution administration. Other uses, including reproductio ..."
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This article was published in an Elsevier journal. The attached copy is furnished to the author for noncommercial research and education use, including for instruction at the author’s institution, sharing with colleagues and providing to institution administration. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit:
Results 1  10
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54,908