Estimating the Support Set from the ℓ 1 Minimization
Weighted ℓ₁ -Minimization for Generalized Non-Uniform Sparse Model
... the support of the underlying signal is constrained to belong to a given known ...the set of all k-sparse signals, which is now the set of allowable ...
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One bit compressive sampling via ℓ 0 minimization
... mapping from a continuous real value to a discrete value over some finite ...of estimating a sparse signal from a set of quan- tized measurements has been addressed in recent litera- ...that ...
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Sparse subspace clustering via smoothed ℓ<sub><i>p</i></sub> minimization
... 1-10, 11-20, 21-30 and 31-38, and consider all the possible choices for N ∈ { 2, 3, 5, 8, 10 } in each group, as done in [4]. Thus we can obtain a set of data for each N. For AR dataset, we use all face ...
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Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization
... the 1-norm, typically set up as linear programs (Mangasarian, 2000; Bradley and Mangasarian, 1998), are formulated here as a completely unconstrained mini- mization of a convex differentiable ...
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Little by Little: Semi Supervised Stemming through Stem Set Minimization
... wg(t) 1 1 + 1 4 1 1 + 1 4 1 1 Table 2: Possible Stems, their Inf l() and wg() 7 Experimentation Two new stemming systems based on the greedy algorithms for MSS ...
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Estimating Optimal Recommendation Set Sizes for Individual Consumers
... If Dell were to extend its advisor to allow consumers to specify the importance of the price attribute, it would become possible to compute each consumer’s willingness-to-pay. In Figure 4, we see that the consumer has ...
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Quartile ranked set sampling for estimating the distribution function
... n from the target ...interest. From the first set of n units the smallest ranked unit is ...selected. From the second set of n units the second small- est ranked unit is ...measured ...
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Soft Margin Support Vector Classification as Buffered Probability Minimization
... soft-margin support vector classifier is equivalent to minimization of Buffered Probability of Exceedance (bPOE), a recently intro- duced characterization of ...derived from a simple bPOE ...
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Technical Support Set-up Procedure
... Technical Support Set-up Procedure How to Setup the Amazon S3 Application on the DSN-320 Amazon S3 (Simple Storage Service) is an online storage web service offered by AWS (Amazon Web Services), and it ...
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Estimating an EQ-5D population value set: the case of Japan
... noise from the translation process of the instrument have been simultaneously and effectively ...change from 11333 to 22222) is likely to be appreciated less by an average Japanese than a ...
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RANDOM CLOSED SET MODELS: ESTIMATING AND SIMULATING BINARY IMAGES
... If we want to use a RACS model to simulate similar images to a natural one these steps should be followed. A model that fits the data should first be found. Then the goodness of this fit should be checked. Thirdly the ...
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Estimating probabilities of default with support vector machines
... Non-linear extensions of popular methods such as DA or logistic regression also exist when instead of original variables the transformed ones are used. Non-linear DA and logistic regression can be as powerful as SVM, ...
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Estimating Probabilities of Default With Support Vector Machines
... sure from markets and regulators, banks build their trust to an ever increasing degree on statistical techniques for corporate bankruptcy prediction known as rating or ...
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ESTIMATING BILATERAL TRADE IN SERVICES BY INDUSTRY THE EBTSI DATA SET. 1. Francesca Spinelli and Sébastien Miroudot, OECD
... Figure 1 illustrates how the percentage of geographically unallocated bilateral export flows for total services has evolved over time and in most cases has ...moved from very large shares of unallocated ...
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Fast ℓ 1-minimization algorithm for robust background subtraction
... In this paper, we propose a sparse-based BGS strategy that can be distinguished from the above classic methods owing to looser model assumptions. We employ a dictio- nary learning algorithm to train bases, which ...
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A fast and accurate algorithm for ℓ 1 minimization problems in compressive sampling
... m2 1 1 , b m2 3 1 , b m2 3 1 for dynamic range parameters θ = 1, 3, 5, respectively, for better perfor- mance in terms of accuracy and computational ...data from all three ...
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Accurate prediction of major histocompatibility complex class II epitopes by sparse representation via ℓ 1-minimization
... the 1 -minimization method delivered higher AUC values of than any of the AUC of SVM for the same encoding ...the 1 -minimization approach proposed here, where no model selection is involved, ...
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Accelerating ℓ 1 − ℓ 2 deblurring using wavelet expansions of operators
... (a) Gaussian (b) Skewed (c) Motion (d) Airy (e) Defocus Figure 10: The different blurs used to analyze the method’s efficiency. 6.6 Dependency on the blur kernel In this paragraph, we analyze the method behavior with ...
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Measurement of the ZZ production cross section in proton-proton collisions at √s=8 TeV using the ZZ → ℓ−ℓ+ℓ′−ℓ′+ and ZZ→ℓ−ℓ+νν⎯⎯⎯ZZ→ℓ−ℓ+νν¯ decay channels with the ATLAS detector
... JHEP01(2017)099 V.E. Ozcan 20a , N. Ozturk 8 , K. Pachal 145 , A. Pacheco Pages 13 , L. Pacheco Rodriguez 137 , C. Padilla Aranda 13 , M. Pag´aˇcov´a 50 , S. Pagan Griso 16 , M. Paganini 180 , F. Paige 27 , P. Pais 88 , ...
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Search for heavy ZZ resonances in the ℓ+ℓ−ℓ+ℓ− and ℓ+ℓ−νν¯ final states using proton–proton collisions at s√=13 TeV with the ATLAS detector
... G. Volpi 13 , H. von der Schmitt 103 , E. von Toerne 23 , V. Vorobel 131 , K. Vorobev 100 , M. Vos 170 , R. Voss 32 , J. H. Vossebeld 77 , N. Vranjes 14 , M. Vranjes Milosavljevic 14 , V. Vrba 130 , M. Vreeswijk 109 , R. ...
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