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A ridge type of regularization was incorporated into a variety of NSCA: Ordinary NSCA, and Partial and/or Constrained NSCA.
Herein, three model updating techniques are considered involving different type of regularization: classical Tikhonov regularization, constitutive relation error based updating method and Bayesian approach.
In the iterative SIRT method, choosing a certain number of iterations can be viewed as a type of regularization: in general, using few iterations will result in images with less noise, but less high-frequency details as well, while using many iterations results in images with more details but more noise as well.
Note that, even under the smoothness assumption, obtaining a more or less dense set of local PSF estimates is not enough to fully characterize the blur, and a certain type of regularization and interpolation of that information to the rest of the image locations is necessary.
A popular approach has been certain type of regularization of t-test.
This type of regularization, however, can significantly distort the solution by contributing to the broadening of peaks.
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As presented previously, the type of regularization-based methods, specifically the DL method, is extensively used in practice and can be selected as a typical example to compare with the proposed method.
For different types of regularization, we establish energy estimates.
BAM balances the number of false detections against false non-detections by means of a special type of inferential regularization (i.e. borrowing strength across the data).
This type of l1-regularization was applied to parameter estimation for several statistical models, such as Cox's proportional hazard model [ 19], least absolute deviation regression model [ 20], and graphical Gaussian model [ 21].
As discussed in the Introduction, in this work we use a smoothness-type regularizer in which the amount of regularization is allowed to vary for each chromophore.
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