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The selection of a range of regularization parameter values for solving a specific inverse problem is specific to that inverse problem.
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A range of the regularization parameter, rather than one single value, can be determined.
Figure 6 c shows the prediction bias-variance trade-off for a range of the regularization parameter (see computational details in Additional file 1).
In order to answer these questions, here we present a critical comparison of a wide range of regularization methods applicable to nonlinear kinetic models.
In order to demonstrate the effectiveness of our regularization method, we evaluate a tomographic reconstruction over a range of values for the regularization parameter α.
To tune parameters, we perform a grid search over a range of possible parameter values and combinations of learning algorithms (SVM or LR) and type of regularization (ℓ1 or ℓ2).
A Range of Arrangements Q.
— to a range of Southern Californians.
Offer a range of products.
Wear a range of outfits.
Select a range of cells.
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