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This parameter controls the regularization effect.
Krasnosel'skii [1] was first to notice the regularization effect of this average mapping.
Moreover, the regularization effect through the convexification of non-convex potential is embedded to represent fine scale behavior.
We believe that the regularization effect of soft targets can be particularly important for avoiding overfitting in such cases.
Also, n g is used for considering the asymptotic property of adaptation, which means relaxation of regularization effect including sparsity as adaptation data increase.
We also find that adding white noise to the synthetic observation data, with magnitude consistent with current pressure monitoring techniques, generally reduces error in solutions, likely due to a regularization effect.
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We show that the regularization effects induced by the alternative model can be vastly different from those induced by Burgers viscosity depending on the smoothness of initial data in the limit of zero regularization.
Furthermore, by choosing a certain number of iterations or using additional regularization, the effect of noise in iterative methods can be minimized.
The non-linear estimation rules in (30) and (31) are depicted in Figure 2a,b, respectively, for λ = 2. Observe that both regularization functions effect sparsity, since coefficients with small amplitude are set to 0. The Schwarz-like regularization yields unbiased estimates, but the solution is not continuous with respect to y.
Next, we use them to illustrate the key ideas behind the methods presented above, including the bias-variance trade-off, the tuning of the regularization, the effect of the quality of the prior knowledge on the regularization, and their impact on cross-validation results.
We also examine the lack of symmetry in tensor product regularizations and effects of dissipative error in hyperbolic problems.
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