regularize

Regularizers such as energy balance and tangential energy balance, which ensure the smoothness of the overall structure, are used with decaying weights.

Another approach to exploit the graph Laplacian is an output regularizer based on the random Walker (RW) algorithm.

Thus, the penalty term acts as a regularizer that was inspired from the neighborhood expectation maximization algorithm.

Besides, there are a few hyperparameters such as learning rate, dropout rate, and the strength of regularizer which need to be tuned with specific knowledge.

The regularizer parameter y tunes the strength of this penalization.

It will imply that the operator D has a two-sided regularizer, and thus will have a Fredholm property [5].

This method does not solve data fitting problem in an optimal way, as using regularizer for denoising images is not a specialized one.

It should be emphasized that the need for regularizer is an attribute of the computational imaging technique used, CGS, and is not a consequence of the metamaterial aperture.

For the analysis of annihilation rate probability (probability density function [PDF]) solutions, we used a grid of 90 in the annihilation rates from 0.35 to 9 [ns.sup.-1], and the appropriate [ALPHA] regularizer was found and constrained at 2.5 [times] [10.sup.-3] in the next analysis to derive the influence of template molecule presence or absence.

As we know, the first term in (3) is a smoothness regularizer which can be used to encourage the similar vertices, which have the large [w.sub.ij] in between, to be much more similar.

Thus, the penalty parameter (regularizer) A controls the level of sparsity of the resulting model.