Sentence examples for regularization as from inspiring English sources

This paper addresses the problem of regularization as well as some other relevant problems that occur when the crash of parts built from randomly reinforced plastics such as GMT have to be simulated.

In this way the disorder-to-order phase transition from the δ to α form was found to occur through the conformational ordering of the chains accompanied with the chain packing regularization as well as the matching of the neighboring domain height to give a large single domain of the α form.

We propose, too, an approximation for the SV-matrix inversion, under the assumption of having a smoothly varying PSF field and enough regularization as to ensure its local regularized inverse also changes smoothly in the space.

The non-word in vogue in Washington and Mexico City is "regularization," as in the need to bring clarity and coherence to immigration policy along the border.

This approach circumvents the need for the regularization, as it would be necessary for continuum damage models with softening characteristics.

The classifier parameters, θ, are estimated by minimizing the cross-entropy error with standard L2 regularization, as per Equation 1.

We explored L1-regularization as well as explicitly limiting the number of regulators allowed for each gene.

In this paper, all these regularization methods, as well as two combinations of them, namely, the elastic net and sparse group lasso, are presented in a unified framework.

The superior regularization capabilities, as well as the accuracy and efficiency of the proposed higher-order gradient damage model are demonstrated by the standard benchmark examples.

Equation (7) can be interpreted in a regularization framework as well as in a Bayesian point of view, and it is also known as the Tykhonov-Phillips regularization (Tykhonov, 1963; Phillips, 1962) or ridge regression (Hoerl and Ken-nard, 1970).

Nevertheless, we found that non-linear ANN with Bayesian regularization behaved as well as the linear models when predicting an additive target.