Sentence examples for strength of regularization from inspiring English sources

In some cases, better solutions can be obtained by using S a that are different from S e and adjusting the strength of regularization (i.e., constraint from the a priori data).

Since latent factors are affected by the strength of regularization, the choice of parameter λ is important.

This is the role of β which can adjust the strength of regularization applied to the variable x = (x1, x2,..., x n).

By penalizing the L1-norm of the variables, part of the regression coefficients will be driven to zero with the level of sparsity controlled by the strength of regularization.

In LASSO (Tibshirani, 1996),, whereas in standard ridge regression where α1 and α2 are regularization constants and determine the strength of the regularization.

The parameters λ3 and λ2 control the strength of the regularization.

(lambda _3) and (lambda _2) determine the strength of the regularization applied to the model time dependence during the entire modelled interval and at the endpoints, respectively.

The Gaussian prior variance controls the strength of the regularization, so that reducing σ lowers the ability of the model to fit the data, increasing generalization and decreasing the chance of overfitting.

The parameters λ 3 and λ 2 control the strength of the regularization applied to the model time dependence during the entire modelled interval and at the endpoints, respectively.

end{aligned} (5) Here ({{varvec{I}}}) is the identity matrix of size (Mtimes M), (alpha ^2) is a parameter controlling the strength of the regularization, and ({varvec{W}}^d_i) is the data weight matrix.

The strength of the regularization term, determined by λ, was chosen carefully.