Sentence examples for regularization to control from inspiring English sources

D iscern combines three ingredients in making a prediction the use of phylogenomic scores, information from structure and features computed at structural neighbors, and a statistical regularization to control for overfitting.

Once again, λ is the critical regularization parameter to control the weight we assign to the regularizer relatively to the data misfit term.

The weight vector w of the linear logistic regression is usually learned with L2-regularization as follows: min || w w || 2 + C ∑ i = 1 n D ∑ j = 1 n P log 1 + exp - y i j w T Φ D i, P j, where || · ||2 is L2 norm (the sum of squared values) and C is a regularization parameter to control the penalty.

To induce sparsity in the model, the weight vector w of the linear logistic regression is learned with L1-regularization as follows: min || w w || 1 + C ∑ i = 1 n D ∑ j = 1 n P log 1 + exp - y i j w T Φ D i, P j, where || · ||1 is L1 norm (the sum of absolute values) and C is a regularization parameter to control the sparsity.

Meanwhile, in order to derive a sparse ensemble, l1 regularization is introduced to control the size of ensembles.

The purpose of the regularization term is to control over-fitting of data by unrealistic, spurious oscillations of the resistivity model.

In the case of NN, one can reduce the number of hidden nodes or use regularization (weight decay), to control magnitude of weights [ 27].

where the non-negative parameter δ can be chosen to control the regularization and the multiplication by P is used to ensure the balance with the weight of the diagonal of (mathring {underline {mathbf {S}}}(n)).

n ω is the number of pixels in window ω k. (overline {p}_{k}) is the mean of the guided image in window ω k. ε is the regularization parameter which is used to control the structural similarity.

The ith frequency domain inverse filter V i (k) is the following: {V}_i k)=frac{H_i^{ast } k)}{{left|{H}_i k)right|}^2+beta }, (10 where ( {H}_i^{ast }(k) ) denotes the complex conjugate of H i (k) and β is a regularization index that is used to control the power output of the inverse filter [8].

A regularization (penalization) parameter is included to control the trade-off.