The regularization allows control of physical instabilities.
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.
It is clear that it is the applied regularization that controls the SV before 2002.0 and after 2008.5.
The crucial parameter of these methods is degree of regularization, which controls sparsity of the networks.
Here, λ ≥ 0 is a regularization parameter controlling the nuclear norm of estimated value L t, where (7) T λ W = U D λ V ′, with D λ = diag d 1 − λ +, …, d r − λ +.
The penalty is defined for U as Reg (U ) = (1 − λ ) 1 2 | | U | | 2, 1 2 + λ | | U | | 1, 1, where λ ≥ 0 is a regularization parameter controlling the amount of sparsity in the node neighborhood.
Combing this network regularizer R with the objective function in Eq. (8), we obtain the objective function of RNMF as follows (11) O = | | X - U V T | | 2 + α | | W ⊙ (Y - B V T ) | | 2 + β Tr (V T L V ) where β is the regularization parameter controlling the importance of the network regularization term.
Once again, λ is the critical regularization parameter to control the weight we assign to the regularizer relatively to the data misfit term.
Moreover, regularization of unstable control matrices is not seen to prevent the proposed array to provide free-of-interference amplitude and relative-phase control, but the system performance is degraded, as a function of the amount of regularization needed.
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.
