Typically, the strength of the L1-penalization is determined by the regularization parameter λ.
This model was obtained by modifying the regularization term mostly known as the Dirichlet regularization term given by ∫ Ω | ∇ u ( x ) | 2 d x, (9).
By decreasing the regularization parameter, the weights of the features gradually increase, making the model more complex.
As the regularization parameter vanishes, the expressions reduce to the known images for singular Stokeslets.
Mr. Bush, for instance, emphasized that he was not considering amnesty, a word that has been stricken from the vocabulary of the working group and replaced by the terms "regularization" and "earned adjustment".
For the noised thermal cycles the solution is stabilized by the iterative regularization.
An iterative regularization model (IRM) [13], which replaces the regularization term by a generalized Bregman distance [14, 15], was proposed.
We investigate the control of the "softness" of the structural constraints by changing the weight of the regularization.
The regularization employed by the GLM-path algorithm is designed to minimize prediction error over independent validation datasets [ 34].
We hypothesize that the conversion of gene expression data to module activity functions as a regularization step, where the extent of the regularization is controlled by the specificity of the modules, resulting in more stable classifiers.
