The phrase "automatic regularization" is correct and usable in written English.
It can be used in contexts related to machine learning, statistics, or any field where regularization techniques are applied automatically to improve model performance.
Example: "The algorithm employs automatic regularization to prevent overfitting and enhance generalization on unseen data."
Alternatives: "automated regularization" or "self-regulating regularization".
A procedure to identify this angle together with the flexural stiffness parameters is proposed, as well as an automatic regularization procedure to overcome the high sensitivity of the inverse problem to measurement noise.
The fact that ||θ|| is minimized during training provides SVMs with an automatic regularization mechanism.
One automatic regularization procedure found in literature to this problem is to the search for solution of equation of form Σ x = b (which involves a formal inversion of matrix Σ), which can be achieved by slightly incrementing all diagonal elements in Σ by a constant factor (Tikhonov regularization, [22]).
The local automatic regularization in LLARRMA implies a different perspective: that λ is a parameter intrinsic to, and only meaningful in the context of, a single LASSO path on a single (subsampled) realization of the data.
We demonstrate a principled approach, LASSO local automatic regularization resample model averaging (LLARRMA), that characterizes sensitivity of locus choice to sampling variability and uncertainty due to missing genotype data, and that provides LASSO shrinkage automatically regularized through either predictive- or discovery-based criteria.
Our method, LASSO local automatic regularization resample model averaging (LLARRMA), combines LASSO shrinkage with resample model averaging and multiple imputation, estimating for each SNP the probability that it would be included in a multi-SNP model in alternative realizations of the data.
Among others, one can cite the automatic tuning of regularization coefficients, the selection of the most important input variables, the derivation of an uncertainty interval on the model output and the possibility to perform a comparison of different models and, therefore, select the optimal model.
While this method provides automatic selection for the regularization parameters, it is not clear what would be an appropriate choice for the column vector b and to what extent the optimization obtained for this inverse problem yields optimal results for the classification problem.
The L1-SVM performs automatic feature selection, and a regularization parameter λ controls the amount of regularization.
Moreover, an automatic procedure for selecting the regularization parameter is necessary, and finally, a jittered pulsing scheme could be applied to randomize the undersampling in order to weaken the amplitude of azimuth image ambiguities.
They extract position invariant features using a sparse codebook on aligned images, and apply a local regularization framework on these features for automatic image annotation.
