Sentence examples for enforcing regularization from inspiring English sources

We used sparsity/parsimony enforcing regularization techniques in a nested cross validation grid search to select features for 17 unique supervised learning models, encoding missing values as additional indicator features.

In the first extension, we use group-sparsity enforcing regularization term to impose the sparse structure.

The innovative idea of this paper is to aggregate the DEDR-optimal fixed-point iterative reconstruction/enhancement procedures developed in the previous studies [7, 8, 10] with the multi-level robustness and convergence enforcing regularization via constructing the proper projections onto convex sets (POCS) in the solution domain.

In order to avoid overfitting, we also enforce regularization on the model parameters.

Finally, through enforcing the ℓ2,1-norm regularization, the imaging feature selection across most SNPs are coupled (Argyriou et al., 2007; Obozinski et al., 2006), so that the identified imaging phenotypes have common influence on all the SNPs.

Functions Φ are chosen in such a way to enforce a global regularization constraint on the cross-spectra.

Here one has to augment the problem with some additional regularization, e.g. enforcing the weights of neighboring time-lags to be similar [ 39].

Here, the regularization term R : X → [ 0, + ∞ ] is intended to enforce certain regularity properties of the approximate solution and to stabilize the process of solving (1).

Thus, enforcing model sparsity using L1-regularization resulted in dropping the feature of presence in a cleft or pocket, but retained residue centrality and solvent accessibility which allow this defining characteristic of active site residues to be recognized.

Following the POCS regularization formalism [9], the convergence enforcing projectors in the iterated procedure (32) are to be constructed formally as (33).

{leftVert mathbf{x}rightVert}_1le c (1 where ℒ : ℝ D  → ℝ is a convex and differentiable loss function, || ⋅ ||1 indicates an L 1 norm operator enforcing the sparse solution, and c is a constant for controlling regularization and sparsity, meaning how many zeros are in the optimal solution vector.