Sentence examples for regularization under from inspiring English sources

The one- and the zero-norm penalties are used in combination with the Tikhonov regularization under a robust setting designed to correctly classify instances, up to a predefined error rate, even for the worst data distribution.

In order to provide a comparison between the ISMU estimates and actual data, we compare survey results with the most recent alternative dataset based on applications for regularization under the Bossi-Fini amnesty law of 2002.

Here, we show the well-posedness of the method and derive convergence rates both for convex and non-convex regularization under rather weak conditions.

Significance tests show that with λ1=10 and λ3=30, the sparse group lasso is significantly better than all other regularization methods under all testing conditions.

To the best of the authors' knowledge, there are few papers to choose the regularization parameter under the a posteriori rule for this problem.

The inverse problem is posed as a constrained optimization problem under regularization and solved utilizing the adjoint equations.

And their result for the regularization (1.5) is under dramatically less restrictive conditions than Moudafi and Mainge's [5].

The problem of blind restoration of double regularization CT images under incomplete projection data can be achieved by the following unconstrained minimization objective function.

The nitrogen desorption data were used to compute the pore size distributions (PSD, differential f V~dV p/dR and f S~dS/dR) using a self-consistent regularization (SCR) procedure under nonnegativity condition (f V ≥ 0 at any pore radius R) at a fixed regularization parameter α = 0.01 with voids (V) between spherical nonporous nanoparticles packed in random aggregates (V/SCR model) [49].

The nitrogen desorption data were used to compute the pore size distributions (PSD, differential fV(R) ~ dVp/dR and fS R) ~ dS/dR) using a self-consistent regularization (SCR) procedure under non-negativity condition (fV(R) ≥ 0 at any pore radius R) at a fixed regularization parameter α = 0.01.

The nitrogen desorption data were used to compute the pore size distributions (differential f V ~ dV p/dR and f S ~ dS/dR) using a self-consistent regularization (SCR) procedure under non-negativity condition (f V ≥ 0 at any pore radius R) at a fixed regularization parameter α = 0.01 with voids (V) between spherical nonporous nanoparticles packed in random aggregates (V/SCR model) [27].