Sentence examples for better regularization from inspiring English sources

It is expected that similar norms will be used for the actual mission analysis, but development is continuing on the CI algorithm and could quite possibly lead to better regularization techniques.

Although the recovered absorption contrast does not improve greatly for the φ = 30° case, although shape is recovered much better when regularization is introduced.

In each case, the medium and the best case regularization scenarios clearly outperformed the non-regularized estimation, leading to better generalizable calibrated models.

They found that the least regularized subset selected by their random forests with minimal regularization ensures better accuracy than the complete feature set.

Levenberg Marquardt algorithm under Bayesian regularization has better generalization capability, and is more stable than the classical method.

Based on this metric, MTGV regularization performs better than Tikhonov and L1 regularization in all cases except when the distribution is known to only comprise of discrete peaks, in which case L1 regularization is slightly more accurate than MTGV regularization.

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.

The numerical results show solutions with first-order regularization provide better results, where the accuracy of the local solution increases by 0.2 cm compared to that obtained from zero-order regularization.

The results show solutions with first-order regularization are better than those obtained from zero-order regularization, which indicates the former may be more preferable for regional gravity field modeling.

This study proposes an iterative dual-regression (DR) approach with sparse prior regularization to better estimate an individual's neuronal activation using the results of an independent component analysis (ICA) method applied to a temporally concatenated group of functional magnetic resonance imaging (fMRI) data (i.e., Tc-GICA method).

Further, the optimization algorithm exhibited better convergence properties with regularization, although no significant improvements in the model predictions was observed.