Sentence examples for to achieve regularization from inspiring English sources

In cases where the number of probes/features exceeds N for a particular gene (e.g. microRNA features), we allow for a Bayesian lasso prior, as described earlier in the text, to achieve regularization.

Assigning a prior distribution over the model parameters and inferring quantities of interest by computing their posterior distribution, can also achieve regularization.

To tackle the problem of noisy and incomplete prior networks, we exploit the duality between learning the associations and refining the prior networks to achieve smoother regularization.

In order to achieve a thoroughly regularization, the nonlocal theory is widely adopted in the analyses related to mesh sensitivity strain softening problems since the pioneering work by Bažant (Pijaudier-Cabot and Bažant 1987).

To achieve this, numerous regularization techniques [ 9] can be used to reduce artifacts and improve the overall accuracy.

This paper formulates a point target detection method utilizing super-resolution (SR) strategy concerning infrared scanning oversampling system, with an accelerated SR strategy proposed to realize fast de-aliasing of the oversampled image and an adaptive MRF-based regularization designed to achieve the preserving and aggregation of target energy.

It shares the same idea with linear regression with regularization such as ridge regression [23], which is shown to achieve better performance than regression without adequate regularization.

To deal with the large mesh distortions of the two-phase model, modification of viscous regularization is explored to achieve r-adaptive mesh optimization.

In addition, to achieve better steady-state estimation performance, regularization parameter methods for ZA-NLMS-type algorithms are adopted [13, 14] and set to be ρ = 0.0015 σ n 2. In different SNR regimes, ZA-VSS-NLMS always achieves a better estimation performance than ZA-ISS-NLMS.

It is well known that the use of regularization is necessary to achieve a model that generalizes well to unseen data, particularly if the dimension of features is very high relative to the amount of training data.

It is well-known that the use of regularization is necessary to achieve a model that generalizes well to unseen data, particularly if the dimension of features is high.