Sentence examples for regularization of data from inspiring English sources

A robust data management system was developed, including IT backstopping to ensure data quality, proper lines of responsibilities for data entry and dissemination, and regularization of data input on an annual basis to ensure data are current.

This techniques relies on a discrete regularization of the numerical data.

Imaging scattering has some challenges and it has been stated that the cw method lacks the capability of separating absorption from scattering in the DOT image reconstruction [ 17, 44], but it has been shown that preconditioning of data, regularization and use of multispectral information can separate absorption from scattering coefficient [ 16] and result in good image reconstructions.

(1.4) where α plays a role of regularization parameter, data (g^{delta}) represents the measured data of function g.

Such methods have been developed for peptide binding to class II MHC using a wide variety of fitting techniques including partial least squares (PLS) [18], [19], Gibbs sampling [20], linear programming [21], Support Vector Machines (SVMs) [22], [23], [24], kernel methods [25], non-linear optimization with a regularization penalty [26], or a combination of data fitting techniques [27].

param., where regularization parameters depend on the form of data distributions.

In machine learning, regularization techniques are widely employed to address the problem of data scarcity and model complexity.

Regularization is a technique used to capture the real trend of data by use of functions and avoids over fitting.

The regularization of mm-tSNE put more sparsity to the weights of data points in different maps and less sparsity to the coordinates of data points than previous method.

To do so, we established that the weak limit v of a sequence (vε)ε of solutions of Euler equations, corresponding to a family of regularizations of the Cauchy data, still satisfies the equation.

Three types of methods, quasi-inverse, ridge regularization and Singular Value Decomposition (SVD), are designed to solve low-rank problem of data matrix.