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It combines principles of regularization and robust statistics.
In this paper, we propose a new MRMR criterion combining principles of regularization and robust statistics, together with proposing a novel optimization algorithm for its computation.
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Here we develop a method incorporating the principle of regularization for identification of an optimal pathway in gene regulatory networks starting from a given gene to a target gene.
For the solution of nonlinear ill-posed problems, different variational approaches for constructing the regularizing algorithms, based on Tikhonov's functional and generalized principles for the choice of regularization parameter and formulation of very general conditions for their implementation, is found.
A method of same temperature discrepancy curve (STDC) is designed to estimate the optimal values (α1)opt and (α2)opt of regularization parameters according to discrepancy principle.
For linear discriminant, we used two methods of regularization: principal component analysis, and ridge regularization.
Different norms have different effects of regularization.
For different types of regularization, we establish energy estimates.
This point is illustrated using the Bayesian interpretation of regularization.
It was the first of a series of "regularization programs" that continued over the following decade.
Due to the dimensionality of the features (320 dimensions), some form of regularization was advisable.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com