Sentence examples for regularization framework from inspiring English sources

Manifold regularization (MR) is a promising regularization framework for semi-supervised learning, which introduces an additional penalty term to regularize the smoothness of functions on data manifolds and has been shown very effective in exploiting the underlying geometric structure of data for classification.

A multiple graph ensemble regularization framework is proposed to learn the optimal intrinsic manifold.

The estimated image is recursively updated from its previous estimates using a regularization framework.

To overcome these limitations, several methods have been proposed in the regularization framework.

This paper proposes a new nonconvex variational model for multiplicative noise removal under the Weberized total variation (TV) regularization framework.

In the following, we will consider a total variation (TV) regularization framework, but adding a bilateral filtering part [4, 10].

Because both smooth and sparse regularization frameworks have been shown to improve the prediction power of unregularized models [1], [3], [8], it is likely that combining features of both methods can further improve the quality of the estimates.

In this paper, we propose to unify various dimensionality reduction algorithms by interpreting the Manifold Regularization (MR) framework in a new way.

Considering that in SAR imaging, the underlying field usually exhibits a sparse structure, we previously proposed a sparsity-driven technique for joint SAR imaging of stationary scenes and space-invariant focusing by using a nonquadratic regularization-based framework [12].

A new super-resolution model based on sparsity regularization in Bayesian framework is presented.

Comparing with standard regression approaches with regularization, the proposed framework integrates the information theoretic criteria in the generalized Lasso model, and sets the most informative features penalty-free to improve prediction accuracy and enhance model interpretability.