Sentence examples for information regularization from inspiring English sources

The images are created to test the inverse solution with respect to spectral information, regularization, cross-talk and accuracy.

The semisupervised classifiers used in this paper are low density separation (LDS [ 48]), squared-loss mutual information regularization (SMIR [ 49]), and safe semisupervised support vector machine (S4VM [ 50, 51]).

While for the CRF model utilizing sequence information, the regularization factor are set to 5 and 10 (i.e. λ=5, μ=10).

Detailed information as regards the regularization parameter can be found in [1].

The framework performs a two-step feature selection process based on both information theoretic criteria and regularization concept.

This was to be expected, as aspect model 2 was already capturing some of the contextual information that the spatial regularization can provide (notice also that the maximum is achieved for a smaller value of in aspect model 2).

We formulate the regularization framework for information transferring of auxiliary data as follows: begin{array}{*{20}l} &minlimits_{textbf{U}} F textbf{U} =left|textbf{U}^{T}textbf{X}^{(a)}-textbf{T}^{(a)}right|_{F}^{2}+Omega textbf{U}), end{array} (16).

Within this framework, the prior information is transformed into a regularization term and a sparse optimization problem, which could be solved through block coordinate descent method (BCD), is formulated.

Regularization introduces additional information to a function in order to solve an ill-posed problem or avoid overfitting.

In other words, when some prior information is known for a regularization problem, this selection is more reasonable and intelligent.

Regularization introduces additional information in an inverse problem in order to solve an ill-posed problem or to prevent over-fitting.