Sentence examples for common regularization from inspiring English sources

Total variation (TV) [31] is utilized as the most common regularization.

As a common regularization method, (ell_{1}) regularization ((p=1)) problem has many good properties since it is a convex programming problem.

Reliability of the approach is shown by three key results: (1) a common regularization parameter for all orbits with enough data coverage, (2) 0.95 squared coherence with the Auroral Electrojet index, and (3) 0.97 squared coherence is found between the side-by-side flying satellites, Alpha and Charlie, indicating a method invariant to small changes in data input.

One common regularization is L2-regularization which keeps most elements in the weight vector to be non-zeros.

One common regularization is L2-regularization, which keeps most elements in the weight vector to be non-zeros; therefore, one can suffer from interpreting features from learned weights.

The most common regularization is L2-regularization which keeps most elements in the weight vector to be non-zeros, so one suffers from difficulty in interpreting the predictive model with many non-zero weights.

When the source location is known, the source strength can be estimated successfully by common Tikhonov regularization method, but it is invalid when the information about both source strength and location is absent.

The most common form of regularization is the Tikhonov regularization, which has the following objective function: hbox{min} left{ {phi (f) =frac{1}{2} left| {W (Af - b )} right|^{2},+,frac{alpha}{2} left| {Lf} right|^{2} } right}, (3)where ||·|| means Euclidean norm and W is a weighted matrix whose diagonal elements equal to the reciprocal of the noise level.

Another common method of regularization is to penalize the (entry-wise) <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0014559.e142.PNG" class= inline-graphic"/> -norm of the parameter matrix.

The common schemes include the regularization method, iterative method, stochastic method and so on [ 15, 18– 26].

The most common embedded methods are regularization-based [12], including LASSO, elastic net, or ridge regression.