Sentence examples for good regularization from inspiring English sources

Attributes of a "good" regularization function are delineated in [22], and three properties are identified to this end: Unbiasedness.

Hence, the problem needs to be solved from two aspects: one is how to choose a good regularization functional φ(f), which is an active research area in image science, and the other is how to shorten the computation time without yielding staircase artifacts, which is also a challenging problem.

In accordance with Tikhonov regularization (Doicu et al. 2010), a good regularization strength is achieved by making it stronger within the range in which the residuals of observational parameters, i.e., the first term of the cost function (9), are almost constant.

Typically, good regularization strategies introduce a sufficiently strong bias into parameter estimation that they favor solutions to equation (1) that have non-zero error and therefore avoid over-fitting.

Suffice it to say that there are good regularizations available on the following kinds of spaces: (i) Stein manifolds, i.e., properly embedded submanifolds of ({mathbb C}^N),   (ii) Projective manifolds, and   (iii) manifolds with the property that there is a hypersurface whose complement is Stein.

One may confuse that whether PBC is always good for regularization problems such as signal processing or image processing, since natural signals or images are often asymmetric.

In practice, hard constraints are computationally expensive, and regularization provides good interpretability.

Let denote the observed raw image data and is the original good image; then the regularization functional-based denoising is given by (1).

Hence, group lasso is a good choice for eigenphone matrix regularization.

However, the result of the Lasso type (L1) regularization is not good enough for interpretation, especially in genomic research.

As shown later in section " Ill-conditioning, cross-validation and overfitting", the predictions of the calibrated models using good prior knowledge in the regularization agree with the cross-validation data and thus generalize better.