The phrase "a regularized form" is correct and usable in written English.
It can be used in contexts where you are discussing a standardized or modified version of something, often in technical or academic writing.
Example: "In our study, we present a regularized form of the equation to improve its stability and accuracy."
Alternatives: "a standardized version" or "a modified form".
Parameters for the algorithms were fitted by ridge regression, a regularized form of linear regression, using recorded neural and behavioral (joystick position and velocity) data.
To overcome the rank deficiency issue we propose using a regularized form of logistic regression [ 19].
The Pathifier algorithm was recently supplemented with Cox regression and a regularized form of least-squares regression (the L-LASSO -or L-norm least absolute shrinkage and selection operator method) to achieve more accurate prognosis prediction.
When the sample size was small, a regularized form of the test was used to guard against false positives that may appear due to under-estimated gene-specific variance.
The other exemplar (083M02), is dominated by herbs, consisting of agricultural lands in a regularized pattern.
The approach is very general, which makes it possible to solve the hypersingular BIE numerically in a non-regularized form by using conforming C0 quadratic boundary elements and standard Gaussian quadratures without paying special attention to the corner treatment.
Irregular verbs and nouns may also pose serious problems, resulting in the production of nonexistent regularized forms.
The image reconstruction method, recovering c from Φ s, is formed as a regularized optimization problem of the form (10) θ ^ = argmin ‖ W (Φ s − Kc ‖ 2 2 + α ‖ L θ ‖ 2 where W represents the structure of the noise corrupting the data. The first term in Eq. (10) requires that the estimated value of c is consistent with the observed measurement of Φ s.
In this work we present a data-driven technique for computing boundary values by solving a regularized and well-posed form of the deconvolution problem on an extended domain.
We then apply the discrete weighted matrix form of each stabilizing functional to build a unified form of the objective functional, allowing us to perform a regularized inversion with variety of stabilizing functionals in the same framework.
We provide a M-estimator to estimate principal component subspaces which takes the form of a regularized eigenvalue problem.
