Sentence examples for penalizing coefficient from inspiring English sources

The accuracy of the DDG scheme is heavily dependent on the penalizing coefficient for the odd ordered schemes.

On the other hand, the DDG2 and DDG3 schemes were mildly dependent on the penalizing coefficient for both odd and even orders and retain their accuracy even on highly irregular grids.

From the description of Equation (1), the L1 penalized coefficient vector for our linear model is defined as (2) θ ^ = | | y - H θ | | 2 2 + λ | | θ | | 1 where lambda is the regularization parameter, || θ ||1 is the L1-norm of the parameter vector.

For each choice of the penalty parameter a stationary solution exists often visualized as a regularization path, i.e. the penalized coefficients over all penalty terms.

(2) For each b*, estimate the coefficients β ^ L A S S O in local penalized logistic regressions using each variable as outcome variable and the remainder as predictors for a penalty term t. (3) For each variable a define the neighborhood n e a * as the set of variables b corresponding to non-zero penalized coefficients β ^ b a : n e a * = { b ∈ υ : β ^ b a ≠ 0 }.

This is done by penalizing predictors' coefficients towards zero according to their association with survival.

Smoothing splines and penalized splines have circumvented the problem of choosing the knot locations by constructing a very large spline basis and then penalizing the spline coefficients to reduce the effective number of df [ 40].

Lasso shrinks regression coefficients toward zero by penalizing the sizes of the coefficients but uses absolute values instead of the squared values.

In fact, we estimate that the probability to partially retire at the age of 60 would be reduced by 50% or more if partial pensions were penalized through reduction coefficients.

However, we also observed in all scenarios that if inbreeding was not penalized, large inbreeding coefficients could be accumulated over time.

From this representation we can understand that the added regularization term λ β T Lβ imposes the smoothness of parameters (coefficients) β over the network via penalizing the weighted sum of squares of the scaled difference of coefficients between neighboring vertices in the network.