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Exact(38)
We can see that the regularization method gives better predictions than the non-regularized for a wide range of prior quality.
Using a similar approach as before, the only-regularized version of the NLMS algorithm is considered (also imposing that the regularization parameter is time dependent), with the update begin{array}rcl@ widehat{mathbf{h}}(n) = widehat{mathbf{h}} n-1) + frac{mathbf{x} n-1e(n)} { left| mathbf{x}(n) right|^{2}_{2} + delta(n) }. end{array} (35).
Our numerical experiments show that the regularization successfully restores the design rate of convergence.
Under some conditions, we know that the regularization method is weakly convergent.
Note that the regularization parameter τ n is the same on both sides of the inequality.
It is well known that the regularization method plays an important role in solving a constrained convex minimization problem.
Similar(22)
Numerical examples that illustrate the regularization process show that when the local filtering is applied to the force distribution, calculated from noisy data, reasonable results are obtained.
The function ϕ:ℜ → ℜ is a diffusion function that controls the regularization behavior.
Motivated by these facts, we propose a modified GLRT-based detector that achieves the regularization of the unknown covariance matrix with the help of spatial signatures.
People waiting in line display a sign that reads, "The Regularization Plan is a failure.
A three-dimensional grid is used for the optimization of the parameters, that is, the regularization parameter, the number of genes selected from the microarray data sets, and the number of proteins or CNVs obtained from the proteomics data sets or the genomics data set, respectively.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com