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Finally note that in practice, we used the "regularized" version of (4) proposed in [27]: a diagonal "penalizing" term is added to the inverted matrix in (4) to fix possible ill-conditioning problems.
To this end we introduce a sort of "regularized" version to reduce possible overfitting problems in the learning process.
In order to overcome these disadvantages, in [14] authors provided a regularized version of the NI function.
Preliminary 2D investigations are also conducted by using a regularized version of the adopted formulation.
A regularized version of the model is constructed based on the level-set method.
For the implementation of, we use the compactly supported regularized version which is defined as (21).
Similar(27)
We introduce Iterative Regularized Kernel Regression (IRKR), an iterative nonlinear feature selection method combined with a Lasso-regularized version of the original MLKR formulation that improves on the state-of-the-art results on several AU databases, ranging from prototypical to natural and wild data.
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).
First, the non-regularized version of the NLMS algorithm is considered (also imposing that the normalized step-size is time dependent), with the update begin{array}rcl@ widehat{mathbf{h}}(n) = widehat{mathbf{h}} n-1) + frac{alpha(n)mathbf{x} n-1e(n)} { left| mathbf{x}(n) right|^{2}_{2} }. end{array} (26).
In this case, the blind deconvolution problem can be regularized and the sharp version of the blurry image can be recovered at a new latent semantic level.
Also, it is noted that the smaller the positive number ϵ is, the closer this regularized model is to its original version ((mathcal{M}_{0})).
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