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The purpose of the regularization term is to control over-fitting of data by unrealistic, spurious oscillations of the resistivity model.
The main purpose of the nonlocal regularization is to generalize the local gradient and divergence concepts into the local form.
We assess the accuracy with which the correct regulatory relationships within the networks are extracted, and consider alternative methods of regularization for the purpose of overfitting avoidance.
For this purpose, Tikhonov regularization has been applied, and generalized cross-validation (GCV) has been introduced as an effective method for determining the proper amount of regularization without prior knowledge of either the source distribution or the contaminating errors.
For linear discriminant, we used two methods of regularization: principal component analysis, and ridge regularization.
For different types of regularization, we establish energy estimates.
This point is illustrated using the Bayesian interpretation of regularization.
The second part, we'll move on and actually see an instance of regularization.
The effects of regularization in computer simulations (left) and in a knee scan (right).
Different norms have different effects of regularization.
It was the first of a series of "regularization programs" that continued over the following decade.
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