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This point is illustrated using the Bayesian interpretation of regularization.
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Although regularization may reduce overfitting and sparsity can simplify interpretation of the results, setting the appropriate regularization parameters may be a challenging task.
Interpretation follows the minimization of a functional with two terms: a term of conformity of the 3D interpretation to the image sequence first-order spatio-temporal variations, and a term of regularization based on anisotropic diffusion to preserve the boundaries of interpretation.
For linear discriminant, we used two methods of regularization: principal component analysis, and ridge regularization.
Different norms have different effects of regularization.
For different types of regularization, we establish energy estimates.
It was the first of a series of "regularization programs" that continued over the following decade.
Due to the dimensionality of the features (320 dimensions), some form of regularization was advisable.
Both L1-norm log linear regression and ridge regression solve this problem by means of regularization.
Typically, the amount of regularization needs to be optimized for a given classifier.
The most commonly used form of regularization is the Tikhonov-type regularization.
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