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It is known as the l1-regularized regression model in regularization theory.
The "velocity smoothing" method is the simpler of the two, and removes the discontinuity in regularization from one of the two equations where it occurs.
In "Regularization by sets of functions of ε-approximation-type scale.
Examples are assistance in regularization of residence and labour and gaining access to housing and public services.
This demonstrates the effectiveness of the proposed smoothed CCA approach in regularization when the estimated signals are believed to be continuous and smooth.
Thus, for words with highly unusual spelling-to-sound correspondences, task parameters that bias performance toward direct mapping from spelling to sound can result in regularization errors [25]; see [28] for an alternative interpretation of this finding).
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Support Vector Machines (SVMs) are classifiers that feature a built-in regularization mechanism, and are guaranteed to produce classifiers of optimal complexity.
Since the number of variables is very large and the size of the training set is relatively limited, support vector machine (SVM) classifiers were deemed appropriate because of their built-in regularization mechanism [19].
Since the size of the training set is not very large with respect to the number of variables, support vector machine classifiers were deemed appropriate because of their built-in regularization mechanism.
We note that SVMs have built-in regularization however, which means that the learning algorithm penalizes large weights of predictors thus favoring simpler models by implicitly selecting genes, without using explicit gene selection procedures [49], [50].
Since recent progress in regularization-based system identification has enabled the practical estimation of the third-order Volterra kernel, these tensor factorization based approaches have become attractive.
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