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Although many input indicators are already in use, more regularized use of activity, output, and outcome indicators is needed.
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Another multi-task learning method making use of regularized regression is the trace norm regularized method proposed by Chen et al. [43].
In addition, the computation of the angular velocity of the rod requires the use of regularized (potential) dipoles.
Nair et al. (2015) studied the use of regularized least square algorithm for multi class learning.
Evgeniou et al. [30] first made use of regularized regression concerned with capturing these shared structures among tasks.
We make use of regularized regression-based multi-task learning to simultaneously learn to predict different personality traits to maximize the recognition performance of leadership and extraversion.
The main objective of the study presented in this paper is to demonstrate use of regularized least square algorithm (RLS) along with random kitchen sink algorithm (RKS) to effectively predict the fresh and hardened stage properties of SCC.
From the methodological point of view, the use of regularized large-scale classifiers is nowadays widely spread, especially in the SVM form.
The use of a regularized friction law allows obtaining good convergence of such models.
The results of this study show that use of the regularized constitutive models significantly improved the accuracy of response predictions.
The use of a regularized contact model in the underlying state problem makes it possible to use efficient smooth optimization algorithms for the systematic synthesis of CCMs.
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