Sentence examples for approaches with regularization from inspiring English sources

Comparing with standard regression approaches with regularization, the proposed framework integrates the information theoretic criteria in the generalized Lasso model, and sets the most informative features penalty-free to improve prediction accuracy and enhance model interpretability.

The methods proposed in [14] and [15] belong to the category of SADMM-type approaches with logarithmic-quadratic proximal regularization, which have larger step sizes compared with SADMM.

Some examples include the work of Kang et al. [35], who used a clustering based approach with the MTL regularization method to group similar tasks together, and Chen et al., who used a graph based structured regularization approach to encode the similarity information between tasks [36].

The approach is based on the least squares minimization with regularization constraints provided by the spectral properties of the operator.

The rCCA approach was also applied to these data sets with regularization parameters λ1=0.889 and λ2=0.889 for the first three dimensions (canonical values obtained were of 0.959, 0.925, and 0.881 on each dimension respectively, followed by much lower values).

This study proposes an iterative dual-regression (DR) approach with sparse prior regularization to better estimate an individual's neuronal activation using the results of an independent component analysis (ICA) method applied to a temporally concatenated group of functional magnetic resonance imaging (fMRI) data (i.e., Tc-GICA method).

There is a significant improvement in performance as opposed to a conventional approach with standard L2 regularization, i.e. Figure 9.

For this, we used multiple regression with regularization parameters.

They showed some comparative experiments that FNN with regularization indeed performed better than without regularization.

The embedded methods utilize regression models with regularization.

(2002) with regularization parameter λ = 200.