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However, in all the following experiments, we will consider a constant regularization (delta = 20{sigma _{x}^{2}}) for the NLMS, NPVSS-NLMS, and OSS-NLMS-id algorithms.
Other conditions are the same as in Fig. 5. Nevertheless, the OSS-NLMS-id algorithm still requires a constant regularization parameter, especially in case of non-stationary inputs like speech.
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Nevertheless, we tried optimizing the L1-regularized error function E1 for regularization constant c ranging from 0 to 10 in increments of 0.1.
It can be noticed that the contraction term from (19) always decreases when the regularization constant increases, while the expansion term from (20) always increases when the regularization constant decreases.
There was no constraint posited on the dipole orientation (we used free orientation), the regularization constant was 1% and we did not apply any normalization (although we did use the residual variance fit criterion).
The regularization constant was chosen separately for each set.
The regularization constant is usually obtained through an ad-hoc method (Tarantola, 2005).
where C is a hyperparameter, termed regularization constant, whose value is found by cross-validation.
where is a positive constant called the regularization parameter and called -norm SVM loss.
Grid search is used to find optimal choice of regularization constant C and gamma.
The classifier was a set of linear one-vs-all SVMs with regularization constant C = 0.01.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com