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We force any inversion model to be isotropic by setting the regularization parameter for anisotropy to a value of 106.
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has more flexibility in setting the regularization parameter for each task separately, which is not the case in Coupled.
In our experiments, we set the regularization parameters of the TSS algorithm by CV to c0 = 0.5 and c = 1.
Although regularization may reduce overfitting and sparsity can simplify interpretation of the results, setting the appropriate regularization parameters may be a challenging task.
It was shown that the performance of the similarity measure was substantially improved by setting an extremely small regularization constant, 10−10.
To set the two regularization parameters we conducted a grid search, evaluating each combination of parameters using 10-fold cross-validation on the training set.
We set C, the regularization parameter, to 100 based on the development data.
Because no noise is present, regularization is switched off by setting λ = 0.
For this set the separation of the regularization parameters is very important.
In our experiments, we have observed that adding a small amount of ridge regularization to SW results in a slight performance improvement; the regularization parameter can be set using CV; alternatively, we have observed good performance by setting it to (i.e. 0.1% of the total number of observations).
We set C, the regularization parameter, to 10 000, since preliminary experiments indicate that preferring generalization to overfitting (by setting C to a small value) tends to yield poorer classification performance.
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