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Following on the regularized regression approach, many different models with different regularization methods were proposed.
Some regularization methods are presented.
This can be controlled by several regularization methods, including shrinkage and applying the SGB method.
Model regularization methods, leave-one-out cross-validation (LOOCV), and permutation tests controlled for overfitting.
Effects of these regularization methods are numerically discussed.
Churchill, N. W., Yourganov, G. & Strother, S. C. Comparing within-subject classification and regularization methods in fMRI for large and small sample sizes.
Different regularization methods have been explored in the literature to avoid overfitting in deep learning models.
We compare three regularization methods for logistic regression: variable selection, lasso, and ridge.
For testing the performance of our proposed regularization method, we compare four different regularization methods.
We also discuss the design of efficient regularization methods for ill-conditioned reconstruction problems.
Recently the deconvolution and regularization methods have greatly improved spatial resolution of the beamforming methods.
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