Jørgensen et al. [ 14] used a regularized dynamic programming approach to register multiple OCT volumes with each other in order to improve the signal to noise ratio.
Given any of the input feature sets, we use an L1-regularized logistic regression approach to train a binary classifier using the R glmnet package (Friedman et al., 2010).
The so-called regularized FD (Finite Difference) adaptation, a modified regularized approach where the off-equilibrium part of the stress tensor is computed thanks to a finite difference scheme, is the only tested adaptation that can handle the high Reynolds computation.
The authors in [18] propose a bivariate total variation regularization model to calibrate the volatility (sigma=sigma(S,t)), the existence and convergence for the regularized approach are discussed and some numerical experiments are presented.
The stability and convergence for the regularized approach are also presented in the last section.
Furthermore, the stability and convergence for the regularized approach are discussed.
The method of regularized Stokeslets is a numerical approach to approximating solutions of fluid structure interaction problems in the Stokes regime.
Due to the number of features is much larger than the number of observations, simple, highly regularized approaches are in pressing need.
With the training set, we use the representative features (R1 - R4) and proposed regularized approaches for estimation.
