(1.4) where α plays a role of regularization parameter, data (g^{delta}) represents the measured data of function g.
We describe the framework and present examples in computer graphics and image processing applications, including texture synthesis, flow field visualization, and image and vector field intrinsic regularization for data defined on 3D surfaces.
Even with this region-based regularization, the data term is still not powerful enough to always return the right solution (Table 1), as most of the objects in the volume look very similar (Fig. 1).
A very successful image denoising model is that of Rudin, Osher, and Fatemi (ROF) model [21] which uses TV regularization with data-fidelity term.
Finally, the regularization and data-type weighting parameters andata-type weightinged on an external evaluation discussed in Section 4. So far we have discussed our aparametersr identifying miRNandegulated modules using a C2ndition-specific expression dareset.
The recommendations for this algorithm include early stopping, reweighting used to adjust the sample data, regularization, step size.
where the terms from left are defined as follows: super-resolution or data, regularization potential, and fidelity, respectively.
If (delta =0.01), then the mollification procedure is applied (before the regularization) to the data (g^{delta }), but with time observation T large enough.
Except for the above two methods, the generalized cross-validation (GCV) (Golub et al. 1979) and L-curve (Hansen 1992) methods are often widely used to select the regularization parameter for data inversion in many fields.
To training classifiers, they employ Stochastic Gradient Descent [9] with early stopping, reweighting data, regularization, step size and the computation of dot product is parallelized on 16 cores of their computer.
