Similar to the procedure described by Mumford et al. (2012), a separate general linear model was run for each individual trial, with the first regressor containing a stick function mapped to the onset of the individual trial and the second regressor containing stick functions modeling all of the other trials, with additional motion and nuisance regressors as described above.
Then, in the second step, we use a kernel function to map each VSMMD to a feature space typically used for classification.
The algorithm can use a kernel function to map the problem into a high dimensional space where the problem can be easier to solve.
SVMs use a kernel function to map a set of training vectors into a higher dimensional space.
where is an unknown function, maps from to, and map from to, and satisfies the assumption for.
Figure 2 illustrates an example of GNSS receiver functions mapped on to this canonical architecture.
Furthermore, the output of the SVM classifier was postprocessed by a sigmoid function to map the SVM output into probabilities.
During each Rep, we map a different function to the elements of our array (e.g. if Rep Count = 1024, we map 1024 different functions onto each of the array elements, one per Rep).
This is generally obtained by applying a mapping function to STEC measurements, which leads to important calculation errors.
Even in This lower-dimensional space, turnstructure of anquantizatioptimalely, the boundary of individual cells, can still be difficult to spartitioning
A mapping function to MOS-LQO is described under ITU-T Recommendation P.862.1, covering a range from 1 (bad) to 5 (excellent).
