Either the kernel space distorts the semantics of the visual space or the dataset is not linear separable in the kernel space, the classification performance cannot be guaranteed.
Therefore, there have been several attempts to provide smoother estimates, either by kernel estimates (see, for instance, [30]) or by projection on an orthonormal basis (see, for instance, [7] for the use of splines).
As shown in Supplementary Material 8, the performance of OKLC is also comparable to the best performance obtained either by kernel combination or Laplacian combination.
We study the diameters of sections of convex bodies in RN determined by a random N×n matrix Γ, either as kernels of Γ∗ or as images of Γ. Entries of Γ are independent random variables satisfying some boundedness conditions, and typical examples are matrices with Gaussian or Bernoulli random variables.
Using a stochastic transformation process, a nonlocal stochastic Swift-Hohenberg equation is studied with either a positive kernel or a non-negative kernel.
However, short-range singularities in either the colonisation kernel or landscape correlations can give rise to qualitatively different behaviour.
She spends much of every day slowly working through the piles of corn, picking off the dry kernels either for making cornmeal or for feeding her pigs.
The basic reason is that these OSes can rarely support a thoroughly secure or isolated environment either between OS kernel-mode components or between user-mode softwares.
The observed close temporal correlation or delay of Hα emission with HXR emission points out to the precipitation either of electron (kernels 1 and 3) or protons (4 and 7).
Here integrals are evaluated analytically in the neighborhood of the singularity by expanding the integrands, excluding the singular kernels, either analytically or numerically in a Taylor series or a Maclaurin series.
