Using the property of ExD for kernel G(n, s), we have | u | Z ≤ | p | Z ∑ s = - ∞ n M μ ( n - 1 - s ) ≤ | p | Z M ( 1 - μ ) μ ≤ M ( 1 - μ ) μ ( | p 1 | Z + | P o | Z ), where p(n) = p1(n) + p0(n), that p1(n) is almost periodic function and p0(n) is ergodic perturbation.
As a consequence, one may get a consistency property for the weighted kernel estimators in the model (2.6).
A lifting property for subordinated invariant kernels is proved and extended to operator-valued kernels.
One of the important properties of kernel methods is that they can be applied to any kind of input domain, for which it is possible to construct an appropriate kernel.
Basic mathematical operations such as multiplication, addition, and exponentiation preserve properties of kernel matrices and hence produce valid kernels.
Physical properties of kernels, grains, and seeds are necessary for the design of equipment to handle, transport, process and store the crop.
Prerequisites for kernel learning are similarity measures with the mathematical property of positive semidefiniteness (kernels).
For this property of the kernel structure matrix, exploring of ({varvec{KS}}) values allows us to find potentially intensive economic transactions.
Like apricots, the stone contains a kernel with a bitter almond flavour called a noyau (French for kernel or pit).
For (mathbf {H}(mathbf {curl})) conforming discretizations, which fulfill the discrete sequence property, the kernel of the system matrix is known.
