Sentence examples for kernel Family from inspiring English sources

Therefore, the vector f=[f z1),…,f z P )] is distributed as (p(mathbf {f}|mathbf {Z},kappa,beta,delta)=mathcal {N}(mathbf {f};mathbf {0},mathbf {K})) where 0 is a 1×P vector, K:=κ z i,z j ), for all i,j=1,…,P is a P×P matrix, and we have expressed explicitly the dependence on the choice of the kernel family κ in Eq. (22).

In the following, we name this kernel family "C-kernels".

In most cases, the kernel families that we develop are closed under convolution so that multiple steps of a kernel remain within the same family.

These kernel families are constructed by randomizing convolutions of stepping-stone kernels and have interpretations in terms of population heterogeneity and heterogeneous physical processes.

Following the fast subtree kernel, a family of Weisfeiler-Lehman kernels was later designed [ 29], including the Weisfeiler-Lehman edge kernel and the Weisfeiler-Lehman shortest path kernel.

It is shown that the decomposition of positive definite kernels involving families of forms also belongs to the present context.

satisfying d i δ j  = dj−1δ i for 0 ≤ i < j ≤ k + 1, is said to be the simplicial kernel of the family of face homomorphisms (d0,…,d k ) if it has the following universal property: given any family (x0,…,xk+1) of k + 2 homomorphisms Open image in new window.

Pickus' Spring Breakthrough course came from a "family kernel"—his teaching of the founding period, his wife's work with primary sources at the Rubenstein Rare Book and Manuscript Library, and his daughter's love for music and Hamilton.

which satisfies the equalities d i x j  = dj−1x i  (0 ≤ i < j ≤ k + 1) with the top face maps of the truncated simplicial algebra, there exists a unique homomorphism x = < x0,…,xk+1 > : Y → Xk+1 such that δ i  x = x i. Simplicial kernels exist and given the simplicial kernel Xk+1, the family of homomorphisms ( α k + 1, j, …, α 1 j, α 0 j ) Open image in new window.

It can be shown that the kernel function or family selected determines the functional form of all possible solutions considered [ 19].

Using Markov chains to build kernels for non-standard data is novel and the resulting kernels can potentially benefit the entire family of kernel methods (e.g. support vector machines) for genetic data analysis.