Sentence examples for kernel I from inspiring English sources

To prove the necessity of mSVDD, we also compare mSVDD with CVM using the linear kernel (i.e., LCVM).

We observe strong selective pressure on mean dispersal distance (i.e., the first moment), and weaker, but significant, one on the shape of dispersal kernel (i.e., higher moments).

The translation parameter of the kernel, i.e., ({mathbf {w}}_{t}in {mathbb {R}}^{2}) models the time-varying advective displacement, i.e., the spatial drift of the rain storm, and the dilation parameter of the kernel, i.e., ({mathbf {D}}in {mathbb {S}}^{2}_) models the diffusion.

With each additional order (jerk, snap, crackle, pop, …), the computational cost of the combined M2L kernels is not more than the corresponding multiple of the ordinary M2L kernel (i.e. acceleration plus jerk are twice as costly as just acceleration).

Therefore, the necessity to encode a chemical structure as a numerical vector using a set of explicitly calculated molecular descriptors could be avoided by using a structured kernel (i.e., a kernel that takes non-numerical objects).

Equations 14 and 15 lead to the definition of the quadratic TFDs with a separable kernel, i.e. a function with, respectively, independent Doppler and lag kernel filters: γ ( t, f ) = g ( t ) H ( f ), (16) g = G h.

First, the high dimensional data space is mapped into the reproducing kernel Hilbert space (RKHS) rather than the Hilbert space, with a mixture of kernels, i.e. a linear combination between a local kernel and a global kernel.

Under these circumstances, the response of the noise control treatment can be formally obtained by means of convolution integrals involving simple analytical kernels (i.e. Green functions).

Two different yet complementary network properties, i.e., local connectivity and global topological properties are quantified by computing two different types of kernels, i.e., a vector-based kernel and a graph kernel.

Second, three kernels (i.e., EP, CG and MG) of the NAS parallel benchmarks (NPBs), which are abstracted from many legacy computational fluid dynamics applications, are implemented with the above two patterns.

To describe the aggregation of free clusters, three typical aggregation kernels (i.e. diffusion-limited and reaction-limited cluster aggregation as well as simple shear aggregation) have been used.