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Recent numerical methods for the evaluation of irregular oscillatory integrals are applied to the kernel in theN=2 case.
Feature space normalisation is applied to the kernel rather than to the input vectors.
Then, standard PCA was implicitly applied to the kernel matrix.
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In this paper, a novel error estimation procedure is presented that is based on the enhanced assumed strain (EAS) method and applied to the reproducing kernel particle method (RKPM) as a representative of Galerkin meshfree methods.
This splitting method cannot be applied to the M2L kernel (3b) (because it is not a translation), which occurs many more times in the FMM algorithm than the M2M and L2L kernels.
In this paper we present a preconditioned multi-domain algorithm applied to the elliptic kernels arising from the spectral collocation of the incompressible Navier Stokes equations in three space dimensions with one homogeneous direction.
The p-spectrum-based features also have histogram shapes, and they can be applied to the histogram intersection kernel.
Modifying the Nyström method with local corrections for singular integrands allows the method to be applied to the inherently singular kernels of frequency-domain electromagnetic integral equations.
Later, exploratory data analysis was applied to the processed datasets, namely kernel Principal Component Analysis (kPCA) and spectral and hierarchical clustering, to group contrasts from different microarray experiments and to find genes regulated in a specific cluster.
Additionally, we have investigated the performance of a string kernel (the k-mer kernel) along with other types of kernels, applied to the pure amino-acid sequences (i.e. without any additional information).
We use an adaptive 3D convolution kernel applied to the voxels of the distance transform model.
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