Sentence examples for sum of kernels from inspiring English sources

The KDE approximates the underlying distribution by a sum of kernels.

To classify data, MKL considers a linearly weighted sum of kernels instead of a single kernel.

Fixed weights for kernels are computed in the training phase and the weighted sum of kernels is computed.

In order to prove that the localized weighted sum of kernels is positive semi definite, we use the definition of a quasi-conformal transformation.

If we prove that the localized weighted sum of kernels ( {sum}_{k=1}^m{pi}_kleft({x}_i^kright){pi}_kleft({x}_j^kright){K}_kleft({x}_i^k,{x}_j^kright) ) is a positive semi definite kernel matrix, then (12) can be solved as a standard canonical SVM problem.

In particular f(x) = α + 1- α) f 1 x) - αf 2 x) (for α fixed, 0 < α < 1) where each f l (l = 1,2) is a positive finite sum of kernels centered at arbitrary points { z i} ⊂ V where each 0 < f l (x) < 1 in V. (Denote the class of such f 's by PKα (V ).

A common method for creating a probability density is to use Kernel Density Estimation [4, 5], which approximates the true density by a sum of kernel functions.

A SVM is constructed as a sum of kernel functions K, f ( x ) = ∑ i = 1 L α i t i K ( x, x i ) + d, (9).

In a number of problems in computational physics, a finite sum of kernel functions centered at N particle locations located in a box in three dimensions must be extended by imposing periodic boundary conditions on box boundaries.

When training any kernel method (e.g. an SVM or a kernel regression method) with this kernel, the resulting function is a weighted sum of kernel evaluations (1).

We will assume that f (x) is a constant plus a finite sum of kernel functions centered at arbitrary points in a given sphere V about x0 in k dimensional space.