mlpack_kernel (3)

Classes

class CosineDistance

The cosine distance (or cosine similarity). class EpanechnikovKernel

The Epanechnikov kernel, defined as. class ExampleKernel

An example kernel function. class GaussianKernel

The standard Gaussian kernel. class HyperbolicTangentKernel

Hyperbolic tangent kernel. class KernelTraits

This is a template class that can provide information about various kernels. class KernelTraits< CosineDistance >

Kernel traits for the cosine distance. class KernelTraits< EpanechnikovKernel >

Kernel traits for the Epanechnikov kernel. class KernelTraits< GaussianKernel >

Kernel traits for the Gaussian kernel. class KernelTraits< LaplacianKernel >

Kernel traits of the Laplacian kernel. class KernelTraits< SphericalKernel >

Kernel traits for the spherical kernel. class KernelTraits< TriangularKernel >

Kernel traits for the triangular kernel. class KMeansSelection

class LaplacianKernel

The standard Laplacian kernel. class LinearKernel

The simple linear kernel (dot product). class NystroemMethod

class OrderedSelection

class PolynomialKernel

The simple polynomial kernel. class PSpectrumStringKernel

The p-spectrum string kernel. class RandomSelection

class SphericalKernel

class TriangularKernel

The trivially simple triangular kernel, defined by.

Kernel functions.

This namespace contains kernel functions, which evaluate some kernel function $ K(x, y) $ for some arbitrary vectors $ x $ and $ y $ of the same dimension. The single restriction on the function $ K(x, y) $ is that it must satisfy Mercer's condition:

\[ \int \int K(x, y) g(x) g(y) dx dy \ge 0 \].PP for all square integrable functions $ g(x) $.

The kernels in this namespace all implement the same methods as the ExampleKernel class. Any additional custom kernels should implement all the methods that class implements; in addition, any method using a kernel should rely on any arbitrary kernel function class having a default constructor and a function

double Evaluate(arma::vec&, arma::vec&);

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