mlpack::kernel(3) Kernel functions.

## 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.

## Detailed Description

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:

\.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&);
```

## Author

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