# Crate linfa_kernel

source · [−]## Expand description

### Kernel methods

Kernel methods are a class of algorithms for pattern analysis, whose best known member is the support vector machine. They owe their name to the kernel functions, which maps the features to some higher-dimensional target space. Common examples for kernel functions are the radial basis function (euclidean distance) or polynomial kernels.

### Current State

linfa-kernel currently provides an implementation of kernel methods for RBF and polynomial kernels, with sparse or dense representation. Further a k-neighbour approximation allows to reduce the kernel matrix size.

Low-rank kernel approximation are currently missing, but are on the roadmap. Examples for these are the Nyström approximation or Quasi Random Fourier Features.

## Re-exports

`pub use inner::Inner;`

`pub use inner::KernelInner;`

## Modules

## Structs

A generic kernel

Defines the set of parameters needed to build a kernel

## Enums

The inner product definition used by a kernel.

Kernel representation, can be either dense or sparse

## Type Definitions

Type definition of Kernel that owns its inner matrix

Type definition of Kernel that borrows its inner matrix