Crate linfa_kernel[−][src]
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
inner |
Structs
KernelBase | A generic kernel |
KernelParams | Defines the set of parameters needed to build a kernel |
Enums
KernelMethod | The inner product definition used by a kernel. |
KernelType | Kernel representation, can be either dense or sparse |
Type Definitions
Kernel | Type definition of Kernel that owns its inner matrix |
KernelView | Type definition of Kernel that borrows its inner matrix |