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.
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.
The inner product definition used by a kernel.
Kernel representation, can be either dense or sparse