KernelFunctioncomputes real-valued proximities between vectors. Note that proximity runs in the reverse direction from distance: the more similar two vectors are, the greater their proximity.
Implementations of the standard kernel functions used for
machine learning are provided in this package, including
See those classes' documentation for definitions of the specific
Typically kernel functions will be functions that could,
in theory, be represented by inner products of vectors
f maps an n-dimensional
input vector to an m-dimensional or even infinite-dimensional
f(v). The kernel function is then
kernel(v1,v2) = f(v1) * f(v2), where
f(v) is the embedding function and
represents the dot-product.
The use of kernel functions is usually for the so-called "kernel trick", which allows classification or clustering in high-dimensional spaces by embedding a lower-dimensional space and then working with linear combinations of kernel function results.