public interface KernelFunction extends Proximity<Vector>
KernelFunction computes 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 DotProductKernel, PolynomialKernel, GaussianRadialBasisKernel, and HyperbolicTangentKernel.
See those classes' documentation for definitions of the specific
kernel functions.
Typically kernel functions will be functions that could,
in theory, be represented by inner products of vectors
f(v), where f maps an n-dimensional
input vector to an m-dimensional or even infinite-dimensional
vector f(v). The kernel function is then
defined as 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.
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