Radial basis function
Type of mathematical function / From Wikipedia, the free encyclopedia
Dear Wikiwand AI, let's keep it short by simply answering these key questions:
Can you list the top facts and stats about Radial basis function?
Summarize this article for a 10 year old
In mathematics a radial basis function (RBF) is a real-valued function whose value depends only on the distance between the input and some fixed point, either the origin, so that
, or some other fixed point
, called a center, so that
. Any function
that satisfies the property
is a radial function. The distance is usually Euclidean distance, although other metrics are sometimes used. They are often used as a collection
which forms a basis for some function space of interest, hence the name.
Sums of radial basis functions are typically used to approximate given functions. This approximation process can also be interpreted as a simple kind of neural network; this was the context in which they were originally applied to machine learning, in work by David Broomhead and David Lowe in 1988,[1][2] which stemmed from Michael J. D. Powell's seminal research from 1977.[3][4][5] RBFs are also used as a kernel in support vector classification.[6] The technique has proven effective and flexible enough that radial basis functions are now applied in a variety of engineering applications.[7][8]