A radial basis function (RBF) is a real-valued function whose value depends only on the distance from the origin, so that Phi(x) = Phi(||x||); or alternatively on the distance from some other point c, called a center. Any function Phi that satisfies this property is a radial function. The norm is usually Euclidean distance, although other distance functions are also possible. For example, using Lukaszyk-Karmowski metric, it is possible for some radial functions to avoid problems with ill conditioning of the matrix solved to determine coefficients wi, since the ||x|| is always greater than zero. 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 invented, by David Broomhead and David Lowe in 1988. RBFs are also used as a kernel in support vector classification. … Radial Basis Function (RBF)

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