Definition
Radial basis function networks are a means of approximation by algorithms using linear combinations of translates of a rotationally invariant function, called the radial basis function. The coefficients of these approximations usually solve a minimization problem and can also be computed by interpolation processes. The radial basis functions constitute the so-called reproducing kernels on certain Hilbert-spaces or – in a slightly more general setting – semi-Hilbert spaces. In the latter case, the aforementioned approximation also contains an element from the nullspace of the semi-norm of the semi-Hilbert space. That is usually a polynomial space.
Motivation and Background
Radial basis function networks are a method to approximate functions and data by applying kernel methods to neural networks. More specifically, approximations of...
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Buhmann, M.D. (2011). Radial Basis Function Networks. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_692
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DOI: https://doi.org/10.1007/978-0-387-30164-8_692
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