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In [[machine learning]], the ('''Gaussian''') '''[[radial basis function]] kernel''', or '''RBF kernel''', is a popular [[Positive-definite kernel|kernel function]] used in various [[kernel method|kernelized]] learning algorithms. In particular, it is commonly used in [[support vector machine]] [[statistical classification|classification]].<ref name="Chang2010">{{cite journal | last1 = Chang | first1 = Yin-Wen
The RBF kernel on two samples '''x''' and '''x'''', represented as feature vectors in some ''input space'', is defined as<ref name="primer">Jean-Philippe Vert, Koji Tsuda, and Bernhard Schölkopf (2004). [http://cbio.ensmp.fr/~jvert/publi/04kmcbbook/kernelprimer.pdf "A primer on kernel methods".] ''Kernel Methods in Computational Biology''.</ref>
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==External links==
* [http://charlesmartin14.wordpress.com/2012/02/06/kernels_part_1/ Kernels Part 1: What is an RBF Kernel? Really?]
[[Category:Kernel methods for machine learning]]
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