Radial basis function kernel: Difference between revisions

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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 Chang,| last2 = Hsieh | first2 = Cho-Jui Hsieh,| last3 = Chang | first3 = Kai-Wei Chang,| Michaellast4 = Ringgaard and| first4 = Michael | last5 = Lin | first5 = Chih-Jen Lin| year = (2010). [http://jmlr.org/papers/v11/chang10a.html| title = "Training and testing low-degree polynomial data mappings via linear SVM"] | url = http://jmlr.org/papers/v11/chang10a.html | journal = ''J. Machine Learning Research'' '''| volume = 11''': | issue = | pages = 1471–1490. }}</ref>
 
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]]