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One way to construct such a ''z'' is to randomly sample from the [[Fourier transformation]] of the kernel.<ref>Ali Rahimi and Benjamin Recht (2007). [http://www.eecs.berkeley.edu/~brecht/papers/07.rah.rec.nips.pdf "Random features for large-scale kernel machines"]. ''Neural Information Processing Systems''.</ref> Another approach uses the [[Nyström method]] to approximate the [[eigendecomposition]] of the [[Gramian matrix|Gram matrix]] ''K'', using only a random sample of the training set.<ref>{{cite journal |authors=C.K.I. Williams and M. Seeger|title=Using the Nyström method to speed up kernel machines |journal=Advances in Neural Information Processing Systems |year=2001 |url= http://papers.nips.cc/paper/1866-using-the-nystrom-method-to-speed-up-kernel-machines}}</ref>
==External links==▼
* [http://charlesmartin14.wordpress.com/2012/02/06/kernels_part_1/ Kernels Part 1: What is an RBF Kernel? Really?]▼
==See also==
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{{reflist|30em}}
▲==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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