Radial basis function kernel: Difference between revisions

Revision as of 21:59, 28 November 2016

Revision as of 07:30, 31 December 2016

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==Approximations==

Because support vector machines and other models employing the [[kernel trick]] do not scale well to large numbers of training samples or large numbers of features in the input space, several approximations to the RBF kernel (and similar kernels) have been devisedintroduced.<ref>Andreas Müller (2012). [http://peekaboo-vision.blogspot.de/2012/12/kernel-approximations-for-efficient.html Kernel Approximations for Efficient SVMs (and other feature extraction methods)].</ref>

Typically, these take the form of a function ''z'' that maps a single vector to a vector of higher dimensionality, approximating the kernel: