

Kernel Approximations for Efficient SVMs (and other feature extraction methods) - achompas
http://peekaboo-vision.blogspot.com/2012/12/kernel-approximations-for-efficient.html

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achompas
We recently saw "Why use SVM" hit the front page of HN, but I feel this is a
much more rigorous treatment of the same topic.

In particular, Andreas does a great job discussing the relevance of the kernel
to SVMs, its computational complexity, and methods to minimize that complexity
via inner product space approximation.

Interesting to see how the Nystrom and Fourier approximations to the inner
product space almost match the full kernel's accuracy in 1/3rd and 1/5th as
much time, respectively. It is rare that one needs that extra bit of accuracy,
so I imagine Nystrom would be far more appealing for anyone using sklearn.SVM.

