Kernel-based SVM Algorithm

A Kernel-based SVM Algorithm is an SVM algorithm that ...

• Example(s):
  • a Radial-Kernel SVM.
• See: Linear SVM.

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References

2016

• https://www.quora.com/Why-is-kernelized-SVM-much-slower-than-linear-SVM
  • QUOTE: Basically, a kernel-based SVM requires on the order of n^2 computation for training and order of nd computation for classification, where n is the number of training examples and d the input dimension (and assuming that the number of support vectors ends up being a fraction of n, which is shown to be expected in theory and in practice). Instead, a 2-class linear SVM requires on the order of nd computation for training (times the number of training iterations, which remains small even for large n) and on the order of d computations for classification.

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