Non-Linear Kernel-based Support Vector Machine Algorithm

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A Non-Linear Kernel-based Support Vector Machine Algorithm is an SVM training algorithm that can be implemented by a non-linear SVM training system to solve a non-linear SVM training task (to produce a non-linear SVM based on a non-linear kernel).



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 find 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 find 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.

2010