Kernel-based SVM Algorithm: Difference between revisions
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=== 2016 === | === 2016 === | ||
* https://www.quora.com/Why-is-kernelized-SVM-much-slower-than-linear-SVM | * https://www.quora.com/Why-is-kernelized-SVM-much-slower-than-linear-SVM | ||
** QUOTE: Basically, a [[Kernel-based SVM Algorithm|kernel-based SVM]] requires on the order of n^2 computation for training and order of | ** QUOTE: Basically, a [[Kernel-based SVM Algorithm|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. | ||
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Latest revision as of 17:59, 23 August 2023
A Kernel-based SVM Algorithm is an SVM algorithm that ...
- Example(s):
- See: Linear SVM.
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.