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Version du 8 mars 2019 à 06:47
The kernel support vector machine is essentially the same as the standard SVM, with a cool trick that allows it to discover non-linear decision boundaries.
Instead of using the data as-is, we throw our data into something called a kernel. The kernel is any function that takes an input with a given dimensionality and returns an output with higher dimensionality, effectively adding more features to your examples.
Contributeurs: Claude Coulombe, Gérard Pelletier, Jacques Barolet, wiki