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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.