A support vector machine (SVM) is an example of a fast, lightweight machine learning technique first discussed by Vladimir Vapnik. Support vector machines are often used as non-linear classifiers, although they can always be shown to be equivalent to linear classifiers of hyperplanes in a high-dimensional space.
An SVM uses a kernel function (often non-linear) to transform distances between sample points before making comparisons. Even in the non-linear case, it can be shown that this is equivalent to performing a transformation of the classification space into another, higher dimensional, space and using a linear classifier on this new space. This technique can also be used when the higher-dimensional space is a Hilbert space, and has
an infinite number of dimensions.
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