Redirected from Support Vector Machine
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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