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Support vector machine

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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.

References

  • N. Cristianini and J. Shawe-Taylor. AN INTRODUCTION TO SUPPORT VECTOR MACHINES (and other kernel-based learning methods). Cambridge University Press, 2000. ISBN 0-521-78019-5

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