“The Support Vector method can also be applied to the case of regression, maintaining all the main features that characterise the maximal margin algorithm: a non-linear function is learned by a linear learning machine in a kernel-induced feature space while the capacity of the system is controlled by a parameter that does not depend on the dimensionality of the space.” Cristianini and Shawe-Taylor (2000) “In SVM the basic idea is to map the data x into a high-dimensional feature space F via a nonlinear mapping ?, and to do linear regression in this space (cf. Boser et al. (1992); Vapnik (1995)).” M?uller et al.

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