Support Vector Machines for Regression


“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.

Most Cited



Sergios Theodoridis Avatar

Leave a Reply