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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

  • SMOLA, Alex J. and Bernhard SCHÖLKOPF, A Tutorial on Support Vector Regression, 1998. [Cited by 309]
  • GUNN, S., Support Vector Machines for Classification and Regression, ISIS Technical Report, 1998. [Cited by 164]
  • COLLOBERT, R and S BENGIO, SVMTorch: Support Vector Machines for Large-Scale Regression Problems, Journal of Machine Learning Research, 2001. [Cited by 154]

    Bibliography