Corpus ID: 15046500

Support Vector Regression

@inproceedings{Basak2007SupportVR,
  title={Support Vector Regression},
  author={D. Basak and S. Pal and D. C. Patranabis},
  year={2007}
}

D. Basak, S. Pal, D. C. Patranabis

Published 2007

Mathematics

Instead of minimizing the observed training error, Support Vector Regression (SVR) attempts to minimize the generalization error bound so as to achieve generalized performance. The idea of SVR is based on the computation of a linear regression function in a high dimensional feature space where the input data are mapped via a nonlinear function. SVR has been applied in various fields - time series and financial (noisy and risky) prediction, approximation of complex engineering analyses, convex… CONTINUE READING

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