ML Anthology

Support Vector Method for Function Approximation, Regression Estimation and Signal Processing

Vladimir Vapnik, Steven E. Golowich, Alex J. Smola
NeurIPS 1996 pp. 281-287
/neurips/1996/vapnik1996neurips-support/

Abstract

The Support Vector (SV) method was recently proposed for es(cid:173) timating regressions, constructing multidimensional splines, and solving linear operator equations [Vapnik, 1995]. In this presenta(cid:173) tion we report results of applying the SV method to these problems.

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Text

Vapnik et al. "Support Vector Method for Function Approximation, Regression Estimation and Signal Processing." Neural Information Processing Systems, 1996.

Markdown

[Vapnik et al. "Support Vector Method for Function Approximation, Regression Estimation and Signal Processing." Neural Information Processing Systems, 1996.](https://mlanthology.org/neurips/1996/vapnik1996neurips-support/)

BibTeX

@inproceedings{vapnik1996neurips-support,
  title     = {{Support Vector Method for Function Approximation, Regression Estimation and Signal Processing}},
  author    = {Vapnik, Vladimir and Golowich, Steven E. and Smola, Alex J.},
  booktitle = {Neural Information Processing Systems},
  year      = {1996},
  pages     = {281-287},
  url       = {https://mlanthology.org/neurips/1996/vapnik1996neurips-support/}
}