Support vector method for function approximation, regression estimation, and signal processing (1997) [116 citations — 26 self]
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by Vladimir Vapnik, Steven E. Golowich, Alex Smola
Advances in Neural Information Processing Systems 9
http://www.kernel-machines.org/papers/vapgolsmo96.ps.gz
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@INPROCEEDINGS{Vapnik97supportvector,author = {Vladimir Vapnik and Steven E. Golowich and Alex Smola},
title = {Support vector method for function approximation, regression estimation, and signal processing},
booktitle = {Advances in Neural Information Processing Systems 9},
year = {1997},
pages = {281--287},
publisher = {MIT Press}
}
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Abstract:
The Support Vector (SV) method was recently proposed for estimating regressions, constructing multidimensional splines, and solving linear operator equations [Vapnik, 1995]. In this presentation we report results of applying the SV method to these problems. 1
Citations
| 4514 | Statistical Learning Theory – Vapnik - 1998 |
| 73 | A statistical model for positron emission tomography – Vardi, Shepp, et al. - 1985 |
| 1 | Polynomial Splines and Wevelets - A Signal Perspectives – Unser, Aldroubi - 1992 |
