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

Part of Advances in Neural Information Processing Systems 9 (NIPS 1996)

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Vladimir Vapnik, Steven Golowich, Alex Smola


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.