conference paper

Predicting time series with support vector machines

K. Robert Müller^{a b}✉, Alexander Johannes Smola^{a b}, Gunnar Rätsch^{a b}, Bernhard Schölkopf^{c d}, Jens Kohlmorgen^{a b}, Vladimir N. Vapnik^{e f}

^aGMD FIRST, Rudower Chaussee 5, 12489, Berlin, Germany
^bGMD FIRST
^cMax-Planck-Institut f. biol. Kybernetik, Spemannstr. 38, 72076, Germany
^dMax-Planck-Institut f. biol. Kybernetik
^eAT&T Research, 101 Crawfords Corner Rd, PO 3030, 07733, Holmdel, USA
^fAT&T — Research
✉Corresponding author

Lecture notes in computer science Springer Science+Business Media1997-01-01 1,082 citations17 references

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TL;DRTwo different cost functions for Support Vectors are made use: training with an e insensitive loss and Huber's robust loss function and how to choose the regularization parameters in these models are discussed. (Semantic Scholar)

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Predicting time series with support vector machines

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