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  Predicting time series with support vectur machines

Müller, K.-R., Smola AJ, Rätsch, G., Schölkopf, B., Kohlmorgen, J., & Vapnik, V. (1997). Predicting time series with support vectur machines. 7th International Conference on Artificial Neural Networks, ICANN 97, Lausanne, Switzerland, 999-1004.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E9D4-0 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E9D5-E
Genre: Conference Paper

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 Creators:
Müller, K-R, Author
Smola AJ, Rätsch, G1, Author              
Schölkopf, B1, Author              
Kohlmorgen, J, Author
Vapnik, V, Author
Gerstner, Editor
W., Editor
Germond, A., Editor
Hasler, M., Editor
Nicoud, J.-D., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two different cost functions for Support Vectors: training with (i) an e insensitive loss and (ii) Huber's robust loss function and discuss how to choose the regularization parameters in these models. Two applications are considered: data from (a) a noisy (normal and uniform noise) Mackey Glass equation and (b) the Santa Fe competition (set D). In both cases Support Vector Machines show an excellent performance. In case (b) the Support Vector approach improves the best known result on the benchmark by a factor of 29.

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 Dates: 1997-10
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: ISBN: 3-540-63631-5
DOI: 10.1007/BFb0020283
BibTex Citekey: 416
 Degree: -

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Title: 7th International Conference on Artificial Neural Networks
Place of Event: Lausanne, Switzerland
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Title: 7th International Conference on Artificial Neural Networks, ICANN 97, Lausanne, Switzerland
Source Genre: Journal
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Affiliations:
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 999 - 1004 Identifier: -