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Please use this identifier to cite or link to this item:
http://hdl.handle.net/10016/7264
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| Title: | Robust g-filter using support vector method |
| Author(s): | Camps-Valls, G.
Martínez-Ramón, Manel
Rojo-Álvarez, José Luis
Soria-Olivas, E. |
| Publisher: | Elsevier |
| Issued date: | Dec-2004 |
| Citation: | Neurocomputing, vol. 62, Dec. 2004, pp. 493-499 |
| URI: | http://hdl.handle.net/10016/7264 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2004.07.003 |
| Abstract: | This Letter presents a new approach to time series modelling using the support vector machines (SVM). Although the g filter can provide stability in several time series models, the SVM is proposed here to provide robustness in the estimation of the g filter coefficients. Examples in chaotic time series prediction and channel equalization show the advantages of the joint SVM g filter. |
| Review: | PeerReviewed |
| Publisher version: | http://dx.doi.org/10.1016/j.neucom.2004.07.003 |
| Keywords: | Suuport vector machines
Gamma filter
Iterated prediction
Channel equalization |
| Rights: | © Elsevier B.V. |
| Appears in Collections: | DTSC - G2PI - Artículos de Revistas
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