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Please use this identifier to cite or link to this item:
http://hdl.handle.net/10016/11671
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| Title: | Non-linear System Identification with Composite Relevance Vector Machines |
| Author(s): | Camps-Valls, Gustavo
Martínez-Ramón, Manel
Rojo-Álvarez, José Luis
Muñoz-Marí, Jordi |
| Publisher: | IEEE |
| Issued date: | Apr-2007 |
| Citation: | G. Camps-Valls, M. Martínez-Ramón, J. Luis Rojo-Álvarez and J. Muñoz-Marí, "Non-linear System Identification with Composite Relevance Vector Machines", IEEE Signal Processing Letters, Vol. 14, No 4, pp. 279-282, April, 2007 |
| URI: | http://hdl.handle.net/10016/11671 |
| ISSN: | 1070-9908 |
| DOI: | 10.1109/LSP.2006.885290 |
| Abstract: | Nonlinear system identification based on relevance vector machines (RVMs) has been traditionally addressed by stacking the input and/or output regressors and then performing standard RVM regression. This letter introduces a full family of composite kernels in order to integrate the input and output information in the mapping function efficiently and hence generalize the standard approach. An improved trade-off between accuracy and sparsity is obtained in several benchmark problems. Also, the RVM yields confidence intervals for the predictions, and it is less sensitive to free parameter selection |
| Publisher version: | http://dx.doi.org/10.1109/LSP.2006.885290 |
| Keywords: | Composite kernels
Nonlinear System Identification
Relevance Vector Machines
RVM |
| Rights: | © IEEE |
| Appears in Collections: | DTSC - G2PI - Artículos de Revistas
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