A Robust Support Vector Algorithm for Nonparametric Spectral Analysis
A new approach to the nonparametric spectral estimation on the basis of the support vector method (SVM) framework is presented. A reweighted least squared error formulation avoids the computational limitations of quadratic programming. The application to a synthetic example and to a digital communic...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2009 |
| País: | España |
| Institución: | Universidad Rey Juan Carlos |
| Repositorio: | BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos |
| OAI Identifier: | oai:burjcdigital.urjc.es:10115/1914 |
| Acceso en línea: | http://hdl.handle.net/10115/1914 |
| Access Level: | acceso abierto |
| Palabra clave: | Telecomunicaciones 3325 Tecnología de las Telecomunicaciones |
| Sumario: | A new approach to the nonparametric spectral estimation on the basis of the support vector method (SVM) framework is presented. A reweighted least squared error formulation avoids the computational limitations of quadratic programming. The application to a synthetic example and to a digital communication problem shows the robustness of the SVM spectral analysis algorithm. |
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