Abstract
A new approach to the non-parametric spectral estimation on the basis of the Support Vector (SV) framework is presented. Two algorithms are derived for both uniform and non-uniform sampling. The relationship between the SV free parameters and the underlying process statistics is discussed. The application in two real data examples, the sunspot numbers and the Heart Rate Variability, shows the higher resolution and robustness in SV spectral analysis algorithms.
This work has been partially supported by the project 07T/0046/2000 of the CAM
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Rojo- Álvarez, J.L., García-Alberola, A., Martínez-Ramón, M., Valdés, M., Figueiras-Vidal, A.R., Artés-Rodríguez, A. (2002). Support Vector Robust Algorithms for Non- parametric Spectral Analysis. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_178
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DOI: https://doi.org/10.1007/3-540-46084-5_178
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