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Support Vector Machines Framework for Linear Signal Processing

dc.contributor.authorRojo-Álvarez, José Luis
dc.contributor.authorCamps Valls, Gustavo
dc.contributor.authorMartínez Ramón, Manel
dc.contributor.authorSoria Olivas, Emilio
dc.contributor.authorNavia Vázquez, Ángel
dc.contributor.authorFigueiras Vidal, Aníbal R
dc.description.abstractThis paper presents a support vector machines (SVM) framework to deal with linear signal processing (LSP) problems. The approach relies on three basic steps for model building: (1) identifying the suitable base of the Hilbert signal space in the model, (2) using a robust cost function, and (3) minimizing a constrained, regularized functional by means of the method of Lagrange multipliers. Recently, autoregressive moving average (ARMA) system identification and non-parametric spectral analysis have been formulated under this framework. The generalized, yet simple, formulation of SVM LSP problems is particularized here for three different issues: parametric spectral estimation, stability of Infinite Impulse Response filters using the gamma structure, and complex ARMA models for communication applications. The good performance shown on these different domains suggests that other signal processing problems can be stated from this SVM
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.titleSupport Vector Machines Framework for Linear Signal Processinges
dc.subject.unesco3325 Tecnología de las Telecomunicacioneses
dc.description.departamentoTeoría de la Señal y Comunicaciones

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Atribución-NoComercial-SinDerivadas 3.0 EspañaExcept where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España