Examinando por Autor "Soria Olivas, Emilio"

Mostrando 1 - 3 de 3

Robust Gamma-filter Using Support Vector Machines
(2009-07-23T09:19:34Z) Camps Valls, Gustavo; Martínez Ramón, Manel; Rojo-Álvarez, José Luis; Soria Olivas, Emilio
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.
Support Vector Machines for Nonlinear Kernel ARMA System Identification
(2006-11-01) Martínez Ramón, Manel; Rojo-Álvarez, José Luis; Camps Valls, Gustavo; Muñoz Marí, Jordi; Navia Vázquez, Ángel; Soria Olivas, Emilio; Figueiras Vidal, Aníbal R
Nonlinear system identification based on support vector machines (SVM) has been usually addressed by means of the standard SVM regression (SVR), which can be seen as an implicit nonlinear autoregressive and moving average (ARMA) model in some reproducing kernel Hilbert space (RKHS). The proposal of this letter is twofold. First, the explicit consideration of an ARMA model in an RKHS (SVM-ARMA 2k) is proposed. We show that stating the ARMA equations in an RKHS leads to solving the regularized normal equations in that RKHS, in terms of the autocorrelation and cross correlation of the (nonlinearly) transformed input and output discrete time processes. Second, a general class of SVM-based system identification nonlinear models is presented, based on the use of composite Mercer¿s kernels. This general class can improve model flexibility by emphasizing the input¿output cross information (SVM-ARMA 4k), which leads to straightforward and natural combinations of implicit and explicit ARMA models (SVR-ARMA 2k and SVR-ARMA 4k). Capabilities of these different SVM-based system identification schemes are illustrated with two benchmark problems.
Support Vector Machines Framework for Linear Signal Processing
(2009-07-23T09:14:14Z) Rojo-Álvarez, José Luis; Camps Valls, Gustavo; Martínez Ramón, Manel; Soria Olivas, Emilio; Navia Vázquez, Ángel; Figueiras Vidal, Aníbal R
This 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 framework.