Sparse deconvolution using support vector machines

DOI10.1155/2008/816507zbMath1184.68425OpenAlexW2021703224WikidataQ59215892 ScholiaQ59215892MaRDI QIDQ966912

Gustavo Camps-Valls, Aníbal R. Figueiras-Vidal, Manel Martínez-Ramón, Carlos M. Cruz, Jordi Muñoz-Marí, José Luis Rojo-Álvarez

Publication date: 24 April 2010

Published in: EURASIP Journal on Advances in Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2008/816507

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• ForWaRD

Cites Work

• Support vector machines framework for linear signal processing
• Sparse deconvolution using adaptive mixed-Gaussian models
• Practical selection of SVM parameters and noise estimation for SVM regression
• Support vector machines: a nonlinear modelling and control perspective
• Atomic Decomposition by Basis Pursuit
• Robust Deconvolution of Deterministic and Random Signals
• Maximum likelihood detection and estimation of Bernoulli - Gaussian processes
• Nonuniform Interpolation of Noisy Signals Using Support Vector Machines
• Support Vector Method for RobustARMA System Identification
• ForWaRD: Fourier-Wavelet Regularized Deconvolution for Ill-Conditioned Systems
• A Competitive Minimax Approach to Robust Estimation of Random Parameters
• Linear Minimax Regret Estimation of Deterministic Parameters with Bounded Data Uncertainties
• Robust mean-squared error estimation in the presence of model uncertainties

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