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Aníbal R. Figueiras-Vidal
Researcher at Charles III University of Madrid
Publications - 131
Citations - 4377
Aníbal R. Figueiras-Vidal is an academic researcher from Charles III University of Madrid. The author has contributed to research in topics: Artificial neural network & Adaptive filter. The author has an hindex of 29, co-authored 126 publications receiving 3843 citations. Previous affiliations of Aníbal R. Figueiras-Vidal include University of Cambridge & Complutense University of Madrid.
Papers
More filters
Journal ArticleDOI
Pattern classification with missing data: a review
TL;DR: The aim of this work is to analyze the missing data problem in pattern classification tasks, and to summarize and compare some of the well-known methods used for handling missing values.
Journal ArticleDOI
Sparse Spectrum Gaussian Process Regression
Miguel Lázaro-Gredilla,Joaquin Quiñonero-Candela,Carl Edward Rasmussen,Aníbal R. Figueiras-Vidal +3 more
TL;DR: The achievable trade-offs between predictive accuracy and computational requirements are compared, and it is shown that these are typically superior to existing state-of-the-art sparse approximations.
Journal ArticleDOI
Mean-square performance of a convex combination of two adaptive filters
TL;DR: This paper studies the mean-square performance of a convex combination of two transversal filters and shows how the universality of the scheme can be exploited to design filters with improved tracking performance.
Journal ArticleDOI
K nearest neighbours with mutual information for simultaneous classification and missing data imputation
TL;DR: This article proposes a novel KNN imputation procedure using a feature-weighted distance metric based on mutual information (MI), which provides a missing data estimation aimed at solving the classification task.
Journal ArticleDOI
Support vector method for robust ARMA system identification
José Luis Rojo-Álvarez,Manel Martínez-Ramón,M. de Prado-Cumplido,Antonio Artés-Rodríguez,Aníbal R. Figueiras-Vidal +4 more
TL;DR: This paper presents a new approach to auto-regressive and moving average (ARMA) modeling based on the support vector method (SVM) for identification applications that allows the linking of the fundamentals of SVM with several classical system identification methods.