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Alain Rakotomamonjy
Researcher at University of Rouen
Publications - 180
Citations - 7914
Alain Rakotomamonjy is an academic researcher from University of Rouen. The author has contributed to research in topics: Support vector machine & Kernel embedding of distributions. The author has an hindex of 35, co-authored 167 publications receiving 6451 citations. Previous affiliations of Alain Rakotomamonjy include Aix-Marseille University & Institut national des sciences appliquées de Rouen.
Papers
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A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces: A 10-year Update
Fabien Lotte,Laurent Bougrain,Andrzej Cichocki,Andrzej Cichocki,Maureen Clerc,Marco Congedo,Alain Rakotomamonjy,Florian Yger +7 more
TL;DR: A comprehensive overview of the modern classification algorithms used in EEG-based BCIs is provided, the principles of these methods and guidelines on when and how to use them are presented, and a number of challenges to further advance EEG classification in BCI are identified.
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Optimal Transport for Domain Adaptation
TL;DR: A regularized unsupervised optimal transportation model to perform the alignment of the representations in the source and target domains, that consistently outperforms state of the art approaches and can be easily adapted to the semi-supervised case where few labeled samples are available in the target domain.
Journal Article
Variable selection using svm based criteria
TL;DR: New methods to evaluate variable subset relevance with a view to variable selection based on weight vector derivative achieves good results and performs consistently well over the datasets used.
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BCI Competition III: Dataset II- Ensemble of SVMs for BCI P300 Speller
TL;DR: This paper addresses the problem of signal responses variability within a single subject in such brain-computer interface P300 speller with a method that copes with such variabilities through an ensemble of classifiers approach.
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Recovering Sparse Signals With a Certain Family of Nonconvex Penalties and DC Programming
TL;DR: Experimental results demonstrate the effectiveness of the proposed generic framework compared to existing algorithms, including iterative reweighted least-squares methods, and several algorithms in the literature dealing with nonconvex penalties are particular instances of the algorithm.