Alain Rakotomamonjy spends much of his time researching Support vector machine, Artificial intelligence, Pattern recognition, Classifier and Machine learning. Alain Rakotomamonjy interconnects Variable, Histogram, Mathematical optimization and Regularization in the investigation of issues within Support vector machine. He is involved in the study of Artificial intelligence that focuses on Feature extraction in particular.
The study incorporates disciplines such as Relevance and Sensitivity in addition to Pattern recognition. His work in Classifier addresses issues such as Brain–computer interface, which are connected to fields such as Speech recognition and Statistical classification. The various areas that Alain Rakotomamonjy examines in his Machine learning study include Quadratic programming and Maximization.
Alain Rakotomamonjy mainly focuses on Artificial intelligence, Support vector machine, Pattern recognition, Algorithm and Mathematical optimization. The Artificial intelligence study combines topics in areas such as Machine learning, Brain–computer interface and Computer vision. His study in Support vector machine is interdisciplinary in nature, drawing from both Regularization, Filter and Feature selection.
His Pattern recognition study incorporates themes from Feature, Invariant and Kernel. He combines subjects such as Kernel embedding of distributions and Lasso with his study of Mathematical optimization. His Classifier research is multidisciplinary, incorporating perspectives in Speech recognition and Data analysis.
His scientific interests lie mostly in Algorithm, Artificial intelligence, Machine learning, Gradient descent and Mathematical optimization. His Artificial intelligence study integrates concerns from other disciplines, such as Matrix decomposition, Quadratic equation and Pattern recognition. His biological study spans a wide range of topics, including Initialization, Feature extraction and Concave function.
He studies Optimization problem, a branch of Mathematical optimization. His research in Transfer of learning intersects with topics in Classifier and Brain–computer interface. His study looks at the intersection of Classifier and topics like Random forest with Linear discriminant analysis.
His primary scientific interests are in Artificial intelligence, Machine learning, Algorithm, Function and Transfer of learning. His work on Deep learning, Feature extraction and Representation as part of general Artificial intelligence study is frequently linked to Generalization and Complete information, bridging the gap between disciplines. His Deep learning study combines topics from a wide range of disciplines, such as Discriminative model and Pattern recognition.
The various areas that he examines in his Transfer of learning study include Classifier and Brain–computer interface. His work deals with themes such as Probability distribution, External Data Representation, Invariant and Data analysis, which intersect with Classifier. His studies examine the connections between Brain–computer interface and genetics, as well as such issues in Statistical classification, with regards to Linear discriminant analysis.
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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.
Journal of Neural Engineering (2018)
Variable selection using svm based criteria
Journal of Machine Learning Research (2003)
Optimal Transport for Domain Adaptation
Nicolas Courty;Remi Flamary;Devis Tuia;Alain Rakotomamonjy.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2017)
BCI Competition III: Dataset II- Ensemble of SVMs for BCI P300 Speller
A. Rakotomamonjy;V. Guigue.
IEEE Transactions on Biomedical Engineering (2008)
Pedestrian Detection using Infrared images and Histograms of Oriented Gradients
F. Suard;A. Rakotomamonjy;A. Bensrhair;A. Broggi.
ieee intelligent vehicles symposium (2006)
More efficiency in multiple kernel learning
Alain Rakotomamonjy;Francis Bach;Stéphane Canu;Yves Grandvalet.
international conference on machine learning (2007)
Recovering Sparse Signals With a Certain Family of Nonconvex Penalties and DC Programming
G. Gasso;A. Rakotomamonjy;S. Canu.
IEEE Transactions on Signal Processing (2009)
Joint Distribution Optimal Transportation for Domain Adaptation
Nicolas Courty;Rémi Flamary;Amaury Habrard;Alain Rakotomamonjy.
neural information processing systems (2017)
Histogram of gradients of time-frequency representations for audio scene classification
Alain Rakotomamonjy;Gilles Gasso.
IEEE Transactions on Audio, Speech, and Language Processing (2015)
A Pedestrian Detector Using Histograms of Oriented Gradients and a Support Vector Machine Classifier
M. Bertozzi;A. Broggi;M. Del Rose;M. Felisa.
international conference on intelligent transportation systems (2007)
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