What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
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.
His most cited work include:
- Variable selection using svm based criteria (564 citations)
- A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces: A 10-year Update (516 citations)
- BCI Competition III: Dataset II- Ensemble of SVMs for BCI P300 Speller (379 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (50.98%)
- Support vector machine (33.99%)
- Pattern recognition (32.03%)
What were the highlights of his more recent work (between 2015-2021)?
- Algorithm (20.92%)
- Artificial intelligence (50.98%)
- Machine learning (18.30%)
In recent papers he was focusing on the following fields of study:
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.
Between 2015 and 2021, his most popular works were:
- A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces: A 10-year Update (516 citations)
- Optimal Transport for Domain Adaptation (355 citations)
- Joint Distribution Optimal Transportation for Domain Adaptation (102 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
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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