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Amaury Lendasse
Researcher at University of Houston
Publications - 315
Citations - 7831
Amaury Lendasse is an academic researcher from University of Houston. The author has contributed to research in topics: Extreme learning machine & Feature selection. The author has an hindex of 39, co-authored 315 publications receiving 7167 citations. Previous affiliations of Amaury Lendasse include Ikerbasque & FedEx Institute of Technology.
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Proceedings Article
Adaptive Kernel Smoothing Regression for Spatio-Temporal Environmental Datasets.
TL;DR: In this paper, a simple and fast combination of incremental vector quantization with kernel smoothing regression using adaptive bandwidth is shown to be effective for online modeling of environmental datasets and is illustrated on publicly available datasets corresponding to the Tropical Atmosphere Ocean array and the Helsinki Commission hydrographic database for the Baltic Sea.
Proceedings ArticleDOI
A shadow-removal based saliency map for point feature detection of underwater objects
TL;DR: A streamline AUV system that adopts the side scan sonar on board has been set up to explore underwater visual tasks and it is shown that the proposed model could achieve great performances in the point feature detection with both robustness and effectiveness.
Book ChapterDOI
Solve Classification Tasks with Probabilities. Statistically-Modeled Outputs
TL;DR: The paper contains detailed description and analysis of the HP method by the example of the Iris dataset, and results for combination with Extreme Learning Machines (ELM) and Support Vector Machines (SVM) are presented.
Journal ArticleDOI
A Framework for Privacy Quantification: Measuring the Impact of Privacy Techniques Through Mutual Information, Distance Mapping, and Machine Learning
TL;DR: The results allow for an objective quantification of the effects of the k-anonymity and differential privacy algorithms, and illustrate on the toy data used, that such privacy techniques have very non-linear effects on the information content of the data.
Sélection de variables spectrales par information mutuelle multivariée pour la construction de modèles non-linéaires
TL;DR: Amaury Lendasse, Damien Francois, Fabrice Rossi, Vincent Wertz, Michel Verleysen 1 Helsinki University of Technology Lab. Computer and Information Science, Neural Networks Research Centre, P.O.P. 5400, FIN-02015 HUT, Finlande, lendasse@hut.fi 2 Universite catholique de Louvain -machine learning group, CESAME, 4 av. G. Lemaitre, 1348 Lévain-la-Neuve, Belgium, francois@auto.ucl.ac.