A
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 ArticleDOI
Long-term prediction of time series using NNE-based projection and OP-ELM
TL;DR: A combination of methodologies based on a recent development -called Extreme Learning Machine (ELM)- decreasing drastically the training time of nonlinear models is proposed, resulting in an Optimally-Pruned ELM (OP-ELM).
Journal ArticleDOI
Blood-Based Biomarkers for Predicting the Risk for Five-Year Incident Coronary Heart Disease in the Framingham Heart Study via Machine Learning.
Meeshanthini V. Dogan,Steven R. H. Beach,Ronald L. Simons,Amaury Lendasse,Brandan Penaluna,Robert A. Philibert +5 more
TL;DR: A novel DNA-based precision medicine tool capable of capturing the complex genetic and environmental relationships that contribute to the risk of CHD, and being mapped to actionable risk factors that may be leveraged to guide risk modification efforts is described.
Journal ArticleDOI
An improved methodology for filling missing values in spatiotemporal climate data set
TL;DR: An improved methodology for the determination of missing values in a spatiotemporal database is presented and it is based on an original linear projection method called empirical orthogonal functions (EOF) pruning.
Journal ArticleDOI
Fast bootstrap methodology for regression model selection
TL;DR: The fast bootstrap (FB) methodology to select the best model structure is presented; this methodology is applied here to regression tasks and illustrated on multi-layer perceptrons, radial-basis function networks and least-square support vector machines.
Book ChapterDOI
Mutual information and k-nearest neighbors approximator for time series prediction
TL;DR: This paper presents a method that combines Mutual Information and k-Nearest Neighbors approximator for time series prediction that is repeated to build a large number of models that are used for long-term prediction of time series.