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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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Journal ArticleDOI
Forecasting electricity consumption using nonlinear projection and self-organizing maps
TL;DR: This work presents a quasi-automatic method using a nonlinear projection named curvilinear component analysis to build a regressor, corresponding to the minimum number of variables necessary to forecast the future values of the time series.
Proceedings Article
Time Series Prediction using DirRec Strategy
Antti Sorjamaa,Amaury Lendasse +1 more
TL;DR: A third strategy is presented, DirRec, which combines the advantages of the two already used ones called Recursive and Direct in the prediction purposes and is applied to two benchmarks: Santa Fe and Poland Electricity Load time series.
Journal ArticleDOI
Adaptive and online network intrusion detection system using clustering and Extreme Learning Machines
TL;DR: The main objective of this paper is to propose an adaptive design of intrusion detection systems on the basis of Extreme Learning Machines that offers the capability of detecting known and novel attacks and being updated according to new trends of data patterns provided by security experts in a cost-effective manner.
Journal ArticleDOI
Mixture of Gaussians for distance estimation with missing data
Emil Eirola,Amaury Lendasse,Vincent Vandewalle,Vincent Vandewalle,Christophe Biernacki,Christophe Biernacki +5 more
TL;DR: Using the mixture model for estimating distances is on average more accurate than using the same model to impute any missing values and then calculating distances, and more accurately estimating distances lead to improved prediction performance for classification and regression tasks when used as inputs for a neural network.
Journal ArticleDOI
Minimal Learning Machine
TL;DR: A comprehensive set of computer experiments illustrates that the proposed method achieves accuracies that are comparable to more traditional machine learning methods for regression and classification thus offering a computationally valid alternative to such approaches.