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.
Papers
More filters
Journal ArticleDOI
Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation
TL;DR: F fuzzy models are shown to be consistently more accurate for prediction in the case of time series coming from real-world applications and the advantages of the proposed methodology are shown in terms of linguistic interpretability, generalization capability and computational cost.
Journal ArticleDOI
A Two-Stage Methodology Using K-NN and False-Positive Minimizing ELM for Nominal Data Classification
TL;DR: Experimental results using a specific dataset provided by F-Secure Corporation show that this methodology provides a rapid decision on new samples, with a direct control over the false positives and thus on the decision capabilities of the model.
Proceedings ArticleDOI
Prediction of electric load using Kohonen maps - Application to the Polish electricity consumption
TL;DR: In this article, the problem of electrical load forecasting is divided into three parts: prediction of the daily mean, the daily standard deviation and the normalized daily profile, and a method based on Kohonen maps is proposed.
Journal ArticleDOI
Ensemble delta test-extreme learning machine (DT-ELM) for regression
Qi Yu,Mark van Heeswijk,Yoan Miche,Rui Nian,Bo He,Eric Séverin,Amaury Lendasse,Amaury Lendasse,Amaury Lendasse +8 more
TL;DR: A method is proposed, which operates in an incremental way to create less complex ELM structures and determines the number of hidden nodes automatically, and uses Bayesian Information Criterion as well as Delta Test to restrict the search and consider the size of the network and prevent overfitting.
Journal ArticleDOI
Minimising the delta test for variable selection in regression problems
TL;DR: New methodologies based on the delta test, such as tabu search, genetic algorithms and the hybridisation of them, are presented, to determine a subset of variables which is representative of a function.