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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
OP-ELM: Optimally Pruned Extreme Learning Machine
TL;DR: The proposed OP-ELM methodology performs several orders of magnitude faster than the other algorithms used in this brief, except the original ELM, and is still able to maintain an accuracy that is comparable to the performance of the SVM.
Book
Extreme Learning Machine
Erik Cambria,Guang-Bin Huang,Liyanaarachchi Lekamalage Chamara Kasun,Hongming Zhou,Chi-Man Vong,Jiarun Lin,Jianping Yin,Zhiping Cai,Qiang Liu,Kuan Li,Victor C. M. Leung,Liang Feng,Yew-Soon Ong,Meng-Hiot Lim,Anton Akusok,Amaury Lendasse,Francesco Corona,Rui Nian,Yoan Miche,Paolo Gastaldo,Rodolfo Zunino,Sergio Decherchi,Xuefeng Yang,Kezhi Mao,Beom-Seok Oh,Jehyoung Jeon,Kar-Ann Toh,Andrew Beng Jin Teoh,Jaihie Kim,Hanchao Yu,Yiqiang Chen,Junfa Liu +31 more
TL;DR: This special issue includes eight original works that detail the further developments of ELMs in theories, applications, and hardware implementation.
Journal ArticleDOI
Methodology for long-term prediction of time series
TL;DR: A global input selection strategy that combines forward selection, backward elimination (or pruning) and forward-backward selection is introduced and is used to optimize the three input selection criteria (k-NN, MI and NNE).
Journal ArticleDOI
High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications
TL;DR: This paper presents a complete approach to a successful utilization of a high-performance extreme learning machines (ELM) Toolbox for Big Data, and summarizes recent advantages in algorithmic performance; gives a fresh view on the ELM solution in relation to the traditional linear algebraic performance; and reaps the latest software and hardware performance achievements.
Journal ArticleDOI
Mutual information for the selection of relevant variables in spectrometric nonlinear modelling
Fabrice Rossi,Amaury Lendasse,Damien François,Vincent Wertz,Michel Verleysen,Michel Verleysen +5 more
TL;DR: The mutual information measures the information content in input variables with respect to the model output, without making any assumption on the model that will be used; it is suitable for nonlinear modelling and allows therefore a greater interpretability of the results.