World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
46
Citations
9334
World Ranking
6813
National Ranking
2995

Overview

Amaury Lendasse is affiliated with the University of Houston in the United States. Their research spans multiple areas within computer science and engineering, with a focus on artificial intelligence and related subfields.

The main fields of study in which they have contributed include:

  • Computer Science
  • Engineering

Within these fields, their subfields of specialization include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Molecular Biology
  • Electrical and Electronic Engineering
  • Neurology

The topics central to their research work are:

  • Machine Learning and ELM (Extreme Learning Machines)
  • Domain Adaptation and Few-Shot Learning
  • Neural Networks and Applications
  • Face and Expression Recognition
  • Brain Tumor Detection and Classification
  • Machine Learning and Data Classification
  • Advanced Memory and Neural Computing

Among their recent publications, the following papers stand out:

  • "A modified Lanczos Algorithm for fast regularization of extreme learning machines" (2020), published in Neurocomputing
  • "A systematic review of phenotypic and epigenetic clocks used for aging and mortality quantification in humans" (2024), published in Aging
  • "Embedded spectral descriptors: learning the point-wise correspondence metric via Siamese neural networks" (2020), published in Journal of Computational Design and Engineering
  • "Quantifying Time-Frequency Co-movement Impact of COVID-19 on U.S. and China Stock Market Toward Investor Sentiment Index" (2021), published in Frontiers in Public Health
  • "Ensemble Learning with Highly Variable Class-Based Performance" (2024), published in Machine Learning and Knowledge Extraction

Lendasse's frequent coauthors include:

  • Kaj-Mikael Björk
  • Edward Ratner
  • Anton Akusok
  • Leonardo Espinosa-Leal
  • Brandon Warner

The venues where Lendasse has commonly published are:

  • ESANN 2021 proceedings
  • Aging
  • Neurocomputing
  • Frontiers in Public Health
  • Machine Learning and Knowledge Extraction

Best Publications

  • OP-ELM: Optimally Pruned Extreme Learning Machine

    Yoan Miche;A. Sorjamaa;P. Bas;O. Simula

  • Extreme Learning Machine

    Erik Cambria;Guang-Bin Huang;Liyanaarachchi Lekamalage Chamara Kasun;Hongming Zhou

  • Methodology for long-term prediction of time series

    Antti Sorjamaa;Jin Hao;Nima Reyhani;Yongnan Ji

  • High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications

    Anton Akusok;Kaj-Mikael Bjork;Yoan Miche;Amaury Lendasse

  • Mutual information for the selection of relevant variables in spectrometric nonlinear modelling

    Fabrice Rossi;Amaury Lendasse;Damien François;Vincent Wertz

  • TROP-ELM: A double-regularized ELM using LARS and Tikhonov regularization

    Yoan Miche;Mark van Heeswijk;Patrick Bas;Olli Simula

  • Nonlinear projection with curvilinear distances: Isomap versus curvilinear distance analysis

    John Aldo Lee;Amaury Lendasse;Michel Verleysen

  • GPU-accelerated and parallelized ELM ensembles for large-scale regression

    Mark van Heeswijk;Yoan Miche;Erkki Oja;Amaury Lendasse

  • Non-linear financial time series forecasting application to the Bel 20 stock market index

    Amaury Lendasse;Eric de Bodt;Vincent Wertz;Michel Verleysen

  • Bankruptcy prediction using Extreme Learning Machine and financial expertise

    Qi Yu;Yoan Miche;Eric Séverin;Amaury Lendasse;Amaury Lendasse;Amaury Lendasse

  • A robust nonlinear projection method

    John Aldo Lee;Amaury Lendasse;Nicolas Donckers;Michel Verleysen

  • Regularized extreme learning machine for regression with missing data

    Qi Yu;Yoan Miche;Emil Eirola;Mark Van Heeswijk

  • A robust non-linear projection method.

    John Aldo Lee;Amaury Lendasse;Nicolas Donckers;Michel Verleysen

  • Curvilinear Distance Analysis versus Isomap

    John Aldo Lee;Amaury Lendasse;Michel Verleysen

  • Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction

    Mark Heeswijk;Yoan Miche;Tiina Lindh-Knuutila;Peter A. Hilbers

  • Extreme learning machine for missing data using multiple imputations

    Dušan Sovilj;Emil Eirola;Yoan Miche;Kaj-Mikael Björk

  • OP-ELM: Theory, Experiments and a Toolbox

    Yoan Miche;Antti Sorjamaa;Amaury Lendasse

  • Width optimization of the Gaussian kernels in Radial Basis Function Networks

    Nabil Benoudjit;Cédric Archambeau;Amaury Lendasse;John Aldo Lee

  • Long-term time series prediction using OP-ELM

    Alexander Grigorievskiy;Yoan Miche;Anne-Mari Ventelä;Eric Séverin

  • Direct and recursive prediction of time series using mutual information selection

    Yongnan Ji;Jin Hao;Nima Reyhani;Amaury Lendasse

  • Model selection with cross-validations and bootstraps: application to time series prediction with RBFN models

    Amaury Lendasse;Vincent Wertz;Michel Verleysen

Frequent Co-Authors

Michel Verleysen
Michel Verleysen Université Catholique de Louvain
John Aldo Lee
John Aldo Lee Université Catholique de Louvain
Christian Jutten
Christian Jutten Grenoble Alpes University
Guang-Bin Huang
Guang-Bin Huang Nanyang Technological University
Erkki Oja
Erkki Oja Aalto University
Ignacio Rojas
Ignacio Rojas University of Granada
Kezhi Mao
Kezhi Mao Nanyang Technological University
Juha Karhunen
Juha Karhunen Aalto University
Héctor Pomares
Héctor Pomares University of Granada
Yew-Soon Ong
Yew-Soon Ong Nanyang Technological University

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