H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 42 Citations 8,145 243 World Ranking 4106 National Ranking 2076

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

Amaury Lendasse mostly deals with Artificial intelligence, Machine learning, Extreme learning machine, Time series and Pattern recognition. His studies deal with areas such as Big data and Nonlinear system as well as Artificial intelligence. In general Machine learning study, his work on Ensemble forecasting, Selection, Cross-validation and Feedforward neural network often relates to the realm of Toolbox, thereby connecting several areas of interest.

His biological study spans a wide range of topics, including Feature, Support vector machine and Robustness. The concepts of his Time series study are interwoven with issues in Linear model, Long-term prediction, Benchmark, Generalization and Series. His study in Pattern recognition is interdisciplinary in nature, drawing from both Pixel and Functional data analysis.

His most cited work include:

  • OP-ELM: Optimally Pruned Extreme Learning Machine (609 citations)
  • Extreme Learning Machine (558 citations)
  • Methodology for long-term prediction of time series (253 citations)

What are the main themes of his work throughout his whole career to date?

His primary scientific interests are in Artificial intelligence, Machine learning, Extreme learning machine, Pattern recognition and Data mining. His Artificial intelligence study combines topics from a wide range of disciplines, such as Computer vision and Time series. His Machine learning study combines topics from a wide range of disciplines, such as Algorithm and Benchmark.

Amaury Lendasse works mostly in the field of Extreme learning machine, limiting it down to topics relating to Ensemble forecasting and, in certain cases, Incremental learning. His research integrates issues of Visualization and Projection in his study of Pattern recognition. His Data mining research is multidisciplinary, incorporating perspectives in Mutual information, Missing data and Data set.

He most often published in these fields:

  • Artificial intelligence (76.37%)
  • Machine learning (40.92%)
  • Extreme learning machine (38.62%)

What were the highlights of his more recent work (between 2016-2021)?

  • Artificial intelligence (76.37%)
  • Extreme learning machine (38.62%)
  • Pattern recognition (34.58%)

In recent papers he was focusing on the following fields of study:

His primary areas of study are Artificial intelligence, Extreme learning machine, Pattern recognition, Machine learning and Artificial neural network. As part of his studies on Artificial intelligence, Amaury Lendasse frequently links adjacent subjects like Computer vision. His Extreme learning machine research integrates issues from Word, Data mining and Missing data.

As a member of one scientific family, he mostly works in the field of Pattern recognition, focusing on Spectral shape analysis and, on occasion, Similarity. His biological study spans a wide range of topics, including Training set and Data visualization. Amaury Lendasse has included themes like Multiscale modeling, Support vector machine and Algorithm in his Artificial neural network study.

Between 2016 and 2021, his most popular works were:

  • Adaptive and online network intrusion detection system using clustering and Extreme Learning Machines (37 citations)
  • Anomaly-Based Intrusion Detection Using Extreme Learning Machine and Aggregation of Network Traffic Statistics in Probability Space (22 citations)
  • Generating Word Embeddings from an Extreme Learning Machine for Sentiment Analysis and Sequence Labeling Tasks (20 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Machine learning

His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Extreme learning machine, Image segmentation and Artificial neural network. He combines subjects such as Machine learning and Conditional probability distribution with his study of Artificial intelligence. His Machine learning research includes themes of Network intrusion detection, Training set and Data mining.

The Feature extraction, Feature selection, Discriminative model and Feature vector research Amaury Lendasse does as part of his general Pattern recognition study is frequently linked to other disciplines of science, such as Invariant, therefore creating a link between diverse domains of science. His Extreme learning machine study integrates concerns from other disciplines, such as Word, Projection, Nonlinear system, Visualization and Dimensionality reduction. His work deals with themes such as Multiscale modeling and Algorithm, which intersect with Artificial neural network.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

OP-ELM: Optimally Pruned Extreme Learning Machine

Yoan Miche;A. Sorjamaa;P. Bas;O. Simula.
IEEE Transactions on Neural Networks (2010)

798 Citations

Methodology for long-term prediction of time series

Antti Sorjamaa;Jin Hao;Nima Reyhani;Yongnan Ji.
Neurocomputing (2007)

391 Citations

Extreme Learning Machines [Trends & Controversies]

Erik Cambria;Guang-Bin Huang;Liyanaarachchi Lekamalage Chamara Kasun;Hongming Zhou.
IEEE Intelligent Systems (2013)

338 Citations

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

Fabrice Rossi;Amaury Lendasse;Damien François;Vincent Wertz.
Chemometrics and Intelligent Laboratory Systems (2006)

255 Citations

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

Anton Akusok;Kaj-Mikael Bjork;Yoan Miche;Amaury Lendasse.
IEEE Access (2015)

234 Citations

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

Yoan Miche;Mark van Heeswijk;Patrick Bas;Olli Simula.
Neurocomputing (2011)

229 Citations

Nonlinear projection with curvilinear distances: Isomap versus curvilinear distance analysis

John Aldo Lee;Amaury Lendasse;Michel Verleysen.
Neurocomputing (2004)

215 Citations

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

Mark van Heeswijk;Yoan Miche;Erkki Oja;Amaury Lendasse.
Neurocomputing (2011)

172 Citations

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

Amaury Lendasse;Eric de Bodt;Vincent Wertz;Michel Verleysen.
European Journal of Economic and Social Systems (2000)

170 Citations

A robust nonlinear projection method

John Aldo Lee;Amaury Lendasse;Nicolas Donckers;Michel Verleysen.
the european symposium on artificial neural networks (2000)

169 Citations

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