Amaury Lendasse
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OP-ELM: optimally pruned extreme learning machine
Y Miche, A Sorjamaa, P Bas, O Simula, C Jutten, A Lendasse
IEEE transactions on neural networks 21 (1), 158-162, 2009
Methodology for long-term prediction of time series
A Sorjamaa, J Hao, N Reyhani, Y Ji, A Lendasse
Neurocomputing 70 (16-18), 2861-2869, 2007
Extreme learning machines [trends & controversies]
E Cambria, GB Huang, LLC Kasun, H Zhou, CM Vong, J Lin, J Yin, Z Cai, ...
IEEE intelligent systems 28 (6), 30-59, 2013
High Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications
A Akusok, KM Bjork, Y Miche, A Lendasse
Access, IEEE, 2015
Mutual information for the selection of relevant variables in spectrometric nonlinear modelling
F Rossi, A Lendasse, D François, V Wertz, M Verleysen
Chemometrics and Intelligent Laboratory Systems 80 (2), 215-226, 2006
TROP-ELM: a double-regularized elm using lars and tikhonov regularization
Y Miche, M van Heeswijk, P Bas, O Simula, A Lendasse
Neurocomputing 74 (16), 2413-2421, 2011
Nonlinear projection with curvilinear distances: Isomap versus curvilinear distance analysis
JA Lee, A Lendasse, M Verleysen
Neurocomputing 57, 49-76, 2004
GPU-accelerated and parallelized ELM ensembles for large-scale regression
M van Heeswijk, Y Miche, E Oja, A Lendasse
Neurocomputing, 2011
Non-linear financial time series forecasting-application to the bel 20 stock market index
A Lendasse, E De Bodt, V Wertz, M Verleysen
European Journal of Economic and Social Systems 14 (1), 81-92, 2000
Bankruptcy prediction using extreme learning machine and financial expertise
Q Yu, Y Miche, E Séverin, A Lendasse
Neurocomputing 128, 296-302, 2014
A robust nonlinear projection method
JA Lee, A Lendasse, N Donckers, M Verleysen
Proceedings of ESANN, 13-20, 2000
Regularized extreme learning machine for regression with missing data
Q Yu, Y Miche, E Eirola, M Van Heeswijk, E Séverin, A Lendasse
Neurocomputing 102, 45-51, 2013
OP-ELM: theory, experiments and a toolbox
Y Miche, A Sorjamaa, A Lendasse
Artificial Neural Networks-ICANN 2008, 145-154, 2008
Extreme learning machine for missing data using multiple imputations
D Sovilj, E Eirola, Y Miche, KM Björk, R Nian, A Akusok, A Lendasse
Neurocomputing 174, 220-231, 2016
Adaptive ensemble models of extreme learning machines for time series prediction
M Van Heeswijk, Y Miche, T Lindh-Knuutila, P Hilbers, T Honkela, E Oja, ...
Artificial Neural Networks–ICANN 2009, 305-314, 2009
Width optimization of the Gaussian kernels in radial basis function networks
N Benoudjit, C Archambeau, A Lendasse, J Lee, M Verleysen
Proc. of ESANN, 425-432, 2002
Curvilinear distance analysis versus isomap
JA Lee, A Lendasse, M Verleysen
Proceedings of ESANN, 185-192, 2002
Time series prediction using DirRec strategy.
A Sorjamaa, A Lendasse
Esann 6, 143-148, 2006
Model selection with cross-validations and bootstraps—application to time series prediction with RBFN models
A Lendasse, V Wertz, M Verleysen
Artificial Neural Networks and Neural Information Processing—ICANN/ICONIP …, 2003
Long-term prediction of time series by combining direct and MIMO strategies
S Ben Taieb, G Bontempi, A Sorjamaa, A Lendasse
Neural Networks, 2009. IJCNN 2009. International Joint Conference on, 3054-3061, 2009
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