| OP-ELM: optimally pruned extreme learning machine Y Miche, A Sorjamaa, P Bas, O Simula, C Jutten, A Lendasse Neural Networks, IEEE Transactions on 21 (1), 158-162, 2010 | 914 | 2010 |
| 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 | 453 | 2013 |
| Prototyping a digital twin for real time remote control over mobile networks: Application of remote surgery H Laaki, Y Miche, K Tammi Ieee Access 7, 20325-20336, 2019 | 339 | 2019 |
| High-performance extreme learning machines: a complete toolbox for big data applications A Akusok, KM Björk, Y Miche, A Lendasse IEEE Access 3, 1011-1025, 2015 | 313 | 2015 |
| 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 | 270 | 2011 |
| GPU-accelerated and parallelized ELM ensembles for large-scale regression M Van Heeswijk, Y Miche, E Oja, A Lendasse Neurocomputing 74 (16), 2430-2437, 2011 | 201 | 2011 |
| Bankruptcy prediction using extreme learning machine and financial expertise Q Yu, Y Miche, E Séverin, A Lendasse Neurocomputing 128, 296-302, 2014 | 197 | 2014 |
| 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 | 166 | 2013 |
| 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 | 138 | 2016 |
| OP-ELM: theory, experiments and a toolbox Y Miche, A Sorjamaa, A Lendasse International conference on Artificial Neural networks, 145-154, 2008 | 136 | 2008 |
| Adaptive ensemble models of extreme learning machines for time series prediction M Van Heeswijk, Y Miche, T Lindh-Knuutila, PAJ Hilbers, T Honkela, ... Artificial Neural Networks–ICANN 2009: 19th International Conference …, 2009 | 135 | 2009 |
| Long-term time series prediction using OP-ELM A Grigorievskiy, Y Miche, AM Ventelä, E Séverin, A Lendasse Neural Networks 51, 50-56, 2014 | 113 | 2014 |
| Adaptive and online network intrusion detection system using clustering and extreme learning machines S Roshan, Y Miche, A Akusok, A Lendasse Journal of the Franklin Institute 355 (4), 1752-1779, 2018 | 111 | 2018 |
| Feature selection for nonlinear models with extreme learning machines F Benoît, M Van Heeswijk, Y Miche, M Verleysen, A Lendasse Neurocomputing 102, 111-124, 2013 | 106 | 2013 |
| A Methodology for Building Regression Models using Extreme Learning Machine: OP-ELM. Y Miche, P Bas, C Jutten, O Simula, A Lendasse ESANN, 247-252, 2008 | 85 | 2008 |
| Minimal learning machine: A novel supervised distance-based approach for regression and classification AH de Souza Junior, F Corona, GA Barreto, Y Miche, A Lendasse Neurocomputing 164, 34-44, 2015 | 80 | 2015 |
| Fast face recognition via sparse coding and extreme learning machine B He, D Xu, R Nian, M van Heeswijk, Q Yu, Y Miche, A Lendasse Cognitive Computation 6, 264-277, 2014 | 67 | 2014 |
| A feature selection methodology for steganalysis Y Miche, B Roue, A Lendasse, P Bas International Workshop on Multimedia Content Representation, Classification …, 2006 | 57 | 2006 |
| Anomaly-based intrusion detection using extreme learning machine and aggregation of network traffic statistics in probability space BG Atli, Y Miche, A Kalliola, I Oliver, S Holtmanns, A Lendasse Cognitive Computation 10 (5), 848-863, 2018 | 55 | 2018 |
| Extreme learning machine towards dynamic model hypothesis in fish ethology research R Nian, B He, B Zheng, M Van Heeswijk, Q Yu, Y Miche, A Lendasse Neurocomputing 128, 273-284, 2014 | 55 | 2014 |
Amaury LendasseUniversity of HoustonVerified email at central.uh.edu
