| 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 |
| 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 |
| 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 | 135 | 2009 |
| 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 |
| 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 |
| 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 |
| Binary/ternary extreme learning machines M van Heeswijk, Y Miche Neurocomputing 149, 187-197, 2015 | 47 | 2015 |
| Ensemble delta test-extreme learning machine (DT-ELM) for regression Q Yu, M Van Heeswijk, Y Miche, R Nian, B He, E Séverin, A Lendasse Neurocomputing 129, 153-158, 2014 | 40 | 2014 |
| Air quality forecasting using neural networks C Zhao, M van Heeswijk, J Karhunen 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 1-7, 2016 | 22 | 2016 |
| Extreme learning machine: A robust modeling technique? Yes! A Lendasse, A Akusok, O Simula, F Corona, M van Heeswijk, E Eirola, ... Advances in Computational Intelligence: 12th International Work-Conference …, 2013 | 19 | 2013 |
| Fast feature selection in a gpu cluster using the delta test A Guillén, MIG Arenas, M Van Heeswijk, D Sovilj, A Lendasse, LJ Herrera, ... Entropy 16 (2), 854-869, 2014 | 15 | 2014 |
| Solving Large Regression Problems using an Ensemble of GPU-accelerated ELMs M van Heeswijk, Y Miche, E Oja, A Lendasse European Symposium on Artificial Neural Networks (ESANN) 2010, 2010 | 12 | 2010 |
| Method for detecting aging related failures of process sensors via noise signal measurement T Toosi, M Sirola, J Laukkanen, M van Heeswijk, J Karhunen International Scientific Journal of Computing 18 (2), 135-146, 2019 | 11 | 2019 |
| Advances in extreme learning machines M van Heeswijk Aalto University, 2015 | 9 | 2015 |
| Evolutive approaches for variable selection using a non-parametric noise estimator A Guillén, D Sovilj, M van Heeswijk, LJ Herrera, A Lendasse, H Pomares, ... Parallel architectures and bioinspired algorithms, 243-266, 2012 | 5 | 2012 |
| Variable Selection in a GPU Cluster Using Delta Test A Guillén, M van Heeswijk, D Sovilj, M Arenas, L Herrera, H Pomares, ... Advances in Computational Intelligence, 393-400, 2011 | 4 | 2011 |
| Detecting aging of process sensors with noise signal measurement T Toosi, M Sirola, J Laukkanen, M van Heeswijk, J Karhunen 2017 9th IEEE International Conference on Intelligent Data Acquisition and …, 2017 | 2 | 2017 |
| Fast feature selection in a GPU cluster using the Delta Test A Guillén Perales, MI García Arenas, M Heeswijk, D Sovilj, A Lendasse, ... MDPI, 2014 | | 2014 |
| Compressive ELM: Improved Models through Exploiting Time-Accuracy Trade-Offs M van Heeswijk, A Lendasse, Y Miche Engineering Applications of Neural Networks: 15th International Conference …, 2014 | | 2014 |
