Следене
Mark van Heeswijk
Mark van Heeswijk
Aalto University School of Science
Потвърден имейл адрес: aalto.fi - Начална страница
Заглавие
Позовавания
Позовавания
Година
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
2402011
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
1822011
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
1472013
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
1222009
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
982013
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 (2), 264-277, 2014
652014
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
542014
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
372014
Binary/ternary extreme learning machines
M van Heeswijk, Y Miche
Neurocomputing 149, 187-197, 2015
362015
Extreme learning machine: A robust modeling technique? Yes!
A Lendasse, A Akusok, O Simula, F Corona, M Heeswijk, E Eirola, ...
International Work-Conference on Artificial Neural Networks, 17-35, 2013
202013
Air quality forecasting using neural networks
C Zhao, M van Heeswijk, J Karhunen
2016 IEEE Symposium Series on Computational Intelligence (SSCI), 1-7, 2016
162016
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
142014
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
122010
Advances in extreme learning machines
M van Heeswijk
Aalto University, 2015
72015
Evolutive approaches for variable selection using a non-parametric noise estimator
A Guillén, D Sovilj, M Heeswijk, LJ Herrera, A Lendasse, H Pomares, ...
Parallel architectures and bioinspired algorithms, 243-266, 2012
52012
Method for detecting aging related failures of process sensors via noise signal measurement
T Toosi, M Sirola, J Laukkanen, M van Heeswijk, J Karhunen
42019
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
32011
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
12017
Compressive ELM: Improved Models through Exploiting Time-Accuracy Trade-Offs
M Heeswijk, A Lendasse, Y Miche
International Conference on Engineering Applications of Neural Networks, 165-174, 2014
2014
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
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