| 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 | 167 | 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 |
| Mixture of Gaussians for distance estimation with missing data E Eirola, A Lendasse, V Vandewalle, C Biernacki Neurocomputing 131, 32-42, 2014 | 80 | 2014 |
| Distance estimation in numerical data sets with missing values E Eirola, G Doquire, M Verleysen, A Lendasse Information Sciences 240, 115-128, 2013 | 71 | 2013 |
| Using the Delta Test for Variable Selection. E Eirola, E Liitiäinen, A Lendasse, F Corona, M Verleysen ESANN, 25-30, 2008 | 65 | 2008 |
| Gaussian mixture models for time series modelling, forecasting, and interpolation E Eirola, A Lendasse Advances in Intelligent Data Analysis XII: 12th International Symposium, IDA …, 2013 | 49 | 2013 |
| A pragmatic android malware detection procedure P Palumbo, L Sayfullina, D Komashinskiy, E Eirola, J Karhunen Computers & Security 70, 689-701, 2017 | 47 | 2017 |
| Efficient detection of zero-day Android Malware using Normalized Bernoulli Naive Bayes L Sayfullina, E Eirola, D Komashinsky, P Palumbo, Y Miche, A Lendasse, ... IEEE TrustCom 2015, 2015 | 38 | 2015 |
| Diurnal variations of depression-related health information seeking: case study in Finland using Google Trends data JC Tana, J Kettunen, E Eirola, H Paakkonen JMIR mental health 5 (2), e9152, 2018 | 34 | 2018 |
| When is prime-time in streaming media platforms and video-on-demands services? New media consumption patterns and real-time economy J Tana, E Eirola, M Nylund European journal of communication 35 (2), 108-125, 2020 | 31 | 2020 |
| Ensembles of local linear models for bankruptcy analysis and prediction L Kainulainen, Y Miche, E Eirola, Q Yu, B Frénay, E Séverin, A Lendasse Case Studies In Business, Industry And Government Statistics 4 (2), 116-133, 2011 | 25 | 2011 |
| 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 |
| Extreme Learning Machines for Multiclass Classification: Refining Predictions with Gaussian Mixture Models E Eirola, A Gritsenko, A Akusok, KM Björk, Y Miche, D Sovilj, R Nian, B He, ... | 14 | 2015 |
| Ensemble Modeling with a Constrained Linear System of Leave-One-Out Outputs. Y Miche, E Eirola, P Bas, O Simula, C Jutten, A Lendasse, M Verleysen ESANN, 28-30, 2010 | 13 | 2010 |
| ELM-SOM: a continuous self-organizing map for visualization R Hu, V Roshdibenam, HJ Johnson, E Eirola, A Akusok, Y Miche, ... 2018 international joint conference on neural Networks (IJCNN), 1-8, 2018 | 10 | 2018 |
| Brute-force Missing Data Extreme Learning Machine for Predicting Huntington's Disease A Akusok, E Eirola, KM Björk, Y Miche, H Johnson, A Lendasse Proceedings of the 10th International Conference on PErvasive Technologies …, 2017 | 10 | 2017 |
| A new application of machine learning in health care KM Björk, E Eirola, Y Miche, A Lendasse Proceedings of the 9th ACM International Conference on PErvasive …, 2016 | 10 | 2016 |
| Predicting huntington’s disease: extreme learning machine with missing values E Eirola, A Akusok, KM Björk, H Johnson, A Lendasse Proceedings of ELM-2016, 195-206, 2018 | 9 | 2018 |
| Android malware detection: building useful representations L Sayfullina, E Eirola, D Komashinsky, P Palumbo, J Karhunen 2016 15th IEEE International Conference on Machine Learning and Applications …, 2016 | 8 | 2016 |
| The delta test: The 1-NN estimator as a feature selection criterion E Eirola, A Lendasse, F Corona, M Verleysen 2014 International Joint Conference on Neural Networks (IJCNN), 4214-4222, 2014 | 8 | 2014 |
Amaury LendasseUniversity of HoustonVerified email at central.uh.edu
