Emil Eirola
Emil Eirola
Silo AI
Geverifieerd e-mailadres voor silo.ai
Geciteerd door
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Regularized extreme learning machine for regression with missing data
Q Yu, Y Miche, E Eirola, M Van Heeswijk, E SÚverin, A Lendasse
Neurocomputing 102, 45-51, 2013
Extreme learning machine for missing data using multiple imputations
D Sovilj, E Eirola, Y Miche, KM Bj÷rk, R Nian, A Akusok, A Lendasse
Neurocomputing 174, 220-231, 2016
Using the Delta Test for Variable Selection.
E Eirola, E Liitińinen, A Lendasse, F Corona, M Verleysen
ESANN, 25-30, 2008
Mixture of Gaussians for distance estimation with missing data
E Eirola, A Lendasse, V Vandewalle, C Biernacki
Neurocomputing 131, 32-42, 2014
Distance estimation in numerical data sets with missing values
E Eirola, G Doquire, M Verleysen, A Lendasse
Information Sciences 240, 115-128, 2013
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
A pragmatic android malware detection procedure
P Palumbo, L Sayfullina, D Komashinskiy, E Eirola, J Karhunen
Computers & Security 70, 689-701, 2017
Ensembles of local linear models for bankruptcy analysis and prediction
L Kainulainen, Y Miche, E Eirola, Q Yu, B FrÚnay, E SÚverin, A Lendasse
Case Studies In Business, Industry And Government Statistics 4 (2), 116-133, 2011
Gaussian mixture models for time series modelling, forecasting, and interpolation
E Eirola, A Lendasse
International Symposium on Intelligent Data Analysis, 162-173, 2013
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), e43, 2018
Extreme learning machine: A robust modeling technique? Yes!
A Lendasse, A Akusok, O Simula, F Corona, M van Heeswijk, E Eirola, ...
International Work-Conference on Artificial Neural Networks, 17-35, 2013
Extreme Learning Machines for Multiclass Classification: Refining Predictions with Gaussian Mixture Models
E Eirola, A Gritsenko, A Akusok, KM Bj÷rk, Y Miche, D Sovilj, R Nian, B He, ...
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
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
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 Applicationsá…, 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
Brute-force Missing Data Extreme Learning Machine for Predicting Huntington's Disease
A Akusok, E Eirola, KM Bj÷rk, Y Miche, H Johnson, A Lendasse
Proceedings of the 10th International Conference on PErvasive Technologiesá…, 2017
Probabilistic methods for multiclass classification problems
A Gritsenko, E Eirola, D Schupp, E Ratner, A Lendasse
Proceedings of ELM-2015 Volume 2, 385-397, 2016
Ensembles of Locally Linear Models: Application to Bankruptcy Prediction.
L Kainulainen, Q Yu, Y Miche, E Eirola, E SÚverin, A Lendasse
DMIN, 280-286, 2010
Predicting Huntington’s Disease: Extreme Learning Machine with Missing Values
E Eirola, A Akusok, KM Bj÷rk, H Johnson, A Lendasse
Proceedings of ELM-2016, 195-206, 2018
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