558 citations · 1.1k across the 26 of their papers we have counts for
3 papers · 2 filters
Uncertainty Quantification in Extreme Learning Machine: Analytical Developments, Variance Estimates and Confidence Intervals
Fabian Guignard, Federico Amato, Mikhail Kanevski
Uncertainty quantification is crucial to assess prediction quality of a machine learning model. In the case of Extreme Learning Machines (ELM), most methods proposed in the literat…
On Feature Selection Using Anisotropic General Regression Neural Network
Federico Amato, Fabian Guignard, Philippe Jacquet +1
The presence of irrelevant features in the input dataset tends to reduce the interpretability and predictive quality of machine learning models. Therefore, the development of featu…
A Novel Framework for Spatio-Temporal Prediction of Environmental Data Using Deep Learning
Federico Amato, Fabian Guignard, Sylvain Robert +1
As the role played by statistical and computational sciences in climate and environmental modelling and prediction becomes more important, Machine Learning researchers are becoming…