no code implementations • 29 Jul 2021 • Federico Amato, Fabian Guignard, Alina Walch, Nahid Mohajeri, Jean-Louis Scartezzini, Mikhail Kanevski
These include (i) insufficient consideration of spatio-temporal correlations in wind-speed data, (ii) a lack of existing methodologies to quantify the uncertainty of wind speed prediction and its propagation to the wind-power estimation, and (iii) a focus on less than hourly frequencies.
no code implementations • 15 Apr 2021 • Devis Tuia, Michele Volpi, Loris Copa, Mikhail Kanevski, Jordi Munoz-Mari
Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines.
no code implementations • 3 Nov 2020 • Fabian Guignard, Federico Amato, Mikhail Kanevski
Uncertainty quantification is crucial to assess prediction quality of a machine learning model.
1 code implementation • 12 Oct 2020 • Federico Amato, Fabian Guignard, Philippe Jacquet, Mikhail Kanevski
The presence of irrelevant features in the input dataset tends to reduce the interpretability and predictive quality of machine learning models.
1 code implementation • 23 Jul 2020 • Federico Amato, Fabian Guignard, Sylvain Robert, Mikhail Kanevski
As the role played by statistical and computational sciences in climate and environmental modelling and prediction becomes more important, Machine Learning researchers are becoming more aware of the relevance of their work to help tackle the climate crisis.
no code implementations • 4 Oct 2017 • Mohamed Laib, Jean Golay, Luciano Telesca, Mikhail Kanevski
In this paper, we applied the multifractal detrended fluctuation analysis to the daily means of wind speed measured by 119 weather stations distributed over the territory of Switzerland.
no code implementations • 27 Jun 2017 • Mohamed Laib, Mikhail Kanevski
It is implemented in a filter algorithm for unsupervised feature selection problems.
no code implementations • 19 Aug 2016 • Jean Golay, Mikhail Kanevski
This paper deals with a new filter algorithm for selecting the smallest subset of features carrying all the information content of a data set (i. e. for removing redundant features).
no code implementations • 31 Jan 2016 • Jean Golay, Michael Leuenberger, Mikhail Kanevski
It can identify relevant features and distinguish between redundant and irrelevant information.