no code implementations • 19 Dec 2019 • Anton Akusok, Kaj-Mikael Björk, Leonardo Espinosa Leal, Yoan Miche, Renjie Hu, Amaury Lendasse
This concept paper highlights a recently opened opportunity for large scale analytical algorithms to be trained directly on edge devices.
no code implementations • 19 Dec 2019 • Anton Akusok, Emil Eirola, Kaj-Mikael Björk, Amaury Lendasse
The paper proposes a new variant of a decision tree, called an Extreme Learning Tree.
no code implementations • 19 Dec 2019 • Anton Akusok, Mirka Saarela, Tommi Kärkkäinen, Kaj-Mikael Björk, Amaury Lendasse
The paper proposes to analyze a data set of Finnish ranks of academic publication channels with Extreme Learning Machine (ELM).
no code implementations • 19 Dec 2019 • Anton Akusok, Yoan Miche, Kaj-Mikael Björk, Amaury Lendasse
Prediction intervals in supervised Machine Learning bound the region where the true outputs of new samples may fall.
no code implementations • 18 Dec 2019 • Anton Akusok, Emil Eirola, Yoan Miche, Ian Oliver, Kaj-Mikael Björk, Andrey Gritsenko, Stephen Baek, Amaury Lendasse
An incremental version of the ELMVIS+ method is proposed in this paper.
no code implementations • 18 Dec 2019 • Leonardo Espinosa Leal, Kaj-Mikael Björk, Amaury Lendasse, Anton Akusok
The results show that the best method of classifying a webpage into the studies classes is to assign the class according to the maximum probability of any image belonging to this (weapon) class being above the threshold, across all the retrieved images.