Model variance for extreme learning machine
F. Guignard, M. Laib, and M. Kanevski
in the Proceedings of the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020, Virtual, Online, Belgium, 2-4 October, ISBN: 978-287587074-2, pp. 703-708, 2020
We derived theoretical formulas for the variance of extreme learning machine ensemble in a general case of a heteroskedastic noise. They provide a decomposition of the variance, which helps in the understanding of how the different sources of randomness contribute. The application of the proposed method to simulated datasets shows the effectiveness of the newly introduced estimations in replicating the expected variance behaviours.