Learning in Nonstationary Environments: A Hybrid Approach
Informazioni aggiuntive
Autori
Alippi C.,
Qi W.,
Roveri M.
Tipo
Contributo in atti di convegno
Anno
2017
Lingua
Inglese
Sommario
Solutions present in the literature to learn in nonstationary environments can be grouped into two main families: passive and active. Passive solutions rely on a continuous adaptation of the envisaged learning system, while the active ones trigger the adaptation only when needed. Passive and active solutions are somehow complementary and one should be preferred than the other depending on the nonstationarity rate and the tolerable computational complexity. The aim of this paper is to introduce a novel hybrid approach that jointly uses an adaptation mechanism (as in passive solutions) and a change detection triggering the need to retrain the learning system (as in active solutions).
Titolo atti di convegno
Artificial Intelligence and Soft Computing
Editore
Springer International Publishing
Luogo convegno
Cham
ISBN
978-3-319-59060-8
