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2017 | OriginalPaper | Buchkapitel

Learning in Nonstationary Environments: A Hybrid Approach

verfasst von : Cesare Alippi, Wen Qi, Manuel Roveri

Erschienen in: Artificial Intelligence and Soft Computing

Verlag: Springer International Publishing

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Abstract

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).

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Titel: Learning in Nonstationary Environments: A Hybrid Approach
verfasst von: Cesare Alippi
Wen Qi
Manuel Roveri
Verlag: Springer International Publishing
Buch: Artificial Intelligence and Soft Computing
Print ISBN: 978-3-319-59059-2

Electronic ISBN: 978-3-319-59060-8

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-59060-8_63

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