From batch to online learning using copies

Irene Unceta, Jordi Nin, Oriol Pujol

Producció científica: Capítol de llibreContribució a congrés/conferènciaAvaluat per experts

3 Cites (Scopus)

Resum

In many every day examples trained machine learning models are rendered obsolete by an inability to adapt to an ever changing environment. This may happen either because their performance decreases in time or because external agents impose new constraints, for example, in the form of regulations. This situation is particularly worrying in company productions environments where model accuracy needs to be preserved. In such situations, model-agnostic copies have been proposed as a viable method to adapt pre-existing models to the new requirements. In this article we study how the use of copies can be extended to endow classifiers trained in batch with online learning. We propose two online algorithms and validate their performance in a series of well-known problems.

Idioma originalAnglès
Títol de la publicacióArtificial Intelligence Research and Development - Proceedings of the 22nd International Conference of the Catalan Association for Artificial Intelligence, CCIA 2019
EditorsJordi Sabater-Mir, Vicenc Torra, Isabel Aguilo, Manuel Gonzalez-Hidalgo
EditorIOS Press
Pàgines125-134
Nombre de pàgines10
ISBN (electrònic)9781643680149
DOIs
Estat de la publicacióPublicada - 6 de set. 2019
Publicat externament
Esdeveniment22nd International Conference of the Catalan Association for Artificial Intelligence, CCIA 2019 - Mallorca, Spain
Durada: 23 d’oct. 201925 d’oct. 2019

Sèrie de publicacions

NomFrontiers in Artificial Intelligence and Applications
Volum319
ISSN (imprès)0922-6389

Conferència

Conferència22nd International Conference of the Catalan Association for Artificial Intelligence, CCIA 2019
País/TerritoriSpain
CiutatMallorca
Període23/10/1925/10/19

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