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dc.contributor.authorMontiel, Jacoben_NZ
dc.contributor.authorHalford, Maxen_NZ
dc.contributor.authorMastelini, Saulo Martielloen_NZ
dc.contributor.authorBolmier, Geoffreyen_NZ
dc.contributor.authorSourty, Raphaelen_NZ
dc.contributor.authorVaysse, Robinen_NZ
dc.contributor.authorZouitine, Adilen_NZ
dc.contributor.authorGomes, Heitor Muriloen_NZ
dc.contributor.authorRead, Jesseen_NZ
dc.contributor.authorAbdessalem, Talelen_NZ
dc.contributor.authorBifet, Alberten_NZ
dc.date.accessioned2021-06-23T02:46:18Z
dc.date.available2021-06-23T02:46:18Z
dc.date.issued2021en_NZ
dc.identifier.citationMontiel, J., Halford, M., Mastelini, S. M., Bolmier, G., Sourty, R., Vaysse, R., … Bifet, A. (2021). River: Machine learning for streaming data in Python. Journal of Machine Learning Research, 22(10), 1–8.en
dc.identifier.issn1532-4435en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/14402
dc.description.abstractRiver is a machine learning library for dynamic data streams and continual learning. It provides multiple state-of-the-art learning methods, data generators/transformers, performance metrics and evaluators for different stream learning problems. It is the result from the merger of two popular packages for stream learning in Python: Creme and scikit- multiow. River introduces a revamped architecture based on the lessons learnt from the seminal packages. River's ambition is to be the go-to library for doing machine learning on streaming data. Additionally, this open source package brings under the same um-brella a large community of practitioners and researchers. The source code is available at https://github.com/online-ml/river.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.relation.urihttp://jmlr.org/papers/v22/20-1380.htmlen_NZ
dc.rights© 2021 Jacob Montiel, Max Halford, Saulo Martiello Mastelini, Geoffrey Bolmier, Raphael Sourty, Robin Vaysse, Adil Zouitine, Heitor Murilo Gomes, Jesse Read, Talel Abdessalem, Albert Bifet. License: CC-BY 4.0, see https://creativecommons.org/licenses/by/4.0/. Attribution requirements are provided at http://jmlr.org/papers/v22/20-1380.html.
dc.subjectstream learningen_NZ
dc.subjectonline learningen_NZ
dc.subjectdata streamen_NZ
dc.subjectconcept driften_NZ
dc.subjectsupervised learningen_NZ
dc.subjectunsupervised learningen_NZ
dc.subjectPythonen_NZ
dc.subjectcomputer scienceen_NZ
dc.subjectMachine learning
dc.titleRiver: Machine learning for streaming data in Pythonen_NZ
dc.typeJournal Article
dc.relation.isPartOfJournal of Machine Learning Researchen_NZ
pubs.begin-page1
pubs.elements-id258736
pubs.end-page8
pubs.issue10en_NZ
pubs.publication-statusPublisheden_NZ
pubs.volume22en_NZ
dc.identifier.eissn1533-7928en_NZ


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