STUDD: a student–teacher method for unsupervised concept drift detection

Vitor Cerqueira; Heitor Murilo Gomes; Albert Bifet; Luis Torgo

doi:10.1007/s10994-022-06188-7

STUDD: a student–teacher method for unsupervised concept drift detection

Vitor Cerqueira, Heitor Murilo Gomes, Albert Bifet, Luis Torgo

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Research output: Contribution to journal › Article › peer-review

11 Citations (Scopus)

Original language	English
Journal	Machine Learning
DOIs	https://doi.org/10.1007/s10994-022-06188-7
Publication status	Accepted/In press - 2022

ASJC Scopus Subject Areas

Software
Artificial Intelligence

Keywords

Concept drift detection
Data streams
Model compression

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10.1007/s10994-022-06188-7

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title = "STUDD: a student–teacher method for unsupervised concept drift detection",

keywords = "Concept drift detection, Data streams, Model compression",

author = "Vitor Cerqueira and Gomes, {Heitor Murilo} and Albert Bifet and Luis Torgo",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature.",

year = "2022",

doi = "10.1007/s10994-022-06188-7",

language = "English",

journal = "Machine Learning",

issn = "0885-6125",

publisher = "Springer Netherlands",

}

TY - JOUR

T1 - STUDD

T2 - a student–teacher method for unsupervised concept drift detection

AU - Cerqueira, Vitor

AU - Gomes, Heitor Murilo

AU - Bifet, Albert

AU - Torgo, Luis

PY - 2022

Y1 - 2022

KW - Concept drift detection

KW - Data streams

KW - Model compression

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U2 - 10.1007/s10994-022-06188-7

DO - 10.1007/s10994-022-06188-7

M3 - Article

AN - SCOPUS:85132317505

SN - 0885-6125

JO - Machine Learning

JF - Machine Learning

ER -

STUDD: a student–teacher method for unsupervised concept drift detection

ASJC Scopus Subject Areas

Keywords

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