Reservoir of diverse adaptive learners and stacking fast hoeffding drift detection methods for evolv...
Reservoir of diverse adaptive learners and stacking fast hoeffding drift detection methods for evolving data streams
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Publisher
New York: Springer US
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Language
English
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Publisher
New York: Springer US
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Scope and Contents
Contents
The last decade has seen a surge of interest in adaptive learning algorithms for data stream classification, with applications ranging from predicting ozone level peaks, learning stock market indicators, to detecting computer security violations. In addition, a number of methods have been developed to detect concept drifts in these streams. Conside...
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Full title
Reservoir of diverse adaptive learners and stacking fast hoeffding drift detection methods for evolving data streams
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TN_cdi_proquest_journals_2048628649
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2048628649
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ISSN
0885-6125
E-ISSN
1573-0565
DOI
10.1007/s10994-018-5719-z
