Scalable and efficient multi-label classification for evolving data streams
Scalable and efficient multi-label classification for evolving data streams
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Author / Creator
Publisher
Boston: Springer US
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Language
English
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Publication information
Publisher
Boston: Springer US
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Scope and Contents
Contents
Many challenging real world problems involve multi-label data streams. Efficient methods exist for multi-label classification in non-streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as classifiers must be able to deal with huge numbers of examples and to adapt to change using limited time and memory while...
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Full title
Scalable and efficient multi-label classification for evolving data streams
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Record Identifier
TN_cdi_proquest_miscellaneous_1031340371
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_1031340371
Other Identifiers
ISSN
0885-6125
E-ISSN
1573-0565
DOI
10.1007/s10994-012-5279-6
