Research Commons

Browsing by Author "Read, Jesse"

Research Commons

Browsing by Author "Read, Jesse"

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  • Batch-incremental versus instance-incremental learning in dynamic and evolving data 
    Read, Jesse; Bifet, Albert; Pfahringer, Bernhard; Holmes, Geoffrey (Springer, 2012)
    Many real world problems involve the challenging context of data streams, where classifiers must be incremental: able to learn from a theoretically- infinite stream of examples using limited time and memory, while being ...
  • Classifier chains for multi-label classification 
    Read, Jesse; Pfahringer, Bernhard; Holmes, Geoffrey; Frank, Eibe (Springer, 2009)
    The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its label-independence ...
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    Efficient multi-label classification for evolving data streams 
    Read, Jesse; Bifet, Albert; Holmes, Geoffrey; Pfahringer, Bernhard (University of Waikato, Department of Computer Science, 2010)
    Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios. However, learning in evolving streaming scenarios ...
  • Scalable and efficient multi-label classification for evolving data streams 
    Read, Jesse; Bifet, Albert; Holmes, Geoffrey; Pfahringer, Bernhard (Springer, 2012)
    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, ...
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    Scalable Multi-label Classification 
    Read, Jesse (University of Waikato, 2010)
    Multi-label classification is relevant to many domains, such as text, image and other media, and bioinformatics. Researchers have already noticed that in multi-label data, correlations exist between labels, and a variety ...

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Jesse Read has 4 co-authors in Research Commons.

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