Research Commons

Browsing by Author "Bifet, Albert"

Research Commons

Browsing by Author "Bifet, Albert"

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  • 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 ...
  • Ienco, Dino; Bifet, Albert; Žliobaitė, Indrė; Pfahringer, Bernhard (Springer, 2013)
    Data labeling is an expensive and time-consuming task. Choosing which labels to use is increasingly becoming important. In the active learning setting, a classifier is trained by asking for labels for only a small fraction ...
  • Kranen, Philipp; Kremer, Hardy; Jensen, Timm; Seidl, Thomas; Bifet, Albert; Homes, Geoff; Pfahringer, Bernhard (2010)
    In today's applications, evolving data streams are ubiquitous. Stream clustering algorithms were introduced to gain useful knowledge from these streams in real-time. The quality of the obtained clusterings, i.e. how good ...
  • Bifet, Albert; Pfahringer, Bernhard; Read, Jesse; Holmes, Geoffrey (ACM, 2013)
    In the context of a data stream, a classifier must be able to learn from a theoretically-infinite stream of examples using limited time and memory, while being able to predict at any point. Many methods deal with this ...
  • 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 ...

Co-authors for Albert Bifet