Browsing by Author "Pfahringer, Bernhard"

Now showing items 6-10 of 117

  • Bagging ensemble selection

    Sun, Quan; Pfahringer, Bernhard (Springer, 2011)
    Ensemble selection has recently appeared as a popular ensemble learning method, not only because its implementation is fairly straightforward, but also due to its excellent predictive performance on practical problems. The ...
  • Bagging ensemble selection for regression

    Sun, Quan; Pfahringer, Bernhard (Springer, 2012)
    Bagging ensemble selection (BES) is a relatively new ensemble learning strategy. The strategy can be seen as an ensemble of the ensemble selection from libraries of models (ES) strategy. Previous experimental results on ...
  • 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 ...
  • Boosting decision stumps for dynamic feature selection on data streams

    Barddal, Jean Paul; Enembreck, Fabrício; Gomes, Heitor Murilo; Bifet, Albert; Pfahringer, Bernhard (2019)
    Feature selection targets the identification of which features of a dataset are relevant to the learning task. It is also widely known and used to improve computation times, reduce computation requirements, and to decrease ...
  • Bound analysis for Whiley programs

    Weng, Min-Hsien; Utting, Mark; Pfahringer, Bernhard (Elsevier, 2015)
    The Whiley compiler can generate naive C code, but the code is inefficient because it uses infinite integers and dynamic array sizes. Our project goal is to build up a compiler that can translate Whiley programs into ...