Research Commons

Browsing by Author "Pfahringer, Bernhard"

Research Commons

Browsing by Author "Pfahringer, Bernhard"

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  • Sun, Quan; Pfahringer, Bernhard (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 ...
  • 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 ...
  • 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 ...
  • Holmes, Geoffrey; Pfahringer, Bernhard; Kirkby, Richard Brendon (2006)
    We present an architecture for data streams based on structures typically found in web cache hierarchies. The main idea is to build a meta level analyser from a number of levels constructed over time from a data stream. ...
  • 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 ...

Co-authors for Bernhard Pfahringer

Supervised by Bernhard Pfahringer

Showing up to 5 theses - most recently added to Research Commons first.

  • Sun, Quan (University of Waikato, 2014)
    When working as a data analyst, one of my daily tasks is to select appropriate tools from a set of existing data analysis techniques in my toolbox, including data preprocessing, outlier detection, feature selection, learning ...
  • Sarjant, Samuel (University of Waikato, 2013)
    Relational Reinforcement Learning (RRL) is a subfield of machine learning in which a learning agent seeks to maximise a numerical reward within an environment, represented as collections of objects and relations, by ...
  • Mutter, Stefan (University of Waikato, 2011)
    Detecting similarity in biological sequences is a key element to understanding the mechanisms of life. Researchers infer potential structural, functional or evolutionary relationships from similarity. However, the concept ...
  • 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 ...