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Browsing by Author "Pfahringer, Bernhard"
Now showing items 1-5 of 65
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(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 ... -
(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 ... -
(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 ...
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(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. ... -
(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 ...
Now showing items 1-5 of 65
Co-authors for Bernhard Pfahringer
Supervised by Bernhard Pfahringer
Showing up to 5 theses - most recently added to Research Commons first.
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Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation (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 ... -
(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 ...