Now showing items 1-5 of 15

  • Batch-Incremental Learning for Mining Data Streams

    Holmes, Geoffrey; Kirkby, Richard Brendon; Bainbridge, David (University of Waikato, 2004)
    The data stream model for data mining places harsh restrictions on a learning algorithm. First, a model must be induced incrementally. Second, processing time for instances must keep up with their speed of arrival. Third, ...
  • Cache Hierarchy Inspired Compression: a Novel Architecture for Data Streams

    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. ...
  • Handling numeric attributes in Hoeffding trees

    Pfahringer, Bernhard; Holmes, Geoffrey; Kirkby, Richard Brendon (Springer, Berlin, 2008)
    For conventional machine learning classification algorithms handling numeric attributes is relatively straightforward. Unsupervised and supervised solutions exist that either segment the data into pre-defined bins or sort ...
  • Improving Hoeffding Trees

    Kirkby, Richard Brendon (The University of Waikato, 2007)
    Modern information technology allows information to be collected at a far greater rate than ever before. So fast, in fact, that the main problem is making sense of it all. Machine learning offers promise of a solution, but ...
  • A logic boosting approach to inducing multiclass alternating decision trees

    Holmes, Geoffrey; Pfahringer, Bernhard; Kirkby, Richard Brendon; Frank, Eibe; Hall, Mark A. (University of Waikato, Department of Computer Science, 2002-03)
    The alternating decision tree (ADTree) is a successful classification technique that combine decision trees with the predictive accuracy of boosting into a ser to interpretable classification rules. The original formulation ...