Now showing items 1-5 of 84

  • Algorithm selection on data streams

    van Rijn, Jan N.; Holmes, Geoffrey; Pfahringer, Bernhard; Vanschoren, Joaquin (Springer International Publishing, 2014)
    We explore the possibilities of meta-learning on data streams, in particular algorithm selection. In a first experiment we calculate the characteristics of a small sample of a data stream, and try to predict which classifier ...
  • Analysing chromatographic data using data mining to monitor petroleum content in water

    Holmes, Geoffrey; Fletcher, Dale; Reutemann, Peter; Frank, Eibe (Springer, 2009)
    Chromatography is an important analytical technique that has widespread use in environmental applications. A typical application is the monitoring of water samples to determine if they contain petroleum. These tests are ...
  • An application of data mining to fruit and vegetable sample identification using Gas Chromatography-Mass Spectrometry

    Holmes, Geoffrey; Fletcher, Dale; Reutemann, Peter (iEMSs, 2012)
    One of the uses of Gas Chromatography-Mass Spectrometry (GC-MS) is in the detection of pesticide residues in fruit and vegetables. In a high throughput laboratory there is the potential for sample swaps or mislabelling, ...
  • Batch-Incremental Learning for Mining Data Streams

    Holmes, Geoffrey; Kirkby, Richard Brendon; Bainbridge, David (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, ...
  • 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 ...

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

  • Scalable Text Mining with Sparse Generative Models

    Puurula, Antti (University of Waikato, 2015)
    The information age has brought a deluge of data. Much of this is in text form, insurmountable in scope for humans and incomprehensible in structure for computers. Text mining is an expanding field of research that seeks ...
  • Parameter Tuning Using Gaussian Processes

    Ma, Jinjin (University of Waikato, 2012)
    Most machine learning algorithms require us to set up their parameter values before applying these algorithms to solve problems. Appropriate parameter settings will bring good performance while inappropriate parameter ...
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation

    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 ...
  • Scalable Multi-label Classification

    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 ...