Browsing by Author "Holmes, Geoffrey"

Now showing items 1-5 of 105

  • Adaptive random forests for evolving data stream classification

    Gomes, Heitor Murilo; Bifet, Albert; Read, Jesse; Barddal, Jean Paul; Enembreck, Fabrício; Pfahringer, Bernhard; Holmes, Geoffrey; Abdessalem, Talel (Springer, 2017)
    Random forests is currently one of the most used machine learning algorithms in the non-streaming (batch) setting. This preference is attributable to its high learning performance and low demands with respect to input ...
  • 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 (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, ...

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  • Regression modelling of spectroscopic data using lazy learning and deep neural networks

    Fraser, Huon (The University of Waikato, 2022)
    Neural networks show promise in modelling infrared (IR) spectroscopic data, but many of the proposed solutions in the literature are either Multilayer Perceptrons with a single hidden layer or are adapted from successful ...
  • A new approach to fitting linear models in high dimensional spaces

    Wang, Yong (The University of Waikato, 2000)
    This thesis presents a new approach to fitting linear models, called “pace regression”, which also overcomes the dimensionality determination problem. Its optimality in minimizing the expected prediction loss is theoretically ...
  • N-gram models of agreement in language

    Smith, Anthony Clive (The University of Waikato, 2000)
    Conventional n-gram language models are well-established as powerful yet simple mechanisms for characterising language structure when low data complexity is the primary objective. Much of their predictive power can be ...
  • Speech analysis and synthesis using an auditory model

    Carnegie, Dale A. (The University of Waikato, 2000)
    Many traditional speech analysis/synthesis techniques are designed to produce speech with a spectrum that is as close as possible to the original. This may not be necessary because the auditory nerve is the only link from ...
  • High-throughput machine learning algorithms

    Mitchell, Rory (The University of Waikato, 2021)
    The field of machine learning has become strongly compute driven, such that emerging research and applications require larger amounts of specialised hardware or smarter algorithms to advance beyond the state-of-the-art. ...

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