Browsing by Author "Frank, Eibe"

Now showing items 1-5 of 119

  • Accelerating the XGBoost algorithm using GPU computing

    Mitchell, Rory; Frank, Eibe (2017)
    We present a CUDA-based implementation of a decision tree construction algorithm within the gradient boosting library XGBoost. The tree construction algorithm is executed entirely on the graphics processing unit (GPU) and ...
  • Accurate photometric redshift probability density estimation - method comparison and application

    Rau, Michael M.; Seitz, Stella; Frank, Eibe; Brimioulee, Fabrice; Friedrich, Oliver; Gruen, Daniel; Hoyle, Ben (Oxford University Press (OUP): Policy P - Oxford Open Option A, 2015)
    We introduce an ordinal classification algorithm for photometric redshift estimation, which significantly improves the reconstruction of photometric redshift probability density functions (PDFs) for individual galaxies and ...
  • Active learning of soft rules for system modelling

    Frank, Eibe; Huber, Klaus-Perter (1996)
    Using rule learning algorithms to model systems has gained considerable interest in the past. The underlying idea of active learning is to learning algorithm influence the selection of training examples. The presented ...
  • Adaptive XGBoost for evolving data streams

    Montiel, Jacob; Mitchell, Rory; Frank, Eibe; Pfahringer, Bernhard; Abdessalem, Talel; Bifet, Albert (IEEE, 2020)
    Boosting is an ensemble method that combines base models in a sequential manner to achieve high predictive accuracy. A popular learning algorithm based on this ensemble method is eXtreme Gradient Boosting (XGB). We present ...
  • Additive Regression Applied to a Large-Scale Collaborative Filtering Problem

    Frank, Eibe; Hall, Mark A. (Springer, 2008)
    The much-publicized Netflix competition has put the spotlight on the application domain of collaborative filtering and has sparked interest in machine learning algorithms that can be applied to this sort of problem. The ...

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

  • Super-resolution of satellite imagery

    Bull, Daniel (The University of Waikato, 2021)
    Can deep neural networks super-resolve satellite imagery to a high perceptual quality? This thesis explores the juxtaposition between the pixel accuracy and perceptual qualities of super-resolved imagery by comparing and ...
  • A study of self-training variants for semi-supervised image classification

    Sahito, Attaullah (The University of Waikato, 2021)
    Artificial neural networks achieve state-of-the-art performance when trained on a vast number of labelled examples. Still, they can easily overfit training examples when few labelled examples are available. The requirement ...
  • 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. ...
  • Learning discrete and Lipschitz representations

    Gouk, Henry (The University of Waikato, 2019)
    Learning to embed data into a low dimensional vector space that is more useful for some downstream task is one of the most common problems addressed in the representation learning literature. Conventional approaches to ...
  • Tree-structured multiclass probability estimators

    Leathart, Timothy Matthew (The University of Waikato, 2019)
    Nested dichotomies are used as a method of transforming a multiclass classification problem into a series of binary problems. A binary tree structure is constructed over the label space that recursively splits the set of ...

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