Browsing by Author "Gouk, Henry"

Now showing items 1-5 of 9

  • Comparing high dimensional word embeddings trained on medical text to bag-of-words for predicting medical codes

    Yogarajan, Vithya; Gouk, Henry; Smith, Tony C.; Mayo, Michael; Pfahringer, Bernhard (Springer, 2020)
    Word embeddings are a useful tool for extracting knowledge from the free-form text contained in electronic health records, but it has become commonplace to train such word embeddings on data that do not accurately reflect ...
  • A comparison of machine learning methods for cross-domain few-shot learning

    Wang, Hongyu; Gouk, Henry; Frank, Eibe; Pfahringer, Bernhard; Mayo, Michael (Springer, 2020)
    We present an empirical evaluation of machine learning algorithms in cross-domain few-shot learning based on a fixed pre-trained feature extractor. Experiments were performed in five target domains (CropDisease, EuroSAT, ...
  • Estimating heading direction from monocular video sequences using biologically-based sensor

    Cree, Michael J.; Perrone, John A.; Anthonys, Gehan; Garnett, Aden C.; Gouk, Henry (2016)
    The determination of one’s movement through the environment (visual odometry or self-motion estimation) from monocular sources such as video is an important research problem because of its relevance to robotics and autonomous ...
  • Experiments in cross-domain few-shot learning for image classification

    Wang, Hongyu; Gouk, Henry; Fraser, Huon; Frank, Eibe; Pfahringer, Bernhard; Mayo, Michael; Holmes, Geoffrey (Informa UK Limited, 2022)
    Cross-domain few-shot learning has many practical applications. This paper attempts to shed light on suitable configurations of feature exactors and ‘shallow’ classifiers in this machine learning setting. We apply ResNet-based ...
  • 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 ...