Browsing by Author "Sahito, Attaullah"

Now showing items 1-3 of 3

  • 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 ...
  • Semi-supervised learning using Siamese networks

    Sahito, Attaullah; Frank, Eibe; Pfahringer, Bernhard (Springer, 2019)
    Neural networks have been successfully used as classification models yielding state-of-the-art results when trained on a large number of labeled samples. These models, however, are more difficult to train successfully for ...
  • Transfer of pretrained model weights substantially improves semi-supervised image classification

    Sahito, Attaullah; Frank, Eibe; Pfahringer, Bernhard (Springer, 2020)
    Deep neural networks produce state-of-the-art results when trained on a large number of labeled examples but tend to overfit when small amounts of labeled examples are used for training. Creating a large number of labeled ...

Attaullah Sahito has 2 co-authors in Research Commons.