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Showing 1 to 2 of 2 for “"Class overlap"”.

  1. Learning from class-imbalanced data: overlap-driven resampling for imbalanced data classification.

    Classification of imbalanced datasets has attracted substantial research interest over the past years. This is because imbalanced datasets are common in several domains such as health, finance and security, but learning algorithms are generally not designed to handle them. Many existing solutions …

    rgu Repository record for Learning from class-imbalanced data: overlap-driven resampling for imbalanced data classification. (opens in a new tab)

  2. Improved hyperspectral classification of vegetation through generative deep learning models.

    … the potential for taxonomic discrimination and classification, though this came with the caveat that misidentification was a frequent impediment as a result of small sample sizes, inter-class similarity and intra-class variability. The aim of this thesis was to develop methods of improving the …

    adelaide Repository record for Improved hyperspectral classification of vegetation through generative deep learning models. (opens in a new tab)