This thesis tackles the challenge of training ML classification models using limited labelled data or data with uncertain labels. Two approaches are explored: semi-supervised learning (SSL), which ...

Supervised learning works well with labelled data, enabling tasks like classification and regression, but it requires large, high-quality datasets. In contrast, unsupervised learning identifies ...

It can be described as an approach that uses data with noisy labels. These labels are usually generated by a computer by applying heuristics to a signal with the unlabelled data to develop their label ...