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Multi-instance learning is a setting in supervised learning where the data consist of bags of instances. Samples in the dataset are groups of individual instances. In classification problems, a decision value is assigned to the entire bag, and the classification of an unseen bag involves the prediction of the decision value based on the instances it contains. In this paper, we develop a framework...
Semi-supervised learning incorporates aspects of both supervised and unsupervised learning. In semi-supervised classification, only some data instances have associated class labels, while others are unlabelled. One particular group of semi-supervised classification approaches are those known as self-labelling techniques, which attempt to assign class labels to the unlabelled data instances. This is...
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