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Labeling data for classification requires significant human effort. To reduce labeling cost, instead of labeling every instance, a group of instances (bag) is labeled by a single bag label. Computer algorithms are then used to infer the label for each instance in a bag, a process referred to as instance annotation. This task is challenging due to the ambiguity regarding the instance labels. We propose...
In this paper, we present an approach for learning in the semi-supervised setting in the presence of novel class instances. In this setting, data consists of a labeled portion and an unlabeled portion that contains novel class instances along with unlabeled known class instances. Novel class instances are instances from concepts that do not have labeled training examples. This setting is appropriate...
The cost associated with manually labeling every individual instance in large datasets is prohibitive. Significant labeling efforts can be saved by assigning a collective label to a group of instances (a bag). This setup prompts the need for algorithms that allow labeling individual instances (instance annotation) based on bag-level labels. Probabilistic models in which instance-level labels are latent...
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