Probabilistic Decoupling of Labels in Classification

A common approach, called transductive In this paper we develop a principled, probabilistic, semi-supervised learning (Zhu & Goldberg, 2009; Triguero unified approach to nonstandard classification et al., 2015), is to attempt to predict labels on the unlabelled tasks, such as semi-supervised, positiveunlabelled, dataset and then use the combined dataset to train final multi-positive-unlabelled and noisylabel models. One transductive method is self-training in which learning. We train a classifier on the given a model switches between training and relabelling its labels to predict the label-distribution.