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Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data
Most of the semi-supervised classification methods developed so far use unlabeled data for regularization purposes under particular distributional assumptions such as the cluster assumption.
Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning
In PU learning, a binary classifier is trained from positive (P) and unlabeled (U) data without negative (N) data.
Transductive Learning with Multi-class Volume Approximation
Given a hypothesis space, the large volume principle by Vladimir Vapnik prioritizes equivalence classes according to their volume in the hypothesis space.
Clustering Unclustered Data: Unsupervised Binary Labeling of Two Datasets Having Different Class Balances
We consider the unsupervised learning problem of assigning labels to unlabeled data.
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