Search Results for author:
Found 3 papers, 0 papers with code
Pool-Based Active Learning with Proper Topological Regions
Machine learning methods usually rely on large sample size to have good performance, while it is difficult to provide labeled set in many applications.
Self-Training: A Survey
Semi-supervised algorithms aim to learn prediction functions from a small set of labeled observations and a large set of unlabeled observations.
Self-Training of Halfspaces with Generalization Guarantees under Massart Mislabeling Noise Model
The pseudo-labeled examples are then added to the training set, and a new classifier is learned.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
