SEG-SSC: a framework based on synthetic examples generation for self-labeled semi-supervised classification
Self-labeled techniques are semi-supervised classification methods that address the shortage of labeled examples via a self-learning process based on supervised models. They progressively classify unlabeled data and use them to modify the hypothesis learned from labeled samples. Most relevant propos...
| Main Authors: | , , |
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| Format: | Article |
| Published: |
Institute of Electrical and Electronics Engineers
2015
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| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/45410/ |