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SEG-SSC: a framework based on synthetic examples generation for self-labeled semi-supervised classification (2014)
Journal Article

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... Read More about SEG-SSC: a framework based on synthetic examples generation for self-labeled semi-supervised classification.