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

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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...

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Bibliographic Details
Main Authors:	Triguero, Isaac, Garcia, Salvador, Herrera, Francisco
Format:	Article
Published:	Institute of Electrical and Electronics Engineers 2015
Subjects:	Prototypes Training Reliability Prediction algorithms Cybernetics Manifolds Standards
Online Access:	https://eprints.nottingham.ac.uk/45410/

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