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PAKDD
2005
ACM

SETRED: Self-training with Editing

10 years 9 months ago
SETRED: Self-training with Editing
Self-training is a semi-supervised learning algorithm in which a learner keeps on labeling unlabeled examples and retraining itself on an enlarged labeled training set. Since the self-training process may erroneously label some unlabeled examples, sometimes the learned hypothesis does not perform well. In this paper, a new algorithm named Setred is proposed, which utilizes a specific data editing method to identify and remove the mislabeled examples from the self-labeled data. In detail, in each iteration of the self-training process, the local cut edge weight statistic is used to help estimate whether a newly labeled example is reliable or not, and only the reliable self-labeled examples are used to enlarge
Ming Li, Zhi-Hua Zhou
Added 28 Jun 2010
Updated 28 Jun 2010
Type Conference
Year 2005
Where PAKDD
Authors Ming Li, Zhi-Hua Zhou
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