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Co-training is one of the major semi-supervised learning paradigms in which two classifiers are alternately trained on two distinct views and they teach each other by adding the predictions of unlabeled data to the training set of the other view. Co-training can achieve promising performance, especially when there is only a small number of labeled data. Hence, co-training has received considerable...
Spectral clustering employs spectral-graph structure of a similarity matrix to partition data into disjoint meaningful groups, because of its well-defined mathematical framework, good performance and simplicity, spectral clustering has gained considerable attentions in the recent past. Despite these virtues, it suffers from several drawbacks, such as it is unable to determine a reasonable cluster...
Semi-supervised learning algorithms that combine labeled and unlabeled data receive significant interests in recent years and are successfully deployed in many practical data mining applications. Manifold regularization, one of the most representative works, tries to explore the geometry of the intrinsic data probability distribution by penalizing the classification function along the implicit manifold...
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