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Co-training is one of the major semi-supervised learning paradigms that iteratively trains two classifiers on two different views, and uses the predictions of either classifier on the unlabeled examples to augment the training set of the other. During the co-training process, especially in initial rounds when the classifiers have only mediocre accuracy, it is quite possible that one classifier will...
In real-world applications, assigning labels to examples usually requires human effort and therefore, labeled training examples are expensive; unlabeled training examples, however, are cheap and abundant. As a consequence, semi-supervised learning which attempts to exploit unlabeled data to help improve learning performance has become a very hot topic in machine learning and data mining. In this talk,...
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