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Inductive transfer learning and semi-supervised learning are two different branches of machine learning. The former tries to reuse knowledge in labeled out-of-domain instances while the later attempts to exploit the usefulness of unlabeled in-domain instances. In this paper, we bridge the two branches by pointing out that many semi-supervised learning methods can be extended for inductive transfer...
Traditional feature selection algorithms require a large number of labeled training instances to find out the most informative subset of features. However, in many real-world applications, the labeled data are often difficult, expensive or time-consuming to obtain. Recently, several semi-supervised feature selection algorithms were proposed, which aim at doing feature selection with the help of some...
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