A transfer approach to detecting disease reporting events in blog social media
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Compact coding for hyperplane classifiers in heterogeneous environment
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Transfer learning for cross-company software defect prediction
Information and Software Technology
Transfer learning with local smoothness regularizer
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
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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 learning, if the step of labeling an unlabeled instance is replaced by re-weighting a diff-distribution instance. Based on this recognition, we develop a new transfer learning method, namely COITL, by extending the co-training method in semi-supervised learning. Experimental results reveal that COITL can achieve significantly higher generalization and robustness, compared with two state-of-the-art methods in inductive transfer learning.