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An introduction to transfer learning

By Qiang Yang


Many existing data mining and machine learning techniques are based on the assumption that training and test data fit the same distribution. This assumption does not hold, however, as in many cases of Web mining and wireless computing when labeled data becomes outdated or test data are from a different domain with training data. In these cases, most machine learning methods would fail in correctly classifying new and future data. It would be very costly and infeasible to collect and label enough new training data. Instead, we would like to recoup as much useful knowledge as possible from the old data. This problem is known as transfer learning. In this talk, I will give an overview of the transfer learning problem, present a number of important directions in this research, and discuss our own novel solutions to this problem

Year: 2008
DOI identifier: 10.1007/978-3-540-88192-6_1
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