Discriminative learning when training and test data belong to different distributions is a challenging and complex task. Often times we have very few or no labeled data from the test or target distribution, but we may have plenty of labeled data from one or multiple related sources with different distributions. Due to its capability of migrating knowledge from related domains, transfer learning has shown to be effective for cross-domain learning problems.
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- Partial requirement for: Ph.D., Arizona State University, 2015Note typethesis
- Includes bibliographical references (pages 115-124)Note typebibliography
- Field of study: Computer science