Transfer learning refers to statistical machine learning methods that integrate the knowledge of one domain (source domain) and the data of another domain (target domain) in an appropriate way, in order to develop a model for the target domain that is better than a model using the data of the target domain alone. Transfer learning emerged because classic machine learning, when used to model different domains, has to take on one of two mechanical approaches.
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- Partial requirement for: Ph.D., Arizona State University, 2015Note typethesis
- Includes bibliographical references (pages 74-78)Note typebibliography
- Field of study: Industrial engineering