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A probabilistic framework of transfer learning- theory and application

Abstract 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. That is, it will either assume the data distributions of the different domains to be the same and thereby developing one model that fits all, or develop one model for each domain independently. Transfer learning, on the other hand, aims to mitigate the limitations of the two approaches b... (more)
Created Date 2015
Contributor Zou, Na (Author) / Li, Jing (Advisor) / Baydogan, Mustafa (Committee member) / Borror, Connie (Committee member) / Montgomery, Douglas (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Subject Industrial engineering / change detection / degeneracy / network state space model / transfer learning
Type Doctoral Dissertation
Extent 108 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Industrial Engineering 2015
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis