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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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TITLE: The Transfer Learning Exploratory Graphical Models with Application in Alzheimer’s Disease
SPEAKER: Dr. Jing Li
ABSTRACT:
Knowledge discovery in fields such as medical informatics, bioinformatics, and modern manufacturing systems/processes often requires building statistical models in which many random variables interact with each other in complex ways. Graphical models provide a general methodology for learning the complex relationships from observational data. Transfer learning of graphical models deal with the situations where graphical models need to be learned for multiple related groups of subjects, so that the knowledge/information gained during the learning of one group can be effectively transferred to the learning of another group. Such “groups” can be, for example, patients with similar but not identical neurological disorders, or manufacturing products belonging to several product varieties. The concept of “transfer learning” was originated in psychology and recently has been introduced into statistics and machine learning society. This talk will present our recent development in transfer learning of undirected graphical models, which integrates statistics and optimization. The developed methodology is applied to build brain connectivity networks from neuroimaging data for Alzheimer’s Disease studies.