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MS Final Examination – Gaole Jin


Monday, December 3, 2012 1:00 PM - 3:00 PM

On Surrogate Supervision Multi-View Learning
In multi-view learning, data can be represented in multiple views where a classifier can be obtained independently in each of the views. In some situations, the data may be incomplete, i.e., the labels are only available in one of the two views in a two-view scenario. This naturally leads to a semi-supervised learning scenario. The setting of surrogate supervision multi-view learning is such a scenario where a classifier for one view is sought after, however, no labeled examples are available for that view. Instead, the training set consists of only labeled examples for the other view as well as unlabeled two-view data. While it is straightforward to train and test a classifier in one view, it is challenging to perform the same task in the other view where the labeled data is missing. In this work, we propose solutions to the surrogate supervision multi-view learning problem.

Major Advisor: Raviv Raich
Committee: Xiaoli Fern
Committee: Bill Smart
Committee: Sinisa Todorovic
GCR: Yevgeniy Kovchegov 


Kelley Engineering Center (campus map)
1114
Nicole Thompson
1 541 737 3617
Nicole.Thompson at oregonstate.edu
Sch Elect Engr/Comp Sci
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