Home > Seminars > Clayton Scott - Kernel Methods for Transfer Learning

Clayton Scott - Kernel Methods for Transfer Learning

Start:

9/15/2016 at 3:30PM

End:

9/15/2016 at 5:00PM

Location:

356A Fitzpatrick

Host:

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Kevin Bowyer

Kevin Bowyer

VIEW FULL PROFILE Email: kwb@nd.edu
Phone: 574-631-9978
Website: http://www.nd.edu/~kwb
Office: 321 Stinson-Remick Hall

Affiliations

Biometrics, data mining, computer vision, pattern recognition, applications to medical imaging, ethics and computing, computer science education.
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574-631-9978
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In machine learning, transfer learning refers to the problem of classifying a test data set, given one or more training data sets that are governed by different (although similar) distributions. Such problems arise in a range of applications where data distributions fluctuate because of biological, technical, environmental, and other sources of variation. In this talk I will present a framework for applying kernel methods to transfer learning. To begin the talk, I will review the use of reproducing kernel Hilbert spaces in machine learning. Several applications motivate this work and will be presented as time permits.

Seminar Speaker:

Clayton Scott

Clayton Scott

University of Michigan

Clayton Scott is Associate Professor of Electrical Engineering and Computer Science, and of Statistics, at the University of Michigan. He received his AB in mathematics from Harvard University in 1998, and his MS and PhD in electrical engineering from Rice University in 2000 and 2004, respectively. In 2009 he received the NSF CAREER Award. His research interests are in the theory, methods, and applications of statistical machine learning.