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Ingo Steinwart: Recent Results and Open Questions for Kernel Methods: An Overview

June 1, 2009
Time: 2:00 PM
Location: NSEC LARP T226

Abstract: Kernel Machines such as Support Vector Machines are one of the standard machine learning methods. In this talk, I will give an overview of our current understanding of their learning mechanisms and discuss some open questions. I will also illustrate how they can be modified to work on new learning problems that are beyond the classical cases of classification and regression. Finally, I will talk about a few applications.


Biography:  Ingo Steinwart received his Ph.d. in Mathematics from the Friedrich-Schiller University Jena, Germany, in 2000. Betwen 2000 and 2003 he worked as a postdoc at the Friedrich-Schiller University Jena and the Gutenberg University Mainz, Germany. Since 2003 he is a technical staff member at the Los Alamos National Laboratory, NM. He served in the program committee for the leading machine learning conferences and is currently an action editor for the Journal of Machine Learning Research. His work mainly focuses on the analysis of learning methods, such as kernel machines, that minimize a regularized empirical risk. Together with A. Christmann, he has recently finished a book on support vector machine

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