Abstract
Some theoretical aspects of support vector machines and related kernel-based learning methods
Ingo Steinwart
Los Alamos National Laboratory
Support vector machines (SVMs) and related algorithms are used for a variety of learning scenarios including
classification and regression. This talk mainly focuses on some recent progress in the theoretical analysis of these
algorithms for both classification and regression problems. In particular, we will discuss in which sense SVMs can learn and which additional
features they typically have in the learning process. If time permits we finally discuss the role of the used loss function in some more detail.
classification and regression. This talk mainly focuses on some recent progress in the theoretical analysis of these
algorithms for both classification and regression problems. In particular, we will discuss in which sense SVMs can learn and which additional
features they typically have in the learning process. If time permits we finally discuss the role of the used loss function in some more detail.
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