A brief introduction to Support Vector Machines 12:30 Mon 4 Jun 12 :: 5.57 Ingkarni Wardli :: Mr Tyman Stanford :: University of Adelaide
Support Vector Machines (SVMs) are used in a variety of contexts for a range of purposes including regression, feature selection and classification. To convey the basic principles of SVMs, this presentation will focus on the application of SVMs to classification. Classification (or discrimination), in a statistical sense, is supervised model creation for the purpose of assigning future observations to a group or class. An example might be determining healthy or diseased labels to patients from p characteristics obtained from a blood sample.
While SVMs are widely used, they are most successful when the data have one or more of the following properties:
The data are not consistent with a standard probability distribution.
The number of observations, n, used to create the model is less than the number of predictive features, p. (The so-called small-n, big-p problem.)
The decision boundary between the classes is likely to be non-linear in the feature space.
I will present a short overview of how SVMs are constructed, keeping in mind their purpose. As this presentation is part of a double post-grad seminar, I will keep it to a maximum of 15 minutes.