Support Vector Machines have been used to do classification and
regression analysis. One important part of SVMs are the kernels.
Although there are several widely used kernel functions, a carefully
designed kernel will help to improve the accuracy of SVMs. We
present two methods in terms of customizing kernels: one is
combining existed kernels as new kernels, the other one is to do feature selection.
We did theoretical analysis in the interpretation of
feature spaces of combined kernels. Further an experiment on a
chemical data set showed improvements of a linear-Gaussian combined
kernel over single kernels. Though the improvements are not
universal, we present a new idea of creating kernels in SVMs.
| Identifer | oai:union.ndltd.org:WATERLOO/oai:uwspace.uwaterloo.ca:10012/3063 |
| Date | 18 May 2007 |
| Creators | Zhang, Zhanyang |
| Source Sets | University of Waterloo Electronic Theses Repository |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | 2152867 bytes, application/pdf |
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