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Support Vector Machines

  • Homayoon Beigi
Chapter

Abstract

In Section B.4 we defined a kernel function,K (s, t) – see Definition B.56. Recently, quite a lot of attention has been given to kernel methods for their inherent discriminative abilities and the capability of handling nonlinear decision boundaries with good discrimination scalability, in relation with increasing dimensions of observations vectors. Of course using kernel techniques is nothing new. Integral transforms, for example, are some of the oldest techniques which use kernels to be able to transform a problem from one space to another space which would be more suitable for a solution. Eventually, the solution is transformed back to the original space. Appendix B is devoted to the details of such techniques and we have already used different transforms in other parts of the book, especially in doing feature extraction. It is highly recommended to the reader to review Appendix B entirely.

Keywords

Support Vector Machine Fisher Information Matrix Linear Kernel Speaker Recognition Kernel Principal Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Recognition Technologies, Inc.Yorktown HeightsUSA

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