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Support Vector Machines in Neuroscience
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Author(s): Onur Seref (University of Florida, USA), O. Erhun Kundakcioglu (University of Florida, USA)and Michael Bewernitz (University of Florida, USA)
Copyright: 2008
Pages: 11
Source title:
Encyclopedia of Healthcare Information Systems
Source Author(s)/Editor(s): Nilmini Wickramasinghe (Illinois Institute of Technology, USA)and Eliezer Geisler (Illinois Institute of Technology, USA)
DOI: 10.4018/978-1-59904-889-5.ch161
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Abstract
The underlying optimization problem for the maximal margin classifier is only feasible if the two classes of pattern vectors are linearly separable. However, most of the real life classification problems are not linearly separable. Nevertheless, the maximal margin classifier encompasses the fundamental methods used in standard SVM classifiers. The solution to the optimization problem in the maximal margin classifier minimizes the bound on the generalization error (Vapnik, 1998). The basic premise of this method lies in the minimization of a convex optimization problem with linear inequality constraints, which can be solved efficiently by many alternative methods (Bennett & Campbell, 2000).
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