Abstract
Support Vector Data Description(SVDD),which is one of the widely applied kernel methods,has not taken the information of data distribution into full consideration.Concerning this issue,the optimization of SVDD was first reformulated equivalently,and then the distance in the optimization was redefined.Finally,a new algorithm called Minimum Variance Support Vector Data Description(MVSVDD) was presented,which exploited the information of data distribution.The experimental results denote that,in contrast to SVDD,MVSVDD obtains clear enhancement in generalization performance,and has better ability of describing data.
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