In this paper it is proposed a boundary based classifier that is inspired by SVDD and makes an important role for gravity center of training samples. In the proposed method all training samples intervene in determining the classifier boundary. Consequently, the relevant classifier isn’t placed in the group of the support vector machines. Due to the employment of this idea, this method is called "Quasi Support Vector Data Description (QSVDD)". The ability of this method to eliminate the effect of noisy training samples on synthetic data is shown. Experiments on real data sets show that the proposed method describes more accurately lots of real data sets than SVDD.

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