Data domain description using support vectors

This paper introduces a new method for data domain description, inspired by the Support Vector Machine by V.Vapnik, called the Support Vector Domain Description (SVDD). This method computes a sphere shaped decision boundary with minimal volume around a set of objects. This data description can be used for novelty or outlier detection. It contains support vectors describing the sphere boundary and it has the possibility of obtaining higher order boundary descriptions without much extra computational cost. By using the di erent kernels this SVDD can obtain more exible and more accurate data descriptions. The error of the rst kind, the fraction of the training objects which will be rejected, can be estimated immediately from the description.