Machine Learning

, Volume 54, Issue 1, pp 45–66

Support Vector Data Description

  • David M.J. Tax
  • Robert P.W. Duin

DOI: 10.1023/B:MACH.0000008084.60811.49

Cite this article as:
Tax, D.M. & Duin, R.P. Machine Learning (2004) 54: 45. doi:10.1023/B:MACH.0000008084.60811.49


Data domain description concerns the characterization of a data set. A good description covers all target data but includes no superfluous space. The boundary of a dataset can be used to detect novel data or outliers. We will present the Support Vector Data Description (SVDD) which is inspired by the Support Vector Classifier. It obtains a spherically shaped boundary around a dataset and analogous to the Support Vector Classifier it can be made flexible by using other kernel functions. The method is made robust against outliers in the training set and is capable of tightening the description by using negative examples. We show characteristics of the Support Vector Data Descriptions using artificial and real data.

outlier detection novelty detection one-class classification support vector classifier support vector data description 

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • David M.J. Tax
    • 1
  • Robert P.W. Duin
  1. 1.Pattern Recognition Group, Faculty of Applied SciencesDelft University of TechnologyCJ DelftThe Netherlands

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