Solving the One-Class Problem Using Neighbourhood Measures

  • Javier M. Moguerza
  • Alberto Muñoz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)


The problem of estimating high density regions from univariate or multivariate data samples is studied. To be more precise, we estimate minimum volume sets, whose probability is specified in advance. This problem arises in outlier detection and cluster analysis, and is strongly related to One-Class Support Vector Machines (SVM). In this paper we propose a new method to solve this problem, the Support Neighbour Machine (SNM). We show its properties and introduce a new class of kernels. Finally, numerical results illustrating the advantage of the new method are shown.


Support Vector Machine Outlier Detection Support Vector Machine Algorithm High Density Region True Mode 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Javier M. Moguerza
    • 1
  • Alberto Muñoz
    • 2
  1. 1.University Rey Juan CarlosMóstolesSpain
  2. 2.University Carlos IIIGetafeSpain

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