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A Naive Solution to the One-Class Problem and Its Extension to Kernel Methods

  • Alberto Muñoz
  • Javier M. Moguerza
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)

Abstract

In this work, 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 simpler method to solve this problem. We show its properties and introduce a new class of kernels, relating the proposed method to One-Class SVMs.

Keywords

Class Support Vector Machine High Density Region True Mode Naive Algorithm Naive Method 
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 2005

Authors and Affiliations

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

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