Tackling Multiple-Instance Problems in Safety-Related Domains by Quasilinear SVM

  • Christian Moewes
  • Clemens Otte
  • Rudolf Kruse
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 48)

Abstract

In this paper we introduce a preprocessing method for safety-related applications. Since we concentrate on scenarios with highly unbalanced misclassification costs, we briefly discuss a variation of multiple-instance learning (MIL) and recall soft margin hyperplane classifiers; in particular the principle of a support vector machine (SVM). According to this classifier, we present a training set selection method for learning quasilinear SVMs which guarantee both high accuracy and model complexity to a lower degree. We conclude with annotating on a real-world application and potential extensions for future research in this domain.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Christian Moewes
    • 1
  • Clemens Otte
    • 2
  • Rudolf Kruse
    • 1
  1. 1.Department of Knowledge and Language EngineeringUniversity of MagdeburgMagdeburgGermany
  2. 2.Siemens AGCorporate TechnologyMunichGermany

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