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)


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.


Support Vector Machine Fuzzy Rule Positive Instance Linear Support Vector Machine Fuzzy Graph 
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 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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