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Support Vector Machines with Example Dependent Costs

  • Ulf Brefeld
  • Peter Geibel
  • Fritz Wysotzki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2837)

Abstract

Classical learning algorithms from the fields of artificial neural networks and machine learning, typically, do not take any costs into account or allow only costs depending on the classes of the examples that are used for learning. As an extension of class dependent costs, we consider costs that are example, i.e. feature and class dependent. We present a natural cost-sensitive extension of the support vector machine (SVM) and discuss its relation to the Bayes rule. We also derive an approach for including example dependent costs into an arbitrary cost-insensitive learning algorithm by sampling according to modified probability distributions.

Keywords

Support Vector Machine Cost Matrix Soft Margin Support Vector Machine Learning Dependent Cost 
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 2003

Authors and Affiliations

  • Ulf Brefeld
    • 1
  • Peter Geibel
    • 1
  • Fritz Wysotzki
    • 1
  1. 1.Fak. IV, ISTI, AI Group, Sekr. FR5-8TU BerlinBerlinGermany

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