Data Analysis, Machine Learning and Applications

Part of the series Studies in Classification, Data Analysis, and Knowledge Organization pp 29-36

Calibrating Margin-Based Classifier Scores into Polychotomous Probabilities

  • Martin GebelAffiliated withGraduiertenkolleg Statistische Modellbildung, Lehrstuhl für Computergestützte Statistik, UniversitÄt Dortmund
  • , Claus WeihsAffiliated withLehrstuhl für Computergestützte Statistik, UniversitÄt Dortmund

* Final gross prices may vary according to local VAT.

Get Access


Margin-based classifiers like the SVM and ANN have two drawbacks. They are only directly applicable for two-class problems and they only output scores which do not reflect the assessment uncertainty. K-class assessment probabilities are usually generated by using a reduction to binary tasks, univariate calibration and further application of the pairwise coupling algorithm. This paper presents an alternative to coupling with usage of the Dirichlet distribution.