Calibrating Margin-Based Classifier Scores into Polychotomous Probabilities

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Abstract

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.