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.
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Gebel, M., Weihs, C. (2008). Calibrating Margin-Based Classifier Scores into Polychotomous Probabilities. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds) Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78246-9_4
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DOI: https://doi.org/10.1007/978-3-540-78246-9_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78239-1
Online ISBN: 978-3-540-78246-9
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