Evidential calibration of binary SVM classifiers

@article{Xu2016EvidentialCO,
  title={Evidential calibration of binary SVM classifiers},
  author={Philippe Xu and Franck Davoine and Hongbin Zha and Thierry Denoeux},
  journal={Int. J. Approx. Reasoning},
  year={2016},
  volume={72},
  pages={55-70}
}
In machine learning problems, the availability of several classifiers trained on different data or features makes the combination of pattern classifiers of great interest. To combine distinct sources of information, it is necessary to represent the outputs of classifiers in a common space via a transformation called calibration. The most classical way is to use class membership probabilities. However, using a single probability measure may be insufficient to model the uncertainty induced by the… CONTINUE READING
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