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Binary Classifier Calibration Using a Bayesian Non-Parametric Approach
Learning probabilistic predictive models that are well calibrated is critical for many prediction and decision-making tasks in Data mining. This paper presents two new non-parametric methods for calibrating outputs of binary classification models: a method based on the Bayes optimal selection and a...
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| Veröffentlicht in: | Proc SIAM Int Conf Data Min |
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| Hauptverfasser: | , , |
| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
2015
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| Schlagworte: | |
| Online Zugang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4657569/ https://ncbi.nlm.nih.gov/pubmed/26613068 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1137/1.9781611974010.24 |
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