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Classifier calibration: a survey on how to assess and improve predicted class probabilities.

, , , , , and . Mach. Learn., 112 (9): 3211-3260 (September 2023)

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Evaluating Data Representations for Object Recognition During Pick-and-Place Manipulation Tasks., , , , , , , and . SysCon, page 1-6. IEEE, (2022)Learning Under Concept Drift for Regression - A Systematic Literature Review., , , and . IEEE Access, (2022)Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration., , , , , and . NeurIPS, page 12295-12305. (2019)Pattern classifiers with adaptive distances., and . IJCNN, page 1508-1514. IEEE, (2011)Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers., , and . AISTATS, volume 54 of Proceedings of Machine Learning Research, page 623-631. PMLR, (2017)A two-level Item Response Theory model to evaluate speech synthesis and recognition., , , and . Speech Commun., (2022)Explaining Learning Performance with Local Performance Regions and Maximally Relevant Meta-Rules., and . BRACIS (1), volume 13653 of Lecture Notes in Computer Science, page 550-564. Springer, (2022)Automatic Recognition of Units of Measurement in Product Descriptions from Tax Invoices Using Neural Networks., , , and . PROPOR, volume 13208 of Lecture Notes in Computer Science, page 156-165. Springer, (2022)Assessment of volumetric dense tissue segmentation in tomosynthesis using deep virtual clinical trials., , , , , , , and . Pattern Recognit., (2024)Classifier calibration: a survey on how to assess and improve predicted class probabilities., , , , , and . Mach. Learn., 112 (9): 3211-3260 (September 2023)