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Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications.
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Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
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Transforming classifier scores into accurate multiclass probability estimates
Zadrozny, Bianca and Elkan, Charles · 2002
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Caltech-UCSD Birds 200
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Reading digits in natural images with unsupervised feature learning
Netzer, Yuval, Wang, Tao, Coates, Adam, Bissacco, Alessandro, Wu, Bo, and Ng, Andrew Y · 2011
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End to end learning for self-driving cars
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