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There has been increasing interest in building deep hierarchy-aware classifiers that aim to quantify and reduce the severity of mistakes, and not just reduce the number of errors.
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Integrating domain knowledge: using hierarchies to improve deep classifiers
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Mahdi Pakdaman Naeini, Gregory F Cooper, and Milos Hauskrecht · 2015
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Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
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Learning for single-shot confidence calibration in deep neural networks through stochastic inferences
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