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Same-decision probability: A confidence measure for threshold-based decisions

机译：同决策概率：基于阈值决策的置信度

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Choi Arthur; Xue Yexiang; Darwiche Adnan;
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年度 2012
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1. Same-decision probability: A confidence measure for threshold-based decisions [J] . Arthur Choi, Yexiang Xue, Adrian Darwiche International Journal of Approximate Reasoning . 2012,第9期

机译：同决策概率：基于阈值决策的置信度
2. Same-Decision Probability: Threshold Robustness and Application to Explanation [J] . Silja Renooij JMLR: Workshop and Conference Proceedings . 2017,第3期

机译：同决策概率：阈值稳健性和解释应用
3. Algorithms and Applications for the Same-Decision Probability [J] . Chen S. J., Choi A., Darwiche A. The Journal of Artificial Intelligence Research . 2014,第4期

机译：同决策概率的算法和应用
4. An Exact Algorithm for Computing the Same-Decision Probability [C] . Suming Chen, Arthur Choi, Adnan Darwiche Proceedings of the Twenty-Third international joint conference on artificial intelligence . 2013

机译：计算相同决策概率的精确算法
5. Robust Decision Making with the Same-Decision Probability [D] . Chen, Suming Jeremiah 2015

机译：具有相同决策概率的鲁棒决策
6. Confidence Predictions Affect Performance Confidence and Neural Preparation in Perceptual Decision Making [O] . Annika Boldt, Anne-Marike Schiffer, Florian Waszak, -1

机译：置信度预测影响性能置信度和神经准备的感知决策。
7. Figure 3: Proposed approach: (A) The skin lesion image is firstly converted to grayscale, after which the skin lesion is segmented and smoothed, lesion border (edge) detected, and the lesion irregularity measure derived. (B) A CNN is trained on the smoothed segmented image, skin lesion border, and the skin lesion irregularity measure. (C) A Gaussian naive Bayes is trained on the irregularity measure. (D) The generated models are used to predict class probabilities, and a threshold is eventually used to determine the final decision (regular or irregular skin lesion border). [O] . -1

机译：图3：所提出的方法：（a）将皮肤病变图像首先转化为灰度，之后将皮肤病变分段和平滑，检测到病变边界（边缘），并且衍生病变不规则性测量。（b）CNN在平滑的分段图像，皮肤病变边界和皮肤病原不规则测量上培训。（c）高斯天真贝叶斯在不规则措施上培训。（d）所生成的模型用于预测类概率，最终最终用于确定最终决定（常规或不规则的皮肤病变边界）。

Same-decision probability: A confidence measure for threshold-based decisions

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