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相关概念视频

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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相关实验视频

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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利用贝叶斯深度学习和集合方法来量化图像分类中的不确定性:基于排名的方法.

Abdullah A Abdullah1, Masoud M Hassan1, Yaseen T Mustafa2

  • 1Computer Science Department, Faculty of Science, University of Zakho, Duhok, Iraq.

Heliyon
|January 31, 2024
PubMed
概括

本研究引入了一种新的贝叶斯集合方法,用于在分类任务中改进不确定性量化. 该方法通过更好地评估预测信心,提高了医疗成像等关键领域的决策能力.

关键词:
贝叶斯深度学习是贝叶斯的深度学习.组合学习学习 组合学习图像的分类图像的分类.基于排名的模型.不确定性量化不确定性的量化.

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 计算机视觉 计算机视觉

背景情况:

  • 与传统模型相比,贝叶斯深度学习 (BDL) 通过反映现实数据的概率性质,提供了优越的不确定性量化.
  • 贝叶斯模型集合结合了多个BDL模型,提高了预测准确性和不确定性估计,超出了单个模型.
  • 不确定性量化在高风险的应用中至关重要,例如医疗诊断和自动驾驶.

研究的目的:

  • 提出一种新的贝叶斯整体方法,用于在分类中增强不确定性量化.
  • 为了利用预测的类概率之间的差异,在集合中进行模型选择.
  • 在医学图像分类中,评估拟议方法的性能与传统贝叶斯集团的性能.

主要方法:

  • 一种新的贝叶斯集体技术,利用预测的正负类概率之间的差异作为排名指标.
  • 根据排名指标选择顶级"k"模型,以确定每个实例的整体输出.
  • 使用各种医学图像分类数据集进行实验验证.

主要成果:

  • 建议的贝叶斯集团方法与传统的贝叶斯集团相比,表现一致或优越.
  • 该方法有效地完善了图像分类任务中的预测性能.
  • 观察到增强的不确定性评估能力.

结论:

  • 新的贝叶斯集团方法为改善分类中的不确定性量化提供了一个强大的工具.
  • 该技术在需要在不确定性下可靠决策的应用中显示出显著的前景,特别是在医学成像中.
  • 这项研究有助于推进贝叶斯集团在机器学习中的实际实用性.