priors

Chi Lung Cheng1,2, Gup Singh2, Benjamin Nachman2,3,4,5

  • 1University of Wisconsin, Department of Physics, Madison, Wisconsin 53706, USA.

PubMed
概括

我们开发了一种新的机器学习方法,称为预先辅助弱监督 (PAWS),以改善异常检测. 在罕见的信号搜索中,PAWS提高了灵敏度,显著优于以前的方法,特别是在杂的数据中.

相关概念视频

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
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Detection of Black Holes01:10

Detection of Black Holes

Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
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Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
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