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相关概念视频

Standard Deviation01:10

Standard Deviation

27.5K
The most commonly used measure of variation is the standard deviation. It is a numerical value measuring how far data values are from their mean. The standard deviation value is small when the data are concentrated close to the mean, exhibiting slight variation or spread. The standard deviation value is never negative, it is either positive or zero. The standard deviation is larger when the data values are more spread out from the mean, which means the data values are exhibiting more variation.
27.5K
Empirical Method to Interpret Standard Deviation01:09

Empirical Method to Interpret Standard Deviation

9.3K
The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
This rule is used widely in statistics to calculate the proportion of data values...
9.3K
Chebyshev's Theorem to Interpret Standard Deviation01:15

Chebyshev's Theorem to Interpret Standard Deviation

5.0K
Chebyshev’s theorem, also known as Chebyshev’s Inequality, states that the proportion of values of a dataset for K standard deviation is calculated using the equation:
5.0K
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.9K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.9K
Downsampling01:20

Downsampling

605
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
605
Range Rule of Thumb to Interpret Standard Deviation01:13

Range Rule of Thumb to Interpret Standard Deviation

13.4K
The range rule of thumb in statistics helps us calculate a dataset's minimum and maximum values with known standard deviation. This rule is based on the concept that 95% of all values in a dataset lie within two standard deviations from the mean.
For instance, the range rule of thumb can be used to find the tallest and the shortest student in a class, given the mean student height and standard deviation. If the mean student height is 1.6 m and the standard deviation, s is 0.05 m, the height...
13.4K

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相关实验视频

Updated: Jan 16, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

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通过基于标准偏差的频段选择来进行高效的分类来减少超谱成像的尺寸性.

Wolfgang Kurz1, Kun Wang2, Furkan Bektas2

  • 1Department of Electrical Engineering, Institute for Measurement Systems and Sensor Technology, Technical University of Munich, Munich, 80333, Germany. w.kurz@tum.de.

Scientific reports
|October 3, 2025
PubMed
概括
此摘要是机器生成的。

标准偏差有效减少高光谱成像数据大小高达97.3%的器官组织分类. 这种频段选择方法保持了光谱特征,实现了与未处理数据相比的高精度.

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A Multimodal Wide-Field Fourier-Transform Raman Microscope
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A Multimodal Wide-Field Fourier-Transform Raman Microscope

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Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures
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Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures

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相关实验视频

Last Updated: Jan 16, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
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Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

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A Multimodal Wide-Field Fourier-Transform Raman Microscope
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A Multimodal Wide-Field Fourier-Transform Raman Microscope

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Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures
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科学领域:

  • 生物医学光学 生物医学光学
  • 机器学习应用程序 机器学习应用程序
  • 在医疗保健中的数据科学.

背景情况:

  • 超光谱成像 (HSI) 可以产生大量数据集,其中包含丰富的空间和光谱信息.
  • 减小尺寸对于管理HSI数据大小至关重要,同时保持关键的光谱特征.
  • 带选择和特征提取是主要的维度减小策略.

研究的目的:

  • 为了评估标准偏差作为HSI数据的频段选择方法.
  • 为了评估标准偏差与卷积神经网络 (CNN) 结合用于器官组织分类的效率.
  • 为了将标准偏差方法与其他频段选择技术比较,例如相互信息和香农.

主要方法:

  • 在超光谱成像数据集中用于频段选择的标准偏差.
  • 利用卷积神经网络 (CNN) 来分类器官组织.
  • 在11个不同器官样本组 (每组100个数据集) 上测试了该方法.
  • 与未处理的数据和其他频段选择方法相比,对分类准确度和数据减少效率进行比较.

主要成果:

  • 标准偏差将数据大小降低了高达97.3%,同时保留了基本的光谱特征.
  • 通过标准偏差方法实现了97.21%的分类精度.
  • 这种准确性与在没有任何维度减小的情况下达到的99.30%可比.
  • 与相互信息和香农带选择方法相比,证明了更高的稳定性和效率.

结论:

  • 标准偏差是一种有效和高效的频段选择方法,用于高光谱成像.
  • 这种方法可以显著减少数据大小,而不会影响分类的光谱特征完整性.
  • 该方法显示了对高频谱成像分类任务的巨大潜力,这些任务需要大量数据集和高处理速度.
  • 突出了医疗应用中的超光谱成像中减小维度的价值.