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相关概念视频

Distance Measurements by Taping01:18

Distance Measurements by Taping

38
Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...
38
Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.3K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
1.3K
Distance Corrections01:15

Distance Corrections

28
To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
28
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K

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相关实验视频

Updated: Jul 1, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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一种基于距离的色彩测量设备的多目标堆叠回归方法.

Amrinder Singh Brar1, Kawaljeet Singh2

  • 1Department of Computer Science and Engineering, Punjabi University, Patiala, 147002, India. amrinder.web@gmail.com.

Scientific reports
|March 6, 2024
PubMed
概括

这项研究引入了一种新的多目标堆叠回归 (MOSR) 模型,用于准确的远程颜色测量,克服外部噪声挑战. MOSR显著提高了预测准确度,并减少了基于距离的颜色测量设备的组合复杂性.

科学领域:

  • 计算机科学 计算机科学
  • 机器学习 机器学习
  • 颜色科学 颜色科学

背景情况:

  • 从远处准确的颜色识别受到测量噪声的阻碍.
  • 开发可靠的远程颜色测量系统至关重要.

研究的目的:

  • 开发一种新的多目标回归 (MTR) 模型,用于精确的基于距离的颜色测量.
  • 引入多目标堆叠回归 (MOSR) 方法.

主要方法:

  • 拟议的MOSR方法使用堆叠作为一个整体方法.
  • 采用多目标进化学习 (NSGA-II) 进行基础学习者选择.
  • 优化预测准确度,并最大限度地减少组合复杂性.

主要成果:

  • 对于所有三种颜色目标,MOSR实现了最高的确定系数.
  • 在色彩数据集上表现优于六种最先进的方法.
  • 在使用CART回归器对18个基准数据集中的15个数据集表现出卓越的表现.

结论:

  • 在统计学上,MOSR方法优于现有方法.
  • 在基于距离的设备中,MOSR有效地提高了精确的颜色值测量.
关键词:
测量颜色的测量方法组合模型模型组合模型机器学习是机器学习.多目标优化多目标优化多目标回归的多目标回归.

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