Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Improving Translational Accuracy02:07

Improving Translational Accuracy

11.9K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.9K
Multiple Regression01:25

Multiple Regression

3.2K
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...
3.2K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.8K
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...
7.8K
Regression Analysis01:11

Regression Analysis

6.0K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.0K
Correlation and Regression00:53

Correlation and Regression

1.9K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
1.9K
Regression Toward the Mean01:52

Regression Toward the Mean

6.5K
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...
6.5K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Critical Factors for Health Behavior Among University Students: The Role of Health Consciousness, Health Knowledge, and Risk Perception.

Healthcare (Basel, Switzerland)·2026
Same author

Open Diagnostic Reader (ODR): An affordable, modular 3D-printed platform for standardized imaging and quantitative analysis of rapid diagnostic tests.

HardwareX·2026
Same author

Plasma proteomics maps molecular bridges from depression to incident CHD: 15-year proteomic trajectories and enhanced prediction.

Cardiovascular research·2026
Same author

Copper nanoregulator with organelle-level precision reprograms COMMD1-Mediated copper homeostasis for myocardial infarction repair.

Biomaterials·2026
Same author

Potential effects of microorganisms on petroleum asphalt: a state-of-the-art review.

World journal of microbiology & biotechnology·2026
Same author

Alcohol Consumption and Age-Specific Risk of Esophageal Cancer: Prospective Cohort Study.

JMIR public health and surveillance·2026
Same journal

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

TraGraph-GS: Trajectory Graph-based Gaussian Splatting for Arbitrary Large-Scale Scene Rendering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
查看所有相关文章

相关实验视频

Updated: Sep 13, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.6K

张力对张力回归的计算和统计保证与张力列车分解.

Zhen Qin, Zhihui Zhu

    IEEE transactions on pattern analysis and machine intelligence
    |July 31, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究分析了基于张量列车 (TT) 的张量对张量 (ToT) 回归模型,解决计算挑战. 它提出了代硬值 (IHT) 和里曼梯度下降 (RGD) 算法,提供理论错误边界和收率.

    相关实验视频

    Last Updated: Sep 13, 2025

    Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
    09:33

    Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

    Published on: July 28, 2013

    28.6K

    科学领域:

    • 机器学习 机器学习
    • 数字分析 数字分析
    • 张量计计算 张量计计算

    背景情况:

    • 张量对张量 (ToT) 回归概括了张量恢复,但面临着存储和计算方面的挑战.
    • 张量分解,特别是张量列车 (TT) 格式,为ToT回归提供了效率改进.
    • 在基于 TT 的 ToT 模型的理论分析和实际表现之间存在差距.

    研究的目的:

    • 从理论上分析基于TT的ToT回归模型,专注于受限制异位数属性 (RIP) 下的误差边界.
    • 开发和评估高效的优化算法,以解决基于TT的ToT回归中的受约束最小平方问题.
    • 为了比较拟议的算法的性能和复杂性,即代硬值 (IHT) 和里曼梯度下降 (RGD).

    主要方法:

    • 对受约束最小平方优化问题的错误分析,假设回归运算符满足RIP.
    • 开发了两个优化算法:使用TT-单数值分解 (TT-SVD) 和里曼梯度下降 (RGD) 的代硬值 (IHT).
    • 在RIP条件下分析IHT和RGD算法的光谱初始化和线性收率.

    主要成果:

    • 导出了理论上的上限误差和最小值的下限误差,显示了多项式依赖于 $N+M$ 的顺序.
    • 无论是IHT还是RGD算法,在满足RIP时,都能实现带有光谱初始化的线性收率.
    • 与IHT相比,RGD通过优化Stiefel分流器上的因素来降低存储复杂性,但恢复性能略差.

    结论:

    • 阐明了基于TT的ToT回归模型的理论特性和算法解决方案.
    • IHT和RGD提供了有效的方法来解决基于TT的ToT回归,在存储和回收方面有明显的权衡.
    • 实验结果验证了理论发现和算法的有效性.