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

Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.3K
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...
7.3K
Correlation and Regression00:53

Correlation and Regression

1.2K
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...
1.2K
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
Multiple Regression01:25

Multiple Regression

2.9K
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...
2.9K
Regression Analysis01:11

Regression Analysis

5.6K
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:
5.6K
Survival Tree01:19

Survival Tree

61
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
61

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

Effectiveness of stereotactic body radiotherapy for hepatocellular carcinoma with portal vein and/or inferior vena cava tumor thrombosis.

PloS one·2013
Same author

Phylogenomic analyses of nuclear genes reveal the evolutionary relationships within the BEP clade and the evidence of positive selection in Poaceae.

PloS one·2013
Same author

A general and robust strategy for the synthesis of nearly monodisperse colloidal nanocrystals.

Nature nanotechnology·2013
Same author

AG10 inhibits amyloidogenesis and cellular toxicity of the familial amyloid cardiomyopathy-associated V122I transthyretin.

Proceedings of the National Academy of Sciences of the United States of America·2013
Same author

Identification and functional characteristics of chlorpyrifos-degrading and plant growth promoting bacterium Acinetobacter calcoaceticus.

Journal of basic microbiology·2013
Same author

[Study on the chemical constituents of Buddleja davidii].

Zhong yao cai = Zhongyaocai = Journal of Chinese medicinal materials·2013

相关实验视频

Updated: Jun 6, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.2K

一个强大的半监控回归器,具有电流诱导的多重规范化和自适应图.

Xiaohan Zheng1, Li Zhang1, Leilei Yan1

  • 1School of Computer Science and Technology, Soochow University, 215006 Suzhou, China.

Neural networks : the official journal of the International Neural Network Society
|November 22, 2024
PubMed
概括

这项研究引入了一种新的电流诱导半监督回归 (CSSR) 方法,用于处理数据中的噪声. 通过currentropy诱导的多重规范化和自适应图形构造,CSSR提高了稳定性,超过了现有的方法.

关键词:
适应式图表是适应性的图表.电流是目前的.多重规范化的多重规范化坚固性 坚固性半监督的回归研究

更多相关视频

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.2K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

641

相关实验视频

Last Updated: Jun 6, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.2K
Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.2K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

641

科学领域:

  • 机器学习 机器学习
  • 数据科学数据科学数据科学
  • 计算机视觉 计算机视觉

背景情况:

  • 半监督回归任务对于利用有限的标记数据至关重要.
  • 现有的方法往往忽略了噪音对性能的不利影响.
  • 数据噪声是现实世界数据集的固有挑战.

研究的目的:

  • 提出一种新的电流诱导半监督回归 (CSSR) 方法.
  • 为了减轻噪声在半监督回归中的不良影响.
  • 提高回归模型的稳定性和性能.

主要方法:

  • 为图形表示学习开发了一种电流诱导的多重规范化 (CMR).
  • 设计了一个电流诱导的自适应图 (CAG),用于强大的相邻矩阵构造.
  • 在CSSR框架内集成CMR,CAG和currentropy诱导的损失,以代方式解决.

主要成果:

  • 在合成,基准和图像数据集中,CSSR表现出卓越的性能.
  • 该方法有效地减轻噪声,提高回归精度.
  • 理论分析和经验实验验证了CSSR的趋同.

结论:

  • 对于半监督回归任务,CSSR提供了一种有效且强大的解决方案.
  • 拟议的CMR和CAG组件大大有助于噪声弹性.
  • 这些发现突显了基于电流的方法在处理杂数据方面的潜力.