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

Bar Graph01:07

Bar Graph

19.6K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
19.6K
Ogive Graph01:07

Ogive Graph

5.9K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
5.9K
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

13.9K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
13.9K
Multiple Bar Graph01:07

Multiple Bar Graph

7.8K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
7.8K
Residual Plots01:07

Residual Plots

5.1K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
5.1K
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

813
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
813

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

Discrete time information diffusion in online social networks: micro and macro perspectives.

Scientific reports·2018
Same author

Micro-blog user community discovery using generalized SimRank edge weighting method.

PloS one·2018
Same author

A Multiple Kernel Learning Model Based on <i>p</i>-Norm.

Computational intelligence and neuroscience·2018
Same journal

Invaders taking over-Mollusc faunal change in volcanic barrier lakes of the Albertine Rift biodiversity hotspot.

PloS one·2026
Same journal

AI-driven molecular diversification and ligand-based optimization of macitentan derivatives targeting VEGFR1 and endothelin signaling pathways.

PloS one·2026
Same journal

Performance patterns and records in the world aquatics masters championships: Where do the most frequently represented nations among the top-ten masters swimmers come from?

PloS one·2026
Same journal

Modeling diurnal Temperature-Rainfall relationships under multicollinearity using PLS-SEM: A case study of Ghana.

PloS one·2026
Same journal

Organizational culture, social capital, and emergency capacity in primary healthcare institutions: A cross-sectional structural equation modeling study comparing ordinary and older communities.

PloS one·2026
Same journal

Impact of kidney function on the metabolome in the general population.

PloS one·2026
查看所有相关文章

相关实验视频

Updated: Sep 15, 2025

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.3K

歧视性图表正规化表示学习对认可的学习.

Jinshan Qi1, Rui Xu1,2

  • 1School of Computer Science and Technology, Huaiyin Normal University, Huaian, China.

PloS one
|July 17, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的歧视图规则化表示学习 (DGRL) 模型,用于改进特征提取. 为了更好的识别任务,DGRL通过整合全球,地方和标签结构来增强泛化和歧视.

更多相关视频

Investigating the Effect of Visual Imagery and Learning Shape-Audio Regularities on Bouba and Kiki
07:31

Investigating the Effect of Visual Imagery and Learning Shape-Audio Regularities on Bouba and Kiki

Published on: September 13, 2019

10.2K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.1K

相关实验视频

Last Updated: Sep 15, 2025

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.3K
Investigating the Effect of Visual Imagery and Learning Shape-Audio Regularities on Bouba and Kiki
07:31

Investigating the Effect of Visual Imagery and Learning Shape-Audio Regularities on Bouba and Kiki

Published on: September 13, 2019

10.2K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.1K

科学领域:

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

背景情况:

  • 功能提取对于机器学习应用程序至关重要.
  • 现有的方法通常将维度缩小和表示学习分开处理.
  • 需要模型来捕捉复杂的数据结构,以提高识别能力.

研究的目的:

  • 提出一种新的歧视图规则化表示学习 (DGRL) 模型.
  • 将全球,地方和标签数据结构整合到一个统一的框架中.
  • 改进特征表示,以便在识别任务中进行概括和歧视.

主要方法:

  • 开发了一个歧视图规范化表示学习 (DGRL) 模型.
  • 集成的尺寸缩小与回归来捕捉子空间结构.
  • 引入了一个使用本地类信息的图形调整器,以提高准确性并防止过度拟合.
  • 建议使用内核技巧为非线性数据提供内核版本 (KDGRL).
  • 提供了使用交叉验证的理论推导和参数估计程序.

主要成果:

  • 该DGRL模型有效地结合了全球,地方和标签结构.
  • 提出的方法在基准实验中表现优越.
  • KDGRL能够成功处理复杂的非线性数据.
  • 该框架统一了多种现有方法,澄清了它们之间的关系.

结论:

  • 新的DGRL和KDGRL模型为特征提取提供了有效的解决方案.
  • 这些方法提高了识别任务的概括和区分能力.
  • 综合方法为代表性学习提供了一个强大的框架.