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

Reasoning01:30

Reasoning

98
Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
Inductive reasoning involves deriving generalizations from specific observations. This type of reasoning helps form beliefs about the world. For example,...
98
Deductive Reasoning01:16

Deductive Reasoning

55.4K
Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
55.4K
Inductive Reasoning00:59

Inductive Reasoning

60.6K
Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
60.6K
Ogive Graph01:07

Ogive Graph

5.7K
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...
5.7K
Bar Graph01:07

Bar Graph

16.6K
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...
16.6K
Multiple Bar Graph01:07

Multiple Bar Graph

5.2K
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...
5.2K

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 journal

Artificial intelligence applications in surgical education and training: a systematic review.

Frontiers in artificial intelligence·2026
Same journal

AI product liability under EU and Canadian laws.

Frontiers in artificial intelligence·2026
Same journal

Statistical limits and conditional complexity in real-world reinforcement learning: a tutorial survey.

Frontiers in artificial intelligence·2026
Same journal

Editorial: Advancing human wellbeing: environment-focused AI technologies.

Frontiers in artificial intelligence·2026
Same journal

Enhancing financial data collection and reporting in small businesses through IoT integration: an exploration of IFRS standard.

Frontiers in artificial intelligence·2026
Same journal

Automatic speech recognition for Telugu: a comparative analysis of Wav2Vec 2.0 model variants and hyperparameter tuning.

Frontiers in artificial intelligence·2026

相关实验视频

Updated: Jul 17, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

267

通过图形数据学习和推理.

Manfred Jaeger1

  • 1Department of Computer Science, Aalborg University, Aalborg, Denmark.

Frontiers in artificial intelligence
|September 7, 2023
PubMed
概括
此摘要是机器生成的。

本综述绘制了人工智能领域的图形学习和推理,统一不同的方法,如图形神经网络和逻辑推理在一个共同的模型概念下. 它为分析和整合各种图形数据方法提供了一个框架.

关键词:
数据图表数据图表数据图形神经网络的神经网络归纳逻辑编程 归纳逻辑编程神经符号集成是神经符号集成.代表性学习学习学习统计关系学习是统计关系学习.

更多相关视频

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K
The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
05:48

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

Published on: June 12, 2020

5.8K

相关实验视频

Last Updated: Jul 17, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

267
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K
The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
05:48

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

Published on: June 12, 2020

5.8K

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 图形是人工智能,逻辑和统计学习中的基本结构.
  • 图形表示学习和图形神经网络是快速发展的领域.
  • 有多种方法可以用图形数据进行推理和学习.

研究的目的:

  • 为学习和借助图形推理提供统一的概念框架.
  • 为了绘制基于图形的AI方法的多样化景观.
  • 促进不同建模范式的理论分析和整合.

主要方法:

  • 引入了适用于各种框架 (例如知识库,GNN,SVM) 的一般语义模型概念.
  • 调查了模型规范的常见策略 (概率因子化,特征构造).
  • 开发了一个推理任务的分类法,并通过最大概率原则来表达学习.

主要成果:

  • 在逻辑演,节点嵌入和其他图形学习技术中建立了统一的视角.
  • 为各种图形建模方法提供了一个共同的语义基础.
  • 创建了推理任务的分类学和跨框架学习的原则.

结论:

  • 该综述为了解图形数据方法的优点和局限性提供了一个连贯的框架.
  • 拟议的框架有助于结合和整合不同的建模范式.
  • 这项工作作为进一步基于图形的AI理论进步的基础.