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相关概念视频

Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Survival Tree01:19

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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
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How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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Vector Algebra: Graphical Method01:10

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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.
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Cluster Sampling Method01:20

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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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...
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相关实验视频

Updated: Jul 14, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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一般化异构图数据增强用于节点分类.

Bisheng Tang1, Xiaojun Chen2, Shaopu Wang1

  • 1School of Cyber Security, University of Chinese Academy of Sciences, Zhongguancun Nanyitiao, Beijing, 100190, China; Institute of Information Engineering, Chinese Academy of Sciences, Shangdi Street, Shucun Road, 19, Beijing, 100080, China.

Neural networks : the official journal of the International Neural Network Society
|October 5, 2023
PubMed
概括

通过使用自主监督学习和数据增强,GePHo增强了异构图的图形神经网络 (GNN). 这种方法提高了同型和异型图数据集上的节点分类性能.

关键词:
图形数据增强 图形数据增强异性恋是一种异性恋.自己监督的自我监督.

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 图形数据增强对于同性图形神经网络 (GNN) 有效.
  • 这些增强显示在异构图上效果和性能降低.
  • 现有的方法很难适应增强策略来适应异性图形学习.

研究的目的:

  • 提出GePHo,一个统一的增强方法用于异性性GNNs.
  • 为了提高GNN性能,利用自我监督学习和图形数据增强.
  • 为了更广泛的适用性,开发一种类型不可知伪同类关系图表生成.

主要方法:

  • GePHo采用基于自我监督学习的规范化技术.
  • 它生成了一个类型不可知的伪同类图表来指导模型学习.
  • 用一个利技术和辅助伪标签来规范邻居和限制节点表示.

主要成果:

  • 在多个数据集的节点分类任务中,GePHo表现出了竞争效率.
  • 实验表明,在同型和异型图形上,性能显著提高.
  • 废除研究证实了GePHo图形数据增强策略的有效性.

结论:

  • GePHo为GNN中的图形数据增强提供了一种统一而有效的方法.
  • 该方法成功地解决了对异性图的传统增强的局限性.
  • 通过新的增强技术,GePHo通过限制本地和全球节点表示来提高GNN性能.