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Survival Tree01:19

Survival Tree

85
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...
85
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.6K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.6K
Types of Skewness01:09

Types of Skewness

11.6K
If the frequency distribution of a data set is more inclined towards smaller or larger values, the distribution is said to be skewed. If data values are skewed to the right, then the distribution is called positively skewed. Conversely, if the plot is skewed to the left, the distribution is called negatively skewed.
For instance, in the middle of a pandemic, the geographical distribution of vaccine coverage may be positively skewed towards populations in the global north countries. However,...
11.6K
Cluster Sampling Method01:20

Cluster Sampling Method

11.9K
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.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.9K
Outliers and Influential Points01:08

Outliers and Influential Points

4.0K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
4.0K
Bar Graph01:07

Bar Graph

16.4K
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...
16.4K

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相关实验视频

Updated: Jul 2, 2025

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

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走向强大的图形 半监督学习 针对极端的数据稀缺性

Kaize Ding, Elnaz Nouri, Guoqing Zheng

    IEEE transactions on neural networks and learning systems
    |February 29, 2024
    PubMed
    概括
    此摘要是机器生成的。

    增强图形自我训练 (AGST) 提高了图形神经网络的性能,使用有限的标记数据. 这种新的框架通过结合结构和语义数据增强来提高节点的稳定性,以更好地分类节点.

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    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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    Deep Neural Networks for Image-Based Dietary Assessment
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    Deep Neural Networks for Image-Based Dietary Assessment

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    相关实验视频

    Last Updated: Jul 2, 2025

    Generating Strictly Controlled Stimuli for Figure Recognition Experiments
    05:39

    Generating Strictly Controlled Stimuli for Figure Recognition Experiments

    Published on: March 18, 2019

    5.2K
    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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    Deep Neural Networks for Image-Based Dietary Assessment
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    Deep Neural Networks for Image-Based Dietary Assessment

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    科学领域:

    • 机器学习 机器学习
    • 数据科学数据科学数据科学
    • 图形分析分析 图形分析

    背景情况:

    • 图形神经网络 (GNN) 需要大量的人类注释数据来进行网络挖掘.
    • 在图表上进行半监督学习是具有挑战性的,因为在特征标签传播和决策边界学习方面存在困难,标记节点很少.

    研究的目的:

    • 为低数据场景开发一个强大的图形预测模型.
    • 通过捕获结构和语义知识来解决对图形结构数据的自我训练的局限性.

    主要方法:

    • 提出了一个新的图形数据增强框架,称为增强图形自训练 (AGST).
    • AGST包含两个新的增强模块:结构和语义.
    • 使用了解的图形自训练 (GST) 骨干.

    主要成果:

    • 对半监督节点分类任务进行了全面的评估.
    • 该框架在各种有限的标记节点数据场景下进行了测试.
    • 实验结果表明,在数据不足的情况下,框架的有效性.

    结论:

    • 拟议的AGST框架显著提高了带有有限标记数据的图形预测模型的稳定性.
    • 新型数据增强模块为节点分类性能做出了独特的贡献.
    • AGST为可持续的图形半监督学习提供了一个有希望的解决方案.