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相关概念视频

Survival Tree01:19

Survival Tree

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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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Ogive Graph01:07

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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...
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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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Time-Series Graph00:54

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Self-Schemas02:16

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In general, a schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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Histogram01:05

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The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
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相关实验视频

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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
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分层对比的硬样本采矿用于图形自主监督预训

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    此摘要是机器生成的。

    本研究介绍了层次对比硬样本采矿 (HCHSM),一种新的图形自我监督预训方法. 通过专注于困难的图形样本和整合多层次信息来改善节点分类和集群,HCHSM改善了表示学习.

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    科学领域:

    • 图表表示学习学习学习图表表示学习.
    • 自主监督学习学习
    • 机器学习是机器学习.

    背景情况:

    • 对比式学习是图形自主监督预训 (GSP) 的关键技术.
    • 现有的GSP方法与样本不平衡和有限的对比模式作斗争,阻碍了表示质量.
    • 在GSP中最大化相互信息 (MI) 可能导致忽视关键的"硬"样本.

    研究的目的:

    • 为了解决当前GSP对比算法的局限性.
    • 提出一个新的GSP算法,分层对比硬样本采矿 (HCHSM).
    • 通过专注于硬样本和整合多层次图形特征来增强图形表示学习.

    主要方法:

    • 开发了HCHSM,一种新的GSP算法.
    • 实现了一个基于MI的硬样本选择 (MHSS) 模块用于层次过.
    • 引入了多层次功能集成的层次对比方案.

    主要成果:

    • 在七个基准数据集中,HCHSM在节点分类和聚类任务上表现优于现有的方法.
    • MHSS模块有效地过容易节点,专注于更难的样本.
    • 层次对比学习增强了硬样本的歧视,并提高了图形嵌入质量.

    结论:

    • HCHSM提供了一种更有效的方法来绘制自我监督的预训.
    • 提出的方法成功地解决了GSP中的样本不平衡和有限的对比模式.
    • 在下游图形分析任务中,HCHSM表现出卓越的性能.