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相关概念视频

Quantifying and Rejecting Outliers: The Grubbs Test01:02

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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...
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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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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.
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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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相关实验视频

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SAMCL:用于检测图形异常的子图对齐多视图对比学习.

Jingtao Hu, Bin Xiao, Hu Jin

    IEEE transactions on neural networks and learning systems
    |November 7, 2023
    PubMed
    概括

    这项研究引入了SAMCL,一种新的图形异常检测方法,它比较子图,而不仅仅是节点. 它通过对准子图相似性来有效地识别异常,优于现有技术.

    科学领域:

    • 图形理论是指图形的理论.
    • 机器学习是机器学习.
    • 数据挖掘是一种数据挖掘.

    背景情况:

    • 图形异常检测 (GAD) 对社交和金融系统等网络至关重要.
    • 图形对比学习 (GCL) 是一个领先的GAD方法,但往往忽略了子图-子图比较.
    • 由于大小或节点索引差异,现有的GCL方法与非对齐的子图对进行斗争.

    研究的目的:

    • 为图形异常检测提出一种新的分图对齐的多视图对比方法 (SAMCL).
    • 通过引入子图-子图水平对比来解决现有的GCL方法的局限性.
    • 为了克服"非对齐"问题,在子图对对比中进行准确的相似度测量.

    主要方法:

    • 通过捕捉目标节点的各种邻居来生成多视图增强子图.
    • 开发了一个使用地球移动器距离 (EMD) 的子图对齐策略,用于非对齐的子图相似性,考虑节点嵌入和拓.
    • 集成的子图对齐的对比学习,视图内节点-子图对比学习,以及用于异常得分的掩盖子图重建.

    主要成果:

    • SAMCL有效地解决了GAD中的子图-子图对比级别差距.
    • 拟议的分图对齐策略准确地测量了非对齐的分图对之间的相似性.
    • 实验表明,与最先进的方法相比,ACM数据集的性能有了显著的提高,在ACM数据集上提高了6.36%.

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    结论:

    • SAMCL通过结合子图-子图对比,为图形异常检测提供了一种新且有效的方法.
    • 该方法成功处理非对齐子图比较,提高异常检测的准确性.
    • 结合的对比学习和重建模块提供了一个强大的框架,用于识别图形数据中的异常.