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相关概念视频

Associative Learning01:27

Associative Learning

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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.
Classical conditioning, also known...
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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Bar Graph01:07

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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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Normal and Tangetial Components: Problem Solving01:24

Normal and Tangetial Components: Problem Solving

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Consider a man with a mass of 70 kg seated in a chair connected to a pin support through a member BC. If the man maintains an upright position, the task is to determine the horizontal and vertical reactions of the chair on the man when the member makes a 45° angle with the horizontal. At this moment, the man has a speed of 5 m/s, increasing at a rate of 1 m/s².
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Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958).
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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.
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当二分位图形学习与属性网络中的异常检测相遇时:了解每个属性的异常.

Zhen Peng1, Yunfan Wang2, Qika Lin3

  • 1School of Computer Science and Technology, Xi'an Jiaotong University, China.

Neural networks : the official journal of the International Neural Network Society
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概括

,在属性网络中检测异常的新型深度框架,在细粒度特征层面分析异常. 它解开实例和属性,使得人们能够更好地理解不同特征组合中的异常.

关键词:
两部分图形建模模的模拟图形.图形异常检测检测的异常自主监督学习学习

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科学领域:

  • 图形神经网络的神经网络
  • 机器学习 机器学习
  • 数据挖掘 数据挖掘

背景情况:

  • 在各种应用中,在赋值网络中检测异常至关重要.
  • 现有的图形神经网络方法经常汇总节点属性,限制细粒度分析.
  • 有需要的方法,可以根据个别特征尺寸来表征异常.

研究的目的:

  • 提出Eagle,这是一个深度框架,用于在赋值网络中检测异常.
  • 通过解开节点实例及其属性来实现异常的细粒度分析.
  • 提供一个用户友好的方法,从多个特征的角度来理解异常.

主要方法:

  • 使用双分图结构,将实例和属性分开成不连接的节点集.
  • 它将赋值网络建模为一个具有两种关系类型的内部连接的双部分图.
  • 一个自我监督的边缘级预测任务,亲和推理,用于学习.

主要成果:

  • 在传导和感应异常检测设置中都表现出有效性.
  • 该框架允许在各个属性维度中对异常进行表征.
  • 案例研究证实了Eagle的易用性和可解释性.

结论:

  • 提供了一种有效的方法,用于在赋值网络中检测细粒度异常.
  • 解的表示和亲和推理任务为异常提供了可解释的见解.
  • 通过考虑特征组合来增强对网络异常的理解.