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相关概念视频

Associative Learning01:27

Associative Learning

298
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...
298
Multiple Regression01:25

Multiple Regression

2.9K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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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...
305
Multiple Comparison Tests01:13

Multiple Comparison Tests

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
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Introduction to Learning01:18

Introduction to Learning

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
337
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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相关实验视频

Updated: Jun 8, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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适应性多图形对比学习,用于捆绑推.

Qian Tao1, Chenghao Liu1, Yuhan Xia1

  • 1School of Software, South China University of Technology, Higher Education Mega Centre, Panyu District, Guangzhou, 510006, Guangdong, China.

Neural networks : the official journal of the International Neural Network Society
|November 7, 2024
PubMed
概括
此摘要是机器生成的。

适应式多图对比学习为捆绑推 (AMCBR) 通过建模复杂的用户-项目关系来改进捆绑推. 这种新的方法通过自适应地结合图形嵌入和使用对比学习来提高用户偏好建模的准确性来提高准确性.

关键词:
捆绑推建议是一套建议.相反的学习学习.图表神经网络的神经网络超图形 (Hypergraph) 是一个超图形.

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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:

  • 捆绑推系统正在获得引力,利用图形神经网络 (GNN) 来建模用户-项目交互.
  • 现有的GNN模型难以捕捉复杂的三元关系,并且由于不同的图形组合而遭受噪声.

研究的目的:

  • 提出一种新的方法,即针对捆绑推的自适应多图谱对比学习 (AMCBR),以解决当前捆绑推模型的局限性.
  • 增强复杂的三元关系的建模,并提高组合图嵌入的稳定性.

主要方法:

  • AMCBR构建多个图形:一个捆绑偏好图,一个协作社区图,以及一个项目级偏好超图.
  • 它采用 (超) 图形卷积用于嵌入生成和自适应聚合模块,用于来自不同图形的嵌入的稳健融合.
  • 用一种对比式学习策略来优化联合模型,并加强图表间的协作链接.

主要成果:

  • AMCBR有效地模拟三元相互作用,并通过自适应聚合减轻噪声.
  • 对比式学习策略加强了各个图形之间的协作联系.
  • 实验表明,AMCBR在三个真实数据集上的Top-K捆绑建议中表现优于最先进的基线.

结论:

  • 通过捕捉复杂的关系和自适应地整合来自多个图形结构的信息,AMCBR为捆绑推提供了强大的和有效的解决方案.
  • 拟议的自适应多图形对比学习框架显著提升了个性化捆绑建议的最新技术.