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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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Observational Learning01:12

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Cognitive Learning01:21

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
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Cross Product01:25

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The cross product is a fundamental concept in vector algebra that is a vector operation on two different vectors to obtain a third vector. Unlike the scalar product, the cross product results in a vector quantity perpendicular to both the original vectors.
The magnitude of the cross product is obtained by multiplying the magnitude of both the vectors and the sine of the angle between them. This means that a larger angle between the vectors will lead to a greater magnitude of the cross product.
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相关实验视频

Updated: Jul 13, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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在缺乏监督下对知识图的调整:一个具有积极交叉视图对比学习的一般框架.

Weixin Zeng, Xiang Zhao, Jiuyang Tang

    IEEE transactions on neural networks and learning systems
    |October 17, 2023
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    此摘要是机器生成的。

    本研究引入了知识图对齐 (KGA) 的新框架,该框架同时匹配实体和关系. 它通过使用关系增强的活跃实例选择和使用有限的标记数据进行交叉视图对比学习来提高KG覆盖率.

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    科学领域:

    • 人工智能的人工智能
    • 数据科学数据科学数据科学
    • 知识表示 知识表示

    背景情况:

    • 知识图 (KG) 对于数据集成至关重要,但很少完成.
    • 知识图对齐 (KGA) 增强了KG覆盖范围,但现有的方法往往忽视了关系,需要大量的标记数据.

    研究的目的:

    • 提出KG中同时对实体和关系进行对齐的一般框架.
    • 解决缺乏监督和忽视传统KGA中关系的局限性.

    主要方法:

    • 开发了一个具有两个核心组件的框架:关系增强的活跃实例选择 (RAS) 和交叉视图对比学习 (CCL).
    • RAS指导使用关系信息进行标记的有价值实例的选择.
    • CCL利用不同视角的对比学习来放大有限的监督信号.

    主要成果:

    • 拟议的框架在稀缺的监督下,在各种KG对中持续改善KG对齐性能.
    • 无论是RAS和CCL组件都会有助于提高对齐准确度.
    • 该框架不依赖于模型,可以适应现有的实体和关系对齐技术.

    结论:

    • 新的框架有效地增强了KG覆盖范围,同时对实体和关系进行调整.
    • 这种方法显著提高了对齐性能,即使有有限的标记数据.
    • 这项工作为实际的KG调整挑战提供了可行的解决方案.