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相关概念视频

Associative Learning01:27

Associative Learning

353
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...
353
Observational Learning01:12

Observational Learning

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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...
170
Cognitive Learning01:21

Cognitive Learning

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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.
Tolman introduced the idea that behavior is influenced by...
239
Introduction to Learning01:18

Introduction to Learning

379
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...
379
Network Function of a Circuit01:25

Network Function of a Circuit

286
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
286
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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在动态网络上进行对比的表示学习.

Pengfei Jiao1, Hongjiang Chen2, Huijun Tang2

  • 1School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China; Data Security Governance Zhejiang Engineering Research Center, Hangzhou, 310018, China.

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

本研究介绍了一种新的动态网络对比表示学习 (DNCL) 模型,以改善动态网络的表示学习. 在稀疏或杂的网络中,DNCL通过专注于时间演变而不是仅仅是快照细节来提高稳健性.

关键词:
相反的学习学习.动态网络的动态网络.互助信息互助信息互助信息互助信息代表性的学习学习.

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科学领域:

  • 机器学习 机器学习
  • 网络科学 网络科学
  • 数据挖掘 数据挖掘

背景情况:

  • 动态网络表示学习旨在捕捉时间网络结构和节点属性.
  • 现有的方法往往过度强调静态快照细节,导致稀疏或杂数据的性能差.
  • 需要更强大的方法来有效地解释时间进化.

研究的目的:

  • 为动态网络提出一个新的对比学习框架,命名为动态网络对比表示学习 (DNCL).
  • 提高节点嵌入在动态网络中的稳定性和准确性,特别是在稀疏或杂的条件下.
  • 为了捕捉内部快照拓和内部快照时间演变信息.

主要方法:

  • 开发了一个动态网络对比表示学习 (DNCL) 模型,利用对比学习原理.
  • 基于快照内和快照间对比的构建对比对象函数.
  • 节点之间在不同的时间步骤和生成的视图之间最大限度地交换信息,避免直接估计基本真相特征.

主要成果:

  • 与最先进的方法相比,DNCL在链接预测,节点分类和集群任务方面表现出卓越的表现.
  • 实验是在现实世界和合成动态网络上进行的.
  • 结果验证了对动态网络表示学习提出的对比方法的有效性.

结论:

  • 拟议的DNCL模型为学习动态网络表示提供了一个强大而有效的方法.
  • 通过最大限度地增加相互信息,对比式学习为捕捉时间动态提供了一个强大的机制.
  • 对于需要对不断变化的网络数据进行准确分析的应用程序,DNCL显示出巨大的潜力.