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相关概念视频

Associative Learning01:27

Associative Learning

2.1K
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...
2.1K
Reinforcement01:23

Reinforcement

1.2K
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
1.2K
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

4.2K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
4.2K
Primary and Secondary Reinforcers01:23

Primary and Secondary Reinforcers

1.8K
In psychology, reinforcement is a key concept in behavior modification. B.F. Skinner demonstrated this with his experiments involving rats in what is known as a Skinner box. The rats learned to press a lever to receive food, a primary reinforcer that fulfilled their innate need for nourishment.
Effective reinforcers for humans vary depending on the individual and the context. Primary reinforcers, such as food, water, sleep, shelter, and pleasure, have inherent value and satisfy basic biological...
1.8K
Reinforcement Schedules01:24

Reinforcement Schedules

743
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
743
Observational Learning01:12

Observational Learning

1.5K
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...
1.5K

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相关实验视频

Updated: May 5, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

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联邦离线增强学习学习

Doudou Zhou1, Yufeng Zhang2, Aaron Sonabend-W1

  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health.

Journal of the American Statistical Association
|February 20, 2026
PubMed
概括
此摘要是机器生成的。

联合离线强化学习 (RL) 能够使用分布式医疗数据实现个性化医疗. 这种新算法在多个站点高效地优化了治疗策略,实现了与集中数据相比的性能.

关键词:
动态处理方案 动态处理方案电气健康记录 电气健康记录多种来源的学习学习.

相关实验视频

Last Updated: May 5, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

6.2K

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 医疗保健信息学 医疗保健信息学

背景情况:

  • 个性化医疗需要动态的治疗方案,经常利用线下强化学习 (RL).
  • 由于隐私问题和特定站点的数据异质性,各机构之间共享敏感的医疗保健数据受到限制.
  • 现有的方法很难有效地利用分布式数据集来开发强大的治疗策略.

研究的目的:

  • 开发一个新的联合离线RL框架,解决多站点医疗保健数据中的隐私和异质性.
  • 为了使网站级特征在统一模型中进行分析.
  • 设计一种有效的通信算法,以优化动态治疗方案.

主要方法:

  • 提出了一个多站点马尔科夫决策过程模型,适应同质和异质站点效应.
  • 开发了第一个用于离线RL的联合政策优化算法,并保证了样本复杂性.
  • 算法只需要通过汇总统计数据交换进行一轮通信.

主要成果:

  • 拟议的联合离线RL算法证明了政策次优化的理论保证,与集中数据场景相比较.
  • 广泛的模拟证实了算法在学习最佳政策方面的有效性.
  • 该方法成功地应用于多部位败血症数据集.

结论:

  • 联邦离线RL是个性化医疗的可行方法,使用分布式的私人医疗数据.
  • 拟议的算法为优化多站点处理方案提供了高效和有效的解决方案.
  • 这项工作促进了先进的RL技术在现实世界医疗保健环境中的临床应用.