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相关概念视频

Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it.
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Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
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政策通用化的效果不变机制

Sorawit Saengkyongam1, Niklas Pfister2, Predrag Klasnja3

  • 1Seminar for Statistics, ETH Zürich, Zürich, Switzerland.

Journal of machine learning research : JMLR
|July 31, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了政策学习的效果不变性 (e-invariance),使模型能够适应新的任务. 这种方法可以在未见的环境中提高概括性,而不需要完全的分布不变性.

关键词:
有关因果关系的因果关系分布 泛化 一般化 分布域名适应 域名适应不变性 不变性 不变性政策学习学习政策学习

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科学领域:

  • 机器学习 机器学习
  • 人工智能的人工智能
  • 强化学习是一种强化学习.

背景情况:

  • 政策学习对现实世界系统至关重要,但在适应新环境时却很难.
  • 现有的方法依赖于条件分布的完全不变性,这往往过于限制性.
  • 将政策推广到未见的场景中仍然是机器学习的一个重大挑战.

研究的目的:

  • 引入一个宽松的不变性条件,效果不变性 (e-invariance),以改善政策概括.
  • 为了证明在特定假设下,e-不变性足以进行零射击政策概括.
  • 扩展使用有限的测试环境数据进行短暂政策概括的方法.

主要方法:

  • 开发了一种新的效果不变性 (e-不变性) 概念,作为完全不变性的放松.
  • 证明了e-invariance对于零射击政策概括的充分性.
  • 在不假设因果图或结构因果模型的情况下,为e-不变性设计数据驱动的测试程序.
  • 调查了使用小样本测试的一些射击概括的扩展.

主要成果:

  • 效果不变性 (e-invariance) 已被证明足以实现零射击政策的一般化.
  • 扩展使有效的短暂的政策概括成为可能.
  • 使用模拟和现实世界移动卫生干预数据的经验验证证证实了该方法的有效性.
  • 该方法成功地直接从数据中测试了e-不变性.

结论:

  • 效果不变性为政策学习提供了完全不变性的实用和强大的替代方案.
  • 拟议的方法增强了在未见的环境中政策概括能力.
  • 这项工作为有效调整政策提供了一个强大的框架,零或少量学习.