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相关概念视频

Improving Translational Accuracy02:07

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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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.
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Vector Algebra: Method of Components01:08

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It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
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强大的基于角度的转移学习在高维度中.

Tian Gu1, Yi Han2, Rui Duan3

  • 1Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY 10032, USA.

Journal of the Royal Statistical Society. Series B, Statistical methodology
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概括
此摘要是机器生成的。

转移学习提高了使用现有数据的模型性能,特别是对于有限的目标数据集. 我们的新型基于角度转移学习 (angleTL) 方法有效地将知识从源向目标群体转移,即使数据异质.

关键词:
高维的非对称性高维的非对称性模型聚合模型的聚合.风险预测风险预测转移学习转移学习

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科学领域:

  • 统计遗传学 统计遗传学
  • 机器学习 机器学习
  • 生物信息学是一种生物信息学.

背景情况:

  • 转移学习对于在稀缺的目标数据下改善模型性能至关重要.
  • 在具有有限数据和异质源群的高维回归中出现了挑战.
  • 现有的方法通常需要个人级别的源数据,这些数据可能无法获得.

研究的目的:

  • 开发一种新的转移学习方法,用于使用有限的目标数据进行高维回归.
  • 在只有模型参数可用时,应对异质源种群的挑战.
  • 提出一种减轻负传输和适应目标信号强度的方法.

主要方法:

  • 提出了一种新的基于角度的转移学习 (angleTL) 方法,使用预训练的源模型的参数估计.
  • 扩展角度TL,以纳入具有不同相关性的多个源模型.
  • 利用高维的非对称分析来理解转移效益.

主要成果:

  • AngleTL统一了几种基准方法,并适应目标信号强度.
  • 该方法有效地减轻了在存在人口异质性的情况下的负面转移.
  • 高维分析证实了angleTL对现有方法的优越性.

结论:

  • AngleTL提供了一个有效的解决方案,用于在高维回归中转移学习,使用有限和异质数据.
  • 该方法可用于跨生物库转移遗传风险预测模型.
  • 利用参数估计可以实现知识转移,而不需要个人级别的源数据.