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Gauss's law helps determine electric fields even though the law is not directly about electric fields but electric flux. In situations with certain symmetries (spherical, cylindrical, or planar) in the charge distribution, the electric field can be deduced based on the knowledge of the electric flux. In these systems, we can find a Gaussian surface S over which the electric field has a constant magnitude. Furthermore, suppose the electric field is parallel (or antiparallel) to the area...
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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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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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在没有集群的集群上学习学习集群学习.

H N Mhaskar1, Ryan O'Dowd1

  • 1Institute of Mathematical Sciences, Claremont Graduate University, Claremont, CA 91711, United States of America.

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概括
此摘要是机器生成的。

这项研究引入了一种用于机器学习的新型一拍式函数近似方法. 它绕过了多重估计,为过球体上的粗略函数提供了最佳的近似率.

关键词:
在未知多元组的近似值.直接近似与错误界限的直接近似.在球体上定位多项式内核.

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科学领域:

  • 机器学习 机器学习
  • 数据科学数据科学数据科学
  • 应用数学 应用数学 应用数学

背景情况:

  • 来自随机数据的函数近似是机器学习的一个核心挑战.
  • 多元假设假设数据位于一个低维子多元.
  • 现有的方法往往涉及多步骤的过程与固有的近似误差.

研究的目的:

  • 开发一种一拍式函数近似技术.
  • 为了避免与估计多重性质相关的错误.
  • 在超球上存储的数据上对函数进行近似计算.

主要方法:

  • 将未知的数据分组投射到环境的超球体上.
  • 使用一系列局部化的球形多项式内核.
  • 在没有多重预处理的情况下制定一次性近似策略.

主要成果:

  • 在相对"粗"的函数中实现了最佳的近似率.
  • 展示了一种不需要超出其尺寸的多重结构估计的方法.
  • 引入了一种更直接的方法,用于对多元体的函数近似.

结论:

  • 拟议的方法为传统的两步方法提供了有效的替代方案.
  • 这种技术通过消除中间估计步骤来减少近似误差.
  • 局部化的球形多项式内核为多元学习和函数近似提供了强大的工具.