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相关概念视频

Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
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Scalar and Vector Triple Products01:06

Scalar and Vector Triple Products

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Two vectors can be multiplied using a scalar product or a vector product. The resultant of a scalar product is scalar, while with vector products, the resultant is a vector. These rules of the scalar or vector product between two vectors can be applied to multiple vectors to obtain meaningful combinations. The scalar triple product is the dot product of a vector with the cross product of two vectors.
The scalar triple product is the dot product of a vector with the cross product of two vectors....
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Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

295
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
295
Vector Components in the Cartesian Coordinate System01:29

Vector Components in the Cartesian Coordinate System

20.2K
Vectors are usually described in terms of their components in a coordinate system. Even in everyday life, we naturally invoke the concept of orthogonal projections in a rectangular coordinate system. For example, if someone gives you directions for a particular location, you will be told to go a few km in a direction like east, west, north, or south, along with the angle in which you are supposed to move. In a rectangular (Cartesian) xy-coordinate system in a plane, a point in a plane is...
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Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.4K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
1.4K
Routh-Hurwitz Criterion I01:15

Routh-Hurwitz Criterion I

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Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
280

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相关实验视频

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Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
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无内核的二次面支持向量回归与非负约束.

Dong Wei1,2, Zhixia Yang1,2, Junyou Ye1,2

  • 1College of Mathematics and Systems Science, Xinjiang University, Urumuqi 830046, China.

Entropy (Basel, Switzerland)
|July 29, 2023
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概括
此摘要是机器生成的。

一种新的无内核二次支向量回归与非负约束 (NQSSVR) 提供可解释的回归模型. 这种方法确保了单调的增加,通过实验和真实世界的空气质量数据来验证.

关键词:
空气质量综合指数数据集没有核的无核.其他非负面约束.一个正方形的表面.回归问题回归问题

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科学领域:

  • 机器学习 机器学习
  • 回归分析是一种回归分析.
  • 数据科学数据科学数据科学

背景情况:

  • 回归问题往往需要复杂的模型.
  • 选择内核函数和参数可能具有挑战性.
  • 关于数据单调性的先验信息通常是可用的.

研究的目的:

  • 提出一个无内核的二次面支持向量回归与非负约束 (NQSSVR).
  • 开发一个可解释和处理单调数据的回归模型.
  • 为了确保回归函数与增加趋势的先前知识保持一致.

主要方法:

  • 使用二次面无核技术进行回归.
  • 引入了对回归系数的非负约束.
  • 为NQSSVR.构建了一个优化问题.
  • 解决了原始和双重问题的存在,独特性和关系.

主要成果:

  • NQSSVR模型提供了一个可解释的二次回归函数.
  • 理论分析证实回归函数与先验信息相匹配.
  • 在人工和基准数据集上的实验结果证明了可行性和有效性.
  • 使用真实世界的空气质量数据验证了该方法.

结论:

  • NQSSVR是一种有效和可解释的方法,用于单调趋势的回归问题.
  • 无内核的方法简化了模型选择,并增强了理解.
  • 该方法在环境数据分析等应用中表现有前途.