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相关概念视频

Binomial Probability Distribution01:15

Binomial Probability Distribution

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A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Line Loss01:10

Line Loss

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The different configurations of source-load connections include wye (star) and delta connections. The relationship between line and phase voltages and currents varies depending on the configuration. When the source is supplying power, it is transmitted through the wires to the load, and during this transmission, some power is absorbed by the wires, leading to line loss.
Line loss impacts power delivery efficiency in a balanced three-phase circuit. The symmetry in such a circuit simplifies the...
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Reducing Line Loss01:18

Reducing Line Loss

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
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Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

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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...
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Distance Problem01:29

Distance Problem

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When an object's velocity changes over time, the total distance traveled can be determined by summing small displacement intervals over short increments. This approach approximates the true distance through numerical summation and the use of integral calculus. An estimate of the total displacement can be obtained by measuring velocity at regular intervals and multiplying each value by the corresponding time step.If a runner accelerates over the first three seconds of a race, speed measurements...
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相关实验视频

Updated: Jul 12, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

HasLoss:一个新的 Hassanat 基于距离的损失函数用于二进制分类.

Ahmad S Tarawneh1

  • 1Faculty of Information Technology, Department of Data Science, Mutah University, Karak, Jordan.

Frontiers in artificial intelligence
|February 26, 2026
PubMed
概括

新的基于距离的损失函数,适应了Hassanat距离用于二进制分类,为机器学习模型提供了强大的训练. 这些Hassanat损失提供了理论上的保证和竞争性表现,显示出对异常值和噪声的强度提高.

科学领域:

  • 机器学习 机器学习
  • 计算机科学 计算机科学
  • 优化理论 优化理论

背景情况:

  • 损失函数对于训练机器学习模型,特别是分类中的神经网络至关重要.
  • 现有的损失函数可能缺乏对异常值和噪声的稳定性,影响模型性能.

研究的目的:

  • 建立基于距离的损失函数的理论框架,使用Hassanat距离进行二进制分类.
  • 开发和经验验证基于Hassanat的新损失函数变体.

主要方法:

  • 调整了Hassanat距离用于二进制分类,以创建基于距离的损失函数的理论框架.
  • 进行了梯度分析,以证明 Hassanat 损失的边界梯度和有限的利普希茨常数.
  • 在合成数据集和9个现实数据集上经验评估了6个Hassanat损失变体,与二进制交叉 (BCE),焦点损失,平均平方误差 (MSE) 和L1损失进行比较.

主要成果:

  • 基于Hassanat的损失显示出具有竞争力的性能,与可比或改进的校准,融合速度,精度,召回,F1得分和AUC.
  • 拟议的损失显示出对异常值和噪声的显著稳定性.
  • 科恩的数据分析表明,一些Hassanat变体的实际效果大小比BCE.

结论:

关键词:
距离指标 距离指标 距离指标损失功能 损失功能 损失功能机器学习是机器学习.神经网络的神经网络的神经网络优化的优化优化优化.

相关实验视频

Last Updated: Jul 12, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

  • 建立了基于距离的Hassanat损失函数的理论基础和经验验证.
  • 有限梯度和有限的利普希茨常数提供了优化保证,并解释了观察到的稳定性.
  • 该框架允许系统地开发针对特定应用的强大的损失函数.