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相关概念视频

Truncation in Survival Analysis01:09

Truncation in Survival Analysis

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Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
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Trimmed Mean01:10

Trimmed Mean

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While measuring the mean of a data set, care needs to be taken when associating the mean to its central tendency. The same goes for the arithmetic mean, the geometric mean, or the harmonic mean. This is because the presence of a single outlier data value can significantly affect the mean. That is, the mean is sensitive to fluctuations in the data set.
Although certain measures of central tendency are not sensitive to outliers, there are alternative versions of the mean that get around the...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Censoring Survival Data01:09

Censoring Survival Data

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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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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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定期截断的M估计器用于用噪音标签学习.

Xiaobo Xia, Pengqian Lu, Chen Gong

    IEEE transactions on pattern analysis and machine intelligence
    |December 28, 2023
    PubMed
    概括

    这项研究引入了定期截断的M估计器 (RTME),以改善使用噪音标签的深度学习. 通过选择干净的样本,并利用可能被错误标记的样本进行更好的概括,RTME有效地处理噪音数据.

    科学领域:

    • 机器学习 机器学习
    • 深度学习 (Deep Learning) 是一种深度学习.
    • 计算机视觉 计算机视觉

    背景情况:

    • 样本选择是深度网络中使用噪音标签的学习中很受欢迎的.
    • 现有的方法往往错误地将小损失的例子视为干净的,并丢弃大损失的例子,忽略了潜在的信息.
    • 这种方法可能是低于最佳的,因为在选定的样本中有噪音标签的影响,以及废弃数据的不足利用.

    研究的目的:

    • 解决当前在噪音标签学习中的样本选择方法的局限性.
    • 提出一种新的方法,减轻噪音标签在选定样本中的负面影响,并利用丢弃的数据.
    • 增强深度网络的概括能力,训练用标签噪音的数据集.

    主要方法:

    • 引入定期截断的M估计器 (RTME),一种在截断和原始M估计器模式之间交替使用的方法.
    • 截断的M估计器可自适应地选择小损失的例子,减少噪音标签的副作用,而无需事先了解噪音率.
    • 原始的M估计器包含了大损失的例子,这些例子可能是干净的或包含有价值的信息来进行概括.

    主要成果:

    • 理论分析证明了拟议策略的标签噪声耐受性.
    • 经验结果表明,RTME在使用噪音标签的学习中表现优于多种基线方法.

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  • 该方法在各种噪声类型和噪声水平中表现出强度.

    • 结论:

    • 在杂的标签学习中,RTME为现有的样本选择技术的缺点提供了同时解决方案.
    • 拟议的方法有效地处理噪音标签,提高模型的概括性和稳定性.
    • 在训练具有不完美的数据的深度网络方面,RTME代表了重大进步.