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相关概念视频

Sample Size Calculation01:19

Sample Size Calculation

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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
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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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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Typical Model Studies01:30

Typical Model Studies

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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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相关实验视频

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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在语音,语言和听觉科学中实现通用化的机器学习模型:估计样本大小和减少过度装配.

Hamzeh Ghasemzadeh1,2,3, Robert E Hillman1,2,4,5, Daryush D Mehta1,2,4,5

  • 1Center for Laryngeal Surgery and Voice Rehabilitation, Massachusetts General Hospital, Boston.

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PubMed
概括
此摘要是机器生成的。

嵌套的k-fold交叉验证在语音,语言和听觉科学中提供了比单个分割更强大的机器学习 (ML) 结果. 这种方法提高了统计能力和信心,减少了对可靠的ML研究设计的样本大小需求.

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科学领域:

  • 语音,语言和听力科学
  • 计算语言学 计算语言学
  • 生物医学数据科学 生物医学数据科学

背景情况:

  • 语音,语言和听觉科学中的机器学习 (ML) 研究通常使用单个数据分割进行交叉验证.
  • 这种方法可能会导致偏见的结果和对模型准确性的高估.
  • 强大的数据分割对于在这些领域可靠的ML模型开发至关重要.

研究的目的:

  • 为ML研究提供量化证据,促进嵌套k-fold交叉验证,而不是单个数据分割.
  • 介绍ML研究设计中功率分析的方法和MATLAB代码.
  • 提高语音,语言和听力研究中的ML应用程序的可靠性和有效性.

主要方法:

  • 我们比较了四种交叉验证方法:单一持久,十倍,列车验证测试和嵌套十倍.
  • 利用现实世界的临床数据和蒙特卡洛模拟来评估ML的结果.
  • 交叉验证之间的量化相互作用,特征区分能力,特征空间维度,模型维度和样本大小.

主要成果:

  • 单个持久交叉验证产生了较低的统计能力和信心,膨胀了准确性估计.
  • 嵌套的10倍交叉验证证明了优越的统计信心和力量,提供了无偏见的准确性.
  • 嵌套的k-fold交叉验证可以将所需的样本大小减少高达50%与单个持久相比.
  • 在嵌套的k-fold交叉验证中,统计信心高达四倍.

结论:

  • 嵌套的k-fold交叉验证对于言语,语言和听觉科学中的公正和强大的ML研究至关重要.
  • 实施嵌套k-fold交叉验证可确保更可靠和更准确的ML模型性能.
  • 该研究提供了工具 (MATLAB代码,查找表),以帮助研究人员对ML研究的样本大小进行估计.