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相关概念视频

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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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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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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Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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相关实验视频

Updated: Jul 24, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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通过可控自适应图形学习和歧视性特征学习进行无监督的特征选择.

Pei Huang, Mengying Xie, Xiaowei Yang

    IEEE transactions on neural networks and learning systems
    |July 4, 2023
    PubMed
    概括

    两个新的无监督特征选择方法,可控制的自适应图形学习 (CAG-U和CAG-I),通过自适应学习图形和选择无关联的特征来解决机器学习中的挑战. 这些方法通过控制图形差异和减少维度来改进现有技术.

    科学领域:

    • 机器学习 机器学习
    • 数据挖掘 数据挖掘
    • 模式识别 模式识别

    背景情况:

    • 无监督的特征选择是复杂的,需要同时保留内在数据结构和选择无关联的特征.
    • 由于初始和最终图之间的显著差异,需要先前的子空间维度知识,以及高维数据的低效率,现有的方法经常失败.

    研究的目的:

    • 提出新的无监督特征选择方法,克服当前方法的局限性.
    • 开发适应性学习图形的技术,同时控制差异,并选择相对不相关/独立的特征.

    主要方法:

    • 无监督特征选择 (CAG-U) 的可控自适应图形学习.
    • 可控制的自适应图形学习用于独立的特征选择 (CAG-I).
    • 使用离散投影矩阵进行特征选择.

    主要成果:

    • 拟议的CAG-U和CAG-I方法在与现有方法相比显示出更高的性能.
    • 在12个不同的数据集上的实验验证证证了新方法的有效性.

    结论:

    • CAG-U和CAG-I有效地解决了无监督特征选择的关键挑战.
    • 这些方法通过自适应式学习图形和选择无关联/独立特征,在各种领域提供了更好的性能和适用性.

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