Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

3.4K
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...
3.4K
Entropy02:39

Entropy

34.7K
Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
34.7K
Entropy01:18

Entropy

3.4K
The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
3.4K
Frequency-dependent Selection01:21

Frequency-dependent Selection

23.0K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
23.0K
Survival Tree01:19

Survival Tree

369
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...
369
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

7.0K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
7.0K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

TAFNet: Trusted Multiview Associative Fusion Neural Networks for Analyzing Dynamic Brain Networks.

IEEE transactions on neural networks and learning systems·2026
Same author

NIR-Programmable Stealth 2D Black Phosphorus Nanobiointerfaces for Deep Tumor Penetration and Photoimmunotherapy.

ACS nano·2026
Same author

A wearable paper-based SGR/MCC microneedle array sensor for continuous glucose monitoring.

Microsystems & nanoengineering·2026
Same author

Local Surrogate Models With Residual Fuzzy Rules for Model-Agnostic Explanations.

IEEE transactions on cybernetics·2026
Same author

MGA-CLIP: A Multigranularity Attribution Framework for Cross-Modal Explainability in CLIP.

IEEE transactions on neural networks and learning systems·2026
Same author

MCAMamba: A multimodal method with bidirectional cross-attention and state space model for cancer survival prediction.

Science progress·2026
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
查看所有相关文章

相关实验视频

Updated: Jan 7, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K

强大的半监督特征选择与多颗粒度的缩模型建模.

Kehua Yuan, Duoqian Miao, Weiping Ding

    IEEE transactions on pattern analysis and machine intelligence
    |December 24, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一种新的多颗粒度心模型 (Ze-MGM) 框架,用于在高维和弱监督数据中进行强大的半监督特征选择. Ze-MGM通过有效捕捉信息细节性和减少不确定性来提高准确性和可靠性.

    更多相关视频

    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
    11:15

    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

    Published on: June 27, 2013

    34.3K
    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
    03:37

    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

    Published on: March 1, 2024

    1.2K

    相关实验视频

    Last Updated: Jan 7, 2026

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    7.9K
    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
    11:15

    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

    Published on: June 27, 2013

    34.3K
    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
    03:37

    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

    Published on: March 1, 2024

    1.2K

    科学领域:

    • 机器学习 机器学习
    • 模式识别 模式识别
    • 数据科学数据科学数据科学

    背景情况:

    • 高维和弱监督 (HiDWS) 数据对传统的机器学习构成挑战.
    • 现有的半监督特征选择方法由于不可靠的未标记数据和不确定的建模而缺乏稳定性.

    研究的目的:

    • 为高精度和强大的半监督特征选择提出一种新的多颗粒度心模型 (Ze-MGM) 框架.
    • 为了解决处理HiDWS数据的现有方法的局限性.

    主要方法:

    • 引入了一种战略软标签 ($S2-$Label) 学习方法,集成对象近距离和分类确定性.
    • 通过分析标签-决策-类等级结构,构建了一个多颗粒度的知识空间和centropy不确定性度量.
    • 为特征评估和选择定义了两个多细分性意义指标.

    主要成果:

    • 拟议的Ze-MGM框架有效地捕捉了HiDWS数据中的信息细分性.
    • Ze-MGM通过选择兼容的实例来减少特征和标签之间的不确定性.
    • 在广泛的实验中,与最先进的方法相比,实现了优越的概括性能和稳定性.

    结论:

    • Ze-MGM为HiDWS数据中的半监督特征选择提供了强大而准确的解决方案.
    • 该框架的模型不可知性和捕获多细分信息的能力有助于其有效性.