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

What Are Outliers?01:12

What Are Outliers?

4.9K
Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
4.9K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.0K
Outliers and Influential Points01:08

Outliers and Influential Points

6.1K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
6.1K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

3.6K
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...
3.6K
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

498
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
498
Survival Tree01:19

Survival Tree

390
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...
390

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

Targeting SLK protects against cerebral ischemia-reperfusion injury by regulating USP8-mediated HIF-1α stabilization and RhoA/ROCK activation.

Cellular & molecular biology letters·2026
Same author

Robust Model Fitting via Motion-Aware Pyramid Transformer-Guided Preference Filtering and Consensus Smoothing.

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

Unusual Anti-Thermoplastics and Low Thermal Expansion in 2D Metal Halide Crystals.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Longitudinal Study of Symptom Cluster Trajectories and Sentinel Symptoms in Patients With Cervical Cancer After Surgery and During the First Chemotherapy Cycle.

Cancer nursing·2026
Same author

Impact of DSA-based Cerebral Microcirculation Time on Neurological Improvement at Discharge in Stroke Patients after Successful Recanalization.

AJNR. American journal of neuroradiology·2026
Same author

A wearable non-invasive sonogenetic pacemaker.

Nature biomedical engineering·2026
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

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

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

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

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

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

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

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

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

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

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
查看所有相关文章

相关实验视频

Updated: Jun 9, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

MOOD:利用分布之外的数据来增强不平衡的半监督学习

Yang Lu, Xiaolin Huang, Yizhou Chen

    IEEE transactions on neural networks and learning systems
    |June 9, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究介绍了混合OOD (MOOD),这是一个不平衡的半监督学习 (SSL) 的新方法. MOOD有效地利用分布外 (OOD) 数据来改善不平衡数据集的模型性能.

    相关实验视频

    Last Updated: Jun 9, 2026

    Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
    08:27

    Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

    Published on: January 5, 2024

    科学领域:

    • 机器学习 机器学习
    • 计算机科学 计算机科学

    背景情况:

    • 类不平衡和部分标记的数据在现实世界中很常见,需要对不平衡的半监督学习 (SSL) 进行研究.
    • 自然收集的数据集通常包含分布外 (OOD) 样本,这大大降低了现有的不平衡SSL方法的性能.
    • 在不平衡数据集中的尾部类特别容易受到OOD数据存在时的性能下降的影响.

    研究的目的:

    • 提出一种新的不平衡的SSL方法,混合OOD (MOOD),旨在有效利用OOD数据.
    • 通过利用OOD数据作为一个有价值的资源来增强尾部类的特征多样性.
    • 为解决因存在OOD样本而导致的SSL方法性能恶化的问题.

    主要方法:

    • 从未标记的数据集中过OOD数据.
    • 将过的OOD数据与标记数据合并以增强特征表示,特别是在尾部类中.
    • 开发一个推拉 (Push-and-Pull) 损失函数,以区分分布式 (ID) 和OOD样本,吸引ID实例,并将OOD样本从类中心体中排斥出来.

    主要成果:

    • 与现有的最先进的不平衡SSL方法相比,MOOD表现出卓越的性能.
    • 拟议的方法在不同程度的类不平衡的数据集中显示了稳定性.
    • 即使在未标记的集合中存在不同比例的OOD数据,MOOD也保持了强的性能.

    结论:

    • 利用以前被认为有害的OOD数据,可以显著有利于失衡的SSL.
    • MOOD方法提供了一种有前途的方法,可以在复杂的现实数据场景中提高机器学习模型的准确性和稳定性.
    • 开发的PaP损失对于有效分离ID和OOD数据至关重要,提高了模型的概括性.