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

Improving Translational Accuracy02:07

Improving Translational Accuracy

11.9K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.9K
Aggregates Classification01:29

Aggregates Classification

387
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
387
Associative Learning01:27

Associative Learning

593
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
593
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

220
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
220
Cluster Sampling Method01:20

Cluster Sampling Method

12.8K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.8K
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

212
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
212

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

Decoding the epithelial-stromal interactome in allergic rhinitis through single-cell multi-omics integration.

The Journal of allergy and clinical immunology·2026
Same author

A nomogram integrating DCE-MRI imaging features and clinicopathological parameters for predicting pathological complete response in HER2-positive breast cancer.

Frontiers in oncology·2026
Same author

Metabolic dysfunction-associated fatty liver disease in chronic hepatitis B: dual effects on hepatocarcinogenesis and evolving strategies for risk prediction.

Frontiers in immunology·2026
Same author

Ginsenoside Rk1 suppresses osteosarcoma progression by coordinately activating ferritinophagy and disrupting antioxidant defense to induce ferroptosis.

Free radical biology & medicine·2026
Same author

Burden of Malaria and Dengue Across Global, Asian, and Chinese Populations Based on GBD 2021 Data: A Quantitative Assessment of Importation Risks to China.

Viruses·2026
Same author

Variations in rice yield and quality during the early and late seasons under the HLREP-CC model across different ecological zones.

Journal of the science of food and agriculture·2026

相关实验视频

Updated: Sep 15, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

644

集群匹配:通过统一的正负伪标签学习来改善深度集群.

Jianlong Wu, Zihan Li, Wei Sun

    IEEE transactions on pattern analysis and machine intelligence
    |July 15, 2025
    PubMed
    概括

    ClusMatch通过使用伪标签将其转换为半监督任务来增强深度聚类. 这一框架通过利用有限的注释和改进现有的深度集群方法,显著提高了准确性.

    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 计算机视觉 计算机视觉

    背景情况:

    • 深度聚类方法显示出希望,但缺乏注释,限制了性能.
    • 在深度聚类和半监督分类之间存在性能差距,即使只有很少的标签.

    研究的目的:

    • 为了弥合深度集群和半监督学习之间的差距.
    • 引入ClusMatch,这是一个统一的框架,用于在深度集群中进行积极和消极的伪标签学习.

    主要方法:

    • ClusMatch是一个可插入的框架,可以适应现有的深度集群技术.
    • 它利用预先训练的网络进行初始预测,并为监督学习选择高质量的样本.
    • 对于未经选择的样本,采用了一种新的统一的正负伪标签学习策略,对信心进行自适应值.

    主要成果:

    • 克拉斯马奇在六个广泛使用的数据集和一个大规模数据集中表现出优势.
    • 在六个数据集上,与最新的ProPos方法相比,平均准确度提高了5.4%.

    结论:

    • 集群匹配有效地将无监督的集群转化为半监督的问题.
    • 该框架通过结合伪标签策略,显著提高了深度集群的性能.

    更多相关视频

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    2.6K
    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
    08:20

    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

    Published on: October 27, 2023

    1.7K

    相关实验视频

    Last Updated: Sep 15, 2025

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    644
    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    2.6K
    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
    08:20

    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

    Published on: October 27, 2023

    1.7K