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

Aggregates Classification01:29

Aggregates Classification

963
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...
963
Force Classification01:22

Force Classification

2.3K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.3K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.5K
3.5K
Improving Translational Accuracy02:07

Improving Translational Accuracy

14.1K
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...
14.1K
Survival Tree01:19

Survival Tree

382
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...
382
Classification of Systems-I01:26

Classification of Systems-I

544
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
544

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

Quantitative spectroscopic analysis of light scattering in rough granular coatings: an optimized Kubelka-Munk modeling approach.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026
Same author

Biochemical Diagnosis in Substance and Non-substance Addiction.

Advances in experimental medicine and biology·2025
Same author

LncRNA KIAA0087 suppresses the progression of osteosarcoma by mediating the SOCS1/JAK2/STAT3 signaling pathway.

Experimental & molecular medicine·2023
Same author

Case report: Early thrombosis in left atrial during transcatheter closure of ASD in a child with favorable outcome after use of GPIIb/IIIa receptor antagonist and heparin.

Frontiers in pediatrics·2023
Same author

Characterization of renal artery variation in patients with clear cell renal cell carcinoma and the predictive value of accessory renal artery in pathological grading of renal cell carcinoma: a retrospective and observational study.

BMC cancer·2023
Same author

Gene Co-Expression Network Analysis Reveals the Hub Genes and Key Pathways Associated with Resistance to <i>Salmonella</i> Enteritidis Colonization in Chicken.

International journal of molecular sciences·2023
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

基于内核的代表调整为类不平衡的半监督学习

Zuoyong Li, Jinhuang Ye, Jie Wen

    IEEE transactions on neural networks and learning systems
    |October 28, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了半监督学习 (SSL) 的内核函数映射策略,以提高对不平衡数据集的稳定性. 该方法对数据表示进行了对齐,提高了机器学习模型的性能,使用有限的标记数据.

    相关实验视频

    科学领域:

    • 机器学习 机器学习
    • 人工智能的人工智能
    • 数据科学数据科学数据科学

    背景情况:

    • 半监督学习 (SSL) 利用未标记的数据来克服稀缺的标记数据的局限性.
    • 现实世界的场景往往呈现域名转移和不平衡的类分布,挑战标准的SSL方法.
    • 确保一致的类表示对于不平衡数据集的机器学习稳定性至关重要.

    研究的目的:

    • 为半监督学习开发一种新的内核函数映射策略.
    • 提高机器学习模型的稳定性,以应对不平衡的数据集和域移动.
    • 为了提高准确性,在表示层面上完善伪标签预测.

    主要方法:

    • 提出了一个使用高斯核的内核函数映射策略.
    • 在无限维空间中将未标记的数据表示映射到标记的数据中心.
    • 实施了选择性策略,以纠正多数阶级的预测,同时保持少数阶级的信心.

    主要成果:

    • 拟议的方法有效地调整了类表示,提高了对不平衡数据的稳定性.
    • 在表示层面上精细化伪标签导致了卓越的性能.
    • 与最先进的方法相比,在各种基准上表现出卓越的表现.

    结论:

    • 内核函数映射策略为SSL提供了一个简单而有效的解决方案,用于数据不平衡的SSL.
    • 该方法提高了机器学习模型可靠性,在现实应用中具有偏斜的数据分布.
    • 通过对各种基准和培训设置进行广泛评估,验证了有效性.