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

Survival Tree01:19

Survival Tree

50
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...
50

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

Unsupervised feature selection via row-sparse local preserving projection.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

A Unified Framework for Pseudo-Supervised Clustering via Weighted Sample Aggregation.

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

Projection with mixed-size anchor graphs.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

SimMTC: Simple Multi-View Tensor Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Unsupervised fine-tuning of vision-language models by fusing classifier tuning and visual prompt tuning.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

IB2MC: Information Bottleneck Inspired Balanced Multiview Clustering.

IEEE transactions on pattern analysis and machine intelligence·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: May 24, 2025

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.2K

在大图表上进行快速半监督学习:改进的绿色函数方法.

Feiping Nie, Yitao Song, Wei Chang

    IEEE transactions on pattern analysis and machine intelligence
    |March 3, 2025
    PubMed
    概括
    此摘要是机器生成的。

    基于图形的半监督学习的绿色函数方法在大,稀疏的图形上是不稳定的. 一种基于优化的新方法提供了更好的稳定性和效率,特别是在加速技术中.

    更多相关视频

    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
    09:44

    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

    Published on: March 8, 2024

    4.6K
    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

    451

    相关实验视频

    Last Updated: May 24, 2025

    Generating Strictly Controlled Stimuli for Figure Recognition Experiments
    05:39

    Generating Strictly Controlled Stimuli for Figure Recognition Experiments

    Published on: March 18, 2019

    5.2K
    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
    09:44

    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

    Published on: March 8, 2024

    4.6K
    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

    451

    科学领域:

    • 机器学习 机器学习
    • 图形理论 图形理论
    • 数字分析 数字分析

    背景情况:

    • 基于图形的半监督学习使用绿色函数方法进行计算.
    • 绿色函数方法在大,稀疏的图表上表现出不稳定性和糟糕的性能.

    研究的目的:

    • 在大,稀疏的图形上分析绿色函数方法的局限性.
    • 提出一种新的,基于优化的方法来提高性能.
    • 提升拟议的方法,以提高效率和稳定性.

    主要方法:

    • 分析绿色函数方法在不同图形结构上的行为.
    • 开发一种新的基于优化的方法.
    • 加速技术的整合:高斯消除和定图.

    主要成果:

    • 拟议的方法提供了对完全连接图的绿色函数方法的替代解释.
    • 新方法解释了绿色函数方法在大型稀疏图表上的不稳定性.
    • 改进的方法在大型图表上显示了更高的效率,准确性和稳定性.

    结论:

    • 这种基于优化的新方法为基于图形的半监督学习提供了稳定高效的替代方案.
    • 加速技术显著提高了拟议方法的可扩展性.
    • 改进的格林函数方法对于大规模的图形分析是强大的.