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

Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Classification of Systems-I01:26

Classification of Systems-I

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:
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Aggregates Classification01:29

Aggregates Classification

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...

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

Association of Pre-Transplant Tyrosine Kinase Inhibitor Therapy With Tumor Response and Survival in Hepatocellular Carcinoma: A 10-Year Retrospective Cohort Study of 427 Patients.

World journal of surgery·2026
Same author

Multimodal graph neural network with large language models for node and link prediction.

Frontiers in artificial intelligence·2026
Same author

HO-1 alleviates lipopolysaccharide-induced acute lung injury in mice by downregulating TFE3 expression and nuclear translocation and suppressing Golgi stress response.

International immunopharmacology·2026
Same author

Perioperative tislelizumab for early-stage hepatocellular carcinoma: A phase II trial with integrated tumour microenvironment profiling and predictive modeling.

Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy·2026
Same author

Molecular-Anion Interfacial Engineering for Solid Electrolyte Interphase to Obtain High-Performance Aqueous Zinc-Ion Batteries.

Chemistry (Weinheim an der Bergstrasse, Germany)·2026
Same author

Exploring the Role of Skin Microbiota in Autoimmune Skin Diseases from a Bidirectional Mendelian Randomization Perspective.

Clinical, cosmetic and investigational dermatology·2026

相关实验视频

Updated: Jul 20, 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

一个分层混合专家框架为少数标记的节点分类.

Yimeng Wang1, Zhiyao Yang1, Xiangjiu Che1

  • 1College of Computer Science and Technology, Jilin University, Changchun 130012, Jilin, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of MOE, Jilin University, Changchun 130012, Jilin, China.

Neural networks : the official journal of the International Neural Network Society
|March 29, 2025
PubMed
概括

本研究介绍了分层混合专家 (HMoE) 通过减少过度拟合和增强特征表示来改进少数标记节点分类 (FLNC). 在有限的标签的图形数据上,HMoE实现了更好的性能.

关键词:
数据增强数据增强很少有标记的图形.专家的混合.节点的分类 节点的分类

相关实验视频

Last Updated: Jul 20, 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

科学领域:

  • 图形神经网络的神经网络
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 由于训练节点极为有限,少数标记节点分类 (FLNC) 具有挑战性.
  • 图形神经网络 (GNN) 在FLNC中扎着特征的融合.
  • 专家混合 (MoE) 在直接应用于FLNC时可能会过度.

研究的目的:

  • 提出一个新的框架,专家层次混合 (HMoE),以解决FLNC的过度配合和特征融合.
  • 为了增强具有有限标签的图形数据的特征表示.
  • 在数据稀缺的情况下提高节点分类任务的性能.

主要方法:

  • 实施了三种数据增强技术,以丰富输入特征并减轻过度拟合.
  • 设计了一个分层的专家混合编码器,用于独特和共享的特征提取.
  • 整合了一个辅助任务,具有梯度反转机制,以提高特征表示能力.

主要成果:

  • 拟议的HMoE框架与基线方法相比显示出更高的性能.
  • 在六个不同的数据集中实现了1.2%的平均性能改善.
  • 有效地减少了FLNC的过度装配和改进的特征表示.

结论:

  • HMoE框架为少数标记节点分类提供了一个强大的解决方案.
  • 层次专家,数据增强和辅助任务的结合提高了GNN在低标签场景中的性能.
  • 这种方法提升了GNN在有限的监督下进行现实世界的图形数据分析的能力.