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

Cluster Sampling Method01:20

Cluster Sampling Method

11.6K
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...
11.6K
Survival Tree01:19

Survival Tree

48
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...
48
Sampling Plans01:23

Sampling Plans

162
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
162
Aggregates Classification01:29

Aggregates Classification

295
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...
295
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

211
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
211
Classification of Systems-II01:31

Classification of Systems-II

132
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,
132

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

Gut microbiota-derived short-chain fatty acids and kidney diseases.

Drug design, development and therapy·2017
Same author

Overexpression of HHEX in Acute Myeloid Leukemia with t(8;21)(q22;q22) Translocation.

Annals of clinical and laboratory science·2017
Same author

C5aR1 promotes acute pyelonephritis induced by uropathogenic E. coli.

JCI insight·2017
Same author

ANGPTL8 negatively regulates NF-κB activation by facilitating selective autophagic degradation of IKKγ.

Nature communications·2017
Same author

Transient Receptor Potential Melastatin 2 Negatively Regulates LPS-ATP-Induced Caspase-1-Dependent Pyroptosis of Bone Marrow-Derived Macrophage by Modulating ROS Production.

BioMed research international·2017
Same author

Experimental validation of a subject-specific maximum endurance time model.

Ergonomics·2017

相关实验视频

Updated: May 21, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K

一种新的自我监督图表集群方法,具有可靠的半监督.

Weijia Lu1, Min Wang2, Yun Yu3

  • 1Science and Technology Department, Affiliated Hospital of Nantong University, Nantong, Jiangsu, 226001, China; Jianghai Hospital of Nantong Sutong Science and Technology Park, Nantong, Jiangsu, 226001, China.

Neural networks : the official journal of the International Neural Network Society
|March 22, 2025
PubMed
概括

这项研究引入了一种新的自主监督图表集群模型 (SSGC-RSS) 来解决图表数据中的噪音和稀疏性. 该模型通过将可靠的半监督与深度学习技术相结合,提高了复杂数据集的集群精度.

关键词:
深度集群是指深度集群.格拉普 (GRAPH) 卷积网络 网络自主监督学习学习半监督的学习学习.

更多相关视频

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
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.2K

相关实验视频

Last Updated: May 21, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K
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
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.2K

科学领域:

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

背景情况:

  • 深度聚类在复杂数据方面表现出色,但在图形数据噪声和稀疏性方面却很困难.
  • 图形数据中的噪音和稀疏性阻碍了特征提取,并降低了聚类性能.

研究的目的:

  • 提出一种基于可靠半监督 (SSGC-RSS) 的自主监督图表集群模型.
  • 在深度图表集群中解决噪音和稀疏性的挑战.

主要方法:

  • 开发了一种双解码器图形自编码器,在上游组件中进行了联合集群优化.
  • 在下游组件中实施了一种半监督的图形注意力编码网络,使用可靠的样本和伪标签.
  • 该模型生成集群中心和伪标签,以减轻稀疏性和减少噪音干扰.

主要成果:

  • 在基准图形数据集上,SSGC-RSS显示了与现有方法相比显著的性能改进.
  • 在Cora上实现了0.9%的精度增加,在Citeseer上2.0%,在Pubmed上5.6%.
  • 该模型有效地处理复杂图形数据集群中的噪声和稀疏性.

结论:

  • SSGC-RSS 证明了它对于有噪音和稀疏数据的深度图集群任务的有效性和优越性.
  • 拟议的模型提供了一个强大的解决方案,用于增强对复杂图形结构的无监督学习.