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

Observational Learning01:12

Observational Learning

163
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
163
Aggregates Classification01:29

Aggregates Classification

317
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...
317
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

4.9K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
4.9K
Multiple Comparison Tests01:13

Multiple Comparison Tests

3.9K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
3.9K
Associative Learning01:27

Associative Learning

333
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...
333
Cluster Sampling Method01:20

Cluster Sampling Method

11.9K
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...
11.9K

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

Impacts of In Situ Wheat Straw Incorporation Methods on Cadmium Behavior in Soil-Rice Systems.

Foods (Basel, Switzerland)·2026
Same author

Clinical spectrum and treatment of thyroid lymphoma: results of a cohort study of 61 patients.

Frontiers in oncology·2026
Same author

The relationship of rice yield and quality with the utilization of temperature and light resources in regions at different altitudes.

Frontiers in plant science·2026
Same author

Electric polarization modulation through continuous phase regulation of the KNbO<sub>3</sub> nanocrystals at room temperature.

Nanoscale·2026
Same author

FTO Regulates Porcine Adipogenesis and Postnatal Survival of Cloned Embryos.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology·2026
Same author

Charting the unseen: 3D mapping of latent prostate cancer infers its likely origin and growth patterns.

International urology and nephrology·2026
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jun 23, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K

通过对比的双重学习进行视图驱动的多视图聚类.

Shengcheng Liu1, Changming Zhu1, Zishi Li1

  • 1Information Engineering College, Shanghai Maritime University, Shanghai 201306, China.

Entropy (Basel, Switzerland)
|June 26, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的深度学习方法,用于多视图聚类,平衡信息的一致性和多样性. 视图驱动的对比双重学习方法通过对齐特征和集群分配来提高集群性能.

关键词:
相反的学习学习学习.深度学习是一种深度学习.多视图聚类多视图聚类.

更多相关视频

A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

10.9K
Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

322

相关实验视频

Last Updated: Jun 23, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K
A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

10.9K
Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

322

科学领域:

  • 计算机科学 计算机科学
  • 机器学习 机器学习
  • 数据挖掘 数据挖掘

背景情况:

  • 多视图集群旨在利用来自多个数据源的信息.
  • 现有的深度学习方法难以平衡观点之间的一致性和多样性.
  • 需要采用统一的方法来有效地整合这两个方面,以改善集群.

研究的目的:

  • 为多视图集群提出一种新的深度学习方法,有效平衡一致性和多样性.
  • 通过整合视图驱动信息和双重对比学习来增强特征学习和聚类准确性.
  • 解决当前方法的局限性,这些方法过度强调一致性或多样性.

主要方法:

  • 开发了一种视图驱动的多视图集群 (VMC-CD) 方法.
  • 采用以观点为导向的策略,将其他观点的信息纳入,促进多样性.
  • 实施双重对比学习以对齐功能和跨视图的集群结果.

主要成果:

  • 与最先进的方法相比,VMC-CD方法显示出更高的性能.
  • 三个数据集的实验结果验证了拟议方法的有效性.
  • 该方法成功地平衡了一致性和多样性,以获得更好的集群结果.

结论:

  • 拟议的VMC-CD方法为多视图集群提供了有效的解决方案.
  • 双重对比学习和视觉驱动方法显著提高了集群质量.
  • 这项工作通过解决信息一致性和多样性之间的关键平衡,推进了深度多视角聚类.