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

11.5K
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...
11.5K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

61
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
61
Scalar and Vector Triple Products01:06

Scalar and Vector Triple Products

2.2K
Two vectors can be multiplied using a scalar product or a vector product. The resultant of a scalar product is scalar, while with vector products, the resultant is a vector. These rules of the scalar or vector product between two vectors can be applied to multiple vectors to obtain meaningful combinations. The scalar triple product is the dot product of a vector with the cross product of two vectors.
The scalar triple product is the dot product of a vector with the cross product of two vectors....
2.2K
Aggregates Classification01:29

Aggregates Classification

289
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...
289
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

4.4K
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...
4.4K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

82
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
82

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

Variance-constrained multi-view ensemble broad network for imbalanced data.

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

Learning to Super-Resolve Face Images via Dual-Domain Multi-scale Feature Interaction.

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

Effectiveness of heterologous mRNA vaccine boosters during an Omicron wave of COVID-19: a cross-sectional study in Macao (China).

Journal of thoracic disease·2026
Same author

Fast BCIs: Leveraging Dual-Scale Time Windows with Test-Time Adaptation to Enhance Accuracy.

IEEE transactions on bio-medical engineering·2026
Same author

FRE-GAN : Full-resolution efficient convolutional generative adversarial network for retinal vessel segmentation.

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

Riemannian Acceleration for Sparse PCA With Separable Structure and Second-Order Information Exploration.

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

ASSR-Net: Anisotropic Structure-Aware and Spectrally Recalibrated Network for Hyperspectral Image Fusion.

IEEE transactions on neural networks and learning systems·2026
Same journal

PIMPC-GNN: Physics-Informed Multiphase Consensus Learning for Enhancing Imbalanced Node Classification in Graph Neural Networks.

IEEE transactions on neural networks and learning systems·2026
Same journal

Quantum Rényi α-Entropies for Graph Characterization.

IEEE transactions on neural networks and learning systems·2026
Same journal

LANet: A Lightweight and Accurate Balanced Network Based on State Space Models for Real-Time Semantic Segmentation.

IEEE transactions on neural networks and learning systems·2026
Same journal

MENDNet: Memory-Enhanced Dependency Network for Multistock Movement Prediction.

IEEE transactions on neural networks and learning systems·2026
Same journal

Temporal Mask-Embedding Learning and Query-Refined Head Network for Visual Tracking.

IEEE transactions on neural networks and learning systems·2026
查看所有相关文章

相关实验视频

Updated: May 10, 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

基于规模驱动的张量表征的多视图集群.

Chuanbin Zhang, Long Chen, Weiping Ding

    IEEE transactions on neural networks and learning systems
    |April 24, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了多视图聚类的新框架,将数据统一到多尺度特征中. 它有效地捕捉了从粗到细的集群形状,提高了不同数据类型的集群准确性.

    更多相关视频

    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    19.9K
    Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools
    07:58

    Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

    Published on: November 11, 2020

    5.9K

    相关实验视频

    Last Updated: May 10, 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
    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    19.9K
    Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools
    07:58

    Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

    Published on: November 11, 2020

    5.9K

    科学领域:

    • 数据科学数据科学数据科学
    • 机器学习 机器学习
    • 计算机视觉 计算机视觉

    背景情况:

    • 现实世界的数据通常具有层次结构,提供自然的多视角视角.
    • 现有的多视图集群方法通常会忽略这些层次结构,并专注于单一级别的关系.
    • 目前的算法是为原生多视图数据设计的,这限制了它们的适用性.

    研究的目的:

    • 为多视图聚类开发一个统一的框架,适用于各种数据类型,包括图像.
    • 提取和利用多尺度特征以提高集群性能.
    • 为了解决忽视数据层次的现有方法的局限性.

    主要方法:

    • 一种新的以规模为导向的预处理方法统一了跨数据类型的特征结构.
    • 在多个尺度上探索本地样本关系,以捕捉全球和本地细节.
    • 从不同的尺度学习视图特定的分区,并通过张量低级别表示引出共识特征.

    主要成果:

    • 拟议的方法有效地捕获从粗到细粒度的精确集群形状.
    • 实验性比较显示了在多视图聚类和图像分割中与现有算法相比的最先进 (SOTA) 性能.
    • 该框架在各种数据集中展示了多功能性.

    结论:

    • 开发的框架通过整合层次和多尺度特征提取,为多视图聚类提供了全面的方法.
    • 这种方法提供了一种统一的方式来聚合各种数据类型,增强对复杂数据结构的理解.
    • 该方法通过利用数据层次结构成功克服了传统方法的局限性.