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
The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

7.2K
In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
7.2K
Associative Learning01:27

Associative Learning

243
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...
243
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
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

231
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
231
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

65
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,...
65

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

Identification and Functional Analysis of Tomato MicroRNAs in the Biocontrol Bacterium <i>Pseudomonas putida</i> Induced Plant Resistance to <i>Meloidogyne incognita</i>.

Phytopathology·2022
Same author

Insights From a Large-Scale Whole-Genome Sequencing Study of Systolic Blood Pressure, Diastolic Blood Pressure, and Hypertension.

Hypertension (Dallas, Tex. : 1979)·2022
Same author

Phase II trial of efficacy, safety and biomarker analysis of sintilimab plus anlotinib for patients with recurrent or advanced endometrial cancer.

Journal for immunotherapy of cancer·2022
Same author

Antidepressant-like effect of ginsenoside Rb1 on potentiating synaptic plasticity via the miR-134-mediated BDNF signaling pathway in a mouse model of chronic stress-induced depression.

Journal of ginseng research·2022
Same author

Multigraph Fusion for Dynamic Graph Convolutional Network.

IEEE transactions on neural networks and learning systems·2022
Same author

Genome-wide identification of small interfering RNAs from sRNA libraries constructed from soybean cyst nematode resistant and susceptible cultivars.

Gene·2022

相关实验视频

Updated: May 12, 2025

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

432

为可扩展的不完整多视图集群进行双相关指导的学习.

Wen-Jue He, Zheng Zhang, Xiaofeng Zhu

    IEEE transactions on neural networks and learning systems
    |May 6, 2025
    PubMed
    概括

    本研究介绍了不完整的多视图集群 (IMC) 的双相关指导学习 (DCGA). DCGA提高了质量和连贯性,改善了对异质数据的聚类性能.

    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:

    • 不完整的多视图集群 (IMC) 面临着从异质数据中学习有效表示的挑战.
    • 现有的基于的IMC方法与不稳定的生成和跨视图不平衡的能力作斗争.

    研究的目的:

    • 为可扩展和高效的IMC提出一种新的双相关指导学习 (DCGA) 方法.
    • 解决现有IMC模型中基生成和视界基连贯性的缺陷.

    主要方法:

    • 通过整合视图内和视图间的相关性,DCGA学习了信息空间.
    • 一个为瓶 (A3B) 策略稳定了使用信息瓶 (IB) 原则的内视空间.
    • 一个信息约束 (IAC) 将空间在不同的视图中对齐.

    主要成果:

    • 与7个数据集中的11种最先进的IMC方法相比,DCGA显示出更高的有效性和效率.
    • 该方法成功地产生了稳定和信息性的,提高了聚类准确性.
    • DCGA有效地处理数据冗余,并保留每个视图的基本信息.

    结论:

    • DCGA为不完整的多视图集群提供了强大的和可扩展的解决方案.

    更多相关视频

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
    12:39

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

    Published on: January 18, 2020

    7.5K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    320

    相关实验视频

    Last Updated: May 12, 2025

    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

    432
    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
    12:39

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

    Published on: January 18, 2020

    7.5K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    320
  • 拟议的方法通过有效利用多视图相关性来增强表示学习.
  • DCGA代表了IMC无监督学习的重大进步.