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

213
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...
213
Survival Tree01:19

Survival Tree

117
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...
117
Associative Learning01:27

Associative Learning

452
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...
452
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.8K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
1.8K
Force Classification01:22

Force Classification

1.3K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.3K
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

636
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
636

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

Enhancing GNN learning with node augmentation.

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

Fahr's Syndrome in a Young Adult Male: A Case of Seizure and Cognitive Decline Secondary to Hypoparathyroidism.

Clinical case reports·2026
Same author

Impact of Peri-Procedural Red Blood Cell Transfusion on Clinical Outcomes in Transcatheter Aortic Valve Replacement: A Propensity-Matched Cohort Study.

Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions·2026
Same author

Comparing Efficacy and Safety of Different Anticoagulants in Cerebral Venous Thrombosis: A Systematic Review and Network Meta-Analysis.

Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis·2026
Same author

Safety and Efficacy of Tranexamic Acid in Hepatic Surgery: Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Journal of surgical oncology·2026
Same author

Invasive versus conservative strategy in older adults ≥70 years of age with non-ST-segment-elevation myocardial infarction: a GRADE-assessed systematic review and meta-analysis of randomized controlled trials with trial sequential analysis.

Future cardiology·2026

相关实验视频

Updated: Jul 24, 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

583

通过自我训练和对抗性学习之间的平衡来实现域自适应对象检测.

Muhammad Akhtar Munir, Muhammad Haris Khan, M Saquib Sarfraz

    IEEE transactions on pattern analysis and machine intelligence
    |July 4, 2023
    PubMed
    概括

    这项研究引入了一种新的深度学习方法,用于对象检测领域的适应. 通过利用预测不确定性,它提高了对齐性,并优于对具有挑战性的数据集的现有方法.

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 深度学习 (Deep Learning) 是一种深度学习.

    背景情况:

    • 深度学习对象检测器面临的挑战是将其推广到具有显著差异的新领域.
    • 当前的域调整方法经常使用图像或实例级对抗特征调整,这可能会受到背景噪音的阻碍,并且缺乏类特定的调整.
    • 类级对齐的伪标签受到噪音预测的阻碍,原因是域转移下的模型校准不佳.

    研究的目的:

    • 开发一种平衡对抗特征对齐和类级对齐的技术,以改善对象检测领域的适应.
    • 利用预测不确定性来指导适应过程,增强对新目标领域的概括性.

    主要方法:

    • 对类赋值和边界框预测的量化预测不确定性.
    • 利用低不确定性预测生成伪标签进行自我训练.
    • 采用高不确定性预测来生成对抗特征对齐的,专注于不确定的对象区域.

    主要成果:

    • 拟议的方法表明,在划分不确定的区域和伪标记某些区域之间产生协同效应,捕捉图像和实例级上下文.
    • 废弃性研究证实了该方法中的单个成分的影响和有效性.
    • 与最先进的方法相比,在五种多样化和具有挑战性的域名适应场景中实现了卓越的性能.

    更多相关视频

    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
    08:20

    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

    Published on: October 27, 2023

    1.5K
    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.0K

    相关实验视频

    Last Updated: Jul 24, 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

    583
    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
    08:20

    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

    Published on: October 27, 2023

    1.5K
    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.0K

    结论:

    • 利用预测不确定性为对象检测领域的适应提供了一个强大的战略.
    • 拟议的方法有效地解决了现有技术的局限性,通过整合类特定和实例级对齐.
    • 这种方法在面对领域转移时显著增强了模型概括能力.