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

Cognitive Learning01:21

Cognitive Learning

243
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
243
Language and Cognition01:27

Language and Cognition

346
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
346
Structural Classification of Joints01:20

Structural Classification of Joints

3.4K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.4K
Associative Learning01:27

Associative Learning

375
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...
375

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

Integrative analysis of mortality risk in SFTS using machine learning and genetic approaches.

mSphere·2026
Same author

Future trends in 3D-printed oleogels based on personalized nutrition and applications.

Critical reviews in food science and nutrition·2026
Same author

Adjunctive Intra-Arterial Alteplase After Near-Complete or Complete Reperfusion in Acute Ischemic Stroke: A Post Hoc Analysis of the PEARL Trial.

International journal of stroke : official journal of the International Stroke Society·2026
Same author

Effectiveness of Immediate Angioplasty or Stenting on Functional Outcomes in Acute Ischemic Stroke With Severe Intracranial Stenosis.

Neurology·2026
Same author

Intra-Arterial Alteplase After Successful Endovascular Reperfusion in Acute Stroke: The PEARL Randomized Clinical Trial.

JAMA·2026
Same author

Increased polysaccharide molecular interaction improves foam template oil absorption behavior of egg white protein oleogel.

Food chemistry·2026
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

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

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

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

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

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

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

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

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

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

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

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

相关实验视频

Updated: Jul 6, 2025

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.7K

以认知驱动的结构预先对依赖实例的标签过渡矩阵估计.

Ruiheng Zhang, Zhe Cao, Shuo Yang

    IEEE transactions on neural networks and learning systems
    |January 8, 2024
    PubMed
    概括
    此摘要是机器生成的。

    本研究引入了一个结构化过渡矩阵网络 (STMN),通过解决标签噪声来改善机器学习. 该方法使用人类认知来提高估计标签过渡矩阵的准确性和效率.

    更多相关视频

    Creating Objects and Object Categories for Studying Perception and Perceptual Learning
    14:38

    Creating Objects and Object Categories for Studying Perception and Perceptual Learning

    Published on: November 2, 2012

    11.8K
    DiOLISTIC Labeling of Neurons from Rodent and Non-human Primate Brain Slices
    09:21

    DiOLISTIC Labeling of Neurons from Rodent and Non-human Primate Brain Slices

    Published on: July 6, 2010

    24.0K

    相关实验视频

    Last Updated: Jul 6, 2025

    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
    08:45

    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

    Published on: October 24, 2012

    14.7K
    Creating Objects and Object Categories for Studying Perception and Perceptual Learning
    14:38

    Creating Objects and Object Categories for Studying Perception and Perceptual Learning

    Published on: November 2, 2012

    11.8K
    DiOLISTIC Labeling of Neurons from Rodent and Non-human Primate Brain Slices
    09:21

    DiOLISTIC Labeling of Neurons from Rodent and Non-human Primate Brain Slices

    Published on: July 6, 2010

    24.0K

    科学领域:

    • 机器学习 机器学习
    • 计算机科学 计算机科学

    背景情况:

    • 标签噪声是机器学习的一个重大挑战.
    • 取决于实例的噪音需要复杂的缓解方法.
    • 对于标签过渡矩阵估计的现有深度学习方法的效率很低.

    研究的目的:

    • 为了解决当前标签过渡矩阵估计方法的低效和不准确性.
    • 引入一种结合人类认知的新方法,以改善标签噪声减轻.
    • 开发一个结构化过渡矩阵网络 (STMN) 以更有效地处理标签噪声.

    主要方法:

    • 开发了一个结构化过渡矩阵网络 (STMN),利用对抗式学习过程.
    • 来自人类认知的综合结构信息,以指导标签过渡矩阵的估计.
    • 在过渡矩阵中采用稀疏估计方法,专注于有效的类过渡.

    主要成果:

    • 通过节省过渡矩阵,STMN方法证明了更好的估计效率.
    • 通过结合人类的认知先验,可以实现更高的估计准确度.
    • 拟议的方法有效地将噪音标签转换为真正的标签,在合成和现实数据集上表现优于最先进的方法.

    结论:

    • 结构化过渡矩阵网络 (STMN) 为缓解机器学习中的标签噪声提供了更有效,更准确的解决方案.
    • 将人类认知纳入标签过渡矩阵估计过程是改善模型稳定性的有希望的方向.
    • 该方法的有效性通过全面的比较来验证,突出其在处理依赖实例的标签噪声方面的优越性.