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

Reason and Intuition01:37

Reason and Intuition

6.4K
The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
6.4K
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
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

32.4K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
32.4K
Classification of Systems-II01:31

Classification of Systems-II

140
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
140
Classification of Systems-I01:26

Classification of Systems-I

179
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
179
Fundamental Attribution Error01:14

Fundamental Attribution Error

12.8K
According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
12.8K

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

Design and implementation of an automated patient-care dashboard to provide individualized patient care data and quality metrics to emergency medicine residents.

AEM education and training·2025
Same author

Revisiting Ultrasound of Fetal Abdominal Cysts; the Common, the Uncommon and the Rare: A Pictorial Review.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine·2025
Same author

Navigating Entire Collecting System During Supine Percutaneous Nephrolithotomy: Is Rigid Nephroscopy Enough? A Prospective Study by International Alliance of Urolithiasis Supine Percutaneous Nephrolithotomy Working Group.

Journal of laparoendoscopic & advanced surgical techniques. Part A·2024
Same author

Understanding the study of 21-year follow-up results of the Rotterdam section of the European Randomized Study of Screening for Prostate Cancer.

World journal of urology·2023
Same author

Interpreting vision and language generative models with semantic visual priors.

Frontiers in artificial intelligence·2023
Same author

False discovery rate in laser studies.

World journal of urology·2023
Same journal

Structural impact of non-IID heterogeneity on federated behavioral anomaly detection in IoT and IoMT systems.

Frontiers in artificial intelligence·2026
Same journal

DiscoVerse: multi-agent pharmaceutical co-scientist for traceable drug discovery and reverse translation.

Frontiers in artificial intelligence·2026
Same journal

EEG-based cognition-aware task classification and scheduling using enhanced fuzzy transition modeling.

Frontiers in artificial intelligence·2026
Same journal

Autofluorescence and deep learning in early disease detection: biological foundations, clinical applications, and future directions.

Frontiers in artificial intelligence·2026
Same journal

Legal document summarization: a short review.

Frontiers in artificial intelligence·2026
Same journal

Generative AI adoption and its impact on teachers' self-efficacy and instructional confidence in Ghana.

Frontiers in artificial intelligence·2026
查看所有相关文章

相关实验视频

Updated: Jun 24, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

544

人类注释的理由和可解释的文本分类:一项调查调查.

Elize Herrewijnen1,2, Dong Nguyen1, Floris Bex1,3

  • 1Department of Information & Computing Sciences, Utrecht University, Utrecht, Netherlands.

Frontiers in artificial intelligence
|June 10, 2024
PubMed
概括
此摘要是机器生成的。

标注器的理由,或对数据标签的解释,提高机器学习模型的质量. 这些由人类产生的洞察力也有助于开发人工智能解释.

关键词:
标注者推理的理由数据收集数据收集数据收集可解释的人工智能人类注释的理性理由.机器学习是机器学习.自然语言解释自然语言解释协议的理由 协议的理由文字分类 文本分类 文本分类

更多相关视频

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

15.9K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

692

相关实验视频

Last Updated: Jun 24, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

544
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

15.9K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

692

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 自然语言处理自然语言处理.

背景情况:

  • 数据注释对于训练机器学习模型至关重要.
  • 了解注释背后的推理可以提高模型性能和可解释性.
  • 当前的方法往往缺乏对分类决定的详细解释.

研究的目的:

  • 调查收集和使用注释器理性的方法.
  • 突出机器学习中人类注释理性的好处.
  • 探索理性推理在推进可解释的人工智能的作用.

主要方法:

  • 对涉及注释器理性的研究的文献综述.
  • 对理性理由对数据质量影响的分析.
  • 检查在模型开发和评估中使用推理的方法.

主要成果:

  • 人类注释的理性解释显著提高了数据质量.
  • 理性是增强机器学习模型的宝贵资源.
  • 标注者推理启发了模型生成的推理的创建和评估.

结论:

  • 对高质量的数据和改进的人工智能模型来说,注释器理性是必不可少的.
  • 研究理性是开发更容易解释的人工智能的关键.
  • 未来的工作应该集中在利用人类和机器学习的合理性.