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

Weighted Mean00:57

Weighted Mean

5.2K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
5.2K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
Introduction to Learning01:18

Introduction to Learning

472
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
472
Survival Tree01:19

Survival Tree

110
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...
110
Associative Learning01:27

Associative Learning

444
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...
444
Aggregates Classification01:29

Aggregates Classification

345
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...
345

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

Development and Validation of an Explainable Machine Learning Model for Predicting Repeat Catheter Ablation for Atrial Fibrillation: A Single-Center Retrospective Cohort Study.

International journal of general medicine·2026
Same author

<i>SGK1</i> Is Upregulated in Retained Placenta and Mediates Estradiol Effects in Bovine Endometrial Cells.

Cells·2026
Same author

Grounding surgical action triplets with instrument instance segmentation: a dataset and target-aware fusion approach.

International journal of computer assisted radiology and surgery·2026
Same author

Enhanced antimicrobial properties of POSS-modified composite resin with chlorhexidine-loaded bioactive glass.

Journal of dentistry·2026
Same author

Correction: A metabonomic study to explore potential markers of asymptomatic hyperuricemia and acute gouty arthritis.

Journal of orthopaedic surgery and research·2026
Same author

Age-dependent trade-offs between tubular esophagogastric and double-tract anastomosis after laparoscopic proximal gastrectomy: a retrospective cohort study.

Surgical endoscopy·2026
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

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

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

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

IGFD-Net: Illumination-guided frequency decoupling for polarization image fusion.

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

Multiple-Strategies dung beetle optimizer and its applications in engineering optimization and bankruptcy prediction.

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

Aggregating global-scale pixel-wise forgery cues within a graph.

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

Finite-Time intermittent control for secure synchronization of Neutral-Type stochastic delayed neural networks under aperiodic DoS attacks.

Neural networks : the official journal of the International Neural Network Society·2026
查看所有相关文章

相关实验视频

Updated: Jul 19, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K

通过加权损失进行类不平衡的互补标签学习.

Meng Wei1, Yong Zhou1, Zhongnian Li1

  • 1School of Computer Science & Technology, China University of Mining and Technology, Xuzhou, China.

Neural networks : the official journal of the International Neural Network Society
|August 16, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了加权补充标签学习 (WCLL),以解决补充标签学习 (CLL) 中的类不平衡,以提高现实数据集的分类准确性.

关键词:
一个不平衡的阶级.补充标签是补充的标签.多个类别的分类分类.缺乏监督的学习学习.

更多相关视频

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

570
Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

6.6K

相关实验视频

Last Updated: Jul 19, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K
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

570
Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

6.6K

科学领域:

  • 机器学习 机器学习
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 补充标签学习 (CLL) 是一种监督较弱的分类方法.
  • 现实世界数据集经常表现出类不平衡,降低了CLL的性能.
  • 现有的CLL方法无法解决类不平衡问题.

研究的目的:

  • 提出一种新的学习问题设置,在多类分类中使用类不平衡的互补标签.
  • 引入加权补充标签学习 (WCLL) 来应对这一挑战.

主要方法:

  • 开发了一种新的加权补充标签学习 (WCLL) 方法.
  • 为不平衡的补充标签建了一个加权的经验风险最小化损失模型.
  • 导出了一个估计误差,限制了理论保证.

主要成果:

  • WCLL在基准和现实世界数据集上取得了显著的改进.
  • 该方法有效地处理多类不平衡场景.
  • 与最先进的方法相比,实现了更高的性能.

结论:

  • 在补充标签学习中,WCLL成功地解决了阶级不平衡的挑战.
  • 拟议的方法提供了一个强大的解决方案,用于对数据不平衡的低监督分类.
  • WCLL既提供了实际的性能提升,也提供了理论验证.