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

Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.3K
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...
1.3K
Probability Distributions01:32

Probability Distributions

11.8K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
11.8K
Classification of Systems-I01:26

Classification of Systems-I

552
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:
552
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.8K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.8K
Classification of Systems-II01:31

Classification of Systems-II

458
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,
458
Aggregates Classification01:29

Aggregates Classification

970
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...
970

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

Multisource Machine Learning Model for Detecting Referral-Warranted Retinopathy of Prematurity.

Ophthalmology science·2026
Same author

Liposomes containing histidine overcome poly ADP-ribose polymerase inhibitor resistance.

Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy·2026
Same author

The diapause-like colorectal cancer cells induced by SMC4 attenuation are characterized by low proliferation and chemotherapy insensitivity.

Cell metabolism·2026
Same author

Deciphering the immunological landscape of HR + metastatic breast cancer: insights from single-cell transcriptomics.

Human cell·2026
Same author

Multi-granularity transformer contrastive learning and feature reconstruction for prediction of disease-related miRNAs.

BMC bioinformatics·2026
Same author

Towards a general-purpose foundation model for functional MRI analysis.

Nature biomedical engineering·2026
Same journal

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

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

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

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

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

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

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

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

相关实验视频

Updated: Jan 17, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.5K

通过已知的联合分布匹配和未知的分类风险重构来进行开放集域调整.

Sentao Chen, Ping Xuan, Lifang He

    IEEE transactions on neural networks and learning systems
    |January 14, 2026
    PubMed
    概括
    此摘要是机器生成的。

    开放集域调整 (OSDA) 是通过新的KMUR方法解决的,该方法与已知的分布相匹配,并为更好的机器学习模型重新制定未知的风险. 这种方法通过解决源-目标差异和未知的类挑战来提高分类准确性.

    相关实验视频

    Last Updated: Jan 17, 2026

    An R-Based Landscape Validation of a Competing Risk Model
    05:37

    An R-Based Landscape Validation of a Competing Risk Model

    Published on: September 16, 2022

    2.5K

    科学领域:

    • 机器学习 机器学习
    • 计算机视觉 计算机视觉
    • 统计学学习 统计学学习

    背景情况:

    • 开放集域调整 (OSDA) 解决了机器学习的挑战,标记的源数据和未标记的目标数据包含已知的和未知的类.
    • 现有的OSDA方法在源-目标分布差异和估计未知类的风险方面存在困难.
    • 在OSDA中,两个关键的挑战是已知类的源和目标联合分布之间的差异,以及未知类的分类风险估计.

    研究的目的:

    • 引入一种以原则为基础的方法,即已知的联合分布匹配和未知的分类风险重构 (KMUR),以解决关键的OSDA挑战.
    • 通过匹配已知的源和目标联合分布来减少源-目标分布差异.
    • 使用未标记的源和目标数据重新制定和估计目标未知分类风险.

    主要方法:

    • 采用交叉用于分类损失和三角歧视 (TD) 距离用于联合分布匹配.
    • 开发最小平方TD估计 (LSTDE) 以通过将其作为最小平方分类问题来估计TD距离.
    • 训练神经网络以尽量减少估计的目标分类风险和TD距离,以获得有效的OSDA.

    主要成果:

    • KMUR有效地减少了源-目标分布差异,并改善了未知的类风险估计.
    • 在基准和现实世界数据集上的实验结果验证了拟议方法的有效性.
    • 该方法在开放式域调整任务中显示了显著的改进.

    结论:

    • KMUR提供了一个强大的框架来应对OSDA的双重挑战.
    • 该方法提供了一种原则性的方法来处理域调整中的分布转移和未知类.
    • 该研究贡献了一种新的方法,对现实世界的机器学习应用有实际意义.