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

Sampling Methods: Overview01:06

Sampling Methods: Overview

249
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
249
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

188
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
188
Sampling Distribution01:12

Sampling Distribution

12.0K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
12.0K
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

165
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
165
Random Sampling Method01:09

Random Sampling Method

10.9K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
10.9K
Sampling Plans01:23

Sampling Plans

157
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
157

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

Paishi granule inhibits calcium oxalate-induced oxidative stress kidney injury after gut microbiota transformation: a multi-omics analysis combined in vivo and in vitro study.

Chinese medicine·2026
Same author

LLM-Driven Regime-Adaptive strategy synthesis for Polymorphic Network routing.

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

Identification and Functional Characterization of a Novel De Novo <i>SATB1</i> Frameshift Variant in a Patient with Epilepsy-Dominant Neurodevelopmental Disorders.

Genes·2026
Same author

Iterative design leads to a smart probe capable of quantifying autophagic flux with switchable fluorescence via engaging MAP1LC3/LC3.

Autophagy·2026
Same author

Compound heterozygous variants in IRAK4 cause IRAK4 deficiency characterized by recurrent bacterial infections, brain calcification, and severe epilepsy.

Gene·2026
Same author

Evaluating data extraction error by a large language model from randomised controlled trials: a large-scale empirical study.

BMJ evidence-based medicine·2026
Same journal

Q-learning based asynchronous Boolean control networks stabilization with data loss.

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

New results on prescribed-time synchronization of complex networks via intermittent control.

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

Variance-constrained multi-view ensemble broad network for imbalanced data.

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

Dynamic analysis and reliable mechanical optimization application of ring HNN effected with a memristive neuron.

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

DAFF-Net: A detection and search method for small-scale low surface brightness galaxies.

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

Quasi-synchronization for complex networks with hybrid pinning intermittent control.

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: May 16, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.4K

使用带有标签噪声的样本来实现强大的持续学习.

Hongyi Nie1, Shiqi Fan2, Yang Liu3

  • 1School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China; Shenzhen Research Institute, Northwestern Polytechnical University, Shenzhen, 518057, Guangdong, China.

Neural networks : the official journal of the International Neural Network Society
|April 4, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的方法,有效地利用标签噪声在持续的机器学习,解决标签转移的挑战. 拟议的转移适应噪声利用 (SANU) 方法通过重新注释噪声样本以提高性能来提高模型的稳定性.

关键词:
不断的机器学习.标签 噪音学习 标签标签转移 标签转移转移适应噪声利用的使用.

更多相关视频

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

8.9K
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

6.5K

相关实验视频

Last Updated: May 16, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.4K
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

8.9K
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

6.5K

科学领域:

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

背景情况:

  • 利用带有标签噪声的样本可以提高模型的稳定性.
  • 现有的方法假设一个一致的标签空间,这是由于标签转移而失败的持续学习.
  • 持续学习环境中的标签转换可能会加剧噪音并降低性能.

研究的目的:

  • 为了解决持续机器学习现有方法的局限性,用标签噪音.
  • 提出一种新的方法,即转移适应性噪声利用 (SANU),用于将噪声样本转化为可用于持续学习的可用数据.
  • 为了减轻标签转移问题,并在动态学习环境中增强模型性能.

主要方法:

  • SANU采用源检测机制来识别噪音样品的正确标签空间.
  • 使用元知识表示模块来改善检测过程的概括性.
  • 使用标签猜测和生成策略重新注释噪音样本,以适应标签转移.

主要成果:

  • SANU有效地减轻了持续学习中的标签转移问题.
  • 该方法通过利用重新注释的噪音样本,显著提高了模型性能.
  • 三个持续学习数据集的实验结果验证了SANU的有效性.

结论:

  • SANU成功地将噪音数据转化为用于持续学习的有价值的培训输入.
  • 拟议的方法为在标签转移条件下处理标签噪声提供了可靠的解决方案.
  • 这项工作推动了在动态机器学习场景中利用噪音数据的发展.