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

Ordinal Level of Measurement00:55

Ordinal Level of Measurement

24.9K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
24.9K
Sampling Plans01:23

Sampling Plans

219
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...
219
Systematic Sampling Method01:17

Systematic Sampling Method

10.5K
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.
Systematic sampling is one of the simplest methods...
10.5K
Sampling Methods: Overview01:06

Sampling Methods: Overview

390
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...
390
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

300
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...
300
Stratified Sampling Method01:16

Stratified Sampling Method

12.1K
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. 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.
To choose a stratified sample, divide the population into groups called strata and then take a...
12.1K

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

Re-evaluation of the prognosis of patients with IgA nephropathy and mild proteinuria: a longitudinal study with unexpected results.

Inflammation research : official journal of the European Histamine Research Society ... [et al.]·2026
Same author

Corrigendum to "Optimization of a magnetic bead-based DNA extraction method combined with 12S rRNA barcoding for species traceability in fish oil products" [Food Chem. Mol. Sci. 12 (2026) 100408].

Food chemistry. Molecular sciences·2026
Same author

Goat gut microbiome as a reservoir for microorganism-encoded short peptides: regulation by host development age and nematode challenge.

NPJ biofilms and microbiomes·2026
Same author

Ganglion Impar Neurolysis Enhances the Short-term Efficacy of Pudendal Nerve Pulsed Radiofrequency Treatment for Pudendal Neuralgia: A Retrospective Cohort Study.

Pain physician·2026
Same author

Toll-Like Receptors in Diabetes: Immunometabolic Mechanisms and Emerging Precision Therapeutic Strategies.

Journal of inflammation research·2026
Same author

Nutritional restriction during late gestation disrupts placental transcriptional programs related to metabolism, transport, and immune function in a sheep model.

BMC genomics·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
Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

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

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

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

A Survey on Human-Centric Voice-Face Multimodal Learning.

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

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

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

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

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 26, 2025

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.2K

哪些样本应该首先学习:容易还是难?

Xiaoling Zhou, Ou Wu

    IEEE transactions on neural networks and learning systems
    |June 20, 2023
    PubMed
    概括
    此摘要是机器生成的。

    在机器学习中,确定最佳的训练样本顺序至关重要. 这项研究引入了一种灵活的权重方案 (FlexW),它适应数据的难度,优于固定的易先或硬先方法.

    更多相关视频

    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

    7.6K
    Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization
    05:35

    Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization

    Published on: April 19, 2017

    6.7K

    相关实验视频

    Last Updated: Jul 26, 2025

    Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
    08:56

    Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

    Published on: January 13, 2023

    2.2K
    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

    7.6K
    Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization
    05:35

    Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization

    Published on: April 19, 2017

    6.7K

    科学领域:

    • 机器学习 机器学习
    • 人工智能的人工智能
    • 数据科学数据科学数据科学

    背景情况:

    • 在机器学习中,对训练样本的不平等处理是常见的.
    • 现有的权重计划往往采用易先或难先的方法.
    • 对新任务的最佳样本优先级策略仍然是一个开放的问题.

    研究的目的:

    • 调查在新的学习任务中,是否应该优先考虑容易或困难的样本.
    • 提出一个灵活的权重方案,适应不同的数据难度分布.
    • 为"容易或困难"样本优先级困境提供全面的答案.

    主要方法:

    • 使用一般目标函数来导出最佳权重的理论分析.
    • 识别了四种不同的样本优先级模式:轻松第一,艰难第一,中等第一和两端第一.
    • 开发和实验验证灵活加权方案 (FlexW).

    主要成果:

    • 最佳的样本优先级模式取决于训练集的难度分布.
    • 除了传统优先级外,还确定了新的优先级模式 (中等优先级,两端优先级).
    • 在各种学习场景中,FlexW 证明了其有效性,并超过了现有的方法.

    结论:

    • 轻松学习和艰难学习之间的选择取决于任务,并受数据特征的影响.
    • 拟议的FlexW方案通过动态选择最佳优先级模式来提供适应性和最佳性能.
    • 这项研究为优化机器学习培训策略提供了宝贵的见解.