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

266
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...
266
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
Stratified Sampling Method01:16

Stratified Sampling Method

11.7K
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...
11.7K
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

176
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...
176
Downsampling01:20

Downsampling

121
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
121
Bootstrapping01:24

Bootstrapping

575
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
575

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

Identification of the gene cluster for the dithiolopyrrolone antibiotic holomycin in Streptomyces clavuligerus.

Proceedings of the National Academy of Sciences of the United States of America·2010
Same author

Safety evaluation of tea (Camellia sinensis (L.) O. Kuntze) flower extract: assessment of mutagenicity, and acute and subchronic toxicity in rats.

Journal of ethnopharmacology·2010
Same author

Influences of soil properties and leaching on nickel toxicity to barley root elongation.

Ecotoxicology and environmental safety·2010
Same author

Effects of CO2 insufflation on cerebrum during endoscopic thyroidectomy in a porcine model.

Surgical endoscopy·2010
Same author

Plants' use of different nitrogen forms in response to crude oil contamination.

Environmental pollution (Barking, Essex : 1987)·2010
Same author

Overexpression of p35 in Min6 pancreatic beta cells induces a stressed neuron-like apoptosis.

Journal of the neurological sciences·2010
Same journal

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

TraGraph-GS: Trajectory Graph-based Gaussian Splatting for Arbitrary Large-Scale Scene Rendering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
查看所有相关文章

相关实验视频

Updated: May 24, 2025

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

8.7K

为平衡的行人属性识别进行异质特征重新采样.

Yibo Zhou, Bo Li, Hai-Miao Hu

    IEEE transactions on pattern analysis and machine intelligence
    |March 3, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究引入了用于行人属性识别 (PAR) 的新方法,以解决数据不平衡的问题. 功能重新抽样分离学习 (FRDL) 和梯度导向增强翻译 (GOAT) 提高了对现实数据集的性能.

    更多相关视频

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
    08:27

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

    Published on: January 5, 2024

    938
    Trajectory Data Analyses for Pedestrian Space-time Activity Study
    16:14

    Trajectory Data Analyses for Pedestrian Space-time Activity Study

    Published on: February 25, 2013

    13.5K

    相关实验视频

    Last Updated: May 24, 2025

    Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
    10:52

    Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

    Published on: April 13, 2016

    8.7K
    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
    08:27

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

    Published on: January 5, 2024

    938
    Trajectory Data Analyses for Pedestrian Space-time Activity Study
    16:14

    Trajectory Data Analyses for Pedestrian Space-time Activity Study

    Published on: February 25, 2013

    13.5K

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 人工智能的人工智能

    背景情况:

    • 步行者属性识别 (PAR) 涵盖了各种描述,从生物识别到配件.
    • 现实世界数据集通常具有代表性不足的属性,导致显著的数据不平衡.
    • 现有的 PAR 方法可能过于专业化,无法解决数据不平衡的根本挑战.

    研究的目的:

    • 在严重的数据不平衡下,将PAR重新定义为多标签识别任务.
    • 开发新的方法,实现标签平衡的学习,独立于属性共发生.
    • 在多样化,现实的数据集上增强 PAR 系统的稳定性和性能.

    主要方法:

    • 拟议的特征重新抽样分离学习 (FRDL) 是为了平衡属性抽样分布,而不会影响同时发生的标签先验.
    • 导向梯度增强翻译 (GOAT) 被引入以减轻由FRDL加剧的特征噪声和语义失衡.
    • FRDL和GOAT被整合到一个统一的框架中,用于全面提高绩效.

    主要成果:

    • 综合FRDL和GOAT框架显著提升了PAR的最新技术.
    • 通过各种现实的基准来证明性能改进.
    • 这些方法以最小的计算成本增加来实现这些收益.

    结论:

    • 这项工作建立了PAR的第一个标签独立和公正的平衡学习方法.
    • 拟议的方法有效地解决了数据不平衡的挑战,并在PAR中同时出现属性.
    • 这些进步为行人属性识别提供了更基本和更强大的视角.