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関連する概念動画

Random Sampling Method01:09

Random Sampling Method

12.3K
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...
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Sampling Distribution01:12

Sampling Distribution

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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...
13.6K
Stratified Sampling Method01:16

Stratified Sampling Method

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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...
12.8K
Cluster Sampling Method01:20

Cluster Sampling Method

12.7K
Appropriate sampling methods ensure 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 cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.7K
Sampling Methods: Overview01:06

Sampling Methods: Overview

498
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...
498
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

405
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...
405

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Updated: Sep 9, 2025

Sampling Soils in a Heterogeneous Research Plot
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連邦学習における現実的な分布シミュレーションのための堅固なサンプリング技術

Robin Hoepp1,2, Leonhard Rist3,4, Alexander Katzmann3

  • 1Computed Tomography, Siemens Healthineers, Forchheim, Germany. robin.hoepp@fau.de.

International journal of computer assisted radiology and surgery
|September 2, 2025
PubMed
まとめ
この要約は機械生成です。

連邦学習 (FL) のトレーニングは,非IIDデータ配布によって損なわれることがあります. 新しいサンプリングアルゴリズムは,実際のラベル分布をシミュレートし,FLの性能低下を展開する前に分析します.

キーワード:
配給シフト統合学習サンプリング

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An Unbiased Approach of Sampling TEM Sections in Neuroscience
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Last Updated: Sep 9, 2025

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科学分野:

  • 機械学習
  • 人工知能
  • 医療情報学

背景:

  • 連邦学習 (FL) は,プライバシーに敏感な臨床環境において不可欠な分散データに関するディープラーニングモデルのトレーニングを可能にします.
  • 非独立で同一分布のデータ (非IID) は,クライアント間の人口学的変動から生じ,FLモデルのパフォーマンスを著しく低下させる可能性があります.
  • 医療における大規模な FL を導入する前に,非IIDデータ分布の影響を評価することが不可欠です.

研究 の 目的:

  • リアルでクライアントに偏ったラベル配分を作成するための新しいサンプリングアルゴリズムの開発と評価.
  • 模擬非IIDデータシナリオでのFLモデルの性能低下を調査する.
  • FLにおけるデータ異質性の影響を分析するための効率的な方法を提供する.

主な方法:

  • グローバル分布から指定された平均値と標準偏差を持つデータサブセットを生成するためのサンプリングアルゴリズムが開発されました.
  • 複数のグループにおけるラベル分布の数値最適化のために,Chi-squaredとGiniの不純度測定法を使用した.
  • アルゴリズムは3Dカメラベースの体重と身長の推定のための実際の臨床データセットに適用されました.

主要な成果:

  • IIDデータ以外のサンプルで Federated Averaging (FedAvg) 訓練を行った結果,パフォーマンスが低下した.
  • 全体的なモデルでは体重で25.3%,身長で28.7%という現実的な劣化が見られた.
  • 提案されたサンプリングテクニックは,ハードデータ分割ベースラインと比較して,有意な悪影響を示した.

結論:

  • FLの環境でクライアントに偏ったレーベルの配布は,モデルのトレーニングとパフォーマンスを大幅に損なう可能性があります.
  • 開発されたサンプリングアルゴリズムは,非IIDデータ効果の導入前の分析のための効率的なアプローチを提供します.
  • このテクニックは,さまざまなネットワークアーキテクチャ,臨床シナリオ,および非IIDサブポレーションに適用できる多用途です.