Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Confirmation Biases01:31

Confirmation Biases

8.3K
The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
8.3K
Hindsight Biases01:12

Hindsight Biases

4.3K
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
4.3K
Bias01:22

Bias

7.4K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
7.4K
Machines01:19

Machines

581
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
581
Correspondence Bias01:17

Correspondence Bias

232
Correspondence bias, also referred to as the fundamental attribution error, describes the tendency to attribute another person’s behavior to internal characteristics rather than situational influences. This cognitive bias leads individuals to overlook external factors that may be influencing actions, thereby fostering potentially inaccurate assessments of others’ intentions and dispositions.Empirical Evidence for Correspondence BiasResearch has consistently demonstrated the...
232
Self-Serving Bias01:29

Self-Serving Bias

250
Self-serving bias is a cognitive phenomenon in which individuals attribute positive outcomes to internal factors such as their abilities, intelligence, or effort while attributing negative outcomes to external circumstances. This cognitive distortion helps maintain self-esteem but can also impede objective self-assessment.Theoretical Explanations of Self-Serving BiasTwo primary theories explain the self-serving bias: the cognitive explanation and the motivational explanation.The cognitive...
250

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Ambiguity-Aware Multi-Stage Cell-Type Annotation for Spatial Transcriptomics.

bioRxiv : the preprint server for biology·2026
Same author

NanoCellAnnotator: Formalizing Expert Cell Type Annotation with Large Language Models.

bioRxiv : the preprint server for biology·2026
Same author

Pulmonary Hypertension in Adults With a Systemic Right Ventricle (Biventricular Circulation).

JACC. Advances·2026
Same author

Primary prevention of Parkinson's disease: proceedings, the 8C's and a position statement from the Parkinson's disease prevention think tank.

NPJ Parkinson's disease·2026
Same author

Machine Learning-Based Predictive Modeling for the Development of Chronic Rhinosinusitis Using Longitudinal Health Records.

International forum of allergy & rhinology·2026
Same author

Switchable Ultralong Chiral Signal Transmission and Gate Tunability in Organic Chiral Semiconductor.

Research (Washington, D.C.)·2026

関連する実験動画

Updated: Feb 13, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.6K

バイアス耐性機械学習のための再現可能なフレームワーク,小規模ニューロ画像データ上のバイアス耐性機械学習

Jagan Mohan Reddy Dwarampudi, Jennifer L Purks, Joshua Wong

    ArXiv
    |February 12, 2026
    PubMed
    まとめ

    この研究は,小さな神経画像データセットのための信頼性の高い機械学習フレームワークを提示し,深い脳刺激の研究の正確性と解釈性を向上させます.

    科学分野:

    • 神経画像は,神経イメージングによるものです.
    • 機械学習 (Machine Learning) とは,機械学習 (Machine Learning) について学ぶことです.
    • バイオメディカルデータ分析

    背景:

    • 従来のクロス検証方法は,機械学習モデルの偏ったパフォーマンスの見積もりにつながる可能性があります.
    • このバイアスは,特に限られたデータでは,再現性と一般化を妨げます.
    • 小さなサンプルの神経画像データセットは,信頼性の高いモデル開発にユニークな課題を提示します.

    研究 の 目的:

    • 小さなサンプルの神経画像データの再現可能でバイアスに耐える機械学習フレームワークを導入する.
    • モデルの選択と性能推定のための従来のクロス検証の限界に対処するために.
    • データに限られた生物医学分野における信頼性の高い機械学習のための一般化可能なコンピューティング・ブループリントを提供する.

    主な方法:

    • ドメイン情報型機能エンジニアリングの統合.
    • 公正なパフォーマンスの見積もりのためのネストクロス検証の実施.
    • 堅牢な分類のために,決定値の最適化が校正されています.

    主要な成果:

    • このフレームワークは,構造的なMRIデータセットで0.660 ± 0.068のネストクロス検証バランスの取れた精度を達成しました.

    さらに関連する動画

    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
    09:34

    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

    Published on: September 25, 2021

    4.5K
    Basics of Multivariate Analysis in Neuroimaging Data
    06:35

    Basics of Multivariate Analysis in Neuroimaging Data

    Published on: July 24, 2010

    17.4K

    関連する実験動画

    Last Updated: Feb 13, 2026

    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    2.6K
    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
    09:34

    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

    Published on: September 25, 2021

    4.5K
    Basics of Multivariate Analysis in Neuroimaging Data
    06:35

    Basics of Multivariate Analysis in Neuroimaging Data

    Published on: July 24, 2010

    17.4K
  • 特徴のコンパクトで解釈可能なサブセットは,重要性に基づくランキングを使用して選択されました.
  • 従来の方法と比較して,信頼性と解釈性が向上したことが実証されています.

    • 結論:

    • 開発されたフレームワークは,神経イメージングにおける機械学習のための再現可能でバイアスに抵抗するアプローチを提供します.
    • それは,小サンプル生物医学データセットの信頼性の高い評価と機能選択を可能にします.
    • この研究は,データに限られた研究分野における機械学習アプリケーションの進歩のためのコンピューティングの青写真を提供します.