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関連する概念動画

Associative Learning01:27

Associative Learning

2.1K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
2.1K
Reinforcement01:23

Reinforcement

1.2K
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
1.2K
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

4.2K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
4.2K
Primary and Secondary Reinforcers01:23

Primary and Secondary Reinforcers

1.8K
In psychology, reinforcement is a key concept in behavior modification. B.F. Skinner demonstrated this with his experiments involving rats in what is known as a Skinner box. The rats learned to press a lever to receive food, a primary reinforcer that fulfilled their innate need for nourishment.
Effective reinforcers for humans vary depending on the individual and the context. Primary reinforcers, such as food, water, sleep, shelter, and pleasure, have inherent value and satisfy basic biological...
1.8K
Reinforcement Schedules01:24

Reinforcement Schedules

743
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
743
Observational Learning01:12

Observational Learning

1.5K
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
1.5K

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関連する実験動画

Updated: May 5, 2026

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

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フェデレーテッド・オフライン・強化学習

Doudou Zhou1, Yufeng Zhang2, Aaron Sonabend-W1

  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health.

Journal of the American Statistical Association
|February 20, 2026
PubMed
まとめ
この要約は機械生成です。

連邦化されたオフライン強化学習 (RL) は,分散医療データを用いてパーソナライズされた医療を可能にします. この新しいアルゴリズムは,複数のサイトで処理ポリシーを効率的に最適化し,集中データに匹敵するパフォーマンスを達成します.

キーワード:
ダイナミック・トリートメント・レジーム電気ヘルスケアレコードマルチソースの学習

関連する実験動画

Last Updated: May 5, 2026

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

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科学分野:

  • 人工知能 (AI) とは,人工知能 (AI) のことです.
  • 機械学習 (Machine Learning) とは,機械学習 (Machine Learning) について学ぶことです.
  • 医療情報工学 医療情報工学

背景:

  • パーソナライズされた医療には,ダイナミックな治療法が必要であり,多くの場合,オフライン強化学習 (RL) を活用します.
  • プライバシーに関する懸念とサイト固有のデータ異質性のために,機密医療データの機関間での共有は制限されています.
  • 既存の方法は,強力な治療戦略を開発するために,分散型データセットを効果的に利用するために苦労しています.

研究 の 目的:

  • マルチサイト医療データにおけるプライバシーと異質性に対処する新しい連邦化されたオフラインRLフレームワークを開発する.
  • 統一モデル内でサイトレベルの特徴の分析を可能にします.
  • ダイナミックな治療体制の最適化のためのコミュニケーション効率の良いアルゴリズムを設計する.

主な方法:

  • 多サイトマルコフ決定プロセスモデルを提案し,同質的および異質的なサイト効果の両方に対応しました.
  • 保証されたサンプル複雑性を持つオフライン RL のための最初の連結されたポリシー最適化アルゴリズムを開発しました.
  • アルゴリズムは,要約統計の交換を通じて1回の通信のみを必要とします.

主要な成果:

  • 提案された連結されたオフラインRLアルゴリズムは,集中データシナリオと比較できる,政策のサブ最適性に関する理論的保証を示しています.
  • 広範なシミュレーションにより,最適なポリシーを学習するアルゴリズムの有効性が確認されています.
  • この方法は,マルチサイトセプシスのデータセットに成功裏に適用されました.

結論:

  • 連邦化されたオフラインRLは,分散型,プライベートなヘルスケアデータによるパーソナライズド医療のための実行可能なアプローチです.
  • 提案されたアルゴリズムは,マルチサイト処理体制の最適化のための効率的かつ効果的なソリューションを提供します.
  • この研究は,現実の医療環境におけるRL技術の臨床的応用を促進します.