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関連する概念動画

Associative Learning01:27

Associative Learning

566
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...
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Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
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Cognitive Learning01:21

Cognitive Learning

513
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
513
Observational Learning01:12

Observational Learning

305
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...
305
Introduction to Learning01:18

Introduction to Learning

528
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
528
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

781
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
781

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FA-GCL: 機能拡張グラフ対比学習方法

Long Xu1, Honghui Chen1

  • 1National Key Laboratory of Information Systems Engineering, Changsha, 410000, China.

Neural networks : the official journal of the International Neural Network Society
|September 5, 2025
PubMed
まとめ
この要約は機械生成です。

この研究では,グラフの表現を強化するために,特徴増強ベースのグラフ対照学習 (FA-GCL) を導入します. FA-GCLは,ダイナミック・ドロップアウトと単数値分解を使用して,既存の方法を上回る精度と堅実性を改善します.

キーワード:
ディナミック・ドロップアウト機能の拡張グラフ対照学習単数値の分解

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科学分野:

  • グラフ表現の学習
  • 機械学習
  • データサイエンス

背景:

  • 既存のグラフ対照学習方法は,しばしば完全なノード属性または構造情報に依存しています.
  • 構造強化による不完全なノード属性と偽陽性は,現実世界のグラフデータにおけるパフォーマンスを妨げます.
  • データの完全性に敏感な,強力なグラフ表現学習技術が必要である.

研究 の 目的:

  • 特徴増強ベースのグラフ対照学習 (FA-GCL) の新しい方法を提案する.
  • グラフ表現の精度と強さを高めるために.
  • 不完全なノード属性と構造ノイズの処理における既存の方法の限界に対処する.

主な方法:

  • ダイナミックなドロップアウトベースの機能増強技術と,適応的なドロップアウト率のための三角波関数を使用します.
  • 単数値分解 (SVD) ベースの2つの機能増強方法:完全なSVDとランダム化投影SVDを導入する.
  • SVD方法は,単数値に制御されたノイズを追加し,高品質の拡張サンプルの特性を再構築し,ランダム化されたSVDは線形複雑性を提供します.

主要な成果:

  • FA-GCLは12のグラフデータセットで一貫した優れたパフォーマンスを示しています.
  • この方法は,ノード分類,ノードクラスタリング,グラフ分類のタスクにおけるベースラインアプローチを大幅に上回ります.
  • 習得したグラフ表現の品質と堅実性を向上させるのに有効であることが証明されています.

結論:

  • FA-GCLは,特にノード属性が不完全である場合,グラフ表現学習に堅実で効果的なアプローチを提供します.
  • 提案された機能増強戦略は,モデルの性能と汎用性を高めます.
  • この研究は,柔軟で強力なデータ増強技術を導入することで,グラフ対照学習を進めています.