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相关概念视频

Associative Learning01:27

Associative Learning

1.2K
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...
1.2K
Observational Learning01:12

Observational Learning

838
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...
838
Purposive Learning01:22

Purposive Learning

444
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
444
Cognitive Learning01:21

Cognitive Learning

1.0K
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...
1.0K
Introduction to Learning01:18

Introduction to Learning

954
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...
954
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.5K
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...
2.5K

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相关实验视频

Updated: Jan 16, 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

Published on: June 30, 2020

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通过影响函数可靠的积极学习.

Meng Xia1, Ricardo Henao2

  • 1Department of Electrical & Computer Engineering, University of Duke.

Transactions on machine learning research
|October 2, 2025
PubMed
概括
此摘要是机器生成的。

标记数据不足阻碍了深度学习 (DL) 模型的性能. 本研究介绍了一种可靠的主动学习 (AL) 框架,使用影响函数有效地选择有价值的数据,提高模型的准确性并克服AL的不可靠性.

相关实验视频

Last Updated: Jan 16, 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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科学领域:

  • 机器学习 机器学习
  • 人工智能的人工智能
  • 计算机科学 计算机科学

背景情况:

  • 标记数据收集的高成本和时间要求给深度学习 (DL) 模型开发带来了挑战.
  • 标记数据不足会对DL模型在现实应用中的性能产生负面影响.
  • 现有的主动学习 (AL) 算法显示DL架构的性能不可靠,有时表现不如随机选择.

研究的目的:

  • 为了解决当前深度学习中的积极学习算法的不可靠性.
  • 为深度学习架构提出一个理论上有动机的积极学习框架.
  • 提高主动学习中数据选择的效率和有效性.

主要方法:

  • 为深度学习架构提出了一个新的积极学习框架.
  • 利用影响函数,伪标签和多样性选择来估计样本价值.
  • 专注于选择改善整个数据集的整体模型性能的样本,包括未标记的数据.

主要成果:

  • 建议的框架,通过影响功能 (RALIF) 可靠的积极学习,始终优于随机选择.
  • 与其他现有和最先进的积极学习方法相比,RALIF表现出卓越的表现.
  • 该方法有效地估计了未标记数据样本的性能影响.

结论:

  • 拟议的RALIF框架为深度学习中的积极学习提供了可靠和有效的解决方案.
  • 这种方法减轻了在以前的积极学习方法中观察到的性能不可靠性问题.
  • 在深度学习中,RALIF提供了一种切实可行的方法来减少对广泛标记数据集的需求.