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相关概念视频

Purposive Learning01:22

Purposive Learning

426
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...
426
Observational Learning01:12

Observational Learning

802
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...
802
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
Probability Laws01:49

Probability Laws

43.9K
Overview
43.9K
Cognitive Learning01:21

Cognitive Learning

981
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...
981
Introduction to Learning01:18

Introduction to Learning

908
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...
908

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相关实验视频

Updated: Jan 10, 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

8.0K

在概率布尔网络中通过结构政策梯度学习.

Pedro Juan Rivera Torres1,2

  • 1Departamento de Computación y Automatización, Universidad de Salamanca, CB3 0BN Salamanca, Spain.

Entropy (Basel, Switzerland)
|November 26, 2025
PubMed
概括
此摘要是机器生成的。

学习概率布尔网络 (PBNs) 被介绍为可训练的函数近似器. 这些可解释的模型在各种任务上实现了与人工神经网络 (ANN) 竞争的性能.

关键词:
学习概率性的布尔网络一个概率性布尔网络.统计学学习的学习.

相关实验视频

Last Updated: Jan 10, 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

8.0K

科学领域:

  • 计算智能是一种计算智能.
  • 机器学习 机器学习
  • 人工智能的人工智能

背景情况:

  • 概率布尔网络 (PBNs) 是模拟复杂系统的强大工具.
  • PBNs的一个主要限制是它们的不可差异性结构,阻碍了基于梯度的直接训练.
  • 人工神经网络 (ANN) 在函数近似方面表现出色,但往往缺乏可解释性.

研究的目的:

  • 开发一个可训练的PBN模型,克服非区分性问题.
  • 展示PBNs作为通用功能近似器的潜力.
  • 保持PBNs固有的可解释性,同时实现竞争性性能.

主要方法:

  • 将PBN结构作为使用REINFORCE梯度优化的一种随机政策.
  • 训练具有标准梯度的连续输出头.
  • 正式化学习概率布尔网络 (LPBN) 并导出无偏的结构梯度.
  • 证明LPBNs对离散输入的通用近似属性.

主要成果:

  • 在分类,回归,集群和强化学习方面,LPBN的性能与ANN相美.
  • LPBNs提供可解释的,类似规则的内部单元.
  • 分析显示了区分分辨率,操作员集和单位计数对LPBN性能的影响.
  • 在LPBN中学习的逻辑在训练期间稳定.

结论:

  • 学习概率布尔网络是有效的通用学习者.
  • 在表格和噪音数据场景中,LPBNs为ANN提供了有竞争力的替代方案.
  • LPBNs成功地将高性能与模型可解释性相结合.