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相关概念视频

Observational Learning01:12

Observational Learning

163
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...
163
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

107
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
107
Associative Learning01:27

Associative Learning

335
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...
335
Block Diagram Reduction01:22

Block Diagram Reduction

200
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
200
Cognitive Learning01:21

Cognitive Learning

237
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...
237
Introduction to Learning01:18

Introduction to Learning

360
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...
360

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Updated: Jun 23, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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可审计和可验证的联合学习基于区块链启用的去中心化.

Aditya Pribadi Kalapaaking, Ibrahim Khalil, Xun Yi

    IEEE transactions on neural networks and learning systems
    |June 14, 2024
    PubMed
    概括

    本研究引入了一个可审计和可验证的去中心化联合学习 (DFL) 框架,使用区块链技术. DFL系统提高了可信度,降低了通信成本,尽管处理时间略有增加.

    科学领域:

    • * 区块链和机器学习
    • * 分散式系统和安全

    背景情况:

    • *联合学习 (FL) 通常依赖于中央权威,创建单一失败点,破坏隐私和安全.
    • * 传统FL架构中缺乏可审计性和可验证性,阻碍了可信度和稳定性.

    研究的目的:

    • * 提出一个可审计和可验证的去中心化联合学习 (DFL) 框架.
    • * 加强FL流程的透明度,问责制和独立验证.
    • * 解决集中式FL架构的局限性.

    主要方法:

    • *为DFL参与者开发基于智能合约的监控系统.
    • * 部署监控系统以记录本地培训数据进行审计.
    • *利用区块链节点进行模型交换,验证和分散聚合,使用多签名方案.
    • * 实施一个共识协议,用于对验证的全球模型进行防改存储.

    主要成果:

    • * 在CIFAR-10,F-MNIST和MedMNIST数据集上的实验验证.
    • * 时间消耗略有增加,作为改善可审计性和可核查性的权衡.
    • * 对参与者来说,通信成本大大降低了高达95%.

    结论:

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    • * 拟议的区块链支持的DFL框架成功实现了可审计性和可验证性.
    • * 该系统增强了分散式联合学习的可信度和安全性.
    • * 该框架提供了安全和透明的协作机器学习的实际解决方案.