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相关概念视频

Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Associative Learning01:27

Associative Learning

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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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Cognitive Learning01:21

Cognitive Learning

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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...
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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
673
Observational Learning01:12

Observational Learning

210
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...
210
Introduction to Learning01:18

Introduction to Learning

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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...
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对于集群联合学习的活跃客户端选择.

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    此摘要是机器生成的。

    积极学习通过选择信息型客户端来增强集群联合学习 (CFL),加快培训,在数据异质性下提高模型准确性. 这种方法在分布式机器学习中减少了客户参与和通信开销.

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    科学领域:

    • 机器学习 机器学习
    • 分布式系统 分布式系统
    • 人工智能的人工智能

    背景情况:

    • 联合学习 (FL) 是一种具有隐私和通信限制的分布式机器学习框架.
    • 集群FL (CFL) 通过学习客户群的定制模型来解决数据异质性.
    • 现有的CFL方法由于客户选择效率低下,特别是非IID数据,因此存在缓慢的融合和有限的性能问题.

    研究的目的:

    • 为集群联合学习 (ACFL) 引入一种新的主动客户选择策略.
    • 通过选择最具信息性的客户端进行模型更新来提高CFL的效率和准确性.

    主要方法:

    • 拟议的ACFL方法使用主动学习 (AL) 度量 (例如,不确定性抽样,QBC,损失) 来在每个集群中选择客户.
    • 在每一轮中,集群都会从一小部分信息客户中识别和汇总更新.
    • 在类不平衡条件下对MNIST,CIFAR-10和LEAF数据集的实证评估.

    主要成果:

    • 与基线FL和CFL方法相比,ACFL显著加快了学习过程.
    • 该方法需要较少的客户参与,同时实现更好的模型准确性.
    • 在ACFL中,通信开支显著减少.

    结论:

    • 积极的客户端选择是改善集群联合学习的有效策略.
    • 在异质环境中,ACFL为高效准确的分布式机器学习提供了一个有前途的解决方案.
    • 拟议的方法通过智能地根据信息性选择客户来提高CFL的性能.