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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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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
327
Associative Learning01:27

Associative Learning

399
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...
399
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
33.0K
Classification of Systems-I01:26

Classification of Systems-I

188
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
188
Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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相关实验视频

Updated: Jul 9, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

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扩展和缩小:使用集群使用未标记数据的联合学习.

Ajit Kumar1, Ankit Kumar Singh1, Syed Saqib Ali1

  • 1School of Computer Science and Engineering, Soongsil University, Seoul 06978, Republic of Korea.

Sensors (Basel, Switzerland)
|December 9, 2023
PubMed
概括

使用未标记数据的联合学习 (FL) 是通过一种新的集群方法实现的,用于客户端的数据标签. 这种方法在物联网 (IoT) 生态系统中增强了保护隐私的深度学习.

科学领域:

  • 人工智能的人工智能
  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学

背景情况:

  • 物联网 (IoT) 与联合学习 (FL) 的整合承诺先进的深度学习,同时保持数据隐私.
  • 当前的FL模型通常需要标记客户端数据进行监督分类,这在现实世界物联网场景中通常是不切实际的.
  • 在FL中处理未标记数据的现有方法,如类前概率或伪标记,依赖于不现实的或不可用的假设.

研究的目的:

  • 调查在物联网中使用未标记数据进行联合学习的可行性.
  • 提出一种新的基于集群的方法,用于在FL之前对客户端数据进行标签.
  • 在FL框架内为分类任务开发一种普遍适用的解决方案.

主要方法:

  • 在联合培训之前,直接在客户端设备上实施基于集群的样本标签方法.
  • 进行了实验,改变了标记数据的比例,集群的数量和客户参与率.
  • 在不同的实验条件下评估了拟议方法的性能.

主要成果:

  • 使用最小数量的真实标签 (分别为0.01和0.03) 实现了87%和90%的高准确率.
  • 在FL环境中证明了基于集群的标签策略的有效性.
  • 验证了该方法适用于各种分类任务的适用性.
关键词:
物联网的物联网,就是物联网.聚类集群是指聚类的聚类.深度学习是一种深度学习.联合学习的联合学习.标签 标签 标签 标签保护隐私 保护隐私 保护隐私半监督学习 半监督学习监督学习学习监督学习没有标记的数据集.监管能力较弱 监管能力较弱

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结论:

  • 拟议的基于集群的标签方法能够使用未标签的数据进行有效的联合学习,解决了当前FL架构的关键局限性.
  • 这种方法可以在物联网环境中增强数据隐私,允许在源头进行标记.
  • 该方法为各种分类任务中的隐私保护深度学习提供了一种实用且可适应的解决方案.