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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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一个半监督的堆叠自动编码器使用伪标签进行分类任务.

Jie Lai1, Xiaodan Wang1, Qian Xiang1

  • 1College of Air and Missile Defense, Air Force Engineering University, Xi'an 710051, China.

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概括
此摘要是机器生成的。

这项研究引入了一种基于伪标签的新型半监督堆叠自动编码器 (PL-SSAE),以有效地利用标记和未标记的数据来改进分类任务. 通过伪标签,PL-SSAE通过利用未标记的样本来提高性能.

关键词:
深度学习是一种深度学习.伪标签是一种伪标签.规范化 规范化 规范化半监督学习 半监督学习堆叠的自动编码器

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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
  • 数据科学数据科学数据科学

背景情况:

  • 手动标记样本是低效的,导致许多未标记的训练样本在实际场景.
  • 半监督学习旨在利用标记和未标记的数据,这对于克服标记限制至关重要.
  • 传统的堆叠自动编码器 (SAE) 受到监督,并且由于仅依赖标记数据,因此在半监督任务中扎.

研究的目的:

  • 为有效的半监督分类提出基于伪标签的新型半监督堆叠自编码器 (PL-SSAE).
  • 在堆叠的自动编码器模型中增强未标记数据的利用.
  • 在有限的标记数据的情况下改善分类性能.

主要方法:

  • 在堆叠的自编码器框架中引入了一个伪标签方法,创建了PL-SSAE.
  • 在所有可用样本上使用自动编码器 (AE) 来初始化网络参数的未经监督的雇员预培训.
  • 使用标记样本实现代微调,为未标记样本生成伪标签,然后用于规范化.

主要成果:

  • 通过在微调过程中将伪标签数据纳入PL-SSAE,有效地利用了标记和未标记的样本.
  • 经验评估表明,PL-SSAE在基准数据集上显著优于传统的SAE,SSAE,半SAE和半SSAE.
  • 拟议的方法显示了具有竞争力的半监督分类性能.

结论:

  • 通过有效利用未标记的样本,PL-SSAE成功地解决了有限的标记数据的挑战.
  • 伪标签的集成增强了堆叠的自动编码器在半监督学习任务的能力.
  • 与现有的方法相比,PL-SSAE为半监督分类提供了更具竞争力和有效的方法.