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相关概念视频

Longitudinal Research02:20

Longitudinal Research

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Continuing care describes the variety of health, personal, and social services provided over a prolonged period. The need for continuing care is increasing because people are living longer. Many people do not have families or others to care for them. Continuing care is mainly for patients who are disabled, functionally dependent, or suffering from a terminal disease. It is available within institutional settings or in homes. Examples include nursing centers or facilities, assisted living,...
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Evaluation of the teaching process enables the nurse to determine if the patient's learning needs were met and if training was effective. If the expected outcomes are not met, the care plan is revised, and additional education or reinforcement is provided. Nurses can ask questions after the session or obtain feedback to assess the patient's understanding of the topic.
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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.
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持续学习:一个系统的文献综述

Qinwen Yang1, Liyuan Wang2, Joerg Wicker1

  • 1School of Computer Science, University of Auckland, 38 Princes Street, Auckland, 1010, New Zealand.

Neural networks : the official journal of the International Neural Network Society
|November 3, 2025
PubMed
概括

智能系统需要持续学习 (CL) 才能在不遗忘的情况下适应. 本综述分析了189篇论文 (2010-2025年),为灾难性遗忘提供了新的见解,以及在CL中未来的研究方向.

关键词:
灾难性的遗忘.持续的学习 持续的学习增量学习是一种增量学习.终身学习是一项终身学习.系统的文献审查 系统的文献审查

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 计算机科学 计算机科学

背景情况:

  • 智能系统需要不断适应不断变化的数据流.
  • 持续学习 (CL) 使系统能够在没有先前数据的情况下顺序学习.
  • 灾难性遗忘 (CF) 是一个主要的挑战,新学习会损害旧的知识.

研究的目的:

  • 提供从2010年到2025年持续学习 (CL) 研究的系统文献综述 (SLR).
  • 通过统一分类和探索灾难性遗忘 (CF) 的原因,解决现有调查的局限性.
  • 在经验测试之外,为CL挑战推进一个理论框架.

主要方法:

  • 在五个主要数据库中进行自动搜索.
  • 在13个主要的学术场所进行手动搜索.
  • 策划了189篇关于持续学习的同行评审论文的数据集.

主要成果:

  • 介绍了对CL研究趋势和方法的全面概述.
  • 为CL方法和实验环境开发了精细的分类学.
  • 分析了灾难性遗忘 (CF) 的结构和功能原因.
  • 识别了在CL中开放的理论问题和新兴的研究方向.

结论:

  • 该审查为CL提供了一个统一的视角,这对于开发弹性AI至关重要.
  • 了解CF的原因对于推进CL方法至关重要.
  • 这些发现指导了未来的研究,以更强大,更适应的智能系统.