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相关概念视频

Associative Learning01:27

Associative Learning

462
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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Observational Learning01:12

Observational Learning

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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...
231
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Introduction to Learning01:18

Introduction to Learning

479
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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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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相关实验视频

Updated: Jul 27, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
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使用最小标记数据与层次上下文的关键词识别和转移学习.

Rohan Goli1, Keerthana Komatineni1, Shailesh Alluri1

  • 1School of Computing, College of Engineering, Computing and Applied Science, Clemson University, Clemson, SC, USA.

medRxiv : the preprint server for health sciences
|June 9, 2023
PubMed
概括

本研究引入了一种半监督的框架,用于识别临床决策支持系统 (CDSS) 中的关键词 (KP),使用最小的标记数据. 这种新的方法通过自动化本体结构构建来提高医疗信息技术的互操作性.

关键词:
临床决策支持系统 临床决策支持系统域名适应领域适应层次上下文 层次上下文最少的标记数据是标记数据.自然语言处理自然语言处理.半监督学习 半监督学习

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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科学领域:

  • 卫生信息技术 信息技术
  • 自然语言处理自然语言处理.
  • 机器学习 机器学习

背景情况:

  • 可互操作的临床决策支持系统 (CDSS) 规则对于医疗信息技术至关重要,但实现互操作性是一项挑战.
  • 对互操作的CDSS规则至关重要的本体结构传统上依赖于领域专家的手动关键词 (KP) 识别.
  • 自然语言处理 (NLP) 技术可以自动化KP识别,补充手工工作,但需要人类专业知识来标记数据.

研究的目的:

  • 为CDSS子域提供一个半监督的关键词识别框架.
  • 解决临床NLP任务中有限的人类标记数据的挑战.
  • 为了提高互操作CDSS规则的实体结构的效率和准确性.

主要方法:

  • 使用BiLSTM-CRF模型开发了一个半监督框架,具有层次关注和域调整.
  • 在初始培训中使用合成标签,并使用最小的人类标签数据进行微调.
  • 优化了NLP预处理和机器学习管道,评估不同的编码方案和上下文学习策略.

主要成果:

  • 拟议的框架通过有效利用合成标签和文档层次上下文,优于先前的神经架构.
  • 域调整技术改善了合成标签的质量,BIO编码方案显示出稍微更好的性能.
  • 整合文档级上下文,预训练的语言模型和词嵌入,显著提高了模型性能.

结论:

  • 这是CDSS子域中用于KP识别的第一个功能框架,基于有限的人类标记数据进行训练.
  • 该框架通过提供轻量级的深度学习方法来实时识别KP,为临床NLP做出贡献.
  • 这种自动化方法补充了人类专家,解决了在专业领域手动数据标签的挑战.