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相关概念视频

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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Force Classification01:22

Force Classification

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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.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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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...
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Introduction to Learning01:18

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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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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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Cognitive Learning01:21

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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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利用转移学习与深度学习进行犯罪预测.

Umair Muneer Butt1,2, Sukumar Letchmunan1, Fadratul Hafinaz Hassan1

  • 1School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia.

PloS one
|April 17, 2024
PubMed
概括

本研究介绍了一种转移学习方法,使用双向长期短期记忆 (BiLSTM) 网络进行准确的犯罪预测. 这种方法有效地将犯罪模式转移到社区之间,提高执法效率.

科学领域:

  • 计算机犯罪学 计算机犯罪学
  • 人工智能在公共安全中的作用
  • 深度学习用于预测分析.

背景情况:

  • 犯罪预测对公共安全至关重要,深度学习显得有前途.
  • 有限的犯罪数据和资源阻碍了高级深度学习模型的培训.
  • 现有的统计和深度学习方法需要大量数据来有效预测犯罪.

研究的目的:

  • 通过采用转移学习模式来解决犯罪预测中的数据短缺问题.
  • 微调和评估各种犯罪预测的统计和深度学习模型.
  • 提出并验证基于BiLSTM的新型转移学习架构,以提高犯罪预测的准确性和效率.

主要方法:

  • 微调统计方法:简单的移动平均线 (SMA),加权移动平均线 (WMA),指数移动平均线 (EMA).
  • 深度学习模型的微调:长期短期记忆 (LSTM),双向长期短期记忆 (BiLSTM) 和卷积神经网络和长期短期记忆 (CNN-LSTM).
  • 基于BiLSTM的转移学习架构的开发和评估,用于在不同社区中转移犯罪预测知识.

主要成果:

  • 使用BiLSTM提出的转移学习方法与其他方法相比,表现优越.
  • 在不同数据集 (芝加哥,纽约,拉合尔) 中预测每周和每月的犯罪趋势时,取得了显著的低误差值.

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  • 证明了执行时间的缩短,这表明实际执法应用的计算效率.
  • 结论:

    • 转移学习,特别是BiLSTM,有效地克服了犯罪预测中的数据限制.
    • 拟议的模型提高了犯罪预测的准确性和效率.
    • 这种方法为执法机构提供了一种有价值的工具,以改善犯罪控制和预防战略.