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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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Introduction to Learning01:18

Introduction to Learning

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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...
945
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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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...
966
Cognitive Learning01:21

Cognitive Learning

1.0K
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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Upsampling01:22

Upsampling

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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相关实验视频

基于自动编码器的对比学习,用于下一个篮子推.

Ling Huang1, Zhe-Yuan Li1, Xiao-Dong Huang1

  • 1College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China.

Neural networks : the official journal of the International Neural Network Society
|October 12, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了基于自动编码器的对比学习,用于下一个篮子推 (AC-NBR),通过解决数据稀疏性来提高预测准确性. 这种新的方法增强了项目嵌入,以获得更强大的下一个购物篮子建议.

关键词:
自动编码器自动编码器相反的学习学习.数据增强数据增强下一个篮子推建议

相关实验视频

科学领域:

  • 电子商务和推系统
  • 机器学习 机器学习
  • 数据挖掘 数据挖掘

背景情况:

  • 下一个购物篮推 (NBR) 基于历史数据预测用户的未来购买.
  • 数据稀疏性是NBR的一个主要挑战,阻碍了准确的预测.
  • 对于NBR,现有的对比学习 (CL) 方法在嵌入中断和适用性方面存在局限性.

研究的目的:

  • 提出一种新的模型,即基于自动编码器的对比学习为下一个篮子推 (AC-NBR),以克服NBR中的数据稀疏性.
  • 提高篮子嵌入质量和适用于各种NBR场景的适用性.
  • 为了提高预测用户下一个篮子中的物品的准确性.

主要方法:

  • 开发了一个基于AE的篮子增大模块,使用编码器-解码器结构与高斯噪声来实现多样化的正对生成.
  • 实现了基于AE的对比学习模块,以从增强的篮子和初始嵌入中构建正对.
  • 在下一个篮子预测器模块中使用了门式递归单元 (GRU) 和多层感知子 (MLP) 进行最终项目预测.

主要成果:

  • 拟议的AC-NBR模型有效地解决了下一个篮子建议中的数据稀疏性挑战.
  • 基于AE的增强保留了核心篮子信息,同时增强了嵌入多样性和适应性.
  • 在三个现实数据集上进行了全面的实验,验证了AC-NBR.BR的有效性.

结论:

  • 通过利用基于自动编码器的对比学习,AC-NBR为下一个篮子推提供了一个强大的和适用的解决方案.
  • 该方法提供了改进的嵌入表示,导致更准确的下一个篮子预测.
  • 这些发现表明,加强面临数据稀疏性的推系统是一个有希望的方向.