Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Introduction to Learning01:18

Introduction to Learning

354
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...
354
Associative Learning01:27

Associative Learning

317
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...
317
Observational Learning01:12

Observational Learning

152
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...
152
Cognitive Learning01:21

Cognitive Learning

233
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...
233
Aggregates Classification01:29

Aggregates Classification

309
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...
309
Purposive Learning01:22

Purposive Learning

106
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
106

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Adaptive parameterized control for line spectrum vibrations using a genetic algorithm-optimized variable forgetting factor.

ISA transactions·2026
Same author

Establishment of an efficient and reliable Agrobacterium-mediated genetic transformation system for the elite male-sterile poplar 'Siyang-1'.

BMC plant biology·2026
Same author

PcabHLH58/PcabHLH151 regulates adventitious root development and nitrogen uptake in poplar.

Nature communications·2026
Same author

Hydrogen production via gasification of corn stover photo-fermentation Residue: Catalytic mechanism of alkali metals.

Bioresource technology·2026
Same author

The temporal transcriptional regulation enhances genomic prediction accuracy for poplar radial growth.

Journal of genetics and genomics = Yi chuan xue bao·2026
Same author

[Numerical simulation study on the influence of free edge configuration on the performance of polymeric heart valves].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi·2026
Same journal

Anchor-based disentanglement framework for incremental multi-view clustering.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Complex-valued amplitude-phase interference modeling for adversarially robust classification.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

TraNce: Type-aware hypergraph neural network with biological mediators for drug repositioning.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Decentralized ADMM for factorization-based Low-rank matrix estimation.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Memristive neuromorphic circuit design inspired by the neural mechanisms of conditioned fear.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Q-learning based asynchronous Boolean control networks stabilization with data loss.

Neural networks : the official journal of the International Neural Network Society·2026
查看所有相关文章

相关实验视频

Updated: Jun 14, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

523

一种阶级增量学习方法,用于学习功能兼容嵌入式.

Hongchao An1, Jing Yang2, Xiuhua Zhang1

  • 1Guizhou University, School of Mechanical Engineering, Guiyang, 550025, Guizhou, China.

Neural networks : the official journal of the International Neural Network Society
|September 7, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的两阶段学习范式,通过解决功能嵌入不兼容性来解决人工智能的灾难性遗忘问题. 该方法显著提高了基准数据集的模型准确性.

关键词:
灾难性的遗忘.课堂上的增量学习.功能嵌入功能嵌入.知识的蒸知识的蒸.

更多相关视频

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.9K
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.5K

相关实验视频

Last Updated: Jun 14, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

523
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.9K
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.5K

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 计算机视觉 计算机视觉

背景情况:

  • 人工智能的持续学习受到灾难性遗忘的阻碍.
  • 现有的方法在与不兼容的功能嵌入方面扎.

研究的目的:

  • 提出一种新的两阶段学习范式,以克服在持续学习中嵌入不兼容性的特征.
  • 在不断获取知识的过程中,提高人工智能模型的性能和效率.

主要方法:

  • 一个两阶段的学习范式:保留和结以前的模型,同时扩展新的模块,然后是融合知识蒸.
  • 重量修剪和整合技术以优化模型效率.

主要成果:

  • 拟议的方法有效地减轻了功能嵌入不兼容性.
  • 在CIFAR-100,ImageNet-100和ImageNet-1000数据集上实现了最先进的性能.
  • 在ImageNet-100数据集上表现出5.08%的最大精度改进.

结论:

  • 开发的方法通过解决功能嵌入问题,显著提高了持续学习能力.
  • 该方法为需要持续更新知识的AI系统提供了强大的解决方案.
  • 代码的可用性有助于进一步的研究和应用.