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Related Concept Videos

Associative Learning01:27

Associative Learning

636
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...
636
Observational Learning01:12

Observational Learning

356
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...
356
Cognitive Learning01:21

Cognitive Learning

692
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...
692
Introduction to Learning01:18

Introduction to Learning

587
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...
587
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

860
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...
860
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

1.0K
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
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Related Experiment Videos

Transfer Learning Algorithm With Knowledge Division Level.

Honggui Han, Hongxu Liu, Cuili Yang

    IEEE Transactions on Neural Networks and Learning Systems
    |March 1, 2022
    PubMed
    Summary
    This summary is machine-generated.

    A novel Knowledge Division Transfer Learning (KDTL) algorithm addresses domain drift in transfer learning by subdividing source knowledge. This approach effectively mitigates negative transfer and enhances target task performance.

    Related Experiment Videos

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Transfer learning faces challenges with domain drift, where source knowledge becomes unsuitable for target tasks.
    • Negative transfer, caused by irrelevant source knowledge, hinders model performance in target domains.

    Purpose of the Study:

    • To propose a novel transfer learning algorithm, Knowledge Division Transfer Learning (KDTL), to address the domain drift problem.
    • To effectively subdivide source scene knowledge and leverage it based on varying degrees of domain drift.

    Main Methods:

    • KDTL employs a comparative evaluation mechanism to categorize knowledge into ineffective, usable, and efficient.
    • An integrated framework prunes ineffective knowledge, reconstructs usable knowledge, and learns efficient knowledge.
    • Theoretical analysis covers convergence, error bounds, and computational complexity of KDTL.

    Main Results:

    • KDTL successfully identifies and avoids negative transfer by distinguishing knowledge types.
    • The algorithm demonstrates improved learning performance by acquiring efficient knowledge.
    • Experimental results show significant improvements over state-of-the-art algorithms on benchmark and real-world problems.

    Conclusions:

    • KDTL offers a robust solution to the domain drift challenge in transfer learning.
    • The proposed method enhances model adaptability and performance in target domains.
    • KDTL provides theoretical guarantees and empirical validation for its effectiveness.