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Related Concept Videos

Introduction to Learning01:18

Introduction to Learning

709
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...
709
Observational Learning01:12

Observational Learning

644
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...
644
Associative Learning01:27

Associative Learning

884
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...
884
Cognitive Learning01:21

Cognitive Learning

854
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...
854
Purposive Learning01:22

Purposive Learning

303
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...
303
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.1K
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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Related Experiment Videos

A Hybrid Recursive Implementation of Broad Learning With Incremental Features.

Di Liu, Simone Baldi, Wenwu Yu

    IEEE Transactions on Neural Networks and Learning Systems
    |December 22, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an improved broad learning system (BLS) for efficient supervised learning. The new method avoids large matrix operations, enabling scalable training for big data challenges.

    Related Experiment Videos

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Computational Science

    Background:

    • The broad learning system (BLS) offers computational efficiency in supervised learning.
    • Standard BLS relies on least-squares, risking efficiency with large matrices in big data.

    Purpose of the Study:

    • To propose a novel BLS implementation mitigating large matrix storage and inversion.
    • To enhance BLS scalability for big data applications.

    Main Methods:

    • Developed a hybrid recursive learning approach for memory-iteration balance.
    • Implemented incremental learning to avoid retraining when expanding networks.

    Main Results:

    • The new framework achieves training weights equivalent to standard BLS.
    • Demonstrated successful training of significantly larger networks compared to standard BLS.

    Conclusions:

    • The proposed BLS implementation effectively addresses big data limitations.
    • This advancement projects BLS capabilities towards the big data frontier.