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Related Concept Videos

Observational Learning01:12

Observational Learning

250
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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Associative Learning01:27

Associative Learning

474
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...
474
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...
492
Cognitive Learning01:21

Cognitive Learning

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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...
461
Purposive Learning01:22

Purposive Learning

174
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...
174
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

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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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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Class-Incremental Learning Method With Fast Update and High Retainability Based on Broad Learning System.

Jie Du, Peng Liu, Chi-Man Vong

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    |April 8, 2023
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    Summary
    This summary is machine-generated.

    A new Broad Learning System-based Class-Incremental Learning (BLS-CIL) method offers fast, accurate updates for machine learning models handling evolving data streams. This approach significantly reduces training and update times while retaining knowledge of older data classes.

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    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Computer Science

    Background:

    • Real-world applications like health monitoring involve continuous data streams with potentially new classes.
    • Class-Incremental Learning (CIL) is crucial for updating models with new data while preserving existing knowledge.
    • Current deep learning CIL methods are computationally expensive and can suffer from knowledge forgetting.

    Purpose of the Study:

    • To propose a novel Broad Learning System-based CIL (BLS-CIL) method.
    • To achieve fast model updates and high retention of old class knowledge.
    • To overcome the limitations of existing deep learning CIL approaches.

    Main Methods:

    • Developed a BLS-CIL method utilizing a class-correlation loss function.
    • Introduced a closed-form solution for the loss function, eliminating iterative optimization.
    • Derived a recursive update rule for CIL (RULL) that avoids replaying all old class exemplars.

    Main Results:

    • BLS-CIL achieved high accuracy by considering correlations between old and new classes.
    • Demonstrated significantly reduced training and update times compared to deep learning CIL methods.
    • Showcased high retainability of old class knowledge without extensive data replay.

    Conclusions:

    • The proposed BLS-CIL method offers a computationally efficient and effective solution for class-incremental learning.
    • BLS-CIL significantly outperforms traditional shallow networks and achieves comparable or better accuracy than deep learning CIL methods.
    • This approach is well-suited for real-world applications with continuous data streams and evolving class structures.