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Related Concept Videos

Purposive Learning01:22

Purposive Learning

199
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...
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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...
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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...
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Metacognition01:26

Metacognition

271
Metacognition is a conscious process where individuals are aware of their cognitive and executive processes, such as planning before solving a problem or self-monitoring during reading. For instance, a writer may need help with composing a piece. The situation involves a writer who is working on a piece of writing, but while doing so, they realize that something is missing. They notice that their characters lack depth or details. This realization occurs because the writer is reflecting on their...
271
Observational Learning01:12

Observational Learning

297
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...
297
Learning Disabilities01:25

Learning Disabilities

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Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
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Self-Paced Broad Learning System.

Licheng Liu, Luyang Cai, Ting Xie

    IEEE Transactions on Cybernetics
    |June 29, 2022
    PubMed
    Summary
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    This study introduces a self-paced Broad Learning System (SPBLS) for noisy data regression. The novel approach enhances robustness to outliers by adaptively reweighting training samples, improving model performance.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Neural Networks

    Background:

    • Broad Learning System (BLS) offers efficient training and extensibility.
    • Conventional BLS using least square loss is sensitive to noisy data and outliers.
    • Need for robust regression models in the presence of data imperfections.

    Purpose of the Study:

    • To propose a Self-Paced Broad Learning System (SPBLS) model.
    • To enhance the robustness of BLS against noise and outliers in regression tasks.
    • To develop incremental learning algorithms for flexible model updates.

    Main Methods:

    • Incorporation of the self-paced learning (SPL) strategy into the BLS architecture.
    • Utilizing model output as feedback to learn sample importance weights.
    • Development of two incremental learning algorithms for SPBLS.

    Main Results:

    • The SPBLS model demonstrates robustness to noise and outliers in regression.
    • Adaptive sample reweighting effectively distinguishes between easy and difficult samples.
    • Incremental learning algorithms allow for quick and flexible system updates without retraining.

    Conclusions:

    • SPBLS effectively addresses the limitations of conventional BLS in noisy environments.
    • The proposed model maintains the efficiency and extensibility of the original BLS.
    • SPBLS achieves satisfying performance for noisy data regression tasks.