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Related Concept Videos

Observational Learning01:12

Observational Learning

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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

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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...
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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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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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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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Aggregates Classification01:29

Aggregates Classification

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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.
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Deep Neural Networks for Image-Based Dietary Assessment
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Deep Class-Incremental Learning From Decentralized Data.

Xiaohan Zhang, Songlin Dong, Jinjie Chen

    IEEE Transactions on Neural Networks and Learning Systems
    |October 31, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces data-decentralized class-incremental learning (DCIL) for continuous data streams across repositories. The proposed decentralized composite knowledge incremental distillation (DCID) framework effectively transfers knowledge, establishing a new benchmark for decentralized machine learning.

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    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Data Science

    Background:

    • Decentralized machine learning presents challenges with continuous data streams across multiple repositories.
    • Existing class-incremental learning (CIL) methods are primarily centralized, limiting their applicability in decentralized settings.

    Purpose of the Study:

    • To formulate and establish a benchmark for data-decentralized class-incremental learning (DCIL).
    • To propose a novel framework, decentralized composite knowledge incremental distillation (DCID), for effective knowledge transfer in DCIL.

    Main Methods:

    • Formulation of the DCIL problem and development of an experimental protocol.
    • Introduction of a baseline decentralized counterpart to centralized CIL approaches.
    • Proposal of the DCID framework, comprising local CIL, collaborative knowledge distillation (KD) among local models, and aggregated KD to a general model.

    Main Results:

    • The proposed DCID framework was comprehensively investigated with various component implementations.
    • Extensive experimental results demonstrated the effectiveness of the DCID framework in the DCIL setting.

    Conclusions:

    • The study successfully initiated the investigation of DCIL, providing a foundational framework and benchmark.
    • The DCID framework shows significant promise for continuous learning in decentralized data environments.