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Related Concept Videos

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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Observational Learning01:12

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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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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...
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Related Experiment Video

Updated: Apr 27, 2026

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
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Transfer learning for visual categorization: a survey.

Ling Shao, Fan Zhu, Xuelong Li

    IEEE Transactions on Neural Networks and Learning Systems
    |July 12, 2014
    PubMed
    Summary
    This summary is machine-generated.

    Transfer learning overcomes limitations in standard machine learning by leveraging related data from different domains. This approach enhances model performance in visual categorization tasks, addressing issues like concept drift.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Standard machine learning assumes future data matches training data distribution, risking overfitting due to limited labeled data.
    • Real-world applications often require knowledge from related, distinct domains to improve target task performance.
    • Transfer learning addresses these cross-domain challenges by transferring knowledge from a source to a target domain.

    Purpose of the Study:

    • To survey state-of-the-art transfer learning algorithms.
    • To highlight the application of transfer learning in visual categorization.
    • To address challenges like view divergence and concept drifting.

    Main Methods:

    • Surveying existing transfer learning algorithms.
    • Analyzing their application in visual categorization tasks.
    • Focusing on object recognition, image classification, and human action recognition.

    Main Results:

    • Transfer learning effectively solves problems like view divergence in action recognition.
    • It also addresses concept drifting issues in image classification tasks.
    • The surveyed algorithms demonstrate significant improvements in visual categorization.

    Conclusions:

    • Transfer learning is a powerful technique for visual categorization.
    • It enables the use of related domain data to improve target task performance.
    • This survey provides insights into current transfer learning methods for visual AI.