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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.
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Related Experiment Video

Updated: Nov 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Multi-Source Contribution Learning for Domain Adaptation.

Keqiuyin Li, Jie Lu, Hua Zuo

    IEEE Transactions on Neural Networks and Learning Systems
    |April 9, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel multi-source contribution learning method for domain adaptation (MSCLDA). It effectively leverages diverse knowledge from multiple sources by learning domain similarities and differences, outperforming existing methods in visual data tasks.

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    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Transfer learning is crucial for leveraging knowledge from source domains to improve target domain tasks.
    • Existing methods often use single-source domain adaptation or average predictions from multiple sources, neglecting varied domain contributions.
    • The need for advanced multi-source domain adaptation techniques that account for differential source domain importance is evident.

    Purpose of the Study:

    • To propose a novel multi-source contribution learning method for domain adaptation (MSCLDA).
    • To simultaneously learn domain similarities and diversities by extracting multi-view features.
    • To enhance the transferability of latent features and improve target task prediction accuracy.

    Main Methods:

    • Developed MSCLDA to extract multi-view features, representing commonalities and unique characteristics across domains.
    • Employed multi-level distribution matching to reduce misclassification by aligning feature distributions.
    • Adjusted source prediction weights using pseudo target labels to emphasize significant source contributions.

    Main Results:

    • The proposed MSCLDA method demonstrated superior performance compared to existing approaches on real-world visual datasets.
    • Simultaneous learning of domain similarities and diversities led to more effective knowledge transfer.
    • Weight adjustment using pseudo target labels improved the final predictor's performance by highlighting distinct source contributions.

    Conclusions:

    • MSCLDA offers a significant advancement in multi-source domain adaptation by intelligently combining knowledge from diverse sources.
    • The method's ability to learn and utilize differential source contributions is key to its improved performance.
    • This approach holds promise for enhancing various machine learning tasks where data from multiple related domains is available.