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Related Experiment Video

Updated: Mar 26, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Published on: June 30, 2020

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Scalable Algorithms for Multi-Instance Learning.

Xiu-Shen Wei, Jianxin Wu, Zhi-Hua Zhou

    IEEE Transactions on Neural Networks and Learning Systems
    |February 11, 2016
    PubMed
    Summary
    This summary is machine-generated.

    New multi-instance learning (MIL) algorithms, miVLAD and miFV, efficiently handle large datasets. These scalable methods achieve high accuracy and are hundreds of times faster than existing approaches.

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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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    Published on: June 30, 2020

    8.2K

    Area of Science:

    • Computer Science
    • Machine Learning
    • Data Science

    Background:

    • Multi-instance learning (MIL) is crucial for complex data like images and genes.
    • Existing MIL algorithms struggle with large-scale datasets.
    • Efficient and scalable MIL solutions are needed.

    Purpose of the Study:

    • To introduce two novel, efficient, and scalable MIL algorithms: miVLAD and miFV.
    • To address the limitations of current MIL methods in handling large datasets.
    • To improve the performance and speed of MIL.

    Main Methods:

    • Proposed MIL based on the vector of locally aggregated descriptors (miVLAD).
    • Proposed MIL based on the Fisher vector representation (miFV).
    • Mapped MIL bags into new vector representations preserving bag-level information.

    Main Results:

    • miVLAD and miFV demonstrate high efficiency and scalability for large-scale MIL.
    • Achieved accuracy comparable to state-of-the-art MIL algorithms.
    • Exhibited hundreds of times faster processing speeds.
    • Multiview representations using miVLAD/miFV further improved accuracy.
    • Algorithms performed well with default parameters, enhancing practical usability.

    Conclusions:

    • miVLAD and miFV offer a significant advancement in scalable multi-instance learning.
    • These methods provide a computationally efficient and effective solution for large-scale MIL problems.
    • The proposed algorithms are practical for real-world applications due to their speed and ease of use.