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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Batch Mode Active Learning for Regression With Expected Model Change.

Wenbin Cai, Muhan Zhang, Ya Zhang

    IEEE Transactions on Neural Networks and Learning Systems
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    Summary
    This summary is machine-generated.

    This study introduces Expected Model Change Maximization (EMCM), a new active learning (AL) framework for regression tasks. EMCM efficiently selects the most informative unlabeled data points to improve regression model accuracy.

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    Area of Science:

    • Machine Learning
    • Statistical Modeling

    Background:

    • Active learning (AL) is well-established for classification but underexplored for regression.
    • Regression tasks require specialized AL strategies to maximize model improvement.

    Purpose of the Study:

    • Introduce a novel active learning framework for regression: Expected Model Change Maximization (EMCM).
    • Develop AL algorithms for linear and nonlinear regression models under the EMCM framework.
    • Extend algorithms for batch mode active learning.

    Main Methods:

    • Quantify model change by the difference in model parameters before and after labeling new instances.
    • Approximate model change using the gradient of the loss function with respect to candidate instances, based on stochastic gradient descent.
    • Develop sequential and batch mode AL algorithms for regression.

    Main Results:

    • Proposed EMCM framework and associated algorithms are effective for regression.
    • Algorithms demonstrate high efficiency in selecting informative instances.
    • Experimental results validate performance on benchmark datasets (UCI, StatLib).

    Conclusions:

    • EMCM provides a robust framework for active learning in regression.
    • The developed algorithms offer efficient and effective solutions for data selection in regression.
    • This work advances the application of active learning to regression problems.