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Decomposition Techniques for Multilayer Perceptron Training.

Luigi Grippo, Andrea Manno, Marco Sciandrone

    IEEE Transactions on Neural Networks and Learning Systems
    |September 29, 2015
    PubMed
    Summary
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    This study introduces block decomposition for training multilayer perceptrons (MLPs), simplifying complex optimization problems. This novel approach enhances learning efficiency for neural networks.

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Neural Networks

    Background:

    • Training multilayer perceptrons (MLPs) involves minimizing a smooth error function, which is a complex nonlinear, nonconvex optimization problem.
    • MLP training is challenging due to flat regions, steep valleys in the error surface, large datasets, and numerous network parameters.

    Purpose of the Study:

    • To develop a novel class of batch learning algorithms for MLPs.
    • To address the inherent difficulties in MLP training by employing block decomposition techniques.

    Main Methods:

    • Formulated the MLP learning problem as minimizing a smooth error function.
    • Defined a class of batch learning algorithms using block decomposition for error function minimization.
    • Decomposed the learning problem into a sequence of smaller, structured minimization problems.

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    Main Results:

    • Established theoretical convergence results for the proposed algorithms.
    • Constructed and evaluated a specific algorithm through extensive numerical experimentation.
    • Demonstrated the effectiveness of the proposed techniques compared to state-of-the-art methods.

    Conclusions:

    • The block decomposition approach offers an effective strategy for optimizing MLP training.
    • The developed algorithms show significant improvements over existing state-of-the-art techniques.
    • This method advantageously exploits the structure of the objective function for efficient learning.