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    Adaptive Second-order Derivative Approximate Greatest Descent Optimization for Deep Learning Neural Networks

    Tan H 2019.pdf (2.943Mb)
    Access Status
    Open access
    Authors
    Tan, Hong Hui
    Date
    2019
    Supervisor
    Hann Lim
    Type
    Thesis
    Award
    PhD
    
    Metadata
    Show full item record
    Faculty
    Curtin Malaysia
    School
    Curtin Malaysia
    URI
    http://hdl.handle.net/20.500.11937/77991
    Collection
    • Curtin Theses
    Abstract

    Backpropagation using Stochastic Diagonal Approximate Greatest Descent (SDAGD) is a novel adaptive second-order derivative optimization method in updating weights of deep learning neural networks. SDAGD applies two-phase switching strategy to seek for solution at far using long-term optimal trajectory and automatically switch to Newton method when nearer to optimal solution. SDAGD has the advantages of steepest training roll-off rate, adaptive adjustment of step-length and the ability to deal with vanishing gradient issues in deep architecture.

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