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    Stochastic diagonal approximate greatest descent in neural networks

    Access Status
    Fulltext not available
    Authors
    Tan, H.
    Lim, Hann
    Harno, H.
    Date
    2017
    Type
    Conference Paper
    
    Metadata
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    Citation
    Tan, H. and Lim, H. and Harno, H. 2017. Stochastic diagonal approximate greatest descent in neural networks, pp. 1895-1898.
    Source Title
    Proceedings of the International Joint Conference on Neural Networks
    DOI
    10.1109/IJCNN.2017.7966081
    ISBN
    9781509061815
    School
    Curtin Malaysia
    URI
    http://hdl.handle.net/20.500.11937/57712
    Collection
    • Curtin Research Publications
    Abstract

    © 2017 IEEE. Optimization is important in neural networks to iteratively update weights for pattern classification. Existing optimization techniques suffer from suboptimal local minima and slow convergence rate. In this paper, stochastic diagonal Approximate Greatest Descent (SDAGD) algorithm is proposed to optimize neural network weights using multi-stage backpropagation manner. SDAGD is derived from the operation of a multi-stage decision control system. It uses the concept of control system consisting of: (1) when the local search region does not contain a minimum point, the iteration shall be defined at the boundary of the local search region, (2) when the local region contains a minimum point, the Newton method is used to search for the optimum solution. The implementation of SDAGD on Multilayer perceptron (MLP) is investigated with the goal of improving the learning ability and structural simplicity. Simulation results showed that two layer MLP with SDAGD achieved a misclassification rate of 4.7% on MNIST dataset.

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