Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Radial effect in stochastic diagonal approximate greatest descent

    Access Status
    Fulltext not available
    Authors
    Tan, H.
    Lim, Hann
    Harno, H.
    Date
    2017
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Tan, H. and Lim, H. and Harno, H. 2017. Radial effect in stochastic diagonal approximate greatest descent, pp. 226-229.
    Source Title
    Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017
    DOI
    10.1109/ICSIPA.2017.8120611
    ISBN
    9781509055593
    School
    Curtin Malaysia
    URI
    http://hdl.handle.net/20.500.11937/65903
    Collection
    • Curtin Research Publications
    Abstract

    © 2017 IEEE. Stochastic Diagonal Approximate Greatest Descent (SDAGD) is proposed to manage the optimization in two stages, (a) apply a radial boundary to estimate step length when the weights are far from solution, (b) apply Newton method when the weights are within the solution level set. This is inspired by a multi-stage decision control system where different strategies is used at different conditions. In numerical optimization context, larger steps should be taken at the beginning of optimization and gradually reduced when it is near to the minimum point. Nevertheless, the intuition of determining the radial boundary when the optimized parameters are far from the solution is yet to be investigated for high dimensional data. Radial step length in SDAGD manipulates the relative step length for iteration construction. SDAGD is implemented in a two layer Multilayer Perceptron to evaluate the effects of R on artificial neural networks. It is concluded that the greater the value of R, the higher the learning rate of SDAGD algorithm when the value of R is constrained in between 100 to 10,000.

    Related items

    Showing items related by title, author, creator and subject.

    • Analyzing organic richness of source rocks from well log data by using SVM and ANN classifiers: A case study from the Kazhdumi formation, the Persian Gulf basin, offshore Iran
      Bolandi, V.; Kadkhodaie, Ali; Farzi, R. (2017)
      Determination of TOC is critical to the evaluation of every source rock unit. Methods which are dependent upon extensive laboratory testing are limited by the availability and integrity of the rock samples. Prediction of ...
    • A strategy of global convergence for the affine scaling algorithm for convex semidefinite programming
      Qian, X.; Liao, L.; Sun, Jie (2018)
      The affine scaling algorithm is one of the earliest interior point methods developed for linear programming. This algorithm is simple and elegant in terms of its geometric interpretation, but it is notoriously difficult ...
    • Multi-scale modelling and controlled synthesis of Titania nanoparticles
      Akindeju, Michael Kehinde (2013)
      Considering the level of current interests in the continuous synthesis of Titania, the Chemical and Manufacturing Industry is expected to benefit from the results of this work which proposed and implemented a tailor-made ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.