BIROn - Birkbeck Institutional Research Online

    Improved sign-based learning algorithm derived by the composite nonlinear Jacobi process

    Anastasiadis, A.D. and Magoulas, George D. and Vrahatis, M.N. (2006) Improved sign-based learning algorithm derived by the composite nonlinear Jacobi process. Journal of Computational and Applied Mathematics 191 (2), pp. 166-178. ISSN 0377-0427.

    [img]
    Preview
    Text
    501.pdf

    Download (490kB) | Preview

    Abstract

    In this paper a globally convergent first-order training algorithm is proposed that uses sign-based information of the batch error measure in the framework of the nonlinear Jacobi process. This approach allows us to equip the recently proposed Jacobi–Rprop method with the global convergence property, i.e. convergence to a local minimizer from any initial starting point. We also propose a strategy that ensures the search direction of the globally convergent Jacobi–Rprop is a descent one. The behaviour of the algorithm is empirically investigated in eight benchmark problems. Simulation results verify that there are indeed improvements on the convergence success of the algorithm.

    Metadata

    Item Type: Article
    Additional Information: Copyright © 2006 Elsevier B.V.
    Keyword(s) / Subject(s): supervised learning, nonlinear iterative methods, nonlinear Jacobi, pattern classification, feedforward neural networks, convergence analysis, global convergence
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centre: Birkbeck Knowledge Lab
    Depositing User: Sandra Plummer
    Date Deposited: 04 Jun 2007
    Last Modified: 02 Dec 2016 13:23
    URI: http://eprints.bbk.ac.uk/id/eprint/501

    Statistics

    Downloads
    Activity Overview
    358Downloads
    342Hits

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item