BIROn - Birkbeck Institutional Research Online

    Sign-based learning schemes for pattern classification

    Anastasiadis, A.D. and Magoulas, George and Vrahatis, M.N. (2005) Sign-based learning schemes for pattern classification. Pattern Recognition Letters 26 (12), pp. 1926-1936. ISSN 0167-8655.

    Full text not available from this repository.


    This paper introduces a new class of sign-based training algorithms for neural networks that combine the sign-based updates of the Rprop algorithm with the composite nonlinear Jacobi method. The theoretical foundations of the class are described and a heuristic Rprop-based Jacobi algorithm is empirically investigated through simulation experiments in benchmark pattern classification problems. Numerical evidence shows that this new modification of the Rprop algorithm exhibits improved learning speed in all cases tested, and compares favorably against the Rprop and a recently proposed modification, the improved Rprop.


    Item Type: Article
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Sarah Hall
    Date Deposited: 22 Jun 2021 12:46
    Last Modified: 09 Aug 2023 12:51


    Activity Overview
    6 month trend
    6 month trend

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item