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    Adaptive self-scaling non-monotone BFGS training algorithm for recurrent neural networks

    Peng, C.-C. and Magoulas, George (2007) Adaptive self-scaling non-monotone BFGS training algorithm for recurrent neural networks. In: de S'a, J.M. and Alexandre, L.A. and Duch, W. and Mandic, D.P. (eds.) Artificial Neural Networks: 17th International Conference. Lecture Notes in Computer Science 4668. Springer, pp. 259-268. ISBN 9783540746904.

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    Abstract

    Book synopsis: This two volume set LNCS 4668 and LNCS 4669 constitutes the refereed proceedings of the 17th International Conference on Artificial Neural Networks, ICANN 2007, held in Porto, Portugal, in September 2007. The 197 revised full papers presented were carefully reviewed and selected from 376 submissions. The 98 papers of the first volume are organized in topical sections on learning theory, advances in neural network learning methods, ensemble learning, spiking neural networks, advances in neural network architectures neural network technologies, neural dynamics and complex systems, data analysis, estimation, spatial and spatio-temporal learning, evolutionary computing, meta learning, agents learning, complex-valued neural networks, as well as temporal synchronization and nonlinear dynamics in neural networks.

    Metadata

    Item Type: Book Section
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 15 Jun 2021 17:05
    Last Modified: 15 Jun 2021 17:05
    URI: https://eprints.bbk.ac.uk/id/eprint/44754

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