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    Boosted Residual Networks

    Mosca, Alan and Magoulas, George D. (2017) Boosted Residual Networks. In: Boracchi, G. and Iliadis, L. and Jayne, C. and Likas, A. (eds.) Engineering Applications of Neural Networks. Communications in Computer and Information Science 744. Springer, pp. 137-148. ISBN 9783319651712.

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    Abstract

    In this paper we present a new ensemble method, called Boosted Residual Networks, which builds an ensemble of Residual Networks by growing the member network at each round of boosting. The proposed approach combines recent developements in Residual Networks - a method for creating very deep networks by including a shortcut layer between different groups of layers - with the Deep Incremental Boosting, which has been proposed as a methodology to train fast ensembles of networks of increasing depth through the use of boosting. We demonstrate that the synergy of Residual Networks and Deep Incremental Boosting has better potential than simply boosting a Residual Network of fixed structure or using the equivalent Deep Incremental Boosting without the shortcut layers.

    Metadata

    Item Type: Book Section
    Additional Information: Series Print ISSN: 1865-0929
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Administrator
    Date Deposited: 02 Jul 2018 09:52
    Last Modified: 02 Jul 2018 09:52
    URI: http://eprints.bbk.ac.uk/id/eprint/20191

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