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    Reliable estimation of a neural network’s domain of validity through interval analysis based inversion

    Adam, S.P. and Karras, D.A. and Magoulas, George D. and Vrahatis, M.N. (2015) Reliable estimation of a neural network’s domain of validity through interval analysis based inversion. In: 2015 International Joint Conference on Neural Networks (IJCNN), 12-17 July 2015, Killarney.

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    Abstract

    Reliable estimation of a neural network’s domain of validity is important for a number of reasons such as assessing its ability to cope with a given problem, evaluating the consistency of its generalization etc. In this paper we introduce a new approach to estimate the domain of validity of a neural network based on Set Inversion Via Interval Analysis (SIVIA), the methodology established by Jaulin andWalter [1]. This approach was originally introduced in order to solve nonlinear parameter estimation problems in a bounded error context and proved to be effective in tackling several types of problems dealing with nonlinear systems analysis. The dependence of a neural network output on the pattern data is a nonlinear function and hence derivation of the impact of the input data to the neural network function can be addressed as a nonlinear parameter estimation problem that can be tackled by SIVIA. We present concrete application examples and show how the proposed method allows to delimit the domain of validity of a trained neural network. We discuss advantages, pitfalls and potential improvements offered to neural networks.

    Metadata

    Item Type: Conference or Workshop Item (Paper)
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centre: Birkbeck Knowledge Lab
    Depositing User: Prof George Magoulas
    Date Deposited: 12 Feb 2016 09:57
    Last Modified: 02 Dec 2016 13:23
    URI: http://eprints.bbk.ac.uk/id/eprint/14187

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