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 Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Research Centres and Institutes: | Birkbeck Knowledge Lab |
| Depositing User: | George Magoulas |
| Date Deposited: | 12 Feb 2016 09:57 |
| Last Modified: | 09 Aug 2025 00:46 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/14187 |
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