Estimations of error bounds for neural-network function approximators
- 1 March 1999
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 10 (2) , 217-230
- https://doi.org/10.1109/72.750542
Abstract
Neural networks are being increasingly used for problems involving function approximation. However, a key limitation of neural methods is the lack of a measure of how much confidence can be placed in output estimates. In the last few years many authors have addressed this shortcoming from various angles, focusing primarily on predicting output bounds as a function of the trained network's characteristics, typically as defined by the Hessian matrix. In this paper the problem of the effect of errors or noise in the presented input vector is examined, and a method based on perturbation analysis of determining output bounds from the error in the input vector and the imperfections in the weight values after training is also presented and demonstratedKeywords
This publication has 20 references indexed in Scilit:
- Nonparametric estimation and classification using radial basis function nets and empirical risk minimizationIEEE Transactions on Neural Networks, 1996
- A Comparison of Some Error Estimates for Neural Network ModelsNeural Computation, 1996
- On radial basis function nets and kernel regression: Statistical consistency, convergence rates, and receptive field sizeNeural Networks, 1994
- A Practical Bayesian Framework for Backpropagation NetworksNeural Computation, 1992
- Predicting the Future: Advantages of Semilocal UnitsNeural Computation, 1991
- Orthogonal least squares learning algorithm for radial basis function networksIEEE Transactions on Neural Networks, 1991
- The self-organizing mapProceedings of the IEEE, 1990
- Sensitivity of feedforward neural networks to weight errorsIEEE Transactions on Neural Networks, 1990
- Fast Learning in Networks of Locally-Tuned Processing UnitsNeural Computation, 1989
- Consistent inference of probabilities in layered networks: predictions and generalizationsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1989