A novel method for estimation of prediction limits for global and local approximating neural networks is presented. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods, and calculates limits that indicate the extent to which one can rely on predictions for making future decisions.
['Richard Chinman', 'J. Ding']
Prediction limit estimation for neural network models