Fast Training Algorithms for Multilayer Neural Nets . An important aspect of the analysis is the need to reject a good fraction of the numerous background events, while keeping most of the signal. Fast training algorithms for multilayer neural nets . While better error functions do lead to better generalisation of learned systems, there is no guarantee. The problem is still there.
Criteria always have to be set for learning.
However , no criteria can guarantee that they correspond to true generalisation. This is a problem faced by all inductive learning methods. There is no way in practice one can solve the problem entirely. Fast training algorithms for multilayer neural nets . Techniques very similar to those used in tree construction, such as information theoretic splitting criteria and pruning, can be found in neural tree construction also.