Some numerical aspects of the training problem for feed-forward neural nets
This paper considers the feed-forward training problem from the numerical point of view, in particular the conditioning of the problem. It is well known that the feed-forward training problem is often ill-conditioned; this affects the behaviour of training algorithms, the choice of such algorithms and the quality of the solutions achieved. A geometric interpretation of ill-conditioning is explored and an example of function approximation is analysed in detail.
feed-forward neural net training numerical ill-conditioning nonlinear least-squares non-uniqueness