Are modified back-propagation algorithms worth the effort?

A wide range of modifications and extensions to the backpropagation (BP) algorithm have been tested on a real world medical problem. Our results show that: 1) proper tuning of learning parameters of standard BP not only increases the speed of learning but also has a significant effect on generalisation; 2) parameter combinations and training options which lead to fast learning do not usually yield good generalisation and vice versa; 3) standard BP may be fast enough when its parameters are finely tuned; 4) modifications developed on artificial problems for faster learning do not necessarily give faster learning on real-world problems, and when they do, it may be at the expense of generalisation; and 5) even when modified BP algorithms perform well, they may require extensive fine-tuning to achieve this performance. For our problem, none of the modifications could justify the effort to implement them