1988
@techreport{Fah1988,
vgclass = {report},
vgproject = {nn},
author = {Scott E. Fahlman},
title = {An Empirical Study of Learning Speed in Back-Propagation
Networks},
number = {CMU-CS-88-162},
institution = {School of Computer Science, Carnegie Mellon University},
address = {Pittsburgh, PA 15213},
month = {September},
year = {1988},
abstract = {Most connectionist or neural network'' learning
systems use some form of the back-propagation algorithm. However,
back-propagation learning is too slow for many applications,
and it scales up poorly as tasks become larger and more complex.
The factors governing learning speed are poorly understood. I have
begun a systematic, empirical study of learning speed in backprop-like
algorithms, measured against a variety of benchmark problems. The goal
is twofold: to develop faster learning algorithms and to contribute to
the development of a methodology that will be of value in future
studies of this kind.
This paper is a progress report describing the results obtained during
the first six months of this study. To date I have looked only at a
limited set of benchmark problems, but the results on these are
encouraging: I have developed a new learning algorithm that is faster
than standard backprop by an order of magnitude or more and that
appears to scale up very well as the problem size increases.},
}