It has frequently been remarked that a major drawback of the back-propagation learning rule, and one that does not bode well for its application to real-world problems, is its poor scaling properties - with large networks, back-propagation can take infeasibly long to converge. The research outlined shows that an intuitively straightforward modification of back-propagation can greatly improve its performance, particularly for large and structured networks.
|Original language||English (US)|
|Number of pages||1|
|Issue number||1 SUPPL|
|State||Published - 1988|
|Event||International Neural Network Society 1988 First Annual Meeting - Boston, MA, USA|
Duration: Sep 6 1988 → Sep 10 1988