Abstract
We have successfully applied this algorithm to several simple problems including some which are nonlinearly separable. An important advantage of this training algorithm is that we do not need to relearn the whole set of learning vectors if new data are added to the system. This is a significant improvement over the back propagation algorithm where new data requires relearning the whole training set. No algorithm however can teach a network functions that the network is incapable of performing. We have discovered that, contrary to the hope of one of the present authors that one could trade extra layers for connectivity, there exist problems which cannot be done on a limited fan-in system.
Original language | English (US) |
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Title of host publication | IEE Conference Publication |
Publisher | Publ by IEE |
Pages | 387-389 |
Number of pages | 3 |
Edition | 313 |
State | Published - 1989 |
Event | First IEE International Conference on Artificial Neural Networks - London, Engl Duration: Oct 16 1989 → Oct 18 1989 |
Other
Other | First IEE International Conference on Artificial Neural Networks |
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City | London, Engl |
Period | 10/16/89 → 10/18/89 |
ASJC Scopus subject areas
- Electrical and Electronic Engineering