DocumentCode :
3050985
Title :
Machine learning using single-layered and multi-layered neural networks
Author :
Sestito, Sabrina ; Dillon, Tharam
Author_Institution :
Dept. of Comput. Sci., La Trobe Univ., Bundoora, Vic., Australia
fYear :
1990
fDate :
6-9 Nov 1990
Firstpage :
269
Lastpage :
275
Abstract :
Methods are proposed which automatically extract a high level knowledge representation in the form of rules from the lower level representation used by neural networks. The strength of neural networks in dealing with noise has made it possible to produce correct rules in a noisy domain. Results obtained when applying the proposed method to a noisy domain suggest that this method can be used in real-world domains. It is believed that this work will lead to an area of machine learning which uses neural networks as the basis of knowledge acquisition which can deal with real-world difficulties
Keywords :
artificial intelligence; knowledge acquisition; knowledge representation; learning systems; neural nets; knowledge acquisition; knowledge representation; lower level representation; machine learning; multilayered neural networks; noise; rules; Artificial intelligence; Artificial neural networks; Automation; Computer networks; Knowledge acquisition; Knowledge based systems; Knowledge representation; Machine learning; Multi-layer neural network; Neural networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Tools for Artificial Intelligence, 1990.,Proceedings of the 2nd International IEEE Conference on
Conference_Location :
Herndon, VA
Print_ISBN :
0-8186-2084-6
Type :
conf
DOI :
10.1109/TAI.1990.130346
Filename :
130346
Link To Document :
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