Fuzzy-DL reasoning over unknown fuzzy degrees
OTM'07 Proceedings of the 2007 OTM Confederated international conference on On the move to meaningful internet systems - Volume Part II
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Error back-propagation--and its many variations--has been used extensively to train neu- ral networks. A multi-layer system cannot be trained in a supervised learning scheme because data are usually provided only as end-to-end input-output pairs for the global system. The central idea of error back-propagation is to derive target input-output pairs for each layer in the system from the global input-output data. We propose a new method for error-back propagation in a fuzzy Description Logic reasoning system. This permits us to derive input- output data pairs in a two-layer setup for training the lower-layer classifiers. To the best of our knowledge, this is the first error back-propagation method for a logic reasoning system.