Extraction of Rules from Discrete-Time Recurrent Neural Networks

Simple item page
dc.contributor.authorOmlin, Christian W.en_US
dc.contributor.authorGiles, C. Leeen_US
dc.date.accessioned2004-05-31T22:32:24Z
dc.date.available2004-05-31T22:32:24Z
dc.date.created1995-05en_US
dc.date.issued1998-10-15en_US
dc.description.abstractThe extraction of symbolic knowledge from trained neural networks and the direct encoding of (partial) knowledge into networks prior to training are important issues. They allow the exchange of information between symbolic and connectionist knowledge representations. The focus of this paper is on the quality of the rules that are extracted from recurrent neural networks. Discrete-time recurrent neural networks can be trained to correctly classify strings of a regular language. Rules defining the learned grammar can be extracted from networks in the form of deterministic finite-state automata (DFA's) by applying clustering algorithms in the output space of recurrent state neurons. Our algorithm can extract different finite-state automata that are consistent with a training set from the same network. We compare the generalization performances of these different models and the trained network and we introduce a heuristic that permits us to choose among the consistent DFA's the model which best approximates the learned regular grammar. (Also cross-referenced as UMIACS-TR-95-54)en_US
dc.format.extent514899 bytes
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/1903/727
dc.language.isoen_US
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_US
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md.)en_US
dc.relation.isAvailableAtTech Reports in Computer Science and Engineeringen_US
dc.relation.isAvailableAtUMIACS Technical Reportsen_US
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-3465en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-95-54en_US
dc.titleExtraction of Rules from Discrete-Time Recurrent Neural Networksen_US
dc.typeTechnical Reporten_US

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
CS-TR-3465.ps
Size:
502.83 KB
Format:
Postscript Files
Download

Collections

Technical Reports from UMIACS
Technical Reports of the Computer Science Department