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dc.contributor.authorLyon, C.
dc.date.accessioned2010-12-09T16:39:23Z
dc.date.available2010-12-09T16:39:23Z
dc.date.issued1996
dc.identifier.citationLyon , C 1996 , Using single layer networks for discrete, sequential data: an example from natural language processing. [extended version] . UH Computer Science Technical Report , vol. 242 , University of Hertfordshire .
dc.identifier.otherPURE: 95401
dc.identifier.otherPURE UUID: 07d8b1a5-9372-4dfb-b4f4-e8be5c6ba1fb
dc.identifier.otherdspace: 2299/5071
dc.identifier.urihttp://hdl.handle.net/2299/5071
dc.description.abstractSupervised, feed-forward networks will, in general, need more than one layer to process data. However, if they can be used, single layer networks offer advantages of functional transparency and operational speed. Now, in some cases data can be pre-processed and then presented in a linearly separable form for processing by a single layer net. In effect, processing at different stages can be de-coupled. The critical issue is finding the pre-processing function to convert data into an appropriate form. For characteristic linguistic data this can be done, and a natural language parser which has been successfully implemented is used to investigate the approach. Single layer nets can then be trained by finding weight adjustments based on (a) factors proportional to the input, as in the Perceptron, (b) factors proportional to the existing weights,and (c) an error minimization mathod. In our experiments generalization ability varies little; method (b) has been used for the prototype parser.en
dc.language.isoeng
dc.publisherUniversity of Hertfordshire
dc.relation.ispartofseriesUH Computer Science Technical Report
dc.rightsOpen
dc.titleUsing single layer networks for discrete, sequential data: an example from natural language processing. [extended version]en
dc.contributor.institutionSchool of Computer Science
dc.relation.schoolSchool of Computer Science
dcterms.dateAccepted1996
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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