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Neural Voting Machines

dc.date.accessioned2005-12-22T02:20:05Z
dc.date.accessioned2018-11-24T10:24:21Z
dc.date.available2005-12-22T02:20:05Z
dc.date.available2018-11-24T10:24:21Z
dc.date.issued2004-12-31
dc.identifier.urihttp://hdl.handle.net/1721.1/30513
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/30513
dc.description.abstract Winner-take-all networks typically pick as winners that alternative with the largest excitatory input. This choice is far from optimal when there is uncertainty in the strength of the inputs, and when information is available about how alternatives may be related. In the Social Choice community, many other procedures will yield more robust winners. The Borda Count and the pair-wise Condorcet tally are among the most favored. Their implementations are simple modifications of classical recurrent networks.
dc.format.extent12 p.
dc.format.extent13714512 bytes
dc.format.extent523751 bytes
dc.language.isoen_US
dc.subjectAI
dc.subjectWTA
dc.subjectBorda machine
dc.subjectCondorcet procedure
dc.subjectneural network
dc.titleNeural Voting Machines


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