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Triangulation by Continuous Embedding

dc.date.accessioned2004-10-20T20:48:48Z
dc.date.accessioned2018-11-24T10:23:13Z
dc.date.available2004-10-20T20:48:48Z
dc.date.available2018-11-24T10:23:13Z
dc.date.issued1997-03-01en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/7176
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/7176
dc.description.abstractWhen triangulating a belief network we aim to obtain a junction tree of minimum state space. Searching for the optimal triangulation can be cast as a search over all the permutations of the network's vaeriables. Our approach is to embed the discrete set of permutations in a convex continuous domain D. By suitably extending the cost function over D and solving the continous nonlinear optimization task we hope to obtain a good triangulation with respect to the aformentioned cost. In this paper we introduce an upper bound to the total junction tree weight as the cost function. The appropriatedness of this choice is discussed and explored by simulations. Then we present two ways of embedding the new objective function into continuous domains and show that they perform well compared to the best known heuristic.en_US
dc.format.extent6 p.en_US
dc.format.extent1326120 bytes
dc.format.extent3467744 bytes
dc.language.isoen_US
dc.subjectAIen_US
dc.subjectMITen_US
dc.subjectbelief networksen_US
dc.subjecttriangulationen_US
dc.subjectcombinatorial optimizationen_US
dc.titleTriangulation by Continuous Embeddingen_US


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