New efficient neural network approach for processing graph structured data

dc.contributor.guide	Gnana jothi, R B
dc.coverage.spatial
dc.creator.researcher	Meena rani, S M
dc.date.accessioned	2016-01-08T11:33:20Z
dc.date.available	2016-01-08T11:33:20Z
dc.date.awarded	n.d
dc.date.completed	December 2013
dc.date.registered	n.d
dc.description.abstract	Graphs serve as important data structures in many applications including so newlinecial network web and Bio informatics Recursive Neural NetworksRNN are a newlineconnectionist model designed to process structured data RNN are particularly newlinesuited to process Directed Positional Acyclic GraphsDPAGs Graph Neural Net newlineworkGNN has been modeled to process data represented in graph domains con newlinesidering the topology of the graph newline newline
dc.description.note
dc.format.accompanyingmaterial	DVD
dc.format.dimensions
dc.format.extent	viii, 116p.
dc.identifier.uri	http://hdl.handle.net/10603/69853
dc.language	English
dc.publisher.institution	Department of Mathematics
dc.publisher.place	Tirunelveli
dc.publisher.university	Manonmaniam Sundaranar University
dc.relation
dc.rights	university
dc.source.university	University
dc.subject.keyword	Directed Positional Acyclic Graphs
dc.subject.keyword	Graph Neural Network
dc.subject.keyword	Graph structure
dc.subject.keyword	neural network
dc.subject.keyword	Recursive Neural Networks
dc.title	New efficient neural network approach for processing graph structured data
dc.title.alternative
dc.type.degree	Ph.D.