Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/69853
Title: | New efficient neural network approach for processing graph structured data |
Researcher: | Meena rani, S M |
Guide(s): | Gnana jothi, R B |
Keywords: | Directed Positional Acyclic Graphs Graph Neural Network Graph structure neural network Recursive Neural Networks |
University: | Manonmaniam Sundaranar University |
Completed Date: | December 2013 |
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 |
Pagination: | viii, 116p. |
URI: | http://hdl.handle.net/10603/69853 |
Appears in Departments: | Department of Mathematics |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 40.11 kB | Adobe PDF | View/Open |
02_certificate.pdf | 13.9 kB | Adobe PDF | View/Open | |
03_declaration.pdf | 17.81 kB | Adobe PDF | View/Open | |
04_acknowledgement.pdf | 14.96 kB | Adobe PDF | View/Open | |
05_content.pdf | 19.25 kB | Adobe PDF | View/Open | |
06_chapter 1.pdf | 58.96 kB | Adobe PDF | View/Open | |
07_chapter 2.pdf | 268.58 kB | Adobe PDF | View/Open | |
08_chapter 3.pdf | 760.2 kB | Adobe PDF | View/Open | |
09_chapter 4.pdf | 76.91 kB | Adobe PDF | View/Open | |
10_chapter 5.pdf | 1.53 MB | Adobe PDF | View/Open | |
11_chapter 6.pdf | 2.52 MB | Adobe PDF | View/Open | |
12_chapter 7.pdf | 21.08 kB | Adobe PDF | View/Open | |
13_reference.pdf | 34.35 kB | Adobe PDF | View/Open | |
14_publication.pdf | 14.25 kB | Adobe PDF | View/Open |
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