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Paper Abstract and Keywords
Presentation 2019-10-04 10:15
[Poster Presentation] A study of similar network generative model using machine learning
Shohei Nakazawa, Kohei Watabe, Kenji Nakagawa (Nagaoka Univ. of Tech.)
Abstract (in Japanese) (See Japanese page) 
(in English) A real topology data are required when we simulate assuming an environment close to a real situation. The real data of topology usually are not opened in public, so it is difficult to get data that accordance with the objective of simulation. It needs to generate a topology by some generating model when the data are not obtained, but almost the conventional generating models can only reproduce one aspect of the real data properties. So it can not say that generated topologies are similar to real data. In this paper, as an advance study to extract the features of the topology, we will build and evaluate an auto-encoder that has the same input/output topology. The purpose of this study is to construct a model using a neural network to generate a topology that reproduces the various features of real data. In advance, we divide the topology of training data into clusters and create a dictionary of clusters.
The auto-encoder trains the topology expressed as a cluster tree based on the dictionary.
Keyword (in Japanese) (See Japanese page) 
(in English) topology generation / machine learning / auto-encoder / feature extraction / / / /  
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Conference Information
Committee MIKA  
Conference Date 2019-10-02 - 2019-10-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Wireless Communication System, etc. 
Paper Information
Registration To MIKA 
Conference Code 2019-10-MIKA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A study of similar network generative model using machine learning 
Sub Title (in English)  
Keyword(1) topology generation  
Keyword(2) machine learning  
Keyword(3) auto-encoder  
Keyword(4) feature extraction  
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1st Author's Name Shohei Nakazawa  
1st Author's Affiliation Nagaoka University of Technology (Nagaoka Univ. of Tech.)
2nd Author's Name Kohei Watabe  
2nd Author's Affiliation Nagaoka University of Technology (Nagaoka Univ. of Tech.)
3rd Author's Name Kenji Nakagawa  
3rd Author's Affiliation Nagaoka University of Technology (Nagaoka Univ. of Tech.)
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Speaker Author-1 
Date Time 2019-10-04 10:15:00 
Presentation Time 50 minutes 
Registration for MIKA 
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