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分類 国際会議
著者名 (author) Tadashi Ae,Keiji Agusa,Satoshi Fujita,Masafumi Yamashita
英文著者名 (author)
編者名 (editor)
編者名 (英文)
キー (key)
表題 (title) On Neural Networks for Graph Isomorphism Problem
表題 (英文)
書籍・会議録表題 (booktitle) Proc. the RNNS/IEEE Symposium on Neuroinformatics and Neurocomputers
書籍・会議録表題(英文)
巻数 (volume)
号数 (number)
ページ範囲 (pages) 1142-1148
組織名 (organization)
出版元 (publisher)
出版元 (英文)
出版社住所 (address)
刊行月 (month) October
出版年 (year) 1992
付加情報 (note) Rostov-von-Don, Russia
注釈 (annote) DOI: 10.1109/RNNS.1992.268621
内容梗概 (abstract) Although the Hopfield neural networks is known to provide an efficient algorithm for hard problems, it cannot always give the correct solution due to local minima. For the graph isomorphism problem (which has not yet been proved to be polynomially solvable or NP-complete), the authors first introduce a Hopfield network that shows a similar behavior, and give some additional initial conditions, which are collectively called condition C. However, the Hopfield network with condition C is still not powerful enough. The authors introduce another type of neural network and show that it can solve the problem correctly at least for small graphs.
論文電子ファイル Not available.


[1-65]  Tadashi Ae, Keiji Agusa, Satoshi Fujita, and Masafumi Yamashita, ``On Neural Networks for Graph Isomorphism Problem,'' In Proc. the RNNS/IEEE Symposium on Neuroinformatics and Neurocomputers, pp. 1142-1148, October 1992. (Rostov-von-Don, Russia)

@inproceedings{1_65,
    author = {Tadashi Ae and Keiji Agusa and Satoshi Fujita and Masafumi
    Yamashita},
    author_e = {},
    editor = {},
    editor_e = {},
    title = {On Neural Networks for Graph Isomorphism Problem},
    title_e = {},
    booktitle = {Proc. the RNNS/IEEE Symposium on Neuroinformatics and
    Neurocomputers},
    booktitle_e = {},
    volume = {},
    number = {},
    pages = {1142-1148},
    organization = {},
    publisher = {},
    publisher_e = {},
    address = {},
    month = {October},
    year = {1992},
    note = {Rostov-von-Don, Russia},
    annote = {DOI: 10.1109/RNNS.1992.268621 }
}

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