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J-GLOBAL ID：201902239009381359
Reference number：19A0418982
A Self-Organization Model Developing Higher Order Units by Self-Test Learning
自己テストにより高次素子を追加し学習する自己組織化モデル
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Author (2)：
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Material：
Volume：
118
Issue：
284(IBISML2018 44-104)(Web)
Page：
419-424 (WEB ONLY)
Publication year：
Oct. 29, 2018
JST Material Number：
S0532B
ISSN：
0913-5685
Document type：
Proceedings
Article type：
原著論文
Country of issue：
Japan (JPN)
Language：
JAPANESE (JA)
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Semi thesaurus term：
Thesaurus term/Semi thesaurus term
Keywords indexed to the article.
All keywords is available on JDreamIII(charged).
On J-GLOBAL, this item will be available after more than half a year after the record posted. In addtion, medical articles require to login to MyJ-GLOBAL.
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JST classification (2)：
JST classification
Category name(code) classified by JST.
Artificial intelligence
, Pattern recognition
Reference (7)：
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D.H. Ackley, G.E. Hinton, and T.J. Sejnowski, “A learning algorithm for Boltzmann machines,” Cognitive Science, vol.9, pp.147-169, 1985.
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S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.PAMI-6, pp.721-741, 1984.
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S. Geman, “Stochastic relaxation methods for image restoration and expert systems,” Maximum-Entropy and Bayesian Methods in Science and Engineering, eds. by E. G. J and C.R. Smith, vol.2, pp.265-311, Kluwer Academic Publisher, 1988.
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S. Geman, “Notes on a self-organizing machine,” Parallel Models of Associative Memory, eds. by G.E. Hinton and J.A. Anderson, pp.237-263, Lawrence Erlbaum Associates, New Jersey, 1981.
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B.R. Davis, “A neurobiological approach to machine intelligence,” PhD thesis, Brown University, 1982.
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Terms in the title
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