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J-GLOBAL ID：201702245526650915   Reference number：17A0740404

Collective mutual information maximization to unify passive and positive approaches for improving interpretation and generalization

解釈と汎化を向上させるために受動的アプローチと積極的アプローチを一体化する集団的な相互情報量最大化

Author (1)： Kamimura Ryotaro (IT Education Center and Graduate School of Science and Technology, Tokai University 4-1-1 Kitakaname, Hiratsuka, Kanagawa 259-1292, Japan)
Material： Neural Networks  (Neural Netw)
Volume： 90  Page： 56-71  Publication year： Jun. 2017 
JST Material Number： T0698A  ISSN： 0893-6080  Document type： Article
Article type： 原著論文  Country of issue： United Kingdom (GBR)  Language： ENGLISH (EN)

Thesaurus term： *neural network(biomodel) ,...

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Author keywords (6)： Passive ,  Positive ,  Potentiality ,  Interpretation ,  Generalization ,  Mutual information

JST classification (5)： Neurocomputers  (JC06010Q) ,  Artificial intelligence  (JE08000Z) ,  System and control theory in general  (IA02010H) ,  Other methods of operations research  (KA03070E) ,  Central nervous system  (EJ12030F)

Reference (32)：

• Bach, F., Jenatton, R., Mairal, J., & Obozinski, G. (2012). Optimization with sparsity-inducing penalties. Foundations and Trends^® in Machine Learning, 4(1), 1-106.
• Bache, K., & Lichman, M. (2013). UCI machine learning repository.
• Barlow, H. B., Kaushal, T. P., & Mitchison, G. J. (1989). Finding minimum entropy codes. Neural Computation, 1(3), 412-423.
• Deco, G., Finnoff, W., & Zimmermann, H. (1995). Unsupervised mutual information criterion for elimination of overtraining in supervised multilayer networks. Neural Computation, 7(1), 86-107.
• Deco, G., & Obradovic, D. (2012). An information-theoretic approach to neural computing. Springer Science & Business Media.

Terms in the title (5)： 汎化 ,  一体化 ,  集団 ,  相互情報量 ,  最大化