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J-GLOBAL ID：201302298869074369   Reference number：13A0026274

Propagating Labels via Sparse Combination of Multiple Graphs

ラベル伝播アルゴリズムにおける複数グラフのスパース結合法

Author (2)： KARASUYAMA Masayuki (Kyoto Univ., Kyoto, JPN) ,  MAMITSUKA Hiroshi (Kyoto Univ., Kyoto, JPN)
Material： 電子情報通信学会技術研究報告  (IEICE Technical Report (Institute of Electronics, Information and Communication Engineers))
Volume： 112  Issue： 279(IBISML2012 34-92)  Page： 171-178  Publication year： Oct. 31, 2012 
JST Material Number： S0532B  ISSN： 0913-5685  Document type： Proceedings
Article type： 原著論文  Country of issue： Japan (JPN)  Language： ENGLISH (EN)

Thesaurus term： *supervised learning ,  systematic classification ,  knowledge acquisition ,  learning model ,  data structure ,  similarity ,  *learning algorithm ,  *machine learning ,  optimization problem
Semi thesaurus term： Laplacian matrix ,  graph algorithm ,  sparsity ,  *Label propagation ,  calculate efficiency ,  optimization algorithm ,  *semi-supervised learning

JST classification (1)： Artificial intelligence  (JE08000Z)

Reference (15)：

• Zhu, X. Semisupervised learning using gaussian fields and harmonic functions. Proceedings of the 20th International Conference I on Machine Learning,2003. 2003, 912-919
• Zhou, D. I,earning with local and global consistency. Advetn'CPs'tn N^riral Information Processing System. 2004, 16, 321-328
• Tsuda, K. Fast protein c l sificatlion with multiple networks. Bioinfrmatics. 2005, 21suppl, 2, 59-65
• KATO, T. Robust label AI propagation on multiple networks. IEEE Traosacations on I Neural Networks. 2009, 20, 35-44
• Wang, M. Unified video annotation via multigraph learning. IEEE Transactions on Circuits and Systems for Video Technology. 2009, 19, 733-746

Terms in the title (4)： ラベル ,  伝播 ,  アルゴリズム ,  グラフ