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J-GLOBAL ID：201902250649706373   Reference number：19A0492261

Breakdown Point of Robust Support Vector Machines

ロバストなサポートベクトルマシンの破壊点【JST・京大機械翻訳】

Author (5)： Kanamori Takafumi (Department of Computer Science and Mathematical Informatics, Nagoya University, Nagoya 464-8601, Japan) ,  Kanamori Takafumi (RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan) ,  Fujiwara Shuhei (TOPGATE Co. Ltd., Bunkyo-ku, Tokyo 113-0033, Japan) ,  Takeda Akiko (Institute of Statistical Mathematics, Tokyo 190-8562, Japan) ,  Takeda Akiko (RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan)
Material： Entropy (Web)
Volume： 19  Issue： 2  Page： 83  Publication year： 2017 
JST Material Number： U7179A  ISSN： 1099-4300  Document type： Article
Article type： 原著論文  Country of issue： Switzerland (CHE)  Language： ENGLISH (EN)

Abstract/Point： Support vector machine (SVM) i...

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Thesaurus term： *robustness ,  hinge ,  penalty ,  failure ,  measure ,  *support vector machine ,  parameter ,  convexity ,  outlier ,  learning
Semi thesaurus term： 理論解析 ,  数値実験 ,  正則化パラメータ ,  有界 , 【Automatic Indexing@JST】

Author keywords (4)： support vector machine ,  breakdown point ,  outlier ,  kernel function

JST classification (1)： Artificial intelligence  (JE08000Z)

Reference (39)：

• Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn. 1995, 20, 273-297.
• Schölkopf, B.; Smola, A.J. Learning with Kernels; MIT Press: Cambridge, MA, USA, 2002.
• Berlinet, A.; Thomas-Agnan, C. Reproducing Kernel Hilbert Spaces in Probability and Statistics; Kluwer Academic: Boston, MA, USA, 2004.
• Perez-Cruz, F.; Weston, J.; Hermann, D.J.L.; Schölkopf, B. Extension of the ν-SVM Range for Classification. In Advances in Learning Theory: Methods, Models and Applications 190; IOS Press: Amsterdam, The Netherlands, 2003; pp. 179-196.
• Schölkopf, B.; Smola, A.; Williamson, R.; Bartlett, P. New support vector algorithms. Neural Comput. 2000, 12, 1207-1245.

Terms in the title (3)： ロバスト ,  サポートベクトルマシン ,  破壊