Art

J-GLOBAL ID：202002224143330752   Reference number：20A1720295

Difficulty Levels of Mathematical Problems and Machine Learnings

数理問題の難易度と機械学習

Author (2)： TOIDA YUTO (名古屋大学大学院多元数理科学研究科) ,  OHIRA TOORU (名古屋大学大学院多元数理科学研究科)
Material： 日本シミュレーション学会論文誌(Web)  (Transaction of the Japan Society for Simulation Technology (Web))
Volume： 12  Issue： 2  Page： 49-53(J-STAGE)  Publication year： 2020 
JST Material Number： U0267A  ISSN： 1883-5058  Document type： Article
Article type： 原著論文  Country of issue： Japan (JPN)  Language： JAPANESE (JA)

Thesaurus term： *machine learning ,  *neural network(biomodel) ,  learning algorithm ,  prime number ,  mathematics
Semi thesaurus term： Learning performance ,  degree of difficulty

Author keywords (6)： Machine Learning ,  Mathematics ,  Problem Classification ,  Machine Learning ,  Mathematics ,  Problem Classification

JST classification (2)： Artificial intelligence  (JE08000Z) ,  Neurocomputers  (JC06010Q)

Reference (17)：

• 1) W. McCulloch and W. Pitts: A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics, 5, 115/133 (1945)
• 2) J. D. Cowan: Neural networks: The early days, In Advances in Neural Information Processing Systems, 2, 828, Morgan Kaufmann (1990)
• 3) A. Krizhevsky, I. Sutskever and G. E. Hinton: Imagenet classification with deep convolutional neural networks, In Advances in Neural Information Processing Systems, 25, 1097, Curran Associates (2012)
• 4) Y. Toida and T. Ohira: Machine Learning as a Robust Index of the Difficulty of Mathematical Problems, In The Proceedings of Artificial Life and Robotics 2019, (AROB 24th 2019, Beppu, Japan, January 2019), 1168 (2019)
• 5) M. Abadi et al.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, https://www.tensorflow.org/ (2015)

Terms in the title (3)： 問題 ,  難易度 ,  機械学習