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Least Squares Method from the View Point of Deep Learning II: Generalization
International College of Arts and Sciences, Yokohama City University, Yokohama, Japan
Department of Mathematical Sciences, Shibaura Institute of Technology, Saitama, Japan
- 1 International College of Arts and Sciences, Yokohama City University, Yokohama, Japan
- 2 Department of Mathematical Sciences, Shibaura Institute of Technology, Saitama, Japan
Advances in Pure Mathematics·Volume 08 (2018)·Pages 782–791·Published 21 September 2018·DOI10.4236/apm.2018.89048
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Abstract
The least squares method is one of the most fundamental methods in Statistics to estimate correlations among various data. On the other hand, Deep Learning is the heart of Artificial Intelligence and it is a learning method based on the least squares method, in which a parameter called learning rate plays an important role. It is in general very hard to determine its value. In this paper we generalize the preceding paper [K. Fujii: Least squares method from the view point of Deep Learning: Advances in Pure Mathematics, 8 , 485-493, 2018] and give an admissible value of the learning rate, which is easily obtained.
KeywordsLeast Squares MethodStatisticsDeep LearningLearning RateGerschgorin’s Theorem
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