Author(s)

Kazuyuki Fujii^1,2

¹International College of Arts and Sciences, Yokohama City University, Yokohama, Japan.
²Department of Mathematical Sciences, Shibaura Institute of Technology, Saitama, Japan.

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.

Least Squares Method, Statistics, Deep Learning, Learning Rate, Gerschgorin’s Theorem

Fujii, K. (2018) Least Squares Method from the View Point of Deep Learning II: Generalization. Advances in Pure Mathematics, 8, 782-791. doi: 10.4236/apm.2018.89048.