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Creators : Maruta Akihiro | Sakai Chihiro | Mikawa Mei | Akita Hinako | Inamitsu Masako | Fujioka Riko | Saiki Yukio | Yamamoto Takeshi Publishers : Yamaguchi University School of Medicine Date Issued : 2021
Creators : Ishimaru Yasutaka | Mahbub MH | Yamaguchi Natsu | Hase Ryosuke | Nakagami Yuki | Takahashi Hidekazu | Watanabe Rie | Saito Hiroyuki | Shimokawa Junki | Yamamoto Hiroshi | Kikuchi Shinya | Tanabe Tsuyoshi Publishers : Yamaguchi University School of Medicine Date Issued : 2021
Creators : Yanagisawa Noboru Publishers : 山口大學經濟學會 Date Issued : 2021-07-31
In this study, we present a new method for estimating input coefficient matrix with deep learning. We use the data of prefectures of Japan as the training data. However, the data is small and is likely to cause overfitting. Therefore, we attempt to avoid the over-fitting by creating new data of virtual areas aggregating multiple prefectures. The approach is based on the concept of data augmentation. As a result of predicting the input coefficient matrix of Yamaguchi prefecture and Gujo city, we showed that the method using deep learning can estimate the matrix with more stable accuracy than RAS method for these areas.
Creators : Fukui Shogo Publishers : 山口大學經濟學會 Date Issued : 2021-07-31
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