文献
J-GLOBAL ID:202002267373942932
整理番号:20A2615203
工学データの学習と予測に対する分数ホットデック補完の影響【JST・京大機械翻訳】
Impacts of Fractional Hot-Deck Imputation on Learning and Prediction of Engineering Data
著者 (6件):
Song Ikkyun
(Department of Civil Engineering, Iowa State University, Ames, IA, USA)
,
Yang Yicheng
(Department of Civil Engineering, Iowa State University, Ames, IA, USA)
,
Im Jongho
(Department of Applied Statistics, Yonsei University, Seoul, Republic of Korea)
,
Tong Tong
(Department of Civil Engineering, Iowa State University, Ames, IA, USA)
,
Ceylan Halil
(Department of Civil Engineering, Iowa State University, Ames, IA, USA)
,
Cho In Ho
(Department of Civil Engineering, Iowa State University, Ames, IA, USA)
資料名:
IEEE Transactions on Knowledge and Data Engineering
(IEEE Transactions on Knowledge and Data Engineering)
巻:
32
号:
12
ページ:
2363-2373
発行年:
2020年
JST資料番号:
T0838A
ISSN:
1041-4347
CODEN:
ITKEEH
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
アメリカ合衆国 (USA)
言語:
英語 (EN)