文献
J-GLOBAL ID:201702217449611394
整理番号:17A0853608
欠測データを用いた高次元変数の多重型の非再環流を予測するための適応的ロジスティックグループLasso法【Powered by NICT】
Adaptive logistic group Lasso method for predicting the no-reflow among the multiple types of high-dimensional variables with missing data
著者 (8件):
Yang Xianglin
(School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China)
,
Yunhai Tong
(School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China)
,
Xiangfeng Meng
(School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China)
,
Shuai Zhao
(School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China)
,
Zhi Xu
(State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing 100094, China)
,
Yanjun Li
(State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing 100094, China)
,
Xin Jia
(State Grid Beijing Haidian Electric Power Supply Company, 100086, China)
,
Shaohua Tan
(School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China)
資料名:
IEEE Conference Proceedings
(IEEE Conference Proceedings)
巻:
2016
号:
ICSESS
ページ:
1085-1089
発行年:
2016年
JST資料番号:
W2441A
資料種別:
会議録 (C)
記事区分:
原著論文
発行国:
アメリカ合衆国 (USA)
言語:
英語 (EN)