文献
J-GLOBAL ID:202002256218980035
整理番号:20A2189943
銅浮選法分類のためのツリーParzen推定量最適化を用いたSMOTE-XGBoost【JST・京大機械翻訳】
SMOTE-XGBoost using Tree Parzen Estimator optimization for copper flotation method classification
著者 (5件):
Dong Haipei
(College of Information Science and Engineering, Northeastern University, Shenyang, 110004, Liaoning, China)
,
He Dakuo
(College of Information Science and Engineering, Northeastern University, Shenyang, 110004, Liaoning, China)
,
He Dakuo
(State Key Laboratory of Synthetical Automation for Process Industries, Northeastern, University, Shenyang 110004, China)
,
Wang Fuli
(College of Information Science and Engineering, Northeastern University, Shenyang, 110004, Liaoning, China)
,
Wang Fuli
(State Key Laboratory of Synthetical Automation for Process Industries, Northeastern, University, Shenyang 110004, China)
資料名:
Powder Technology
(Powder Technology)
巻:
375
ページ:
174-181
発行年:
2020年
JST資料番号:
B0730A
ISSN:
0032-5910
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
オランダ (NLD)
言語:
英語 (EN)