Feng Liu (he/him) -- 刘峰
Feng Liu (he/him) -- 刘峰
Lecturer in Statistics (Data Science), The University of Melbourne
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Zitiert von
Zitiert von
A multi-step wind-speed forecasting model based on WRF ensembles and fuzzy systems
J Zhao, ZH Guo, ZY Su, ZY Zhao, X Xiao, F Liu*
Applied Energy 162, 808-826, 2016
Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models
J Wang, Y Song*, F Liu, R Hou
Renewable and Sustainable Energy Reviews 60, 960-981, 2016
Heterogeneous domain adaptation: An unsupervised approach
F Liu, G Zhang, J Lu*
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2020
Learning Deep Kernels for Non-Parametric Two-Sample Tests
F Liu, W Xu, J Lu, G Zhang, A Gretton, DJ Sutherland
ICML 2020, 2020
A cross-domain recommender system with consistent information transfer
Q Zhang, D Wu, J Lu*, F Liu, G Zhang
Decision Support Systems (DSS) 104, 49-63, 2017
A hybrid forecasting model based on date-framework strategy and improved feature selection technology for short-term load forecasting
P Jiang, F Liu*, Y Song
Energy 119, 694-709, 2017
Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models
S Qin, F Liu*, J Wang, B Sun
Atmospheric environment 98, 665-675, 2014
Open Set Domain Adaptation: Theoretical Bound and Algorithm
Z Fang, J Lu*, F Liu, J Xuan, G Zhang
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021
Does deep learning help topic extraction? A kernel k-means clustering method with word embedding
Y Zhang, J Lu, F Liu, Q Liu, A Porter, H Chen*, G Zhang
Journal of Informetrics (JOI) 12 (4), 1099-1117, 2018
Multisource Heterogeneous Unsupervised Domain Adaptation via Fuzzy Relation Neural Networks
F Liu, G Zhang, J Lu*
IEEE Transactions on Fuzzy Systems (TFS), 2021
A dynamic-choice neural network for electricity price forecasting
J Wang, F Liu*, Y Song, J Zhao
Applied Soft Computing 48, 281-297, 2016
Accumulating regional density dissimilarity for concept drift detection in data streams
A Liu, J Lu*, F Liu, G Zhang
Pattern Recognition (PR) 76, 256-272, 2018
Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation
L Zhong, Z Fang, F Liu, B Yuan, G Zhang, J Lu*
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021
Interval forecasts of a novelty hybrid model for wind speeds
S Qin, F Liu*, J Wang, Y Song
Energy Reports 1, 8-16, 2015
Learning from a Complementary-label Source Domain: Theory and Algorithms
Y Zhang, F Liu, Z Fang, B Yuan, G Zhang, J Lu*
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021
The forecasting research of early warning systems for atmospheric pollutants: A case in Yangtze River Delta region
Y Song, S Qin*, J Qu, F Liu
Atmospheric Environment 118, 58-69, 2015
Development of a hybrid model to predict construction and demolition waste: China as a case study
Y Song, Y Wang*, F Liu, Y Zhang
Waste management 59, 350-361, 2017
Spatial-temporal analysis and projection of extreme particulate matter (PM10 and PM2. 5) levels using association rules: A case study of the Jing-Jin-Ji region, China
S Qin, F Liu*, C Wang, Y Song, J Qu
Atmospheric Environment 120, 339-350, 2015
Unsupervised heterogeneous domain adaptation via shared fuzzy equivalence relations
F Liu, J Lu, G Zhang*
IEEE Transactions on Fuzzy Systems (TFS) 26 (6), 3555-3568, 2018
Fuzzy transfer learning using an infinite gaussian mixture model and active learning
H Zuo, J Lu*, G Zhang, F Liu
IEEE Transactions on Fuzzy Systems (TFS) 27 (2), 291-303, 2018
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