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Smart energy forecasting strategy with four machine learning models for climate-sensitive and non-climate sensitive conditions Journal article
Energy, 2020,Volume: 198
Authors:  Ahmad, Tanveer;  Huanxin, Chen;  Zhang, Dongdong;  Zhang, Hongcai
Favorite |  | TC[WOS]:15 TC[Scopus]:16 | Submit date:2021/09/09
Energy Benchmark  Energy Forecasting  Imbalanced Data Handling  Performance Correlation  Supervised Learning  Utilities And Building Consumption  
A new learning paradigm for random vector functional-link network: RVFL+ Journal article
Neural Networks, 2020,Volume: 122,Page: 94-105
Authors:  Zhang,Peng Bo;  Yang,Zhi Xin
Favorite |  | TC[WOS]:10 TC[Scopus]:16 | Submit date:2021/03/11
KRVFL+  Learning using privileged information  Random vector functional link networks  RVFL+  SVM+  The Rademacher complexity  
Block sparse representation for pattern classification: Theory, extensions and applications Journal article
Pattern Recognition, 2019,Volume: 88,Page: 198-209
Authors:  Wang Y.;  Tang Y.Y.;  Li L.;  Zheng X.
Favorite |  | TC[WOS]:9 TC[Scopus]:8 | Submit date:2019/02/11
Block sparsity  M-estimator  Representation based classifier  Subspace  
Kernel-Based Multilayer Extreme Learning Machines for Representation Learning Journal article
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018,Volume: 29,Issue: 3,Page: 757-762
Authors:  Wong, Chi Man;  Vong, Chi Man;  Wong, Pak Kin;  Cao, Jiuwen
Favorite |  | TC[WOS]:77 TC[Scopus]:86 | Submit date:2018/10/30
Kernel Learning  Multilayer Extreme Learning Machine (Ml-elm)  Representation Learning  Stacked Autoencoder (Sae)  
A Fuzzy Restricted Boltzmann Machine: Novel Learning Algorithms Based on the Crisp Possibilistic Mean Value of Fuzzy Numbers Journal article
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2018,Volume: 26,Issue: 1,Page: 117-130
Authors:  Feng, Shuang;  Chen, C. L. Philip
Favorite |  | TC[WOS]:32 TC[Scopus]:38 | Submit date:2018/10/30
Crisp possibilistic mean value  fuzzy number  fuzzy restricted Boltzmann machine (FRBM)  learning algorithm  
Kernel-based sparse regression with the correntropy-induced loss Journal article
APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS, 2018,Volume: 44,Issue: 1,Page: 144-164
Authors:  Chen, Hong;  Wang, Yulong
Favorite |  | TC[WOS]:11 TC[Scopus]:14 | Submit date:2018/10/30
Learning theory  Kernel-based regression  Correntropy-induced loss  Sparsity  Learning rate  
A novel AdaBoost framework with robust threshold and structural optimization Journal article
IEEE Transactions on Cybernetics, 2018,Volume: 48,Issue: 1,Page: 64-76
Authors:  Zhang P.-B.;  Yang Z.-X.
Favorite |  | TC[WOS]:34 TC[Scopus]:35 | Submit date:2018/12/22
Adaboost Algorithm  Bounds Of Empirical Error  Ensemble Method  Generalization Error  Indoor Positioning System  Robust Threshold  Special Single-layer Neural Network  Structural Optimization  
A MnO2 nanosheet-assisted GSH detection platform using an iridium(III) complex as a switch-on luminescent probe Journal article
NANOSCALE, 2017,Volume: 9,Issue: 14,Page: 4677-4682
Authors:  Dong, Zhen-Zhen;  Lu, Lihua;  Ko, Chung-Nga;  Yang, Chao;  Li, Shengnan;  Lee, Ming-Yuen;  Leung, Chung-Hang;  Ma, Dik-Lung
View | Adobe PDF | Favorite |  | TC[WOS]:73 TC[Scopus]:70 | Submit date:2018/10/30
Error analysis for the semi-supervised algorithm under maximum correntropy criterion Journal article
NEUROCOMPUTING, 2017,Volume: 223,Page: 45-53
Authors:  Zuo, Ling;  Wang, Yulong
Favorite |  | TC[WOS]:0 TC[Scopus]:0 | Submit date:2018/10/30
Semi-supervised learning  Correntropy  Excess generalization error  Manifold error  
Model development and surface analysis of a bio-chemical process Journal article
Chemometrics and Intelligent Laboratory Systems, 2016,Volume: 157,Page: 133-139
Authors:  Jiang D.;  Zhou W.-H.;  Garg A.;  Garg A.
Favorite |  | TC[WOS]:3 TC[Scopus]:2 | Submit date:2018/12/21
Biochemical  Cross-validation  Genetic Programming  Lead Removal  Phytoremediation  Statistical Modelling