UM

Browse/Search Results:  1-3 of 3 Help

Selected(0)Clear Items/Page:    Sort:
On the Benefits of Two Dimensional Metric Learning Journal article
IEEE Transactions on Knowledge and Data Engineering, 2021
Authors:  Wu, Di;  Zhou, Fan;  Wang, Boyu;  Wong, Chi Man;  Shui, Changjian;  Zhou, Yuan;  Lao, Qicheng;  Wan, Feng
Favorite |  | TC[WOS]:0 TC[Scopus]:0 | Submit date:2022/05/13
Boosting  Brain Modeling  Complexity Theory  Electroencephalography  Indexes  Low-rank Matrices  Measurement  Metric Learning  Rademacher Complexity  Symmetric Matrices  Two Dimensional Learning  Upper Bound  
Finite Gaussian Mixture Model Based Multimodeling for Nonlinear Distributed Parameter Systems Journal article
IEEE Transactions on Industrial Informatics, 2020,Volume: 16,Issue: 3,Page: 1754-1763
Authors:  Xu, Kangkang;  Yang, Haidong;  Zhu, Chengjiu;  Hu, Luoke
Favorite |  | TC[WOS]:11 TC[Scopus]:11 | Submit date:2021/12/06
Distributed parameter systems (DPSs)  finite Gaussian mixture model (FGMM)  multiple spatiotemporal modeling  principle component regression (PCR)  Rademacher complexity  
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]:37 TC[Scopus]:34 | Submit date:2021/03/11
Krvfl++  Learning Using Privileged Information  Random Vector Functional Link Networks  Rvfl++  Svm++  The Rademacher Complexity