CSELM‐QE: A Composite Semi‐supervised Extreme Learning Machine with Unlabeled RSS Quality Estimation for Radio Map Construction-Reference-Cited by-同舟云学术

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CSELM‐QE: A Composite Semi‐supervised Extreme Learning Machine with Unlabeled RSS Quality Estimation for Radio Map Construction

Published:2020-11 Issue:6 Volume:29 Page:1016-1024
ISSN:1022-4653
Container-title:Chinese Journal of Electronics
language:en
Short-container-title:Chin. j. electron.

Author:

Zhao Jianli¹,Wang Wei¹,Sun Qiuxia²,Huo Huan³,Sun Guoqiang¹,Gao Xiang¹,Zhu Chendi¹

Affiliation:

1. College of Computer Science and EngineeringShandong University of Science and TechnologyQingdao266510China

2. College of Mathematics and Systems ScienceShandong University of Science and TechnologyQingdao266590China

3. Faculty of Engineering and Information TechnologyUniversity of Technology SydneyAustralia

Funder

Key Technology Research and Development Program of Shandong

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Applied Mathematics,Electrical and Electronic Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/cje.2020.09.002

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Novel Robust Online Extreme Learning Machine for the Non-Gaussian Noise;Chinese Journal of Electronics;2023-01

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