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  4. Machine learning with computer networks: techniques, datasets, and models
 
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Machine learning with computer networks: techniques, datasets, and models

Citation Link: https://doi.org/10.15480/882.9633
Publikationstyp
Journal Article
Date Issued
2024-04-03
Sprache
English
Author(s)
Afifi, Haitham  
Pochaba, Sabrina  
Boltres, Andreas  
Laniewski, Dominic  
Haberer, Janek  
Paeleke, Leonard  
Poorzare, Reza  
Karlsruhe University of Applied Sciences  
Seufert, Michael  
Wehner, Nikolas  
Redder, Adrian  
Samikwa, Eric  
Stolpmann, Daniel  orcid-logo
Kommunikationsnetze E-4  
TORE-DOI
10.15480/882.9633
TORE-URI
https://hdl.handle.net/11420/47702
Journal
IEEE access  
Volume
12
Start Page
54673
End Page
54720
Citation
IEEE Access 12: 54673-54720 (2024)
Publisher DOI
10.1109/ACCESS.2024.3384460
Scopus ID
2-s2.0-85189623730
Machine learning has found many applications in network contexts. These include solving optimisation problems and managing network operations. Conversely, networks are essential for facilitating machine learning training and inference, whether performed centrally or in a distributed fashion. To conduct rigorous research in this area, researchers must have a comprehensive understanding of fundamental techniques, specific frameworks, and access to relevant datasets. Additionally, access to training data can serve as a benchmark or a springboard for further investigation. All these techniques are summarized in this article; serving as a primer paper and hopefully providing an efficient start for anybody doing research regarding machine learning for networks or using networks for machine learning.
Subjects
Computer networking
datasets
machine learning
metrics
tools
MLE@TUHH
DDC Class
004: Computer Sciences
Lizenz
https://creativecommons.org/licenses/by-nc-nd/4.0/
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