Fundamentals of Machine Learning in Data Center Networks

Wang, T; Li, B; Chen, M; Yu, S

Fundamentals of Machine Learning in Data Center Networks

Wang, T Li, B Chen, M Yu, S

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Publisher:: Springer Nature Singapore
Publication Type:: Chapter
Citation:: SpringerBriefs in Computer Science, 2023, pp. 9-14
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Wang, T
dc.contributor.author	Li, B
dc.contributor.author	Chen, M
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2023-11-09T23:32:26Z
dc.date.available	2023-11-09T23:32:26Z
dc.date.issued	2023-01-01
dc.identifier.citation	SpringerBriefs in Computer Science, 2023, pp. 9-14
dc.identifier.isbn	9789811973949
dc.identifier.uri	http://hdl.handle.net/10453/173289
dc.description.abstract	In this chapter, we will briefly review the common learning paradigms of ML and some preliminary knowledge about data collection and processing. Furthermore, to better assess the strengths and weaknesses of the existing research work, we design a multi-dimensional and multi-perspective quality assessment criteria, called REBEL-3S.
dc.language	en
dc.publisher	Springer Nature Singapore
dc.relation.ispartof	SpringerBriefs in Computer Science
dc.relation.isbasedon	10.1007/978-981-19-7395-6_2
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Fundamentals of Machine Learning in Data Center Networks
dc.type	Chapter
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CCSP - Centre for Cyber Security and Privacy
utslib.copyright.status	closed_access	*
dc.date.updated	2023-11-09T23:32:25Z
pubs.publication-status	Published

Abstract:

In this chapter, we will briefly review the common learning paradigms of ML and some preliminary knowledge about data collection and processing. Furthermore, to better assess the strengths and weaknesses of the existing research work, we design a multi-dimensional and multi-perspective quality assessment criteria, called REBEL-3S.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/173289