Reliable Machine Learning for Networking: Key Issues and Approaches
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00318873" target="_blank" >RIV/68407700:21230/17:00318873 - isvavai.cz</a>
Result on the web
<a href="https://www.computer.org/csdl/proceedings/lcn/2017/6523/00/6523a167-abs.html" target="_blank" >https://www.computer.org/csdl/proceedings/lcn/2017/6523/00/6523a167-abs.html</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/LCN.2017.74" target="_blank" >10.1109/LCN.2017.74</a>
Alternative languages
Result language
angličtina
Original language name
Reliable Machine Learning for Networking: Key Issues and Approaches
Original language description
Machine learning has become one of the go-to methods for solving problems in the field of networking. This development is driven by data availability in large-scale networks and the commodification of machine learning frameworks. While this makes it easier for researchers to implement and deploy machine learning solutions on networks quickly, there are a number of vital factors to account for when using machine learning as an approach to a problem in networking and translate testing performance to real networks deployments successfully. This paper, rather than presenting a particular technical result, discusses the necessary considerations to obtain good results when using machine learning to analyze network-related data.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 42nd IEEE Conference on Local Computer Networks
ISBN
978-1-5090-6523-3
ISSN
0742-1303
e-ISSN
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Number of pages
4
Pages from-to
167-170
Publisher name
IEEE Computer Society
Place of publication
USA
Event location
Singapore
Event date
Oct 9, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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