Reliable Machine Learning for Networking: Key Issues and Approaches

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Reliable Machine Learning for Networking: Key Issues and Approaches

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Reliable Machine Learning for Networking: Key Issues and Approaches

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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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Počet stran výsledku

4

Strana od-do

167-170

Název nakladatele

IEEE Computer Society

Místo vydání

USA

Místo konání akce

Singapore

Datum konání akce

9. 10. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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