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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

  • 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

  • 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