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Reliable Machine Learning for Networking : Key Issues and Approaches. / Hammerschmidt, Christian A.; Garcia, Sebastian; Verwer, Sicco; State, Radu.

2017 IEEE 42nd conference on Local Computer Networks, LCN 2017. IEEE, 2017. p. 167-170.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Harvard

Hammerschmidt, CA, Garcia, S, Verwer, S & State, R 2017, Reliable Machine Learning for Networking: Key Issues and Approaches. in 2017 IEEE 42nd conference on Local Computer Networks, LCN 2017. IEEE, pp. 167-170, 2017 IEEE 42nd Conference on Local Computer Networks, LCN 2017, Singapore, Singapore, 9/10/17. https://doi.org/10.1109/LCN.2017.74

APA

Hammerschmidt, C. A., Garcia, S., Verwer, S., & State, R. (2017). Reliable Machine Learning for Networking: Key Issues and Approaches. In 2017 IEEE 42nd conference on Local Computer Networks, LCN 2017 (pp. 167-170). IEEE. https://doi.org/10.1109/LCN.2017.74

Vancouver

Hammerschmidt CA, Garcia S, Verwer S, State R. Reliable Machine Learning for Networking: Key Issues and Approaches. In 2017 IEEE 42nd conference on Local Computer Networks, LCN 2017. IEEE. 2017. p. 167-170 https://doi.org/10.1109/LCN.2017.74

Author

Hammerschmidt, Christian A. ; Garcia, Sebastian ; Verwer, Sicco ; State, Radu. / Reliable Machine Learning for Networking : Key Issues and Approaches. 2017 IEEE 42nd conference on Local Computer Networks, LCN 2017. IEEE, 2017. pp. 167-170

BibTeX

@inproceedings{6b247fca552f4d8997c4f186f2bb4679,
title = "Reliable Machine Learning for Networking: Key Issues and Approaches",
abstract = "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.",
author = "Hammerschmidt, {Christian A.} and Sebastian Garcia and Sicco Verwer and Radu State",
year = "2017",
doi = "10.1109/LCN.2017.74",
language = "English",
isbn = "978-1-5090-6524-0",
pages = "167--170",
booktitle = "2017 IEEE 42nd conference on Local Computer Networks, LCN 2017",
publisher = "IEEE",
address = "United States",

}

RIS

TY - GEN

T1 - Reliable Machine Learning for Networking

T2 - Key Issues and Approaches

AU - Hammerschmidt, Christian A.

AU - Garcia, Sebastian

AU - Verwer, Sicco

AU - State, Radu

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

U2 - 10.1109/LCN.2017.74

DO - 10.1109/LCN.2017.74

M3 - Conference contribution

SN - 978-1-5090-6524-0

SP - 167

EP - 170

BT - 2017 IEEE 42nd conference on Local Computer Networks, LCN 2017

PB - IEEE

ER -

ID: 33113699