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