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2017 IEEE 42nd Conference on Local Computer Networks (LCN) (2017)
Singapore, Singapore
Oct. 9, 2017 to Oct. 12, 2017
ISSN: 0742-1303
ISBN: 978-1-5090-6523-3
pp: 167-170
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.
INDEX TERMS
learning (artificial intelligence),
CITATION

C. A. Hammerschmidt, S. Garcia, S. Verwer and R. State, "Reliable Machine Learning for Networking: Key Issues and Approaches," 2017 IEEE 42nd Conference on Local Computer Networks (LCN), Singapore, Singapore, 2018, pp. 167-170.
doi:10.1109/LCN.2017.74
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