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.
Original language | English |
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Title of host publication | 2017 IEEE 42nd conference on Local Computer Networks, LCN 2017 |
Publisher | IEEE |
Pages | 167-170 |
Number of pages | 4 |
ISBN (Electronic) | 978-1-5090-6523-3 |
ISBN (Print) | 978-1-5090-6524-0 |
DOIs | |
Publication status | Published - 2017 |
Event | 2017 IEEE 42nd Conference on Local Computer Networks, LCN 2017 - Singapore, Singapore Duration: 9 Oct 2017 → 12 Oct 2017 |
Conference
Conference | 2017 IEEE 42nd Conference on Local Computer Networks, LCN 2017 |
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Country/Territory | Singapore |
City | Singapore |
Period | 9/10/17 → 12/10/17 |