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SVM learning of IP address structure for latency prediction
2006
Proceedings of the 2006 SIGCOMM workshop on Mining network data - MineNet '06
We examine the ability to exploit the hierarchical structure of Internet addresses in order to endow network agents with predictive capabilities. Specifically, we consider Support Vector Machines (SVMs) for prediction of round-trip latency to random network destinations the agent has not previously interacted with. We use kernel functions to transform the structured, yet fragmented and discontinuous, IP address space into a feature space amenable to SVMs. Our SVM approach is accurate, fast,
doi:10.1145/1162678.1162682
dblp:conf/minenet/BeverlySB06
fatcat:wmos63722rh75pxofubyilrsf4