Titlebook: Machine Learning for Networking; First International Éric Renault,Paul Mühlethaler,Selma Boumerdassi Conference proceedings 2019 Springer

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Xuanlong Weng,Yin Luo,Jianbo Gao,Haishan Feng,Ke Huangto several subvolumes which cover - in alphabetical order - all binary systems of importance. .Subvolume IV/5.H, the eighth of the series, deals with the systems Li-Mi ... Nd-Zr. Further subvolumes are in preparation.978-3-540-68538-8Series ISSN 1615-1844 Series E-ISSN 1616-9522

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Yue Jin,Dimitre Kostadinov,Makram Bouzid,Armen Aghasaryan

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Towards a Statistical Approach for User Classification in Twitter,s over a twitter dataset and learn-based algorithms in classification task. Several supervised learning algorithms were tested. We achieved high f-measure results of 96.2% using imbalanced datasets and (GBRT), 1.9% were gains when we used imbalanced datasets with Synthetic Minority Oversampling tech

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RILNET: A Reinforcement Learning Based Load Balancing Approach for Datacenter Networks, flows (an aggregation flow is a flow set that includes all flows flowing from the same source edge switch to the same destination edge switch) instead of a single flow. In order to test performance of RILNET, we propose a flow-level simulation and a packet-level simulation, and the both results sho

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Building a Wide-Area File Transfer Performance Predictor: An Empirical Study,0% of the edges, and .32% for 75% of the edges. We present a detailed analysis of these results that provides insights into the cause of some of the high errors. We envision that the performance predictor will be informative for scheduling geo-distributed workflows. The insights also suggest obvious

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Machine-Learned Classifiers for Protocol Selection on a Shared Network,acle would choose TCP-CUBIC for the new foreground traffic if only TCP-CUBIC is in use in the background, for fairness..Empirically, our k-nearest-neighbour (K-NN) classifier, utilizing dynamic time warping (DTW) measure, results in a protocol decision accuracy of 0.80 for .. The OPS approach’s thro

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Common Structures in Resource Management as Driver for Reinforcement Learning: A Survey and Researcde. In this paper, we review the existing resource management systems and unveil their common structural properties. We propose a meta-model and discuss the tracks on how these properties can enhance general purpose RL algorithms.

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: A Deep Learning Approach to Maximising the Utility of Virtualised Millimetre-Wave Backhauls, upon arbitrary combinations of such utilities. Specifically, by employing a stack of convolutional blocks, . can learn correlations between traffic demands and achievable optimal rate assignments. We further regulate the inferences made by the neural network through a simple ‘sanity check’ routine,