Machine Learning for Networking - Third International Conference, MLN 2020, Paris, France, November 24-26, 2020, Revised Selected Papers (Paperback, 1st ed. 2021)

This book constitutes the thoroughly refereed proceedings of the Second International Conference on Machine Learning for Networking, MLN 2019, held in Paris, France, in December 2019. The 26 revised full papers included in the volume were carefully reviewed and selected from 75 submissions. They present and discuss new trends in deep and reinforcement learning, pattern recognition and classification for networks, machine learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection, optimization and new innovative machine learning methods, performance analysis of machine learning algorithms, experimental evaluations of machine learning, data mining in heterogeneous networks, distributed and decentralized machine learning algorithms, intelligent cloud-support communications, ressource allocation, energy-aware communications, software de ned networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, underwater sensor networks.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Information Systems and Applications, incl. Internet/Web, and HCI, 12629
Release date:	March 2021
First published:	2021
Editors:	Eric Renault • Selma Boumerdassi • Paul Muhlethaler
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	375
Edition:	1st ed. 2021
ISBN-13:	978-3-03-070865-8
Categories:	Books > Computing & IT > General theory of computing > Systems analysis & design Books > Computing & IT > Internet > Network computers Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Books > Computing & IT > Applications of computing > Artificial intelligence > Knowledge-based systems / expert systems
LSN:	3-03-070865-9
Barcode:	9783030708658

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