Virtual conference, December 1-3, 2021
MLN 2021 is the fourth edition of the International Conference on Machine Learning for Networking. The goal of the conference is to provide a forum for scientists, engineers and researchers to discuss and exchange novel ideas, results, experiences and work-in-process on all aspects of Machine Learning and Networking. Each year, MLN attendees will appreciate and benefit from multidisciplinary exchanges on these hot topics. MLN 2021 will be held virtually from December 1st to December 3rd, 2021.

Authors are invited to submit complete unpublished papers, which are not under review in any other conference or journal, to https://easychair.org/conferences/?conf=mln2021.

The accepted papers will be published as a post-proceedings in Springer's LNCS (pending). Lecture Notes in Computer Science (LNCS) series is indexed by the ISI Conference Proceedings Citation Index - Science (CPCI-S), included in ISI Web of Science, EI Engineering Index (Compendex and Inspec databases), ACM Digital Library, dblp, Google Scholar, Scopus, etc.

Topics of interest include and are not limited to:

Deep and Reinforcement learning
Pattern recognition and classification for networks
Machine learning for network slicing optimization
Machine learning for 5G system
Machine learning for user behavior prediction
New innovative machine learning methods
Optimization of machine learning methods
Performance analysis of machine learning algorithms
Experimental evaluations of machine learning
Data mining in heterogeneous networks
Machine learning for multimedia
Machine learning for Internet of Things
Machine learning for security and protection
Distributed and decentralized machine learning algorithms
Intelligent cloud-support communications
Intelligent resource allocation
Intelligent energy-aware/green communications
Intelligent software defined networks
Intelligent cooperative networks
Intelligent positioning and navigation systems
Intelligent wireless communications
Intelligent wireless sensor networks
Intelligent underwater sensor networks
Position papers are also welcome and should be clearly marked as such.