Recent Developments in Extreme Learning Machines

Dear Colleagues,

For many years, the general research around Extreme Learning Machines (ELM) has been establishing it as a meaningful and peculiar approach that can be used as stepping stone for creating application oriented models, as well as complex, elaborate machine learning models. Recent work has also been attempting to bridge these current ideas with that of biological learning mechanisms. As such, the original name of ELM nowadays refers not only to the original technique proposed by Guangbin Huang, but also serves as an umbrella term for numerous related approaches directly using the core concepts of the original technique. The focus of this special issue is both on novel theoretical improvements and approaches directly using ELM, and on applications of such techniques to data and network security and privacy issues for future networks, such as in Internet of Things, 5G and Software Defined Networks contexts. Specifically, we invite contributions related to this non-exhaustive list of topics:

- Hierarchical/Layered ELM

- Theoretical/Biological foundations of ELM

- ELM and ELM driven approaches in data and network security and privacy

- ELM (and related) for 5G architectures and scenario

- ELM (and related) for Software Defined Networks and systems

We look forward to receiving novel and disruptive research that addresses the aforementioned topics as well as related ones.

Dr. Yoan Miche
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machine Learning and Knowledge Extraction is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• extreme learning machines
• hierarchical ELM
• layered ELM
• deep ELM
• ELM for data privacy and security
• ELM for 5G
• ELM for SDN