Appel: IEEE CIM Special Issue on New Trends of Learning in Computational Intelligence

[Thierry Hamon]

Date: Sun, 15 Jun 2014 09:43:55 -0500 (EST)
From: SenticNet <feeds at sentic.net>
Message-ID: <63146292.1489152.1402843435062.open-xchange at bosoxweb03.eigbox.net>


Apologies for cross-posting,

A special issue of the IEEE Computational Intelligence Magazine will be
dedicated to New Trends of Learning in Computational
Intelligence. Prospective authors are invited to submit their original
unpublished research and application contributions. Comprehensive
tutorial and survey papers can also be considered for this special
issue.

RATIONALE
Over the past few decades, conventional computational intelligence
techniques faced severe bottlenecks in terms of algorithmic
learning. Particularly, in areas of big data computation, brain science,
cognition and reasoning, it is almost inevitable that intensive human
intervention and time consuming trial and error efforts need to be
employed before any meaningful observations can be obtained. The recent
development of emerging computational intelligence techniques such as
extreme learning machines (ELM) and fast solutions shed some light upon
how to effectively deal with these computational bottlenecks. Based on
the observations that increasing correlation can be found among
apparently different theories from different fields, as well as the
increasing evidence of convergence between computational intelligence
techniques and biological learning mechanisms, this special issue seeks
to promote novel research investigations in computational intelligence
bridging among related areas.

TOPICS
Topics of interest for this special issue include but are not limited
to:
- Theoretical foundations and algorithms:
   - Extreme learning machines (ELM), No-Prop algorithms and random
     kitchen sinks
   - Real-time learning, reasoning and cognition
   - Sequential / incremental learning
   - Clustering and feature extraction / selection
   - Closed form and non-closed form solutions
   - Multiple hidden layers solutions and random networks
   - Parallel and distributed computing / cloud computing
   - Fast implementation of deep learning
- Applications:
   - Biologically-inspired natural language processing
   - Big data analytics
   - Cognitive science / computation
   - Autonomous systems
   - Situation / Intention Awareness

TIMEFRAME
- 15th August, 2014: Submission of Manuscripts
- 15th October, 2014: Notification of Review Results
- 15th November, 2014: Submission of Revised Manuscripts
- 15th December, 2014: Submission of Final Manuscripts
- May, 2015: Publication

PAPER SUBMISSION
The maximum length for the manuscript is typically 25 pages in single
column format with double-spacing, including figures and
references. Authors should specify in the first page of their
manuscripts the corresponding author’s contact and up to 5
keywords. Submission should be made via email to any of the guest
editors listed below.

GUEST EDITORS
- Guang-Bin Huang, Nanyang Technological University (Singapore)
- Erik Cambria, Nanyang Technological University (Singapore)
- Kar-Ann Toh, Yonsei University (South Korea)
- Bernard Widrow, Stanford University (USA)
- Zongben Xu, Xi'an Jiaotong University (China)

-------------------------------------------------------------------------
Message diffuse par la liste Langage Naturel <LN at cines.fr>
Informations, abonnement : http://www.atala.org/article.php3?id_article=48
English version       : 
Archives                 : http://listserv.linguistlist.org/archives/ln.html
                                http://liste.cines.fr/info/ln

La liste LN est parrainee par l'ATALA (Association pour le Traitement
Automatique des Langues)
Information et adhesion  : http://www.atala.org/

ATALA décline toute responsabilité concernant le contenu des
messages diffusés sur la liste LN
-------------------------------------------------------------------------