Connectionists: CFP Special Issue on Online Data Processing

[Hamid Bouchachia]

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                        Call for Papers
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Special Issue of NEUROCOMPUTING 
<http://www.elsevier.com/wps/find/journaldescription.cws_home/505628/description> 
on
ONLINE DATA PROCESSING
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SCOPE:
In contrast to batch learning where data are assumed to be drawn from a 
stationary distribution and are available ahead of the training phase, 
in online learning, the data come over time and can potentially be 
non-stationary. The data may change drastically in the future so that 
the model learned so far will be hardly applicable to the future data.
Online processing of data embraces two issues: (1) online and adaptive 
adjustment of system's parameters and (2) continuous evolution of the 
system's structure. These issues are important for dealing with 
real-world data streams because of the problems: (1) change of the 
system dynamics, (2) change of the (or new) operating conditions and (3) 
increase of the system states over time. Such problems increase the 
difficulties of building reliable intelligent systems, capable of facing 
the challenges of online data processing (one-pass processing, storage 
limitations, non-stationarity, concept drift and shift, etc.).
Online data processing of high-speed and non-stationary data streams has 
prominent relevance in various fields like finance, internet, security, 
smart environments, industrial processes, robotics, etc. Its application 
encompasses various tasks such as monitoring, classification, 
diagnostic, prediction, forecasting, clustering, etc.
Over the recent years, there has been an ever growing interest and 
demand in self-adaptive autonomous systems operating online, capable of 
sequentially processing massively large and continuous streams of data 
in an evolving setting. The present special issue of "Neurocomputing" 
aims at shedding light on the new advances and future avenues of online 
processing of data with a particular focus on the design issues of 
online evolving systems, algorithms and methods dedicated to online 
processing of data and finally novel applications.

The special issue welcomes contributions related to all aspects 
mentioned so far, specifically dealing with the following topics:

  * Online machine learning
      o Online prediction with expert advice
      o Online convex programming
      o Kernel-based online learning
      o Online (incremental and decremental) support vector machines
      o Online active learning
      o Online bagging and boosting
      o Sparse online learning
      o Margin-based online learning
      o Online clustering
      o Online regression
      o Online classification
  * Computational Intelligence for online data processing
      o Neural Networks
          + Self-growing neural networks
          + Online adaptive and life-long learning
          + Plastic neural networks
          + Online dynamic networks
          + Constructive learning
          + Plasticity and stability in neural networks
          + Forgetting and Unlearning in neural networks
          + Online time series prediction with neural networks
          + Online self-organization
          + Online adaptive neuro-fuzzy systems
      o Fuzzy and probabilistic models
          + Online fuzzy and probabilistic regression
          + Online fuzzy and probabilistic classification
          + Online fuzzy and probabilistic rule systems
          + Online fuzzy and probabilistic clustering
          + Online type-2 fuzzy systems
          + Growing mixture models
          + Dynamic probabilistic models
      o Dynamic evolutionary algorithms
          + Change detection in the environment
          + Convergence and computational issues
          + Adaptive evolutionary computation
          + Methods and strategies of dynamic optimization
          + Dynamic multi-objective optimization
          + Real-world applications of dynamic optimization
  * Issues in online processing
      o Online/Incremental feature selection and reduction
      o Online single-pass data mining and pattern recognition
      o Adaptation in changing environments
      o Concept drift  in online learning systems
      o Self-monitoring in online learning systems
      o Self-X systems
      o Novelty detection in online learning
      o Hybrid online learning architectures
  * Applications
      o Smart systems
      o Industrial processes
      o Stock market
      o Web Mining
      o Social computing
      o Robotics
      o Security, etc.

SUBMISSION
This special issue of the journal "Neurocomputing 
<http://www.elsevier.com/wps/find/journaldescription.cws_home/505628/description>" 
welcomes high quality contributions pertaining to online data processing 
which are not under consideration elsewhere. All manuscripts must be 
formatted according to thejournal template 
<http://www.elsevier.com/wps/find/journaldescription.cws_home/505628/authorinstructions> 
and have to be submitted through the online submission system of the 
journal <http://ees.elsevier.com/neucom/default.asp>. Please choose the 
article type "Special Issue: Online data processing" when submitting 
your manuscript. Should you have inquiries, please contact the guest 
editor at hamid at isys.uni-klu.ac.at. <mailto:hamid at isys.uni-klu.ac.at>

IMPORTANT DATES
Submission deadline: December 20th, 2011
First author notification: March 1st, 2012
Revised version: May 1st, 2012
Final notification: August 1st, 2012
Publication:  Last quarter 2012

GUEST EDITOR
Abdelhamid Bouchachia
University of Klagenfurt, Austria
hamid at isys.uni-klu.ac.at <mailto:hamid at isys.uni-klu.ac.at>




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