Connectionists: Special Issue on Incremental Learning: Call for Contribution

[Hamid Bouchachia]

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C A L L F O R C O N T R I B U T I O N
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Special Issue of the Neurocomputing Journal (Elsevier)
on
Adaptive Incremental Learning in Neural Networks
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Guest Editors
Hamid Bouchachia, University of Klagenfurt, Austria 
(hamid at isys.uni-klu.ac.at)
Nadia Nedjah, State University of Rio de Janeiro, Brazil 
(nadia at eng.uerj.br)

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Scope
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Adaptation plays a central role in dynamically changing systems. It is 
about the
ability of the system to “responsively” self-adjust upon change in the 
surrounding
environment. Like in living creatures that have evolved over millions of 
years
developing ecological systems due to their self-adaptation and fitness
capacity to the dynamic environment, systems undergo similar cycle to
improve or at least do not weaken their performance when internal or 
external
changes take place. Internal change bears on the physical structure of 
the system
(the building blocks: hardware and/or software components). External change
originates from the environment due to the reciprocal action and 
interaction.
These two classes of change shed light on the research avenues towards 
smart
adaptive systems. The state of the art draws the picture of challenges that
such systems need to face before they come reality. A sustainable effort is
necessary to develop intelligent hardware on one level and concepts and 
algorithms
on the other level. The former level concerns various analog and digital
accommodations encompassing self-healing, self-testing, reconfiguration and
many other aspects of system development and maintenance. The latter 
level is
concerned with developing algorithms, concepts and techniques which can 
rely
on metaphors of nature and which are inspired from biological and cognitive
plausibility.

To face the different types of change, systems must self-adapt their 
structure
and self-adjust their controlling parameters over time as changes are 
sensed.
A fundamental issue is the notion of “self” which refers to the 
capability of
the systems to act and react on their own. It covers all stages of the 
system’s
working and maintenance cycle starting from online self-monitoring to 
self-growing
and self-organizing. Relying on the two-fold plausibility which is the 
basis for
many computational models, neural networks can be encountered in various 
real-world
dynamical and non-stationary systems that require continuous update over 
time.
There exit many neural models that are theoretically based on incremental
(i.e., online, sequential) learning addressing in particular the notions of
self-growing and self-organizing. However, their strength in practical 
situations
that involve online adaptation is not as efficient as desirable.

The present special issue aims at presenting the latest advances of neural
adaptive models and their application in various dynamic environments.
The special issue is intended for a wide audience including neural network
scientists as well as mathematicians, physicists, engineers, computer 
scientists,
biologists, economists and social scientists. The special issue will 
cover various
topics of neural networks related to the self-organization, 
self-monitoring and
self-growing concepts. It also aims at presenting a coherent view of 
these issues
and a thorough discussion about the future research avenues. A sample of 
the
targeted topics, which is suggestive rather than exhaustive, includes:

Theories and Algorithms
- Self growing neural networks
- Online adaptive and life-long learning
- Constructive learning
- Plasticity and stability in neural networks
- Forgetting and Unlearning in neural networks
- Incremental adaptive neuro-fuzzy systems
- Incremental and single-pass data mining
- Incremental neural classification systems
- Incremental neural clustering
- Incremental neural regression
- Adaptation in changing environments
- Concept drift in incremental learning systems
- Self-monitoring in incremental learning systems
- Incremental diagnostics
- Novelty detection in incremental learning
- Time series prediction with neural networks
- Incremental feature selection and reduction
- Adaptive decision systems
- Methodologies of self-organization
- Neural algorithms for self-organization
- Perception and evolution in learning systems

Applications : Adaptivity and learning in
- Smart systems
- Ambient / ubiquitous environments
- Distributed intelligence
- Intelligent agent technology
- Robotics
- Game theory
- Industrial applications
- Internet applications
- E-commerce, etc

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Schedule
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Submission due date: December 15th , 2009
First acceptance notification: February 20th , 2010
Revised manuscripts due: April 15th , 2010
Final acceptance notification: June 15th , 2010
Final version due: July 15th , 2010
Intended publication date: 3rd/4th quarter, 2010

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Submission
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Manuscripts should be submitted to the Special Issue of Neurocomputing 
on Adaptive
Incremental Learning in Neural Networks following the formatting 
guidelines of the journal at:
http://www.elsevier.com/wps/find/journaldescription.cws_home/505628/authorinstructions. 

The manuscripts must be submitted through the online submission system 
of the journal
http://ees.elsevier.com/neucom/. Please choose article type "SI: 
Incremental Learning" when
submitting your manuscript.