Subject: CFP: ICNNAI-2010 Special Session: Incremental Topological Learning Models and Dimensional Reduction
From: Nistor Grozavu <[email-masked]>
To: [email-masked]
Date: Mon, 1 Mar 2010 12:00:25 -0800 (PST)

CFP: ICNNAI-2010 Special Session:
Incremental Topological Learning Models and Dimensional Reduction

==============================
Submissions Due:  April  4,  2010
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1 - 4 June, 2010
Brest State Technical University 
Belarus
http://icnnai.bstu.by/icnnai-2010.html

SCOPE
======
Incremental Learning is a subfield of the Artificial
Intelligence that deals with data flow. The key hypothesis is that the
algorithms are able to learn data from a data subset and then to re-learn with
new unlabeled data. At the end of the learning, one of the problems is the
clustering analysis and visualization of the results. The topological learning
is one of the most known technique that allows clustering and visualization
simultaneously. At the end of the topographic learning, "similar'' data
will be collect in clusters, which correspond to the sets of similar
observations. These clusters can be represented by more concise information
than the brutal listing of their patterns, such as their gravity center or
different statistical moments. As expected, this information is easier to
manipulate than the original data points.

Dimensionality reduction is another major challenge
in the domain of unsupervised learning which deals with the transformation of a
high dimensional dataset into a low dimensional space, while retaining most of
the useful structure in the original data, retaining only relevant features and
observations. Dimensionality reduction can be achieved by using a clustering
technique to reduce the number of observations or a features selection approach
to reduce the features space.
 
This session would solicit theoretical and applicative
research papers including but not limited to the following topics :

     · Supervised/Unsupervised Topological Learning; 
                  
     · Self-Organization (based on artificial neural
networks, but not limited to); 
                  
     · Clustering Visualization and Analysis; 
                  
     · Time during the learning process; 
                  
     · Memory based systems; 
                  
     · User interaction models; 
                  
     · Fusion (Consensus) based models; 
                  
     · Clustering; 
                  
     · Feature selection;

 
SUBMISSION
================
The special session will be held as
a part of the ICNNAI'2010 conference (The 5th International Conference on
Neural Network and Artificial Intelligence ) . The authors would submit papers
through easychair site : http://www.easychair.org/conferences/?conf=itlmdr10. 
    
All paper submissions will be handled electronically. Detailed instructions for
submitting the papers are provided on the conference home page at : 
http://icnnai.bstu.by/icnnai-2010.html 
    
Papers must correspond to the requirements detailed in the instructions to
authors from the ICNNAI 2010 web site. Accepted papers must be presented by one
of the authors to be published in the conference proceeding. If you have any
questions, do not hesitate to direct your questions to [email-masked] 
 
IMPORTANT DATES
================
Paper Submission
Deadline: 4 April 
Notification of acceptance: 22 April 
Camera-ready papers: 29 April
 
ORGANIZERS
================Nistor GROZAVU,
Post-Doc, Computer Science Laboratory of Paris 13 University, FRANCE
Mustapha LEBBAH, Associate Professor at the Paris 13 University, FRANCE
Younès BENNANI, Full Professor at the Paris 13 University, FRANCE
 Best regards,
Nistor Grozavu
PhD, Computer Science Laboratory of the Paris 13 University (LIPN)
http://www-lipn.univ-paris13.fr/~grozavu/
tel: +33 (0)626901790